Aijrfans vol1 print by iasir journals

ISSN (Print): 2328-3777 ISSN (Online): 2328-3785 ISSN (CD-ROM): 2328-3793

Issue 5, Volume 1 December-2013 to February-2014

American International Journal of Research in Formal, Applied and Natural Sciences

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrfans@gmail.com

PREFACE We are delighted to welcome you to the fifth issue of the American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS). In recent years, advances in science, engineering, formal, applied and natural sciences have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRFANS is publishing high-quality, peer-reviewed papers covering topics such as Biotechnology, Cognitive neurosciences, Physics, Chemistry, Information coding and theory, Biology , Botany & Zoology, Logic & Systems, Earth and environmental sciences, Computer science, Applied and pure Mathematics, Decision Theory & Statistics, Medicine, Algorithms, Anatomy, Biomedical sciences, Biochemistry, Bioinformatics, Ecology & Ethology, Food & Health science, Genetics, Pharmacology, Geology, Astronomy & Geophysics, Oceanography, Space sciences, Criminology, Aerospace, Agricultural, Textile, Industrial, Mechanical, Dental sciences, Pharmaceutical sciences, Computational linguistics, Cybernetics, Forestry, Scientific modeling, Network sciences, Horticulture & Husbandry, Agricultural & Veterinary sciences, Robotics and Automation, Materials sciences and other relevant fields available in the vicinity of formal, applied and natural sciences.

The editorial board of AIJRFANS is composed of members of the Teachers & Researchers community who are actively involved in the systematic investigation into existing or new knowledge to discover new paths for the scientific discovery to provide new logic and design paradigms. Today, modern science respects objective and logical reasoning to determine the underlying natural laws of the universe to explore new scientific methods. These methods

are

quite

useful

develop

widespread

expansion

highâ&#x20AC;?quality common standards and assessments in the formal, applied and natural sciences. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest

researchers in the field of formal, applied and natural sciences. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory,

GetCITED,

CRCnetBASE,

Google

DOAJ,

SSRN,

Scholar,

TGDScholar,

Microsoft

Academic

WorldWideScience, Search,

INSPEC,

CiteSeerX, ProQuest,

ArnetMiner, Base, ChemXSeer, citebase, OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley, ResearchGate, cogprints, OCLC, iSEEK, Scribd, LOCKSS, CASSI, E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRFANS for entrusting us with the important job. We are thankful to the members of the AIJRFANS editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this fifth issue, we received 80 research papers and out of which only 25 research papers are published in one volume as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of formal, applied and natural sciences.

This issue of the AIJRFANS has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in formal, applied and natural sciences and may open new area for research and development. We hope you will enjoy this fifth issue of the American International Journal of Research in Formal, Applied and Natural Sciences and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS), ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 (December-2013 to February-2014, Issue 5, Volume 1). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Biotechnology  Cognitive neurosciences  Physics  Information coding and theory  Chemistry  Biology , Botany & Zoology  Logic & Systems  Earth science  Computer science  Applied and pure Mathematics  Decision Theory  Statistics  Medicine  Algorithms, and formal semantics  Anatomy  Biomedical sciences  Biochemistry  Bioinformatics  Ecology  Ethology  Food science  Genetics  Health sciences  Pharmacology  Geology  Surface sciences  Astronomy  Geophysics  Oceanography  Space sciences  Criminology  Aerospace  Agricultural  Chemical  Textile  Industrial, Mechanical  Military science  Operations research  Healthcare sciences  Dental sciences  Pharmaceutical sciences  Biostatistics  Computational linguistics  Cybernetics  Forestry  Scientific modeling  Network sciences  Horticulture & Husbandry  Agricultural & Veterinary sciences  Neural and fuzzy systems  Robotics and Automation  Materials sciences

TABLE OF CONTENTS (December-2013 to February-2014, Issue 5, Volume 1) Issue 5, Volume 1 Paper Code

Paper Title

Page No.

AIJRFANS 14-101

Effect of sowing dates and meteorological factors on the development of blast disease in finger millet crop R. S. Netam, R.K.S.Tiwari, A.N.Bahadur , D.P. Singh, D.P.Patel

01-05

AIJRFANS 14-102

Comparison of Dental Caries Prevalence in B -Thalassemia Major Patients with their Normal Counterparts in Udaipur Dr. Ruchi Arora, Dr. Sakshi Malik, Dr. Vivek Arora, Dr. Rajesh Malik

06-09

AIJRFANS 14-108

10-13

AIJRFANS 14-109

Thermodynamic and Acoustic Study on Molecular Interactions in Certain Binary Liquid Systems Involving Ethylbenzene at Temperature 313K. Y. C. Morey and P. S. Agrawal

14-20

AIJRFANS 14-113

Convex solutions of the Schröder equation in Hilbert spaces M.A. Alim

21-23

AIJRFANS 14-116

Distribution of ABO and Rh (D) Allele Frequency Among the Type 2 Diabetes Mellitus Patients Shikha Jaggi and Abhay Singh Yadav

24-26

AIJRFANS 14-117

A comparative study of variations in Mangrove biodiversity of Central and Eastern parts of Indian Sundarbans Abhiroop Chowdhury and Subodh Kumar Maiti

27-31

AIJRFANS 14-119

Comparing three approaches to evaluating physics teachers’ effectiveness in instructional delivery to secondary school students in Nasarawa state of Nigeria Amuche Chris Amuche and C. M. Anikweze (PhD)

32-39

AIJRFANS 14-121

Mass production and formulation of herbicidal metabolites from Phoma herbarum FGCC#54 for management of some prominent weeds of Central India. Adarsh Pandey and Sadaf Quereshi

40-47

AIJRFANS 14-122

ASSESSMENT OF GROUND WATER QUALITY IN DIFFERENT VILLAGES OF NALDURG, DIST. OSMANABAD (M.S). Imamuddin Ustad & Gulam Farooq Mustafa

48-50

AIJRFANS 14-124

EFFECT OF ORGANIC AND INORGANIC ADDITIONS ON PHYSICO-CHEMICAL PROPERTIESE IN VERTISOL P.S. Kusro, D.P. Singh, M.S. Paikra and Deepak Kumar

51-53

AIJRFANS 14-125

CONSTRAINTS IN DIVERSIFICATION OF RURAL ECONOMY Mahajan Girish

54-64

AIJRFANS 14-127

Carbon Sequestration in Tree Plantations at Kurukshetra in Northern India Pooja Arora and Smita Chaudhry

65-70

AIJRFANS 14-129

MACROFUNGAL WEALTH OF KUSUMHI FOREST OF GORAKHPUR, UP, INDIA Chandrawati, Pooja Singh, Narendra Kumar, N.N. Tripathi

71-75

AIJRFANS 14-134

Short term Forecasting model of Area Production and Productivity on Lathyrus in Bastar District D.P. Singh, Deepak Kumar, Deo Shankar and P.S. Kusro

76-78

AIJRFANS 14-137

Bio-efficacy of root extract of Boerhaavia diffusa on yellow disease of ginger A. K. Pandey, L. P. Awasthi , V. P. Pandey, N. K. Sharma, A. Kumar

79-80

AIJRFANS 14-141

RESPONSE OF POTASSIUM IN INCEPTISOL UNDER CROPPING PATTERN SYSTEM AFTER FOURTH CROP CYCLE P.S. Kusro, D.P. Singh, Deepak Kumar and Manish Arya

81-82

AIJRFANS 14-142

Single and Combined Effect of Garlic and Carbon Tetrachloride on Serum and Brain Acetylcholinesterase Activity in Rat Kazi Layla Khaled, Ira Ghosh

83-85

AIJRFANS 14-145

Screening of suitable grains substrates for Spawn development, growth and yield of Pleurotus eous Santosh K Sahu, D.P. Singh, Rakesh Patel and G.K. Awadhiya

86-89

AIJRFANS 14-153

ORGANIC AMENDMENTS INFLUENCING GROWTH, HEAD YIELD AND NITROGEN USE EFFICIENCY IN CABBAGE (Brassica oleracea var. capitata L.) Ranjit Chatterjee, S. Bandhopadhyay and J. C. Jana

90-95

AIJRFANS 14-160

PERFORMANCE of CARNATION (DIANTHUS CARYOPHYLLUS L.) GENOTYPES for QUALITATIVE and QUANTITATIVE PARAMETERS to ASSESS GENETIC VARIABILITY among GENOTYPES Tarannum and B. Hemla Naik

96-101

AIJRFANS 14-161

DEVELOPING STATISTICAL MODELS TO STUDY THE GROWTH RATES OF PADDY CROPS IN DIFFERENT DISTRICTS OF CHHATTISGARH D.P. Singh, Deepak Kumar, M.S. Paikra and P.S. Kusro

102-104

AIJRFANS 14-162

A Study of Processing Model and Different Application Aspects of Agricultural Image Processing Jyoti Saini, Amit Kumar, Shrinath Tailor

105-109

AIJRFANS 14-167

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Effect of fenoxycarb, a juvenile hormone analogue, administration to the second instar larvae of rice moth, Corcyra cephalonica Staint. (Lepidoptera: Pyralidae) Akansha Singh and S. K. Tiwari

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American International Journal of Research in Formal, Applied & Natural Sciences

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ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 AIJRFANS is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Effect of sowing dates and meteorological factors on the development of blast disease in finger millet crop 1

R. S. Netam1, R.K.S.Tiwari2, A.N.Bahadur3 , D.P. Singh1, D.P.Patel1 SG College of Agriculture and Research Station, Jagdalpur, 2T. C. B. College of Agriculture Bilaspur, IGKVV, Raipur Chhattisgarh 3Govt. E. R. R. PG. Sc. Collage, Bilaspur (C.G)., INDIA. Abstract: Crop was sowing in different dates to observed incidence and severity of blast of finger millet. Minimum leaf blast severity and neck and finger blast incidence as well as highest grain yields were recorded from 1st June sown crop during both the years. The weather parameters recorded and correlated with disease development, in two years indicated that the average minimum and maximum temperatures of 210C and 290C respectively a 70-81% relative humidity were along with the most important factors favouring blast disease development. Present study indicated that decrease in temperature and increase in humidity may favour the disease development and may cause epidemic of leaf blast or neck blast or finger blast. Key words: Date of sowing, Blast disease, Finger millet, Weather parameters

I. INTRODUCTION Small millets are the traditional crops, agronomically more adapted to less fertile soils. The important small millets grown in India are Finger millet, Kodo millet, little millet, Foxtail millet, Barnyard millet and Proso millets. There has been a consistent decline in area of small millets during the period from 1949-50 (7.63 million ha) to 1994-95 (3.69 million ha). During the same period the production has fluctuated between 3.82 and 3.30 metric tonnes. The area under Finger millet alone fluctuated between 2.21 million ha during the year 194050 to 1.83 million ha during the year 1994-95, but its production has increased from 1.54 to 2.43 metric tons. The production increased mainly due to the raise in productivity from 700 kg per ha to 1327 kg per ha. In contrast to Finger millet, the yield of other small millets has remained more or less stagnant at a range of 421466 kg per ha during the same period (Chakhiyar, 2007). In Bastar (Chhattisgarh), the area under small millet including finger millet is about 12.33 (000 ha). With the production of 3.01(000 metric tons) and productivity is 245 kg / ha (Anonyms, 2006). Millet production can be increased significantly with the use of improved varieties and balanced fertilizer. A study in Bastar has been found that 25 quintal / ha of Ragi yield is possible with improved management. In india millets were grown on about 20 million hectares with annual production of about 18 million tones in 2008 and contributing about 10 per cent of the countries food production. However, in Bastar district about 10-12 per cent of the population belonging to rural poor sector and residing in the millet growing areas forced to manage at least their daily one meal from the millets. The effects of sowing dates (20 June, 30 June, 10 July, 20 July, 30 July, 10 August, 20 August, 30 August, 10 September and 20 September) on the incidence of neck and finger blast disease caused by Pyricularia grisea [Magnaporthe grisea] in 4 susceptible finger millet cultivars (GE-218, GE-283, GGE-295 and GE-301) were studied at Bangalore, Karnataka, India by Kumar et al. (2005). The highest incidence of neck blast was observed in GE-218 45.21%, GE-295 31.37% and GE-30 42.40% sown on 10th July prevailed. Similarly the highest finger blast severity of 48.60, 29.12, 63.00 and 96.25% was recorded in GE-218, GE-283, GE-295 and GE-301, respectively sown on 10th July. The results suggested that the increased neck and finger blast incidence was due to reduced temperature (21.800C) and increased relative humidity (93 %). Whereas, in temperature and humidity during blast disease development. The fungus is Known to prefer low temperature (<20 0C) with high humidity, heavy rainfall and low light for outbreaks (Vishwanath and Channamma, 1988). Jain et al. (1994) while assessing the stable resistance of blast in finger millet reported that moderate temperatures between 21 0C to 290C with more than 80 per cent mean atmospheric relative humidity during reproductive period favoured the disease development. Considering these points, the present study was undertaken to determine the effect of sowing dates and influence of corresponding weather factors on the blast disease development in finger millet.

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II. MATERIALS AND METHODS Effect the date of sowing on the appearance of blast disease in finger millet caused by Pyricularia grisea. The experiment was conducted in RBD design with three replications. Spacing was maintained as 22.5 cm row to row and 10 cm plant to plant. The seeds of PR-202, a highly susceptible finger millet variety were sown in different dates started from 1st June to 20th July at on intervals of 10 days. Fertilizers @ N 50 kg, P 40 kg and K 25kg /ha were applied as basal dose before sowing. Observations recorded separately for the disease leaf, neck and finger. Leaf blast severity was scored on a 0-5 scales as described by (Mackill and Bonman 1992). Whereas, neck blast and finger blast incidence was recorded by counting the number of infected panicles and fingers from total population (Mackill and Bonman, 1992). Disease severity scoring for leaf blast was recorded at the seedling and booting stage whereas, incidence of neck blast and finger blast at the physiological maturity and at harvest. Further per cent disease index (PDI) for leaf blast was calculated (Dubey 1995).The grain yield was recorded after harvesting of crop from individual plots. III. RESULT AND DISCUSSION Effect of sowing dates on the appearance of blast disease in finger millet crop caused by Pyricularia grisea. The leaf blast severity of 24.55 % and 27.44% was recorded respectively from first June sown crop during kharif, 2009-10 and 2010-11 followed by 25.59 % and 31.94 % respectively from 10 th June sown crop, whereas, maximum leaf blast of 50.39 and 50.00 per cent respectively was recorded from 20 th July sown crop during 2009-10 and 2010-11 (Table - 1 and 2). During kharif, 2009-10 per cent neck blast incidence was significantly less on 1 st June sown crop (14.80%), while in 2010-11, neck blast incidence was did not appeared on 1 st June sown crop. Maximum neck blast incidence of 33.14 and 29.74 per cent respectively was recorded from 20 th July of sown crop respectively during 2009-10 and 2010-11 (Table- 3 and 4). Similarly findings were reported by (Ramappa et al., 2000e). Finger blast incidence was significantly less (21.64%) on 30 th June followed by 1st June (23.22%), 20th (23.31%) and 10th June (24.55%) sown crop. Significantly higher finger blast incidence was recorded on 20th July sown crop (60.43%) during 209-10.Whereas during 2010-11, maximum finger blast incidence of 53.41 per cent was observed on 20th July sown crop and minimum of 31.75 per cent incidence was observed on 1 st June sown crop followed by 10th June (34.58%) and 20th June (5.19%) (Table- 5 and 6). The highest grain yield of 20.25 q/ha and 18.75 q/ha respectively was harvested from 1 st June sown crop during both the years. Significantly less grain yield of 4.83 q/ha and 6.67 q/ha respectively were recorded from 20 th July sown crop during 2009-10 and 2010-11 (Table- 7 and 8). The present findings are in agreement with the work of several other scientists who reported that early sowing favoured less disease development in comparison to late sowing (July sown crop). (Sannegowda and Pandurangegowda, 1985, Dodan and Singh, 1995, Sharma et al., 1993 and Vijaya and Balsubramanian 2002, Kumar, et al., 2005, Bisht et al., 1984). Correlation coefficients obtained between weather parameters i.e.; maximum, minimum temperature and leaf blast severity correlated significantly and had negative value (r= -0.609 - 0.649) during 2009-10 and 2010-11. Similarly for neck blast incidence significant negative correlation (r) was obtained and ranged between -0.835 to -0.959 during the both years. Whereas, in 2010-11 correlation coefficient between maximum temperature and date of sowing was positive (r=0.377) and none significant. Significant negative correlation was existed between maximum, minimum temperature and dates of sowing during 2009-10 (r= - 0.921 to â&#x20AC;&#x201C; 0.841) and during 201011 (-0.215 to -0.872) on finger blast incidence. The relative humidity was positively correlated with the leaf blast severity recorded from different dates of sowing and the correlation values (r) ranged between r= 0.679 to 0.670 during 2009-10 and 2010-11 respectively. Whereas neck blast incidence and relative humidity were negatively correlated and values (r) were non significant during 2009-10 (r= -0.375) and 2010-11(r= -0.201). Correlations (r) between finger blast incidence and relative humidity were negative as well as positive and ranged between r= -0.614 to -0.389 and r=0.786 to 0.433 respectively during both the year. The development of blast was low during June, when the temperature was comparatively higher and relative humidity was low. The average minimum and maximum temperatures of 21 0C and 300C, respectively with 6772% relative humidity were observed during crop season 2009-10 whereas, 21 0C and 290C of maximum and minimum temperature respectively with 73-91% relative humidity were recorded during crop season of 201011. Two years mean data indicated that the average minimum and maximum temperature of 21 0C and 29 0C respectively along with RH of 70-81% was existed during the crop season and disease development. Thus, low temperature, high humidity are to be the most important factors favouring blast disease development. Minimum temperature (15-200C), more number of rainy days, higher rainfall and relative humidity were observed to be the most important factors favouring blast development (Bhatt and Chauhan,1985). Minimum temperature (20 or below to 22.30C depending on locations) along with high relative humidity for maximum blast development (Govindaswamy, 1964, Murlidaran and Venkata Rao, 1980 and Sharma et al., 1993). The pathogen Known to prefer low temperature (< 200C) with high humidity, heavy rainfall and low light for outbreaks (Vishwanath and

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Channamma 1988). Jain, et al., (1994) while assessing the stable resistance of blast in finger millet reported that moderate temperatures between 210C to 290C with more than 80 per cent mean atmospheric relative humidity during reproductive period favoured the disease development. It was observed that average minimum and maximum temperatures of 220C and 290C respectively with 85 - 99% RH was favourable for disease development in Madhya Pradesh India (Patel and Tripathi 1998, Pall, 1988). Table 1.Effect the date of sowing and meteorological factors on the development of leaf blast disease in finger millet crop caused by Pyricularia grisea during kharif, 2009-10 Date of sowing

Observation /Met week

1st June

Max. Temp 0C 31.3

Min. Temp 0C 22.0

Rainfall mms 6.9

Relative Humidity I II 70.1 71.0

29.3

21.1

16.2

88.7

73.4

26.9

21.4

21.3

84.4

81.7

26.0

21.3

16.9

94.9

79.9

27.9

21.9

13.7

89.6

73.9

30.2

22.1

1.0

39.4

64.0

29.4

22.1

8.4

23.2

73.6

-0.609

-0.688

0.214

-0.262

0.679

25 June -1 July 10th June 2 July -8 July 20th June 9 July -15 July 30th June 16 July -22 July st

1 July 23 July -29 July 10th July 30 July -5 Aug 20th July 6 Aug -12 Aug SEM Âą CD (P=0.05) CV Correlation Coefficient ( r )

Leaf blast severity % 2009-10 17.33 (24.55) 18.67 (25.59) 22.67 (28.40) 39.33 (38.84) 48.67 (44.24) 57.33 (49.22) 59.33 (50.39) 0.92 2.83 4.26

Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 2.Effect the date of sowing and meteorological factors on the development of leaf blast in finger millet crop caused by Pyricularia grisea during kharif, 2010-11 Date of sowing

Observation /Met week

1st June

Max.

Min.

Rainfall

Temp 0C 30.6

Temp 0C 22.2

mms 15.2

Relative Humidity I 87.9

II 64.6

27.5

22.0

18.5

93.0

79.1

27.6

22.2

8.9

93.0

77.4

30.2

22.7

10.5

85.3

74.0

26.9

22.2

20.7

93.1

86.0

27.5

21.5

28.0

95.6

82.0

26.8

21.1

23.8

93.9

72.3

0.813

-0.649

0.350

0.739

0.670

25 June -1 July 10th June 2 July -8 July 20th June 9 July -15 July 30th June 16 July -22 July 1st July 23 July -29 July 10th July 30 July -5 Aug 20th July 6 Aug -12 Aug CD (P=0.05) Correlation Coefficient ( r )

Leaf blast severity % 2010-11 21.33 (27.44) 28.00 (31.94) 31.33 (33.97) 50.67 (45.38) 55.33 (48.06) 56.00 (48.46) 58.67 (50.00) 4.33

Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 3. Effect the date of sowing and meteorological factors on the development of neck blast in finger millet caused by Pyricularia grisea during kharif, 2009-10 Date of sowing

Observation /Met week 15 Oct -21 Oct

Max. Temp 0C 31.0

Min. Temp 0C 18.0

Rainfall mms 0.0

1st June 10th June

22 Oct -28 Oct

29.8

14.6

0.0

84.0

48.0

20th June

29 Oct-4 Nov

30.0

14.5

0.0

82.3

50.7

30th June

5 Nov -11 Nov

30.6

17.8

2.4

74.9

47.3

1st July

12 Nov -18 Nov

30.7

19.1

5.7

83.9

65.3

10th July

19 Nov -25 Nov

27.3

15.3

0.3

86.1

62.1

20th July

26 Nov-2 Dec

26.7

9.4

0.0

76.0

64.6

-0.835

-0.683

0.054

-0.375

0.304

CD (P=0.05) Correlation Coefficient ( r )

Relative Humidity I II 83.4 62.1

Neck blast incidence % 2009-10 6.67 (14.80) 13.33 (21.37) 14.67 (22.47) 16.00 (23.47) 17.33 (24.57) 21.33 (27.49) 30.00 (33.14) 3.94

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Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 4. Effect the date of sowing and meteorological factors on the development of neck blast in finger millet crop caused by Pyricularia grisea during kharif, 2010-11 Date of sowing

Observation /Met week

Max.

Min. 0

Rainfall

Relative Humidity

Neck blast incidence % 2010-11 0.00 (0.00) 2.00 (8.13) 2.67 (9.27) 4.00 (11.28) 4.00 (11.28) 6.67 (14.93) 24.67 (29.74) 3.83

1st June

15 Oct -21 Oct

Temp C 27.9

Temp C 19.7

mms 15.4

I 92.6

II 74.7

10th June

22 Oct -28 Oct

28.6

18.6

0.9

90.3

73.0

20th June

29 Oct-4 Nov

26.2

17.9

2.0

91.9

73.3

30th June

5 Nov -11 Nov

27.7

17.5

0.8

89.3

74.0

1st July

12 Nov -18 Nov

28.4

17.6

0.9

90.1

78.0

10 July

19 Nov -25 Nov

30.2

17.4

0.4

90.7

66.7

20th July

26 Nov-2 Dec

28.9

15.9

0.0

87.6

73.4

0.377

-0.959

-0.650

-0.872

-0.201

CD (P=0.05) Correlation Coefficient ( r )

Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 5. Effect the date of sowing and meteorological factors on the development of finger blast in finger millet crop caused by Pyricularia grisea during kharif, 2009-10 Date of sowing

Observation /Met week

Max.

Min. 0

Rainfall

Relative Humidity

finger blast incidence % 2009-10 15.56 (23.22) 17.30 (24.55) 15.70 (23.31) 13.61 (21.64) 54.63 (47.66) 63.75 (53.29) 75.40 (60.43) 7.02

1st June

22 Oct -28 Oct

Temp C 29.8

Temp C 14.6

mms 0.0

I 84.0

II 48.0

10th June

29 Oct-4 Nov

30.0

14.5

0.0

82.3

50.7

20th June

5 Nov -11 Nov

30.6

17.8

2.4

74.9

47.3

30th June

12 Nov -18 Nov

30.7

19.1

5.7

83.9

65.3

1st July

19 Nov -25 Nov

27.3

15.3

0.3

86.1

62.1

10 July

26 Nov-2 Dec

26.7

9.4

0.0

76.0

64.6

20th July

3 Dec -9 Dec

27.8

8.2

0.0

56.3

87.7

-0.921

-0.841

-0.508

-0.614

0.786

CD (P=0.05) Correlation Coefficient ( r )

Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 6. Effect the date of sowing and meteorological factors on the development of finger blast in finger millet crop caused by Pyricularia grisea during kharif, 2010-11 Date of sowing

Observation /Met week

Max.

Min. 0

Rainfall

Relative Humidity

finger blast incidence %

Temp C

mms

2010-11

22 Oct -28 Oct

28.6

18.6

0.9

90.3

73.0

10 June

29 Oct-4 Nov

26.2

17.9

2.0

91.9

73.3

20th June

5 Nov -11 Nov

27.7

17.5

0.8

89.3

74.0

30th June

12 Nov -18 Nov

28.4

17.6

0.9

90.1

78.0

1st July

19 Nov -25 Nov

30.2

17.4

0.4

90.7

66.7

10th July

26 Nov-2 Dec

28.9

15.9

0.0

87.6

73.4

20th July

3 Dec -9 Dec

24.2

14.5

3.9

92.1

86.6

27.78 (31.75) 27.78 (31.75) 32.22 (34.58) 34.68 (36.08) 33.23 (35.19) 64.44 (53.41) 55.00 (47.88) 3.94

-0.215

-0.872

0.138

-0.389

0.433

1 June th

CD (P=0.05) Correlation Coefficient ( r )

Data in parenthesis shows Arc sine percentage transformation average of three replications.

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Table 7. Effect the date of sowing and meteorological factors on the development of grain yield in finger millet crop caused by Pyricularia grisea during kharif, 2009-10 Date of sowing

Mean Met. Data of crop periods

Max.

Min.

Rainfall

Relative Humidity

Grain yield (qt/ha)

1st June

28 May-28 Oct.

Temp 0C 30.7

Temp 0C 22.4

mms 6.7

I 68.1

II 62.9

2009-10 18.75

10th June 20th June 30th June

4 June -4 Nov. 18 June -11 Nov 25 June -18 Nov 9 July -25 Nov 16 July -2 Dec 30 July -9 Dec

29.5 30.0 29.7

21.5 20.8 20.6

12.6 6.7 6.8

89.8 69.4 69.9

71.9 66.2 67.3

17.08 14.17 12.29

29.6

20.3

6.0

69.8

66.6

9.79

29.5

19.7

5.0

69.4

65.7

8.08

29.7

18.9

3.7

66.5

65.7

4.83 3.14

0.547

0.984

0.717

0.473

0.137

1st July 10th July

20th July CD (P=0.05) Correlation Coefficient ( r )

Data in parenthesis shows Arc sine percentage transformation average of three replications. Table 8. Effect of date of sowing and meteorological factors on development of grain yield in finger millet crop caused by Pyricularia grisea during kharif, 2010-11 Date of sowing

Mean Met. Data of crop periods

Max.

Min.

Rainfall

Relative Humidity

Grain yield (qt/ha)

1st June

28 May-28 Oct.

Temp 0C 30.1

Temp 0C 21.9

mms 12.5

I 88.5

II 70.0

2010-11 20.25

10th June

4 June -4 Nov.

29.5

21.5

12.6

89.8

71.9

18.54

20th June

18 June -11 Nov

28.8

21.1

12.5

91.1

73.9

15.21

30th June

25 June-18 Nov

28.8

20.9

11.3

91.1

73.8

15.00

1st July

9 July-25 Nov

28.8

20.6

10.2

91.2

73.7

12.33

10th July

16 July-2 Dec

28.9

20.3

9.8

90.9

73.5

9.38

20th July

30 July -9 Dec

28.6

19.8

8.9

91.1

73.5

6.67

0.784

0.994

0.946

-0.744

-0.685

CD (P=0.05) Correlation Coefficient ( r )

1.61

Data in parenthesis shows Arc sine percentage transformation average of three replications. REFERENCES Anonymous.2006. Kharif programme planning report, Office, ZDA, Jagdalpur. Pp 27-31. Bhatt JC and Chauhan VS. 1985. Epidemiological studies on neck blast of rice in U.P. hills. Indian Phytopathology. 38, 126-130. Bisht IS, Bhatt, JC, Chauhan PS and Joshi HC. 1984. Epidemiological studies on blast disease of ragi (Eleusine coracana) in Kumaon hills. Indian Phytopath., 37 : 466-68. Chakhiyar AR. 2007. Production, Consumption and Marketing Pattern of Minor Millets in Surguja District of Chhattisgarh. M.Sc.(Ag.) Thesis submitted at Department of Agriculture and Natural Resource Economics, IGKV, Raipur C.G. Dodan DS and Ram Singh. 1995. Indian phytopath.48:125-186. Dubey SC.1995. Banded blight of finger millet caused by Thanatephorus cucumris. Indian J. Mycol. Pl. Pathol., 25:315-16. Govindaswamy CV. 1964. Madras agrics. J. 51:255-257. Jain K, Gupta JC, Yadav HS and Tikle AN. (1994). Assessment of stable resistance to blast in finger millet. Advances in Plant Science, 7(2) : 330-334. Kumar BS, Kumar TBA and Nagaraja. 2005. Epidemiological studies of neck and finger blast disease in finger millet (Eleusine coracana (L) Gaertn.) caused by Pyricularia grisea (Cke) Sacc. Environment and Ecology. 23S (Special 4): 861-863. Mackill DJ and Bonman JM. 1992. Inheritance of blast resistance in near-isogenic lines of rice. Phytopathology, 82: 746-749. Murlidaran K and Venkatarao GK. 1980. A simple method of forecasting outbreak of rice blast. Indian Phytopathology, 32, 483-485. Pall BS. 1988. Effect of seed borne inoculums of Pyricularia setariae Nishikado on the finger millet blast. Agric.Sci. Digest 8:225-26. Patel RP and Tripathi SK. 1998. Epidemiology of blast of finger millet caused by Pyricularia grisea (Cke) Sacc. Adv. Pl. Sci., 11:73-75. Ramappa HK, Ravishankar CR and Prakash P. 2002e. Time of neck blast infection and relative finger length infection by Pyricularia grisea on grain yield in finger millet. In: Abstr. Proc. IPS (SZ) Symp. On Plant Disease Scenerario in Southern India. Dec. 19-21, 2002. Pp 14. Sannegowda S and Pandurangegowda KT.1985. Epidemiology of blast disease of rice. Source. Indian Phytopathology 38: 1, 143-145. Sharma JP, Kumar S and Verma R N. 1993. Indian Phytopath., 46:78-80. Vijaya M and Balsubramanian K A. 2002. J. Mycol. Pl. Pathol. 32; 251-252. Viswanath S and Channamma L. 1988. Survey and surveillance of Rabi diseases and their management in Karnataka. In: National Seminar on Pest Surveillance for integrated Pest Management, Tamil Nadu Agricultural University, Coimbatore, Pp 35.

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Comparison of Dental Caries Prevalence in Β -Thalassemia Major Patients with their Normal Counterparts in Udaipur Dr. Ruchi Arora1, Dr. Sakshi Malik2, Dr. Vivek Arora3, Dr. Rajesh Malik4 1 Professor, 2Post Graduate Student, 3MD, Pediatrics, 4MD, Pediatrics 1,2 Department of Pedodontics and Preventive Dentistry Darshan Dental College and Hospital; 3 Rabindra Nath Tagore Medical College and Hospital; 4 K.R.K. Government Satellite Hospital; Udaipur (Rajasthan), INDIA Abstract: To determine the association, if any, of patients suffering from β-Thalassemia major with dental caries and compare it with their normal counterparts in Udaipur. Materials and Method: This study was conducted in Rabindra Nath Tagore Medical College and Hospital, Udaipur which included a total of 80 children suffering from β-Thalassemia major and 80 healthy controls (age range 3-17 years). Data was collected from medical records, questionnaires and dental examination. Dental caries was recorded using dmft/ deft Index according to the criteria described by the World Health Organization. Results: Mean DMFT was found to be 0.30±0.863 among β-Thalassemic patients and 0.16±0.489 among controls whereas deft was 2.02±2.611 and 1.96±3.863 respectively. Mean for DMFT/ deft was found to be 2.37±2.708 in males whereas it was 2.35±2.690 in females. Conclusion: There was no statistically significant difference in the dental caries status of children with β-Thalassemia major and their normal counterparts and gender had no effect on caries in thalassemic patients. Keywords: Beta- thalassemia, dental caries, children, gender. I. Introduction Thalassemia is considered as the most common genetic disorder world-wide. It was first described by Thomas B Cooley and Pearl Lee in 1925. It has been derived from the Greek word ‘thalas’ which means the sea. It is an autosomal recessive blood disease involving defects in synthesis of α and β polypeptide chains of hemoglobin. Based on their clinical and genetic orders, thalassemias are classiﬁed mainly into major (homozygous) and minor (heterozygous) types. Thalassemia major (β-thalassemia) or Cooley’s anaemia, exhibits the most severe clinical symptoms while thalassemia minor (α-thalassemia) is mild and is considered to be clinically asymptomatic [1]. β-thalassemia is the most commonly found Thalassemia with an estimated 60-80 million people in the world. There are about 65,000-67,000 β-thalassemic patients in our country with around 9,000-10,000 cases being added every year [2]. Oral health of children suffering from Thalassemia major is reported to be poor by most of the researchers [3-5,7]. As low priority is given to the oral health status by the masses in the country in general, this negligence might be compounded for children already suffering from a life threatening systemic disease because the parents might focus on the medical procedures required to overcome this disease during early childhood. So, this poor oral health in turn leads to further deterioration of systemic health in these children. A number of studies have been conducted relating dental caries with thalassemia. While some studies deny their association, other studies have contradicted this. Therefore, it has been realized that there is a need to further assess the oral health status of the patients with β-thalassemia major. So, the aim of the present study therefore, was to determine the association, if any, of dental caries in patients suffering from β-Thalassemia major and further, to compare it with their normal counterparts in Udaipur. II. Material & Methods This study was conducted in Rabindra Nath Tagore Medical College and Hospital, Udaipur which included a total of 80children suffering from β-Thalassemia major and 80 healthy controls. Institutional

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Ruchi Arora et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 06-09

ethical committee of the hospital approved the study & written informed consent was obtained from all the study participants and parents of children participating in the study before their examination. The study was carried out from July 2013 to September 2013. A. Inclusion criteria were: 1. Age group between 3 to 17 years. 2. Only those patients who were diagnosed previously for β-Thalassemia major were considered as cases. 3. Matching of age, sex & socioeconomic status of cases & controls. 4. The controls were free of thalassemia, both the major and minor forms. B. Exclusion criteria were: 1. Those seeking dental treatment. 2. Those suffering from other diseases known to influence dental caries or severity of periodontal disease such as diabetes. The study consisted of an interview & intraoral examination while they were undergoing routine blood transfusions. Data regarding the age, gender and educational status of all the children were recorded on a proforma. Plane mouth mirror & explorer were used to examine the oral cavity. Single examiner & single recorder were maintained throughout the study period. DMFT index for permanent teeth & dmft index for primary teeth to record dental caries experience in keeping with the criteria described by the World Health Organization [11]. C. Statistical analysis Statistical analysis was done using Statistical Package for Social Sciences, version 17. Descriptive statistics including mean and standard deviation of each clinical parameter were determined for all the groups examined. The student t test was used for comparison of dental caries experience in the permanent and primary dentition of the study and control groups. ANOVA test was used for the comparison DMFT/ deft among different age groups of control group. The level of significance was set at p <0.05. III. Results Table 1. Thalassemic/ non thalassemic patients- a comparison of the occurrence of dental caries p-value Thalassemia Healthy dmft

Mean ± Std. Deviation

0.16± 0. 489

0.30± 0.863

1.96 ± 3.863

2.02 ± 2.611

t-value 0.217* 1.240

Thalassemic Healthy deft Thalassemic

0.168

0.867*

* p > 0.05

2.5 2 1.5

thalessemic healthy

1 0.5 0 deft

dmft

Mean DMFT was found to be 0.30±0.863 among cases as compared to 0.16±0.489 among controls while mean deft was found to be 2.02±2.611 and 1.96±3.863 respectively. Table 1 shows that DMFT/ dmft (P > 0.05) had no relation with thalassemia and the results are not statistically significant.

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Ruchi Arora et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 06-09

Table 2: Comparison of the occurrence of dental caries in male/female thalassemic patients Gender Male DMFT

Mean ± Std. Deviation

2.37 ± 2.708

t- value

p -value

0.973* 0.034

Female

2.35 ± 2.690

* p > 0.05 Mean for DMFT/ deft was found to be 2.37±2.708 in males whereas it was 2.35±2.690 in females. Table 2 shows that gender has no effect on caries in thalassemic patients as no statistically significant difference was seen in caries prevalence between males and females (P > 0.05). Table 3: Comparison of the occurrence of dental caries within age groups Age groups

Mean ± Std. Deviation

1.52 ± 1.834

6-12

3.13 ± 2.990

>12

0.83 ± 1.337

p-value

0.006*

*p < 0.05 The age range of the study population was between 3-17 years. The age groups were divided according to the dentition (primary, mixed and permanent). This table shows the relation between DMFT/deft and age of the thalassemic patients and there was a clear statistical signiﬁcant difference in caries experience amongst thalassemia patients in all the three age groups (P<0.05) IV. Discussion The present study was conducted in order to assess dental caries in children suffering from beta thalassemia major. On the basis of researches done so far, a consensual opinion regarding the relation between thalassemia and gingival status (Al-Wahadni et al [3], Kaur et al [4], Scutellari et al [8], Mehdizadeh et al [9] ) and thalassemia and malocclussions (Scutellari et al [8], Mehdizadeh et al [9], Girinath et al [1] ) was found but no decisive and conclusive findings have been established regarding the relation between the occurrence of dental caries and thalassemia. Because of the prevailing contradictory opinions regarding the relation between dental caries and thalassemia, an attempt has been made in this paper to further investigate and exclusively focus on the relation between them. No difference was found in mean DMFT among cases and controls. The results were similar to the results obtained by Qureshi et al [6] but contradictory to the studies done by Wahadni et al [3], Gomber et al [5] and Kaur et al [4] who found significantly higher caries in case group compared to control group. No difference in mean deft was found among cases and controls. The results of the present study were in agreement with Scutellori et al [8] and Qureshi et al [6] who found similar incidence of dental caries with beta-thalassemia subjects and their controls. On the contrary, the results were in disagreement with Al-Wahadni et al [3], Gomber et al [5] and Kaur et al [4] who found dental caries in primary teeth significantly higher among beta thalassemia subjects than controls. According to Kaplan etnal [12], caries in thalassemic patients might be because of the fact that parents are more concerned about the serious physical problems and pay less attention to the dental ailments, and seek dental care only when the child is in pain whereas in normal patients the occurrence of dental caries can be attributed primarily to the casual approach towards oral health. No difference in mean DMFT/deft might also be attributed to the variation in sample size, area, age range in addition to the method used for determining the prevalence of dental caries. No difference in the deft/ DMFT on gender basis was found in thalassemic patients. Similar results were obtained by Leonardi et al [7] and Kaur et al [4] who reported higher caries prevalence in thalassemia patients with similar def/DMF values for both the sexes. Significant difference was observed in caries experience amongst thalassemia patients among the three age groups. These results were similar to that of Al-Wahadni et al [3] while Mehdizadeh et al [9] found difference was not statistically significant in the 2-5-years-old age group, but in the other two age groups, the DMFT scores of thalassemic patients were significantly higher. According to Al-Hadithi [10], no statistical significant differences between thalassemic and non thalassemic groups in mean value of DMFT at age 6-8 years were observed but significant results were seen at age 9-12 years. In conclusion, our study did not show any relationship between Thalassemia and dental caries experience. There was no statistically significant difference in the dental caries status of children with

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Ruchi Arora et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 06-09

β-Thalassemia major and their normal counterparts. It is evident enough that the occurrence of dental caries can be attributed primarily to the casual approach towards oral health. So there is a need to create awareness about the oral health status in people and educate such group in the prevention of dental caries and periodontal disease. However, the result of the present study needs to be supported by further studies with a greater sample size. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Girinath P, Vahanwala SP, Krishnamurthy V, Pagare SS. Evaluation of Orofacial Manifestations in 50 Thalassemic Patients: A Clinical Study. J of Indian Academy of Oral Med and Radio. 2010; 22(3):126-132. Talsania S, Talsania N, Nayak H. A Cross Sectional Study Of Thalassemia In Ahmedabad City, Gujarat. Healthline. 2011; 2(1): 48-51. Al-Wahadni A M, Taani DQ, Al-Omari MO. Dental diseases in subjects with Beta-Thalassemia major. Community Dent Oral Epidemol. 2002; 30: 418-22 Kaur N, Hiremath S.S. Dental caries and gingival status of 3-14 year old Beta Thalassemia major patients attending paediatric OPD of Vani Vilas Hospital, Bangalore. Archives of Oral Sciences & Res. 2012;2(2):67-70. Gomber S, Dewan P. Physical growth & dental caries in thalassemia. Indian Pediatr. 2006;43:1064- 1069. Qureshi A, Chaudhry S, Shad M A, Izhar F, Khan A A. Is oral health status of children with β-thalassemia worse than that of their normal counterparts? Journal of Khyber College of Dentistry. 2010, Vol. 1, No. Leonardi R, Verzì P, Caltabiano M. Epidemiological survey of the prevalence of dental caries in young Thalassemia major patients. Stomatol Mediterr. 1990;10(2):133-6. Scutellori PN, Orzincolo C, Andraghetti D, Gamberini MR. Anomalies of the masticatory apparatus in beta-thalassemia. The present status after transfusion and iron-chelating therapy. Radio Med. 1994: 87; 389-96. Mehdizadeh M, Mehdizadeh M, Zamani G. Orodental complications in Patients with Major Beta-Thalassemia. Dent Res J. 2008; 5(1):17-20 Kh. Al-Hadithi. Caries Experience among Children 6-12 years with Beta Thalassemia Major Syndrome in Comparison to Healthy Controls in Baghdad-Iraq. J Bagh Coll Dentistry. 2011; 23(128-132). Extracts of the Third edition of “Oral Health Surveys- Basic methods”, Geneva 1987. Kaplan RL, Werther R, Costano FA. Dental and oral findings in Cooley’s anaemia. A study of fifty cases. Ann N Y Acad Sci.1964; 119: 664-6.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

EFFECT OF DENSITY ON GROWTH AND PRODUCTION OF LITOPENAEUS VANNAMEI OF BRACKISH WATER CULTURE SYSTEM IN SUMMER SEASON WITH ARTIFICIAL DIET IN PRAKASAM DISTRICT, INDIA Danya Babu. Ravuru1 and Jagadish Naik. Mude2 Department of Zoology & Aquaculture, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh–522510, India Abstract: The Pacific white shrimp Litopenaeus vannamei (Boone, 1931) is an Ecological important tropical and euryhaline species. The culture was conducted from three ponds each one of 0.7hac for the study. Semi Intensive culture system was selected in Chinaganjam village, Prakasam District under Brackish water conditions. Stocking densities of L.vannamei (post larvae) were taken from three samples, each one contains (3, 50,000) 500 species/m2 and its survival was 86%, 88% and 90%.In summer season in month of March to August, the water quality parameters were measured fortnightly in a month at 7a. m. The production was 8337, 8932and 9450kg/120, 123 and 126 days and FCR was1.78, 1.81 and 1.82 for P1, P2 and P3, respectively. The artificial diet was provided 4times/day with Manamei feed pellets (Protein 35 and 34%).The final growth was 27.7, 29.0 and 30.0g/120,123 and 126days, respectively. Key words: L. vannamei, Temperature, Salinity, Density, Feed, Growth and Production I. Introduction Litopenaeus vannamei (Boone, 1931), is the most important penaeid shrimp species farmed worldwide (Alcivar – Warren et al., 2007). Because of the high demand for shrimps in Japan, the United States and Europe, shrimp aquaculture has expanded rapidly in all around the world, especially in tropical areas, such as Southeast Asia and Latin America (Lombardi et al., 2006). Among all species of shrimp, L. vannamei, which represents over 90% of shrimp culture in the Western hemisphere, is the most commonly cultured shrimp in Central and South American countries, China and Thailand (Frias- Espericueta et al., 2001; Mc Graw et al., 2002; Saoud et al., 2003). India ranks second next to china in shrimp production. India has the one of the longest coastal line of 8118 km. About 90percent of the total landings has commercially most importance for the shrimp culture all over the world. Andhra Pradesh has the second longest coast line 972 km distributed in India. Prakasam District has distributed 102 km coast line in Andhra Pradesh. The L.vannamei is growing much better than Penaeus monodon. The recent trends in shrimp culture shows a considerable increase of farming of L. vannamei replacing P. monodon culture. The optimal stocking density varies depending on the farm system and management practices. In India the production of L.vannamei culture about 18247 (MT) from 2930 ha culture in 2010-11, the production of shrimp 48430.00 (MT). II. Material and Methods All ponds were pumped with creek water. The pond shape is rectangular. The post larvae (PL 15) of L.vannamei was 15 days old for beginning the study. The PL15 collected from BMR hatchery (Iscapalli village) situated about 20 km of Nellore District in Andhra Pradesh. Cost of seed Rs. 50 paisa for each. Water depth maintained 8ft. In the summer season, L.vannamei (post larvae) stocking densities were taken for culture in three ponds, each one contains (3, 50000) 500 species/m2 and also, survival was 86, 88 and 90% (3, 01,000; 3, 08000; 3, 15000), respectively. The temperature, salinity and DO ranges up to 33±2 0C, 14±2ppt and 4.1ppm/day. The artificial diet was given made by Manamei feed pellet (Protein% 35 (Feed No. 1, 2, 3 and 3S) and Protein% 34(Feed No. 3M)).The methodology includes standard techniques to measure the water quality parameters. III. Results In the experiment the stocking density was influenced by the water quality parameters (see Table1) and also, indicated the reduction of survival rate at higher densities. The species L.vannamei was well grownup to 20 gm body weight from 3.75g to 4.25g/15 days in Indian climate conditions, which is better than other countries. In the culture system the growth rate increased due to the artificial feed supplementation in the season. The oxygen consumption was higher in the large size groups than in the smaller shrimp. More the feed is given; more the Ammonia and H2S gas are released. When the electrical aerators and probiotics are used, the shrimp growth rate was increased due to lack of Dissolved Oxygen (DO). The shrimp culture of the mean average weights of the shrimp were 27.7, 29.0 and 30.0g (Tables 1,2 and 3), survival were 86, 88 and 90%.The given feed 4662,

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Danya Babu et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 10-13

4932.3, 5181.6 kg/ 120, 123, 126 days; FCR was1.78, 1.81 and 1.82 for P1,P2 and P3 (Table 1); production was 8337, 8932 and 9450 kg, respectively. Cost of the feed Rs.71.84/kg and Cost of the species at harvesting time Rs.400/kg. IV. Discussion The statistical analysis method was applied “ANOVA” test, comparison of the survival, production, growth rate and FCR in P1, P2, and P3. The maintenance of good water quality is essential for optimum health, survival and growth of shrimp. The present study was concluded that L.vannamei culture is successful in brackish water environments and the growth is directly related to stocking density. The shrimp was relatively inactive about 200C and exhibited low food consumption comparatively at about 35 0C. The shrimp maintained at 350C had the highest rate of food consumption (Araneda et a., 2008) recorded the average growth rate of 0.38 g/wk in the 90 shrimp/m2 and lowest in the180 shrimp/m2 (0.33 g/wk).Despite the growth variation observed, all values of the parameters meet the water quality requirements for shrimp production (Cawthorne, Beard, Devenport and Wickins,1983; Allan and Maguire, 1991; Garcia and Brune, 1991; Lee and Wickins, 1992; Prado-Estepa, Llobrera,Villaluz and Saldes, 1993); early morning Dissolved Oxygen concentration was between 3.0 to 4.5 mg1-1; salinity was about 14% during the first week of grow out pond, which is preferable for post larvae (PL). The initial lower temperatures would have reduced metabolism and diet intake of the shrimp (Lester and Pante 1992), consequently slowing growth during the first weak. The growth rate of L.vannamei at higher salinities of 50ppt and more, showed the possibility of commercial production. As Arnold et al., (2006) observed the lower wet weights at high stocking densities are reduced space and natural food source availability. Likewise, many studies illustrated that artificial substrates could increase shrimp growth and survival (Moss and Moss, 2004; Arnold et al., 2006; Arnold et al., 2009). It is noteworthy that optimum growth is between 3-14 ppt which is little less than Bray et al., observations (1994), but far more than Huang, (1983), Zu et al., (2004) observations. The optimum feeding rate and frequency of presentation must, therefore, be determined for individual feeds and farms by carefully monitoring feed consumption, growth and feed efficiency over several growing seasons (Tacon, 1993). As one of key factors for culture shrimp, water quality not only affects the shrimp growth and survival rate, but also affects the accuracy of the experiment result (Chim et al., 2008). During the course of the attachment, a large number of shrimp could be assembled on the pond bottom from the artificial substrates (Zhang et al., 2010). Protein requirement has been defined by Guillaume (1997) as the minimum or the maximum amount of protein needed per animal per day. Protein requirements change with respect to changes in biotic factors (e.g. species, physiological state, size) and dietary characteristics (e.g. protein quality, energy: protein ratio). Abiotic factors such as temperature and salinity may also affect the protein requirement (Guillaume, 1997). The protein requirement of a given species is often based on the response (e.g. weight gain, feed efficiency, protein conversion efficiency) of the animal to varying levels of dietary protein under a given set of circumstances. Probiotics are provided to all three ponds depending on biomass i. e. “Sana” life for improving the pond bottom. “Back cheak” for controlling Bacteria. “Vibro cheak” for controlling of Vibro. “Detro care” for controlling dead matters. Burunt” lime to develop the water quality and Zooplankton. Minerals are provided to all three ponds depending on biomass i. e. “Booster” for the development of the minerals. EDTA 3 kg/0.7ha for moulting of the species, Burunt lime to enhance the water quality. Sugar 10 kg/0.7 ha for hardening the shell.“Mingrow” (not applicable around 15ppt) for replacing the deficiency of minerals. Bactericidefor controlling of Black gill disease. “Bio curb” for decreasing of ammonia. “Gasonex” to lift of the gas (while it is black soil, it will be given after 70 days).Hydrogen peroxide (H 2O2) for controlling of DO. Zeolite for bottom clears. Potash 25kg/0.7/ha for control the body gram of species.P1 the survival rate was decreased comparatively with P2, P3 and P1Food Conversion Ratio was low compared with P2,P3 (Table1) and P3 the growth was increased in P1,P2(Table 2, 3 and 4). The mean feed and average growth were 66.1, 67.2 and 68.1 and 3.47, 3.62 and 3.75 for P1, P2 and P3 (Table 2, 3 and 4). Table 1: Pond performance Details Pond Details P1 P2 P3

Area (ha) 0.7 0.7 0.7

DOC

Stocking date

120 123 126

27/03/2013 27/03/2013 27/03/2013

PL stocking (days) PL15 PL15 PL15

Density(m2) & Initial stocking 500=3,50,000 500=3,50,000 500=3,50,000

Survival (%) & Numbers 86=3,01000 88=3,08000 90=3,15,000

FCR 1.78 1.81 1.82

Table 2: Pond 1 Water parameters & Growth performance (g) in summer season DOC

Temperatur e (0C)

Salinity (ppt)

DO (ppm)

Giving feed (%)

Feeding/da y (kg)

Total growth (gm)

15 30 45 60 75 90 105 120

28.0±2 30.5±2 31.5±2 32.0±2 33.0±2 32.5±2 31.0±2 29.0±2

10.0±2 11.5±2 12.5±2 13.0±2 14.0±2 13.5±2 12.0±2 11.0±2

3.4 3.6 3.8 3.9 4.1 4.0 3.7 3.5

_ 7.0 5.5 4.5 3.8 3.2 2.9 2.1

180.0 84.2 66.2 54.1 45.7 38.5 34.9 25.2

2.00 5.00 9.35 13.50 17.75 21.90 24.90 27.77

AVG/ fortnightly (gm) 2.00 3.00 4.35 4.15 4.25 4.15 3.00 2.87

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Danya Babu et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 10-13

Mean 30.9±2 12.4±2 3.7 4.1 66.1 15.27 3.47 Total production=8337; Total feed=4662 Table 3: Pond 2 Water parameters & Growth performance (g) in summer season DOC 15 30 45 60 75 90 105 123

Temperatur e (0C) 28.0±2 29.0±2 30.5±2 31.0±2 33.0±2 32.5±2 30.0±2 28.0±2

Salinity (ppt) 10.0±2 11.0±2 12.5±2 13.0±2 14.0±2 13.5±2 12.0±2 10.0±2

DO (ppm) 3.4 3.5 3.8 3.9 4.1 4.0 3.7 3.4

Giving feed (%) _ 7.0 5.5 4.5 3.8 3.2 2.9 2.1

Feeding/da y (kg) 180.0 86.2 67.7 56.4 46.8 39.4 35.7 25.8

Total growth (gm) 2.00 5.35 9.00 14.00 19.00 23.00 26.00 29.00

AVG/ fortnightly (gm) 2.00 3.35 3.65 5.00 5.00 4.00 3.00 3.00

Mean 30.2±2 12.0±2 3.7 4.1 67.2 15.91 Total production=8932; Total feed=4932.3 Table 4: Pond 3 Water parameters & Growth performance (g) in summer season DOC

Temperatur e (0C)

Salinity (ppt)

DO (ppm)

Giving feed (%)

Feeding/da y (kg)

Total growth (gm)

15 30 45 60 75 90 105 126

30.5±2 29.0±2 30.0±2 31.0±2 33.0±2 32.5±2 30.0±2 29.0±2

11.5±2 10.0±2 11.0±2 12.0±2 14.0±2 13.5±2 11.0±2 10.0±2

3.6 3.4 3.5 3.7 4.1 4.0 3.5 3.4

_ 7.0 5.5 4.5 3.8 3.2 2.9 2.1

180.0 88.2 69.3 56.7 47.8 40.3 36.5 26.4

2.00 5.50 9.00 14.00 19.00 23.00 26.50 30.00

AVG/ fortnightly (gm) 2.00 3.50 3.50 5.00 5.00 4.00 3.50 2.50

Mean 29.8±2 11.6±2 4.0 4.1 68.1 16.12 Total production=9450kg; Total feed=5181.6kg Note: P=Pond, DOC=Days of Culture, PL=Post Larvae, FCR=Food Conversion Ratio and DO=Dissolved Oxygen, AVG= Average growth V. Conclusion: In the present study, it has been observed, Temperature, Salinity, Dissolved oxygen, Density and Survival have been observed and the shrimp Growth rate and Production were increased with artificial Manamei feed when compared with control. References: 1. 2. 3. 4. 5.

6. 7. 8. 9.

10. 11. 12. 13. 14. 15.

Arnold, S.A., Sellers, M.J., Crocos, P.J and Coman, G.J, 2006. Allen G.L., & Maguire G.B. (1991). Lethal levels of low dissolved oxygen and effect of short-term oxygen stress on subsequent growth of juvenile Penaeus monodon. Aquaculture 94, 2–37. Arnold, S.J., Coman, F.E., Jackson, C.J. and Groves, S.A, 2009. High–intensity, zero water–exchange production of juvenile tiger shrimp, Penaeus monodon: An evaluation of artificial substrates and stocking density .Aquaculture, 293. Arenda.M., E.P.Perez, and E.Gasca–Leyva .2008. White shrimp Penaeus vannamei culture in fresh water 3densities; condition state based on length and weight. Aquaculture 283; 13–18. Alcivar–Warren AD, Meehan–Meola S, Won Park, Xu Z, Delaney M, Zuniga G. (2007). Shrimp Map: a low-density, microsatellite-based linkage map of the Pacific whiteleg shrimp, Litopenaeus vannamei: identification of sex-linked markers in linkage group 4. Journal of Shellfish Research 26(4): 1259–1277, http://dx.doi.org/10.2983/0730-8000(2007) 26[1259: SALMLM] 2.0.CO; 2 Bray, W.A., Lawrence, A.L., Leung–Trujillo, J.R., 1994. The effect of salinity on growth and survival of Penaeus vannamei, with observations on the interaction of IHHN virus and salinity. Aquaculture 122, 13–146. Chim, L., M. Castex, D. Pham, P. Lemaire, P. Scmidely and M. Mariojouls, 2008. Evaluation of floating cages as an experimental tool for marine shrimp culture studies under practical earthen pond conditions. Aquaculture, 279: 63–69. Cawthorne, D.E., Beard T., Davenport, J and Wickins, J. (1983). Response of juvenile Penaeus monodon Fabricius to natural and artificial sea water of low salinity. Aquaculture 32.165–174. Huang, H.J., 1983; Factors affecting the successful culture of Penaeus stylirostries and Penaeus vannamei at an estuarine power plant site: Temperature, Salinity, Inherent growth variability, damselfly, Nymph predation, Population density, Distribution and Poly Culture. Ph.D. dissertation, Texas A & M University, 221 P. Frías–Espericueta, M.G, Voltolina, D. and Osuna–López, J.I, 2001. Acute toxicity of cadmium, mercury and lead to white leg shrimp (Litopenaeus vannamei) post larvae. Bulletin of Environmental Contamination and Toxicology, 67: 580–586. Garcia, A and Brune, D.E. (1991). Transport limitation of oxygen in shrimp culture pond. Aquaculture engineering 10,269–279. Guillaume, J., 1997. Protein and amino acids. In: D’Abramo, L.R., Conklin, D.E., Akiyama, D.M. (Eds.), Crustacean Nutrition. World Aquaculture Society, Baton Rouge, LA, pp. 26–50. Lee D.O.C. & Wickins J.E. (1992). Crustacean forming Black Well Scientific Publications. Oxford. Lombardi, J.V., M.H .L. De Almeida, L.P.R. Toledo, B.O.J. Salee and E.J. De Paula, 2006. Cage Polyculture of the Pacific white shrimp Litopenaeus vannamei and the Philippines Sea weed Kappaphycusalvarezii. Aquaculture, 258: 412–415. Lester, L. J. and M. J. Pante. 1992. Penaeid temperature and salinity responses. Pages 515–534 in A. W. Fast and L. J. Lester, editors. Marine shrimp culture: principles and practices. Elsevier Scientific Publishing Company, Elsevier, New York, New York, USA.

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17. 18. 19. 20.

21. 22.

McGraw, W.J., Davis, D.A., Teichert-Coddington, D and Rouse, D.B, 2002. Acclimation of Litopenaeus vannamei post larvae to low salinity: influence of age, salinity, endpoint and rate of salinity reduction. Journal of the World Aquaculture Society, 33: 78– 84. Moss, S.M, 2004. Effects of artificial substrate and stocking density on the nursery production of Pacific White shrimp Litopenaeus vannamei. Journal of the World Aqua culture society, 35: 537– 542. Parado–Estpa E.E.D, Llobera A., Villaluz, A and Saldes, R. (1993). Survival and metamorphosis of Penaeus monodon Larvae at different salinity levels. Israel Journal of Aquaculture 45, 3–7. Saoud, I.P., Davis, D.A. and Rouse, D.B, 2003. Suitability studies of inland well waters for Litopenaeus vannamei culture. Aquaculture, 217: 373–383. Tacon, A.G.J. 1993. Feed formulation and on-farm feed management. In M.B. New, A.G.J. Tacon and I. Csavas, eds. Farm-made aquafeeds, p. 61–74. Proceedings of the FAO/AADCP Regional Expert Consultation on Farm–Made Aquafeeds. Bangkok, FAO– RAPA/AADCP. Zhang, B., W.H. Li, J.R. Huang, Y.J. W and R.L. Xu. (2010). Effects of artificial substrates on the growth, survival and spatial distribution of Litopenaeus vannamei in the intensive culture condition. Iran. J. Fish. Sci., 9: 293–304.20. Zhu, C.; Dong, S.; Wang, F and Huang, G, 2004. Effect of Na/K ratio in Sea water on growth and energy budget of juvenile Litopenaeus vannamei. Aquaculture, Vol. 234.pp.485–496.

Page 13

American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Thermodynamic and Acoustic Study on Molecular Interactions in Certain Binary Liquid Systems Involving Ethylbenzene at Temperature 313K. Y. C. Morey1 and P. S. Agrawal1 Department of Chemistry, Hislop College, Nagpur-440001, India.

Abstract: This paper presents experimental data for densities (ρ), viscosities (η) and ultrasonic speeds (U) of pure ethylbenzene (ETB), chlorobenzene (CB), bromobenzene (BB) and nitrobenzene (NB) and of their binary liquid mixtures at temperature 313 K. These parameters were used to determine the adiabatic compressibility (β), intermolecular free length (Lf), molar volume (Vm) and acoustic impendence (Z) and their excess values. The derived functions namely VmE, LfE, ZE and βE were used to have a better understanding of intermolecular interactions between the component molecules of the present liquid mixtures. The variations of these parameters with composition of mixture indicate the nature and extent of interaction between unlike molecules & suggest that the interactions occurring between ethylbenzene, chlorobenzene, bromobenzene and nitrobenzene molecules follow the sequence: Chlorobenzene > Bromo benzene > Nitrobenzene. The excess and deviation functions were fitted to Redlich-Kister type polynomial equation. The results provide information on the molecules in the pure liquids as well as in the binary mixtures. Keywords: Density, Viscosity, Ultrasonic speed, Molar volume, Binary mixtures.

Introduction

Ultrasound waves are high frequency mechanical waves [14]. Ultrasonic wave propagation affects the physical properties of the medium and hence can furnish information about molecular interactions of the liquid and liquid mixtures. The measured ultrasonic parameters are being extensively useful to study intermolecular processes in liquid systems [21]. The sign and magnitude of the non-linear deviations from ideal values of velocities and adiabatic compressibility’s of liquid mixtures with composition are attributed to the difference in molecular size and strength of interaction between unlike molecules. Studies of thermodynamic properties of binary mixtures are of considerable interest in the fundamental understanding of the nature of interactions between the unlike molecules. In recent years, the theoretical and experimental investigations of excess and deviation functions are taken as interaction parameters to improve the results [1-4]. The thermodynamic properties of a binary mixture such as viscosity and density are important from practical and theoretical points of view to understand liquid theory. Accurate knowledge of thermodynamic properties of organic liquid mixtures has relevance in understanding the molecular interactions between the components of the mixture [14]. Binary liquid mixtures due to their unusual behavior have attracted considerable attention [19]. In chemical process industries materials are normally handled in fluid form and as a consequence, the physical, chemical, and transport properties of fluids assume importance. Thus data on some of the properties associated with the liquids and liquid mixtures like Density and viscosity find extensive application in solution theory and molecular dynamics[9][16][5]. The present work deals with the qualitative and quantitative study on the binary mixtures of ethylbenzene (ETB) with chlorobenzene (CB), bromobenzene (BB) and nitrobenzene (NB). The aromatic hydrocarbon ethylbenzene is non-polar compounds with no measurable dipole moment. Thus it involves weak intermolecular interactions. The choice of this solvent was done because of its opposite nature of polarity and their wide range of applicability [13]. Ethyl benzene has been used as a solvent in paints. Ethyl benzene is important in petrochemical industry in the production of styrene which is in tern used for making polystyrene [6]. Para anisaldehyde and ethyl benzene mixture is used to prepare allyl anisole analog repellant pesticides [15]. Chlorine atom in chlorobenzene is an electron- withdrawing atom, tends to attract to π electrons of the benzene ring thereby and decreases the electron density of the benzene ring. As a result, the benzene ring in chlorobenzene becomes relatively poor electron-donor towards the electron seeking proton of any groups. Chlorobenzene is more reactive because chlorine atom is bonded with SP 3 hybridized carbon atom and consequently can be removed easily [6]. Bromobenzene and Nitrobenzene are occasionally used as a solvent for electrophilic substitution reactions. Therefore, a better understanding of the physicochemical properties of mixed solvent System I (Ethylbenzene & Chlorobenzene), System II

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Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20

(Ethylbenzene & Bromobenzene) and System III (Ethylbenzene& Nitrobenzene ) are necessary for interpretation of data obtained from thermo chemical, electrochemical, biochemical and kinetic studies[9][18]. In view of their industrial importance, the present study reports the experimental values of densities (ρ), viscosities (η) and ultrasonic speeds (u) of pure ETB, CB, BB, NB and those of their binary mixtures at the temperature 313 K. The above experimental data were used to evaluate the excess molar volume (V mE), excess intermolecular free length (LfE), excess adiabatic compressibility (βE) and excess acoustic impedance (ZE) at temperature 313K. The excess values were correlated using the Redlich-Kister polynomial equation (6) to obtain their coefficients and standard deviations [9]. The study of molecular interactions in the liquid mixtures is therefore important in elucidation of the structural properties of the molecules. II.

Experimental:

Ethylbenzene (S.D. Fine chem., Pvt. Ltd.) were distilled at atmospheric pressure. Ethylbenzene was dried over phosphorus pentoxide for several days, distilled, stored over 4 A° molecular sieve and used immediately [7]. Chlorobenzene, Nitrobenzene and Bromobenzene (S.D Fine Chem. Ltd. India) also of A.R grade 99.5% were further purified by the method given in the literature [11]. All the chemicals were stored over 0.4 nm molecular sieves to remove the traces of water, if any, and degassed just before use. In all systems, the various concentrations of the binary liquid mixtures were prepared in terms of mole fraction. The mole fractions of the first and second component(X1 andX2) were varied from 0 to 1. Purities of these chemicals were checked by density determination at the temp.313 K which showed an accuracy of 0.0001 gm cm-3 as compared to reported values. The density, viscosity and velocity were measured as a function of composition of binary liquid mixture at the temp. 313K. The density of sample was measured using digital densitometer (Rudolph) with an accuracy of 0.0001. An Ostwald’s viscometer was used for the viscosity measurements. An ultrasonic interferometer having the frequency 2 MHz was used for ultrasonic velocity measurements. An electronically operated constant temperature bath was used to circulate water through measuring cell made up of steel containing experimental solution at 313 K temperature. An ultrasonic interferometer (Model: F81) working at a frequency 3MHz with an overall accuracy of ± 2 ms –1 has been used for velocity measurement. An electronically digital constant temperature bath has been used to circulate water through the double walled measuring cell made up of steel containing experimental mixtures at the desired temperature. The accuracy in the temperature measurement is ±0.1K. Reliability of the experimental data and the purity of the solvents were ascertained by calculating their densities, ultrasonic speeds and viscosities at different temperatures with the values reported as shown in Table 1. III. Results and discussion: The experimental densities (ρ), ultrasonic speeds (U) and viscosities (η) of pure ethylbenzene, chlorobenzene, bromobenzene, nitrobenzene and their binary mixtures are used to calculate excess thermodynamic properties of mixtures which correspond to the difference between the actual property and the property if the system behaves ideally and thus are useful in the study of molecular interactions and arrangements in the mixtures. In particular, they reflect the interactions that take place between solute–solute, solute–solvent and solvent–solvent species. The effects which are expected to operate between the component molecules under study are (i) structural effect which is due to the differences in shape and size of the component molecules (ii) reorientation effect between component molecules and (iii) energetic effect, i.e., molecular interaction that can be weakened or destroyed or established during the mixing process [22]. Thus, in the present study various acoustical parameters were calculated from measured data by using following equations Adiabatic compressibility (β) =1/ ν2ρ ….… (1) Intermolecular Free length (Lf) = K √β ……. (2) Where K is temperature dependant constant, value of K 642 X 10-6 at temp.313 k. Acoustic impedance (Z) = Uρ ….…. (3) Molar Volume (Vm) = M/ρ … (4) Where M is mean molecular weight. It is calculated as M = X1M1 + X2M2 X1 and X2 are mole fractions and M1, M2 are molecular weights of constituent components of binary liquid mixtures. The values of excess molar volumes (V mE), excess intermolecular free length (LfE), excess acoustic impendence, (ZE) and excess adiabatic compressibility (βE) were calculated with the help of the following standard relations: YE = Yexp. - (X1Y1 + X2Y2) ………… (5) Where, Yexp. = experimental values of mixtures Y1 & Y2 = values of parameters for liquids 1 and 2 respectively. X1 & X2 = mole fractions of liquid 1(CB or BB or NB) and liquid 2 (ETB).

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Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20

The values of excess adiabatic compressibility (βE), free length (LfE), acoustic impedance (ZE) and molar volume (VmE) for each mixture have been least-squares fitted to Redlich–Kister type polynomial equation given in literature [10] by taking the limits n=0 to i. F(X) =X1. (1-X1) Σ Ai. (1-2.X1) i, …. (6) Where F(x) refers to VmE or LfE. The coefficient Ai is the polynomial coefficient tabulated by using the least square method computed by the MAPLE software has been used. The values of the standard deviation (σ) were obtained from the expression σ = {∑ (F(x) exp - F(x) cal) ² / (k/n)} ½ … (7) Where k is the number of experimental points excluding the end points and n is order of polynomial equation. The values of F(X) cal are obtained from Eq. (6) by using the best fit values of Ai coefficients. The coefficients A0, A1, A2, A3 and A4 along with standard deviations σ of fit for all the mixtures are listed in Table 4 and it has been observed that standard deviations are very low. The sign of VmE of a system depends upon the relative magnitude of expansion and contraction of the two liquids due to mixing [8] [20]. If the factors causing expansion dominate the contraction factors, the VmE becomes positive. On the other hand if the contraction factors dominate the expansion factors, then VmE become negative. The factors that are responsible for expansion in volume are as follows, i. Loss of dipolar association, ii. The geometry of molecular structure, which does not allow fitting of one component into other component, iii. Steric hindrance, which opposes the proximity of the constituent molecules. The negative VmE values arise due to dominance of the following factors. i. Chemical interaction between constituent chemicals. ii. Accommodation of molecules of one component into the interstitials of the molecules of the other component. Iii.Geometry of the molecular structure that favors fitting of the component molecules with each other. The negative VmE values in the mixtures under study indicate that interactions between molecules of the mixtures are stronger than interactions between molecules in the pure liquids and that associative force dominate the behavior of the solution [9]. The positive VmE values are attributed to weak dipole-dipole interactions between unlike molecules in the mixtures. Fig.1(a), Fig.1(b) & Fig.1(c) reveal that the excess molar volumes, VmE are negative at temperature 313K for all the investigated mixtures of (ethylbenzene+chlorobenzene), (ethylbenzene+bromobenzene) and (ethylbenzene+nitrobenzene).The observed negative values of VmE for the binary mixtures indicate the presence of specific interactions between ethylbenzene and chlorobenzene or bromobenzene or nitrobenzene molecules. The negative VmE values are attributed to strong dipole-dipole interactions between unlike molecules in the mixtures [8]. The value for VmE is more negative for system I (ethylbenzene+chlorobenzene) than system II (ethylbenzene+bromobenzene) & system III (ethylbenzene+nitrobenzene). The more negative V mE for system I attributed to strongest molecular interactions than system II & III. It is clear from fig. 1(a), (b) and (c) that the values of V mE shows negative deviations for all the three mixtures of ETB with CB, BB & NB and follows the sequence: Chlorobenzene> Bromobenzene > Nitrobenzene. Acoustical parameters such as adiabatic compressibility, intermolecular free length, molar volume and adiabatic compressibility were calculated from the measured ultrasonic velocity and density values at temperature 313K have been tabulated in Table 2 for various mole fractions of pure component(X1) of investigated binary mixtures System I (ETB +CB), System II (ETB +BB) and System III (ETB +NB). For all the three systems densities increase with increase in mole fraction. For the system III velocities and viscosities increases with increase in concentration, suggesting thereby more association between solute and solvent molecules [12], but for systems I & II both are decreasing. Acoustical parameters like adiabatic compressibility (β), intermolecular free length (Lf) & molar volume (Vm) decreases for all the three systems but acoustic impendence (Z) increases for all the three systems with increase in mole fraction (X1). Values of excess adiabatic compressibility (βE), free length (LfE), acoustic impedance (ZE) and molar volume (VmE) at 313 K for all three systems have been tabulated in Table 3. Negative excess values are due to closely packed molecules which accounts for the existence of strong molecular interactions, whereas positive excess values reflect weak interactions between unlike molecules [17]. IV. Conclusions The concentration dependencies of ultrasonic velocities (U), viscosities (η) and densities (ρ) of ethylbenzene with chlorobenzene, bromobenzene and nitrobenzene binary systems and of pure liquids have been measured at 313K temperature. The nonlinear variation of the related parameters such as adiabatic compressibility (β), intermolecular free length (Lf), molar volume (Vm), and acoustic impedance (Z) were elaborated to understand the molecular interactions that lead to the volume contraction of different binary mixtures which were under investigation. The positive and negative variations of the excess values with concentration and temperature of the same acoustic parameters supported the presence of interaction between unlike molecules. The present work suggest that due to the variation of the excess molar volume and other acoustical parameters, the strength of interaction between ethylbenzene and chlorobenzene, bromobenzene & nitrobenzene molecules follow the sequence: Chlorobenzene >Bromobenzene > Nitrobenzene and also the order of favorable accommodation of the component molecules into each other’s structure.

Page 16

Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20 ACKNOWLEDGEMENT

The authors wish to express their sincere gratitude to UGC, Delhi for the financial assistance and The Principal Hislop College, Nagpur for providing necessary facilities. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

T. Ramanjappa, E. Raja gopal, Can. J. Chem. 66 (1988) 371–373. H. Kaur, N.S. Samra, B.S. Mahl, J.R. Khurma, M. Bender, A. Heintz, Fluid Phase Equilib. 67 (1991) 241–257. H.Y.Yang, J.P. Zhao, H.P. Li, M. Dai, Thermochem. Acta 69 (1995)253. S.B. Aznarez, M.A. Postigo, J. Solution Chem. 27 (1998) 1045–1053. R.D. Peralta, R. Infante, G. Cortez, J. Wisniak, J. Solution Chem. 33 (2004) 339–351. Thirumaran S.and Karthikeyan N. International Journal of Chemical Research, Vol. 3, Issue 3, 2011, pp-83-98 J. Ferna´ndez, R. Garriga, I. Velasco, S. Ot´ın, Fluid Phase Equilibria 163 (1999) 231–242 Hindawi Publishing Corporation , Journal of Thermodynamics Volume 2013, Article ID 285796, 9 pages , B. Nagarjun,1 A. V. Sarma,2 G. V. Rama Rao,3 and C. Rambabu4 Saravanakumar K.1 and Kubendran T.R.2, Research Journal of Chemical Sciences, Vol. 2(4), 50-56, April (2012) O. Redlick, A.T. Kister, Ind. Eng. Chem. 40 (1948) 345–348. A.I. Vogel, Text Book of Practical Organic Chemistry, 5th Ed.Longmans Green, London, (1989). *G. R. Bedare, ~V. D. Bhandakkar and #B. M. Suryavanshi, Pelagia Research Library, Der Chemica Sinica, 2013, 4(1):132-136 D.S.Wankhede, , Journal of the Korean Chemical Society, 2012, Vol. 56, No. 1 M. Gowrisankar • P. Venkateswarlu • K. Sivakumar • S. Sivarambabu, J Solution Chem (2013) 42:916–935 R. Baskarana and T. R. Kubendranb*, International Journal of Applied Science and Engineering, 2009. 7, 1: 43-52 Mchaweh A., Alsaygh A. and Mosh-Feghian M.A, Fluid Phase Equilib, 224, 157-167 (2004), Sri Devi, U., Samatha, K., Visvanantasarma, A., J. Pure Appl. Ultras. 26, 1–11 (2004) Kenart C. and M. Kenart W., Phys.Chem. Liq., 38, 155-180 (2000) Ewing M.B. Levian B.J. and Marsh, K.N., J. Chem.Thermodyn., 2, 689 – 691(1970) M. M. H. Bhuiyan and M. H.Uddin, Journal of Molecular Liquids, vol. 138, no. 1–3, pp. 139–146, 2008. Palaniappan L. and Karthikeyan V., Indian J. Phys., 2005, 79(2), 155. B. Giner, S. Martin, H. Artigas, M.C. Lopez, C. Lafuenta, J. Phys. Chem. B 110 (2006) 17683–17690.

Symbols and abbreviations ρDensity ηViscosity UUltrasonic Velocity βAdiabatic Compressibility LfIntermolecular free length VmMolar Volume ZAcoustic Impedance β EExcess Adiabatic compressibility LfE Excess Intermolecular Free Length VmEExcess Molar Volume ZE Excess acoustic Impedance σStandard deviation X 1Mole fraction for chlorobenzene or bromobenzene or nitrobenzene X2Mole fractions for Ethylbenzene M1Molecular weight of chlorobenzene or bromobenzene or nitrobenzene M2Molecular weight of Ethylbenzene KKelvin ETBEthylbenzene CBChlorobenzene BBBromobenzene NBNitrobenzene Table 1: Values of density (ρ), viscosity (η), ultrasonic velocity (U), adiabatic compressibility (β), free length (Lf), acoustic impedance (Z) and molar volume (Vm) of pure liquids at 313 K. ρ η U β Lf Z Vm Temp. 3 3 2 -1 10 2 10 -6 -2 6 Kg m x10 Nsm ms x 10 m N x 10 m x10 Kg m s x10 m3mol-1 1

Component

T/K

Ethylbenzene

313K

848.6

0.0569

1252.00

7.5178

176.03

1.0624

125.11

Chlorobenzene

313K

1084.8

0.0534

1208.93

6.3073

161.23

1.3114

103.76

Bromobenzene

313K

1466.5

0.0544

1044.80

6.2467

160.46

1.5322

107.07

Nitrobenzene

313K

1182.7

0.1017

1398.93

4.3205

133.44

1.6545

104.09

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Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20

Table 2: Values of density (ρ), viscosity (η), ultrasonic velocity (U), adiabatic compressibility (β), free length (L f), acoustic impedance (Z) and molar volume (Vm) at 313 K for all the three binary liquid mixtures. Mole ρ 103η U 1010β 1010Lf 10-6Z 106Vm fraction T/K X1 / Kg m3 /( Nsm2 ) /ms-1 /( m2N-1) / ( m) /(Kg m-2s-1) /( m3mol-1)

ETB+CB

0.0000

848.60

0.0569

1252.00

7.5178

176.03

1.0624

125.11

313K

0.2313

899.79

0.0556

1237.80

7.2537

172.91

1.1138

119.64

0.4452

947.70

0.0549

1227.50

7.0030

169.89

1.1633

115.03

0.6435

997.30

0.0538

1214.50

6.7980

167.39

1.2112

110.58

0.8280

1038.00

0.0538

1213.73

6.5397

164.18

1.2599

107.38

1.0000

1084.80

0.0534

1208.93

6.3074

161.23

1.3114

103.76

0.0000

848.60

0.0569

1252.00

7.5178

176.03

1.0624

125.11

0.2258

974.00

0.0557

1203.47

7.0888

170.93

1.1722

120.79

0.4375

1101.20

0.0553

1172.00

6.6112

165.07

1.2906

116.61

0.6363

1224.50

0.0551

1133.00

6.3618

161.93

1.3874

113.13

0.8235

1347.20

0.0547

1089.00

6.2591

160.62

1.4671

109.89

1.0000

1466.50

0.0544

1044.80

6.2467

160.46

1.5322

107.07

0.0000

848.60

0.0569

1252.00

7.5178

176.03

1.0624

125.11

0.2302

916.80

0.0620

1278.13

6.6769

165.89

1.1718

120.06

0.4436

985.00

0.0688

1310.67

5.9099

156.07

1.2910

115.42

0.6421

1051.60

0.0770

1335.87

5.3287

148.20

1.4048

111.30

0.8271

1117.10

0.0887

1366.60

4.7932

140.56

1.5266

107.58

1.0000

1182.70

0.1017

1398.93

4.3205

133.44

1.6545

104.09

ETB+BB 313K

ETB+NB 313K

Table 3: Values of excess adiabatic compressibility (βE), free length (LfE), acoustic impedance (ZE) and molar volume (VmE) at 313 K Mole fraction(X1) 1010βE /(m2N-1 ) 1010 LfE /m 10-6 ZE / 106 VmE ( Kg m-2s-1)

/( m3mol-1 )

System I (ethylbenzene + chlorobenzene) 0.0000

0.0000

0.1180

0.0009

0.1781

-0.0027

-0.5345

0.2313

0.0010

0.3019

-0.0063

-0.5369

0.3403

0.0024

0.4001

-0.0087

-0.5653

0.4452

0.0031

0.4521

-0.0100

-0.5764

0.5462

0.0203

0.6689

-0.0116

-0.5965

0.6435

0.0394

0.8803

-0.0115

-0.7912

0.7374

0.0211

0.5994

-0.0100

-0.4460

0.8280

0.0117

0.3983

-0.0088

-0.0528

0.9155

0.0067

0.2235

-0.0048

-0.0457

1.0000

0.0000

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Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20

System II (ethylbenzene + bromobenzene) 0.0000

0.0000

0.1147

-0.0356

-0.8882

0.0019

-0.1897

0.2258

-0.0670

-1.5805

0.0037

-0.2473

0.3333

-0.1618

-2.9292

0.0122

-0.3736

0.4375

-0.2520

-4.1437

0.0227

-0.6062

0.5384

-0.2996

-4.7567

0.0287

-0.5671

0.6363

-0.2599

-4.1905

0.0260

-0.5025

0.7313

-0.2414

-3.8229

0.0259

-0.4866

0.8235

-0.1601

-2.5888

0.0178

-0.3642

0.9130

-0.0783

-1.2957

0.0093

-0.2381

1.0000

0.0000

System III (ethylbenzene + nitrobenzene) 0.0000

0.0000

0.1173

-0.2307

-0.0257

-0.0162

-0.1720

0.2302

-0.4391

-0.3350

-0.0269

-0.2156

0.3389

-0.6058

-0.7140

-0.0322

-0.3617

0.4436

-0.7220

-1.0651

-0.0341

-0.3710

0.5446

-0.7367

-0.7219

-0.0382

-0.3307

0.6421

-0.7085

-0.4859

-0.0378

-0.3117

0.7362

-0.6310

-0.3355

-0.0335

-0.2504

0.8271

-0.4962

-0.2514

-0.0255

-0.1432

0.9150

-0.2895

-0.1393

-0.0144

-0.0515

1.0000

0.0000

Table (4).Redlich-Kister constants for the deviations of Excess parameters of intermolecular free length, acoustic impedance, adiabatic compressibility and molar volume of different binary mixtures of styrene at temperature 313K Property

T/K

σ (YE)

ETB+CB 1010LfE/(m)

2.47885

-3.08342

10 Vm /(m mol )

-2.64342

10-6ZE/(Kgm-2S-1)

-0.04370

313K

-1

10 β /(m N ) 10 E

-1

0.22616

4.54968

-0.94650

0.1278431

1.52280

1.08842

-8.30780

-2.13711

0.1576142

0.01695

-0.00925

0.01038

0.01382

0.0005477

0.06458

-0.24118

0.01402

0.37239

-0.07471

0.0104003

-18.11647

9.80981

19.59542

-6.70524

-17.61694

0.2445042

-2.33929

0.78436

2.97298

-0.75078

-4.95702

0.0757134

0.10493

-0.10953

-0.15047

0.06711

0.14992

0.0022020

-1.12566

0.78238

1.60345

-0.49798

-1.43506

0.0195878

ETB+BB 1010LfE/(m)

313K

106VmE/(m3mol-1) -6 E

-2 -1

10 Z /(Kgm S ) 10 β /(m N ) 10 E

-1

ETB+NB 1010LfE/(m)

-3.53527

-2.61626

7.90088

6.65231

-5.89980

0.1386764

-1

-1.45881

-0.15043

0.61004

-0.45017

-0.18773

0.0434188

-2 -1

10 Z /(Kgm S )

-0.14674

0.03929

-0.07404

-0.04317

0.05728

0.0012355

10 β /(m N )

-2.94818

0.55961

0.30551

0.63885

-0.44010

0.0104532

313K

10 Vm /(m mol ) -6 E

10 E

-1

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Y. C. Morey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 14-20

Fig. 1 (a) Plot of excess molar volume (VmE) against mole fraction fraction (X1) of Ethylbenzene and Chlorobenzene at temp. 313K.

Fig. 1 (c) Plot of excess molar volume (VmE) against mole (X1) of Ethylbenzene and Nitrobenzene at temp. 313K.

Fig.1 (b) Plot of excess molar volume (VmE) against mole fraction (X1) of Ethylbenzene and Bromobenzene at temp. 313K.

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American International Journal of Research in Formal, Applied & Natural Sciences

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Convex solutions of the Schröder equation in Hilbert spaces M.A. Alim1 Department of Mathematics, University of Chittagong, Chittagong-4331, Bangladesh. Abstract: The problem of the existence and uniqueness of increasing and convex solutions of the Schr öder equation, defined on cones in Hilbert spaces, is examined on a base of the Krein-Rutman theorem. Keywords: Schröder functional equation, convex and increasing solutions, Krein-Rutman theorem. I. INTRODUCTION The aim of this paper is to obtain a theorem on the existence and uniqueness of increasing and convex solutions of the Schröder equation one of the most important equations of linearization, having many applications in various fields of mathematics (see [4] and [5] ). Our result generalizes the theorem of F.M. Hoppe [1], in particular for functions defined on infinite -dimensional Hilbert spaces. The main point is to obtain an infinite-dimensional analogue of [2, Theorem 1] by A. Joffe and F. Spitzer exploiting the famous Krein-Rutman theorem [3, pp. 267-270], cf. also [6, Theorem 2.1]. II. PRELIMINARIES Fix a non-degenerate Hilbert space and a closed cone with non-empty interior, i.e. (cf. [3, p. 217, Definition 2.1]), is a closed subset of such that , for every , and . We define a (partial) order on by iff , and we assume that the inner product is an increasing function on , i.e. implies . (According to [7, p. 216] ), if is a real space and there exists a real constant such that implies , then in the space there exists an equivalent inner product which is increasing on . ) Let be a completely continuous linear operator such that and for every there exists a positive integer such that . By the Krein-Rutman theorem [3, p. 267] the spectral radius of is positive and there exists exactly one vector and exactly one continuous linear functional such that , for every , for every , and . Moreover [3. pp. 269-270], the spectral radius of the operator defined by is less than

and ,

We assume also that a function ,

is given and such that

and there exists a positive constant such that , Let us note that in the case where is finite-dimensional the last condition is always satisfied. III. THE JOFFE-SPITZER SEQUENCE The main result of this section reads: Theorem 1. Assume that either or If Proof. Fix

and and

, , then

such that the closed ball with centre at ,

. and the radius

is contained in . Then

Put

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M.A. Alim, American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 21-23

According to the last part of the Krein-Rutman theorem and Define

, by

and put It follows from

and

that

We shall show for , Applying

and m large enough, say we obtain

and

. ,

Hence, as

increases,

for and making use of ,

. To get the right-hand-side of

assume first

, fix a

and,

such that ,

Then, applying also

with

, ,

with

whence , Now, if

with

is a positive integer such that

. , then

, ,

This

jointly

with ,

ends the proof of which jointly with

. in gives

case .

case

have

Since , it follows from

and the facts that

, we get

and applying

Let be a positive integer such that such that

for

that

Using these inequalities for

Making use of

for

, , and for each

, increases and

. Moreover, implies

, let

be a positive integer

. we obtain

for

Consequently,

for

and

. Hence and from

we get ,

which jointly with ends the proof. Corollary 1. Under the assumptions of Theorem 1 we have

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M.A. Alim, American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 21-23

and . IV. THE SZEKERES SEQUENCE we assume additionally that the function is increasing, convex and , . Observe that then in such a case zero is the only fixed point of and , Passing to solutions of

Fix arbitrarily an

. We shall show that for every

the sequence

is bounded in order to define the function In fact, if on the other hand,

, then

by the formula . for a positive integer . Consequently,

and,

. Hence and from we obtain . Arguing as F.M. Hoppe did in [1], but using our Theorem 1 instead of [2, Theorem 1] by A. Joffe and F. Spitzer, we can prove what follows. Theorem 2. If , then is an increasing and convex solution of and if is an increasing and convex solution of , then , . Corollary 2. If , then , . Applying Theorem 1 and Corollary 2 we obtain also a representation of the solution in which the functional does not occur. Corollary 3. If , then , . Example. Let denote the interval , denote the Hilbert space of all continuous real functions on with the supremum inner product and denote the cone of all non-negative functions on . Let be a continuous function. It is easy to check that the function given by the formula

satisfies all the assumptions of our theorems, with , and , except, may be condition . To get let us observe that putting we have , and for every . In other words, the ball with centre at and the radius is contained in for every . This jointly with [3, p. 210, Lemma 1.2] proves . Remarks. 1. For the sake of simplicity we considered functions defined on the whole cone but similar results hold if we replace by , or by , with . 2. Assuming that the function is concave we can consider increasing and concave solutions of replacing in the definition of the upper limit by the lower limit. References [1] F.M. Hoppe, Convex solutions of a Schröder equation in several variables,Proc. Amer. Math. Soc. 64(1977), 326-330. MR 56:1486 [2] A. Joffe and F. Spitzer, On Multitype Branching Processes with , J. Math. Anal. Appl. 19 (1967), 409-430. MR 35:3760 [3] M.G. Krein and M.A. Rutman, Linear operators leaving invariant a cone in a Banach space, Uspekhi Matem. Nauk (N.S.) 3, no. 1 (23) (1948), 3-95. [English translation: Functional Analysisand Measure Theory, Amer. Math. Soc. Translations- Series 1, vol. 10 (1962).] MR 10: 256c; MR 12:341b [4] M. Kuczma, Functional equations in a single variable, Monografie Matematyczne 46, PWN. Polish Scientific Publishers 1968. MR 34:444l [5] M. Kuczma, B. Choczewski and R. Ger, Iterative Functional Equations, Encyclopedia Math. Appl. 32, Cambridge University Press, Cambridge, 1990. MR 92f:39002 [6] R.D. Nussbaum, Hilbert’s projective metric and iterated nonlinear maps. Memoirs of the American Mathematical Society 391 (1988). MR 89m:47046 [7] H.H. Schaefer, Topological Vector Spaces, Graduate Texts in Mathematics 3, Springer-Verlag 1971. MR 49:7722 [8] Erwin Kreyszig, Introductory Functional Analysis with Applications, John Wiley & Sons, New York Inc, 1978.

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American International Journal of Research in Formal, Applied & Natural Sciences

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Distribution of ABO and Rh (D) Allele Frequency Among the Type 2 Diabetes Mellitus Patients Shikha Jaggi1 and Abhay Singh Yadav2 1

Junior Research Fellow (UGC), 2 Professor,

1,2

Human Genetics Laboratory, Department of Zoology,

Kurukshetra University, Kurukshetra-136119, Haryana, INDIA. Abstract: The aim of the present study was to study the distribution of ABO and Rh (D) blood groups in type 2 diabetes mellitus (T2DM) patients. We evaluated 106 T2DM patients and 58 control subjects. Samples were tested for ABO and Rh (D) blood groups and allele frequencies were calculated. The blood group O (43.40%) was distributed with highest frequency among T2DM patients while AB (6.60%) was least frequent. Frequency of O allele (0.549) was the highest among T2DM patients. Chi-square values were found to be highly significant for ABO blood groups. Keywords: Type 2 Diabetes Mellitus, ABO, Rh blood groups, allele frequency. Ι. Introduction T2DM is an emerging problem worldwide. According to ICMR-INDIAB national study, there are 62.4 million people with T2DM and 77 million people with pre-diabetes in India [1] and the numbers are expected to increase to 101 million by year 2030 [2]. Correlation between ABO blood groups and certain diseases like pancreatic cancer have been reported in past studies [3]-[5]. Thus, aim of the present study was to evaluate the distribution of ABO blood groups in T2DM patients and controls. ΙΙ. Subjects A total of 164 individuals were evaluated. Out of which 106 were T2DM patients and 58 were healthy individuals matched for age, gender, socio-economic status etc. The study was from June to December 2013. The study was approved by Institutional Ethics Committee, Kurukshetra University, Kurukshetra. Written informed consent was obtained from all individuals. ΙΙΙ. Materials and Methods The blood samples were collected by veni-puncture in 4 ml K2EDTA vacutainers, labeled and transported to laboratory for determination of blood groups. ABO and Rh (D) blood grouping was performed simultaneously. Slide agglutination method was followed. Standard technique of serology and manufacturer’s directions enclosed with the different blood grouping reagents was followed. ΙV. Statistical analysis Chi-square test was applied to estimate the probability of difference distributions occurring by chance. p<0.005 was considered to be significant. The allele frequencies of A, B and O alleles were calculated according to Yasuda (1984) [6]. Square root method was used to evaluate d allele frequency. V. Result Phenotype and allele frequencies of ABO and Rh (D) blood groups in T2DM patients and control subjects are depicted in table 1. T2DM patients with blood group B (43.40%) and O (30.19%) were more numerous than controls and least frequent blood group was AB (6.60%). Blood group A (25.86%) and AB (12.07%) were more common in controls than in T2DM patients. Allele frequency for diabetics were in order O>B>A. A allele

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Shikha Jaggi et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 24-26

frequency (0.477) was found to be highest and B allele frequency (0.317) was lowest in controls. The frequency of D allele was higher in T2DM patients (0.755) than in controls (0.679). d allele frequency was found to be higher in controls (0.321) than T2DM patients (0.245). Table 2 shows chi-square values of ABO blood group in T2DM patients and controls. A chi-square value for ABO blood groups was found to be highly significant in both T2DM patients and control subjects. Table 1: Phenotype and allele frequency of ABO and Rh (D) blood groups among T2DM patients and controls. Group

Phenotype frequency

Allele frequency

Rh+

Rh-

100

0.158

0.541

0.549

0.755

0.245

(19.81)

(43.40)

(6.60)

(30.19)

(94.34)

(5.66)

0.477

0.317

0.454

0.679

0.321

T2DM (106)

n (%)

Controls

(58)

(%)

(25.86)

(41.38)

(12.07)

(20.69)

(89.66)

(10.34)

The value in parenthesis shows the number observed. Table 2: Chi-square values for ABO blood group in T2DM patients and controls. Group

χ2

Probability

Remarks

T2DM patients

31.352

P<0.005

Significant

Controls

20.640

P<0.005

Significant

VΙ. Discussion Data on association between the distribution of the ABO and Rh (D) blood types and disease is conflicting. Despite the fact that blood groups play important role in certain diseases, for example, peptic ulcers and gastric cancer [7]. Few studies showed no association between diabetes mellitus and ABO blood groups [8]-[10]. While others suggested that there is an association between ABO blood group and certain diseases [11], [12]. In past studies, increased frequency of blood group B among diabetics was reported [13]-[15]. The same is found in our study. The type B and type O blood group individuals show greater risk of obesity related diabetes due to carbohydrate intolerance as they are not able to dispose off insulin displacing lectins. Lectins cause insulin resistance, hypoglycemia, and obesity leading to T2DM [16]. Individuals with O blood group have about 25% less clotting factor VIII and von willebrand factor in their plasma [17]. These factors have relationship with hypercholesterolemia which in turn has a relationship with diabetes [18]-[20]. VII. References [1]

R. Anjana, R. Pradeepa, M. Deepa, M. Datta, V. Sudha, R. Unnikrishnan et al., On behalf of the ICMR-INDIAB Collaborative Study Group, Prevalance of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase Ι results of the Indian Council of Medical Research- India DIABetes (ICMR-INDIAB) study, Dibetologia, Vol. 54, 2011, pp. 3022-3027.

[2]

D. R. Whiting, L. Guariguata, C. Weil and J. Shaw, IDF Diabetes atlas: Global estimates of the prevalence of diabetes for 2011 and 2030, Diabetes Research and Clinical Practice, Vol. 94, 2011, pp. 311-321.

[3]

J. A. Roberts, Blood groups and susceptibility to diseases: a review, British Journal of Preventive and Social Medicine, Vol. 11, 1957, pp. 107-125.

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Shikha Jaggi et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 24-26 [4]

J. B. Greer, M. H. Yazer, J. S. Raval, M. M. Barmada, R. E. Brand and D. C. Whitcomb, Significant association between ABO blood group and pancreatic cancer, World Journal of Gastroenterology, Vol. 16, 2010, pp. 5588-5591.

[5]

M. S. Jaff, Relation between ABO blood groups and Helicobactor pyroli infection in symptomatic patients. Journal of Clinical and Experimental Gastroenterology, Vol. 4, 2011, pp. 221-226.

[6]

N. Yasuda, A note on gene frequency estimation in the ABO and ABO like system, Japanese Journal of Human Genetics, Vol. 29, 1984, pp. 371-380.

[7]

I. Arid, H. Bentall and J. Roberts, A relationship between cancer of stomach and the ABO blood groups, British Medical Journal, Vol. 1, 1953, pp. 799-801.

[8]

J. Craig and J. Wang, Blood groups in diabetes mellitus, Glasgow Medical Journal, Vol. 36, 1955, pp. 261-266.

[9]

M. Rahman, Non- association of ABO blood groups with diabetes mellitus in Bangladesh, Bangladesh Medical Research Council Bulletin, Vol. 2, 1976, pp. 144-146.

[10]

S. Koley, The distribution of the ABO blood types in patients with diabetes mellitus, Anthropologist, Vol. 10(2), 2008, pp. 129-132.

[11]

G. Garratty, Do blood groups have a biological role? In G. Grarratty Ed., Immunology of Transfusion Medicine, New York, Dekker, 1994, pp. 201-255.

[12]

M. E. Reid and C. Lomas-Francis, The blood group antigen fastbook, New York, Elsevier Academic, 2004, pp. 70-71.

[13]

G. Maehr, Distribution of ABO blood groups in diabetes mellitus, Weiner Klinische Wochenschrift, Vol. 71, 1959, pp. 536-538.

[14]

M. U. Henry and T. Poon-King, Blood groups and diabetes, West Indian Medical Journal, Vol. 10, 1961, pp. 156-160.

[15]

M. Qureshi and R. Bhatti, Frequency of ABO blood groups among the diabetes mellitus type 2 patients, Journal of the College of Physicians and Surgeons Pakistan, Vol. 8, 2003, pp. 453-455.

[16]

P. J. D’Adamo’s, Diabetes: Fight it with the Blood Type Diet, New York, Berkley Books, 2013.

[17]

J. S. O’Donnell and M. A. Lasffan, The relationship between ABO histo-blood group, factor VΙΙΙ and Von Willebrand factor, Transfusion Medicine, Vol. 11, 2001, pp. 343-351.

[18]

J. P. Carton, Defining the Rh blood group antigens: biochemistry and molecular genetics, Blood Reviews, Vol. 8, 1994, pp. 199-212.

[19]

A. I. Adler, I. M. Stratton and H. A. Neil, Association of systolic blood pressure with macrovascular and microvascular complications of type 2 diabetes: prospective observational study, British Medical Journal, Vol. 321, 2000, pp. 412-419.

[20]

A. Kroke, R. Saracci and H. Boeing, The effect of differences in measurement procedure on the comparibility of blood pressure estimates in multi-centre studies, Blood Press Monitoring, Vol. 7, 2002, pp. 95-104.

Conflict of interest There are no conflicts of interest.

Acknowledgement The authors are grateful to the authorities of Kurukshetra University, Kurukshetra for providing laboratory facilities and to UGC, New Delhi for grant of junior research fellowship to Shikha Jaggi.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

A comparative study of variations in Mangrove biodiversity at Central and Eastern parts of the Sundarban Biosphere Reserve, India Abhiroop Chowdhury1 and Subodh Kumar Maiti2 Research Scholar, Professor, Department of Environmental science and Engineering, Indian School of Mines, Dhanbad- 826004, India.

Abstract: Sundarban mangrove ecosystem is the world largest contiguous mangrove forest and is the cradle for both aquatic and terrestrial biota. The mangrove biodiversity of the Central and Eastern parts of Indian Sundarbans shows a difference in species abundance and can also be identified from remote sensing images. There is an exclusion of few fresh water loving true mangrove members and other endangered associates namely Rhizophora sp, Kandelia candel, and Heritiera fomes from the central parts with relatively high abundance of edaphic sub-climax Phoenix paludosa and salt loving Avicennia sp, than in the Eastern parts. The analysis of remote sensing satellite image along with in-field biodiversity assessments have portrayed a variation in mangrove diversity between these two parts of Indian Sundarbans which is discussed in this paper. Keywords: Indian Sundarbans, Mangrove Biodiversity, Percentage cover, Sorenson Similarity Index, Remote Sensing, Normalized Difference Vegetation Index. I. Introduction Mangroves are coastal forests found in sheltered estuaries and along river banks and lagoons in the tropics and subtropics. The term ‘mangrove’ describes both the ecosystem and the plant families that have developed specialized adaptations to live in this tidal environment [1]. Its multifaceted role, including the interactive relationship with the neighboring habitat and sheltering diverse species, has made it a treasured storehouse of the nature particularly production of fish and shellfish[2,3]. In Asia, Sundarbans, is the world’s largest contiguous mangrove patch covering an area of 10,000 Km2 and is the part of the progradation delta of GangaBrahmaputra-Meghna river systems that comprises of an area of 80,000 km2 and recognized internationally as the UNESCO (United Nations Educational, Scientific and Cultural Organization) World-Heritage site [2,3,4]. The transboundary forest of Sundarbans is spread over two countries, of which 60% is in Bangladesh and 40% in India [3]. The spatio-temporal analysis shows that despite having the highest population density in the areal extent of the Sundarban mangrove forest has not changed significantly (approximately 1.2%) in the last 25 years [5]. But the forest is changing due to erosion, aggradation, deforestation and mangrove rehabilitation programs. The net forest area increased by 1.4% from the 1970s to 1990 and decreased by 2.5% from 1990 to 2000 [5]. This paper we use biodiversity assessment and analysis of Remote sensing satellite images to compare the condition of mangrove biodiversity in central and eastern parts of Indian Sundarbans. II. Objective The main objective of this paper is to assess the variations in mangrove biodiversity of Eastern and Central parts of Indian Sundarbans using remote sensing data and ground verification using biodiversity assessment. III. Material and Methods A. Analysis of Remote sensing Image Assessment of mangrove biodiversity using Remote Sensing (RS) and Geographical Information System (GIS) is a modern innovation that is still in the form of development. Vegetation index, namely Normalized Difference Vegetation Index (NDVI) was developed by Donald Daring and Robert Haas to study the rangeland vegetation, which is used to analyze remote sensing image to understand the presence and health of green vegetation. Chlorophyll, the chief photosynthetic pigment in green plants, strongly absorbs visible light (from 0.4 to 0.7 µm). The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). Number of leaves a plant has direct effect on the interference of these wave lengths [6] The index is a ratio represented by the formulae; ,

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Abhiroop Chowdhury et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013February 2014,pp. 27-31

Where NIR is Near Infra red and R is Red band of the spectrum The wavelengths of NIR and R band are generally 0.6 µm and 0.8 µm respectively [6]. The value ranges between -1 to +1. Generally vegetation cover is noted between +3 to +8. For the current period LISS III (Linear Imaging Self Scanning Sensor image with resolution of 24.3 m) image of 16 th October, 2008 (Eastern Sundarbans) and 23rd November, 2009 (Central Sundarbans), was procured freely from National Remote Sensing Institute, Hydrabad (bhuvan@nrsc.gov.in) and image classification was done using “Near Infra Red (NIR), Red(R) and Green (G) bands with the help of TNTmips 2012, image processing software. B. Biodiversity Survey and study area Random 10m/10 m quadrat plots [7] are selected from six natural mangrove patches present in Central Sundarbans and Eastern Sundarbans respectively. 12 random quadrats are selected in each patch and their average value is considered for analysis. Three quadrat points, Site 1( 22o 10’ 31” N Latitude/88o 40’ 17” E Longitude), Site 2 (22o 01’ 05” N/88o 41’ 05” E) and Site 3 (21o 59’ 50” N/88o 38’ 12” E) are taken in the Central sundarbans (west of River Bidya but East of Matla River) and three quadrat points namely, Site 4 (22 o 09’ 16” N/ 88 o 51’ 04” E), Site 5 (22 o 11’ 17” N/88 o 57’ 13” E)and Site 6 (22 o 14’ 57” N/88 o 57’ 20” E) in the Eastern Sundarbans (East of River Bidya till Indo-Bangladesh border) represented in Figure 1. Similiarity between the two habitats are compared by Sorenson’s similarity Index (S) which is; Where a= the species common in both the habitat, b= species specific to site one and c= species specific to site two.

Figure 1: Study area and Quadrat Sites

IV. Result and Discussions Estuarine environments are one of the most productive and sensitive ecosystems [8]. They play a vital role in terms of carbon fixation, nursery of aquatic organisms, nutrient trapping, water storage and sediment stabilization. A. Analysis of Remote sensing LISS III image. NDVI values can designate the health of the ecosystem by drawing an image of the condition of the vegetation cover. The two NDVI images of central Sundarbans (Figure 2) and Eastern Sunderbans (Figure 3) marks an easily observable difference in vegetation health in two regions of the mangrove forest.

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Abhiroop Chowdhury et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013February 2014,pp. 27-31

Figure 2: NDVI Thematic mapping of Central Sundarbans

The above image shows that the maximum NDVI value observed in Central sunderban region is 0.44. Ground verification is done by selecting sample points for biodiversity survey in areas with NDVI values comprising of the last two classes between 0.2-0.44. As central part comprises of the forest area that is in buffer zone of Sunderban Tiger Reserve, making limited extraction of forest product legal and also has an increased salinity due to unavailability of fresh water flow, putting the resident mangrove vegetation in serious anthropogenic and salinity stress which is reflected in the NDVI values.

Figure 3: NDVI Thematic mapping of Eastern Sundarbans

Eastern Sundarbans shows healthy vegetation compared to the central sundarbans region with highest NDVI value of 0.67. Quadrat positions are selected comprising both the two classes of NDVI values of 0.3-0.67. This region receives limited fresh water from Padma river system via Harinbhanga river and the forest is mostly protected by laws making it a Sanctuary (Sajnekhali) that merges into the heavily protected National Park in the south making it safe from anthropogenic pressure. That is reflected in the high NDVI values compared to itâ&#x20AC;&#x2122;s central counterpart. B. Biodiversity Assessment The biodiversity of the three sample sites (average species value of 12 random quadrat is taken from each sites) each in Central Sundarbans and Eastern Sundarbans are assessed and percentage cover of each species is recorded. It gives an accurate estimate of the nature of species present in the area and along with the remote sensing image classification is an effective tool in determining the health of the forest. The comparative percentage cover of the two regions is elucidated in table 1. The quadrats are laid randomly after locating the

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Abhiroop Chowdhury et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013February 2014,pp. 27-31

sampling area from the thematic map of NDVI values, calculated from the LISS III image bands. The selection is so done as to cover the major vegetation patches representing the highest and lowest reflectance values for any natural mangrove patches. While verifying at field it is often seen that mangrove plantation areas (viz: Jharkhali Mangrove Ecological Garden-plantation area at Jharkhali), and agricultural fields also shows similar reflectance/NDVI values as the open natural patch mangrove. To minimize this error, intensive field survey is conducted to identify natural mangrove patches pertaining to particular NDVI value class between 2010 to 2013. Following the above procedure, three sites are selected in each of the regions namely Central and Eastern Sundarbans. And an average estimate is taken from these sites to calculate the representative percentage cover in the two parts. Table 1: Percentage Cover of the Mangrove species of Eastern and Central Sundarbans. Serial

Species

Number

Percentage

cover(Eastern

cover(Central

Sundarbans)

Acanthus ilicifolius

2.747253

7.317073

Acanthus volubilis

1.648352

Aegialitis rotundifolia

1.648352

5.487805

Aegiceras corniculatum

2.197802

4.878049

Aglaia cuculata

1.098901

Avicennia alba

1.098901

7.926829

A. marina

3.296703

12.80488

A. officinalis

0.549451

4.268293

Brownlowia tersa

4.395604

Bruguiera cylindrica

4.395604

1.219512

Bruguiera gymnorrhiza

4.945055

3.658537

Bruguiera sexangula

3.296703

Ceriops decandra

6.593407

1.829268

Ceriops tagal

9.89011

4.878049

Clerodendron inerme

4.395604

4.878049

Derris trifoliata

3.658537

Excoecaria agallocha

5.494505

3.04878

Finlaysonia obovata

1.648352

Heritiera fomes

4.395604

Kandelia candel

8.791209

Lumnitzera racemosa

3.846154

Nypa fruticans

3.296703

Phoenix padulosa

0.549451

22.56098

Rhizophora mucronata

7.692308

0.609756

Sonneratia apetala

5.494505

1.829268

S. grifithii

0.549451

Suaeda sp

9.146341

Xylocarpus granatum

2.197802

X. mekongensis

3.846154

Sorensonâ&#x20AC;&#x2122;s Simiarity index calculated in the two sites is 0.7, which indicates that both the habitat are similar in structure but the percentage cover shows that the central sundarbans have a relatively more abundance of Phoenix padulosa which is a edaphic-subclimax than itâ&#x20AC;&#x2122;s eastern counterpart (Figure 4).

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Figure 4: Comparative study of the species percentage cover of Central and Eastern Sundarbans The salt loving species like Avicennia marina [9], Suaeda sp and Phoenix sp predominates in central sundarbans which makes it difficult for fresh water loving Rhizophoraceae members like Kandelia sp and Rhizophora sp, true mangroves like Heretiera fomes, Nypa fruticans, and endangered mangrove associates [1], like Acanthus volubilis, Aglaia cuculata, Brownlowia tersa, Cassytha filiformis (mangrove parasitic flora) and Finlasonia obovata to survive in the central parts but can be noticed in Eastern part though in limited abundance. The understory like Derris trifoliate is seen in the central parts. V. Conclusion The assessment of biodiversity and analysis of remote sensing LISS III image has shown marked differences in species abundance in Central and Eastern parts of Indian sundarbans. The fresh water loving mangrove associates and true mangrove members are getting rarer in central parts whereas they are prevalent in the east. The analysis also concludes that though structurally both the habitat have similar composition the species assemblages are different. Sundarban is the world’s largest contiguous mangrove forest and is a world heritage site, so periodic in depth assessment of mangrove biodiversity using conventional and remote sensing tools is essential for formulating effective management plans for proper maintenance and sustenance of this unique ecosystem. VI. References

[1] [2] [3]

[4] [5]

[6] [7] [8]

[9]

P.B. Tomlinson, “The botany of mangroves,” Cambridge, UK: Cambridge University Press, 1986. S. Maiti and A. Chowdhury, “Effects of anthropogenic pollution on mangrove biodiversity: A Review” Journal of Environmental Protection”, vol. 4, no. 12, Dec 2013, pp. 1428-1434, doi: 10.4236/jep.2013.412163. A. Chowdhury and S. Maiti, “Mangrove reforestation through participation of vulnerable population: Engineering a sustainable management solution for resource conservation”, International Journal of Environmental Research and Development, vol. 4, issue 1, Jan. 2014, pp. 1-8, http://www.ripublication.com/Volume/ijerdv4n1spl.htm. J. Seidensticker and M.A. Hai, “The Sundarbans wildlife management plan: Conservation in the Bangladesh coastal zone,” IUCN, Gland, Switzerland, 1983, pp 120. C. Giri, B. Pengra, Z. Zhu, A. Singh and L. L. Tieszen, “Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000”, Estuarine, Coastal and Shelf Science, vol. 73, 2007, pp. 91-100. http://dx.doi.org/10.1016/j.ecss.2006.12.019 T. N. Carlson and D. A. Ripley, “On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index”, Remote Sensing and. Environment, Vol 62, 1997, Pp241-252. G. Brahma, H. S. Debnath and S. K. Mukherjee, “Assessment of mangrove Diversity in Sundarban Tiger Reserve, India” Bulletin of. Botanical Survey of India, Vol.50, Nos.1-4, 2008, pp.167-170. S.M.J. Baban, “Environmental Monitoring of Estuaries; Estimating and Mapping Various Environmental Indicators in Breydon Water Estuary, U.K., Using Landsat TM Imagery”, Estuarine, Coastal and Shelf Science, vol. 44, 1997, pp. 589–598. Doi:10.1006/ecss.1996.0142. A. Ahmed, A. Aziz, A. Z. M. N. A. Khan, M. N. Islam, K. F. Iqubal, M. Nazma, and M. S. Islam, “Tree Diversity as Affected by Salinity In The Sundarban Mangrove Forests, Bangladesh”, Bangladesh Journal of Botany, vol. 40, issue 2, Dec. 2011, pp 197202. http://dx.doi.org/10.3329/bjb.v40i2.9778

Acknowledgments The authors are thankful to Mr. Kanailal Sarkar and Nihar Mondol for helping with the field work at Eastern Sundarbans and Central sundarbans respectively. We give thanks to Sri. T. K. Sinha, Senior laboratory technician at Microbiology and Ecology laboratory of Department of Environmental science and Engineering, Indian school of mines, Dhanbad for his support. Last but not the least, thanks to Prof. Pranabes Sanyal (Ex-Field Director, Sunderban Tiger Reserve, India) and Ms. Namrata Dey Roy (Lecturer, Susil kar College, Calcutta university) for helping in compiling and editing this paper

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Comparing three approaches to evaluating physics teachers’ effectiveness in instructional delivery to secondary school students in Nasarawa state of Nigeria Amuche1 Chris Igomu and C. M. Anikweze2 (PhD) School of postgraduate studies, Nasarawa State University Keffi, Nasarawa State, NIGERIA, 2 Faculty of Education, Nasarawa State University Keffi, Nasarawa State, NIGERIA.

Abstract: The study was designed to assess physics teachers’ effectiveness in instructional process using three assessment strategies. It was an evaluative survey, with three types of questionnaires - Students Evaluation Instrument, Peer Evaluation Instrument and Self Evaluation Instrument as instruments for data collection. A total of 180 senior secondary two physics students, 9 physics teachers and 18 peers of the physics teachers were randomly selected from 9 Model Science Schools in Nasarawa State of Nigeria were sampled. Four hypotheses were tested using the t-test, Analysis of Variance, and Pearson’s product moment correlation statistics. Results indicated that there was a significant difference between Student Assessment, Peer Assessment and Self-Assessment methods of evaluating physics teacher effectiveness. Students’ assessment and Peer assessment of physics teacher effectiveness showed a strong positive correlation (r = + 0.60), indicating high degree of objectivity. There was no significant difference between students’ assessment scores and peer assessment scores suggesting that the physics teachers were effective in their instructional delivery. Incidentally, the physics teachers were rather generous in their self-assessment corroborating earlier researches. It was concluded that the two assessment methods should be incorporated into the assessment practices of secondary schools in Nigeria particularly as a strategy for improving physics teachers’ instructional delivery. Keywords: Strategies; Evaluative survey; Students Evaluation Instrument; Peer Evaluation Instrument; Self Evaluation Instrument; Physics; Teacher; Assessment I. Introduction Education in Nigeria has been recognized as an instrument par excellence for effective national development in reference [1]. According to reference [2], education is “the key that opens the door of modernization and globalization.” Education, no doubt, is the key to national development: thus, recent trends in education favour the humanistic approach which puts a strong emphasis on the teacher as the major facilitator of the teaching–learning process. Education is intended to serve the expressed goals and aspirations of the country as enshrined in the National Policy of Education in reference [1]. The thrust is towards the realization of national development through improved educational system which has led to the introduction of new programmes and new syllabuses aimed at improving the curricula, particularly at the secondary school level. To assess the output, it becomes necessary that some form of evaluation must be part of the operation of the educational system. Given that the educational system has objectives, it is expected that the operators of the educational system should be committed to the achievement of these objectives. Educational evaluation is a major process that determines the extent to which objectives have been achieved as well as the quality of human development in a society. The quality of human development process refers essentially to the quality of education and the quality of education is largely recognized as the quality of teaching that goes on in the schools in reference [3]. It is generally acclaimed that the quality of education at any level depends largely on the qualification and commitment of the teacher. Thus, the Federal Ministry of Education in reference [4]states that “no educational system can rise above the quality of its teachers as the standards of our teachers invariably affect the performance of the pupils and students.” Therefore, during the process of human development, evaluation information is generated in a variety of ways to improve school administration, teaching and learning; and also to enhance the likelihood of success by both the learner and the teacher.

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Generally, teachers evaluate their students’ learning and accept the results as evidence of their teaching effectiveness. Scholars, however, believe that teachers’ professional growth and effectiveness in instructional delivery could be enhanced through mentoring, peer assessment, student assessment and self-evaluation. Teacher evaluation is of global concern because of the role of the teacher in the education enterprise. This probably explains why Obanya in reference [5] argued that teachers are the major implementers of a country’s educational policies. The teacher engages in interactive behaviour with learners effecting cognitive, affective and psychomotor changes in them. However, reference [5] posited that the teacher is an engineer in the teaching-learning process because he selects the instructional objectives, contents, method and learning experiences, and also evaluates the outcome of instruction with respect to the stated objectives. Furthermore, reference [6]sees the teacher as the one responsible for the instructional design and so needs to make the best choices amidst subject area influences by using his teaching influences (theory, technology and social system) to overcome certain input constraints or limitations in the way of achieving quality output expected by the society. No doubt, the role of the teacher in the school system cannot be over-emphasized but the decline and deteriorating results, particularly from secondary schools vis-à-vis the huge investment in education, are quite disturbing. The situation has made some stakeholders to associate the quality of school products (in terms of achievement scores/grades) with quality of school personnel who are largely teachers. Some have even wondered whether the achievement scores/grades of learners in and from schools do actually reflect the quality of teaching and by extension, the quality and effectiveness of teachers in reference [6]. In view of the above, the public has become increasingly inquisitive and bothered about the activities going on in schools, particularly the results that schools are producing in the science subjects in reference [5]. Generally, there is a consensus of opinion about poor quality of education in Nigeria reference [7]. Governments, communities, proprietors, employers, parents and learners themselves have had reasons to worry about the results and the products of the educational system. Teachers also complain of students’ low performance at both internal and external examination. The annual releases of Senior Secondary Certificate Examination (SSCE) results conducted by West African Examination Council (WAEC) and National Examination Council (NECO) justify generalization of poor secondary school students’ performance in science subjects. Reference [9] noted that there had been a steady increase in failure rate of secondary school students in the science subjects (Biology, Chemistry and Physics) over the years. The poor performance of students in Biology, Chemistry and Physics reflected in Table I corroborate the impression of critics. Table I: Performance of students in the May/June WAEC Senior Secondary Certificate Examination (1994 – 2004) YEAR

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

BIOLOGY TOTAL NO. & % PASS AT ENTRY CREDIT LEVEL 508,385 57,956 (11.40) 351,353 66,406 (18.90) 506,628 80,554 (15.90) 609,026 96,226 (15.80) 637,021 219,453 (34.45) 745,162 207,230 (27.81) 639,020 123,395 (19.31) 995,345 231,418 (23.25) 1,047,232 328,726 (31.39) 931,219 401,728 (43.14) 838,945 258,647 (30.83)

CHEMISTRY TOTAL NUMBER & % PASS ENTRY AT CREDIT LEVEL 161,232 38,212 (23.70) 133,188 48,880 (36.70) 144,990 48,572 (33.50) 172,383 40,682 (23.60) 185,430 39,682 (21.40) 223,307 69,404 (31.08) 201,369 64,217 (31.89) 301,470 109,283 (36.25) 309,120 107,852 (34.89) 288,324 (146,988) (50.98) 275,078 107,198 (38.97)

PHYSICS TOTAL ENTRY 146000 120,768 132,768 157,700 172,223 210,271 193,052 287,993 298,059 280,880 270,028

NUMBER & % PASS AT CREDIT LEVEL 21,462 (14.70) 22,825 (18.90) 16,994 (12.80) 14,824 (9.40) 19,530 (11.34) 76,896 36.57 58,031 (30.06) 99,242 (34.46) 142,055 (47.66) 133,587 (47.56) 137,768 (51.02)

Source: West African Examination Council Annual Report. Recent results seem to follow the same trend as the 2007 and 2008 examinations recorded 6.45% and 6.90% pass respectively among science-oriented candidates in reference [10]. The 2009 result of the NECO SSCE indicated only about 8.5% pass among all the candidates in reference [11]. Some critics have blamed the poor performance of the students on their low retention, association with wrong peers, and low achievement motivation. However, reference [13] posited that the poor level of academic achievement is attributable to teachers’ non-use of verbal reinforcement strategy. In his attribution, reference [14] maintained that the attitude of some teachers to their job is reflected in their poor attendance to lessons, lateness to school, unsavoury comments about students’ performance that could damage their ego, and poor method of teaching which in concert affect students’ academic performance. Either way, the teacher cannot escape accountability for students’ performance at certificate examinations. Incidentally, evaluation of teacher effectiveness has in recent times become enmeshed in controversies over terms and methods. Reference [14] highlighted various teacher evaluation methods to include: Classroom Observation, Student Evaluation, Peer Evaluation, Self Evaluation, Teaching Portfolio, etc. Reference [15] maintained that there has not been a set of clear indisputable conclusion as to the best ways to evaluate

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teaching. While some experts such as in reference [16] argued in favour of the reasonability of teacher selfevaluation, others such as reference [17] strongly opposed the use of self-evaluation method of teacher effectiveness. Teacher self-evaluation is the method of evaluation whereby the teacher rates him/herself against some pre-determined objectives of instruction in order to ascertain his/her effectiveness in instruction delivery. Reference [18] argued that self-evaluation method encourages the teacher to reflect on his/her teaching thereby enhancing performance. Reference [19] had posited that self-evaluation of teacher effectiveness is of greater value for self-understanding and instructional improvement. On the other hand, in reference [20] argued that student evaluation of teacher effectiveness is one of the several forms of evaluation used to shed light on teacher effectiveness. Student evaluation of instruction means that students as consumers of instruction are made to express their opinion and feelings concerning the effectiveness of the teacher’s instructional process and activities in the classroom and the extent to which they benefited from that process. Since there is lack of standardized and uniform quality assurance instruments for teacher evaluation as reported in the Roadmap for Nigerian Education in reference [21] and in view of controversies over methods of evaluating the teacher, the study sought to comparatively analyze three methods of assessing the teacher with a view to determine which strategy is the most objective and valid. Thus, the thrust of the study is to analytically compare student, peer and self-evaluation of physics teacher effectiveness in Nasarawa state secondary schools. Objectives of the Study The general objective of this study was to compare student evaluation (STEV), peer evaluation (PEEV) and self-evaluation (SEEV) of physics teacher effectiveness in Nasarawa state secondary schools. In specific terms, the study attempted to: (i) Determine whether a significant difference existed between the mean assessment of student, peer and physics teacher self-evaluation. (ii) Determine whether there is a significant relationship between student, peer and self-evaluation methods of assessing physics teacher effectiveness. (iii) Determine whether a significant difference existed between mean score of students’ assessment and the mean score of physics teacher self-assessment. (iv) Determine whether a significant difference existed between mean score of students’ assessment and the mean score of peer assessment. Research Questions The following research questions were raised to facilitate the investigation: (i) Is there any significant difference between mean scores of student, peer and self-evaluation of physics teacher effectiveness? (ii) What relationship exists between student, peer and self-evaluation methods of evaluating physics teacher effectiveness in Nasarawa state secondary schools? (iii) Is there a significant difference in the mean scores between student evaluation of their physics teacher and physics teacher self-evaluation? (iv) Is there a significant difference in the mean scores between student evaluation and evaluation by peers of the physics teacher? Statement of the Hypotheses The following hypotheses were tested: 1. There is no statistically significant difference between mean scores of student, peer and self-evaluation of physics teacher effectiveness in Nasarawa state secondary schools. (Ho1) 2. There is no statistically significant relationship between student, peer and self-evaluation of physics teacher effectiveness in Nasarawa state secondary schools. (Ho2) 3. Students’ evaluation mean scores would not differ significantly from mean scores of peers of physics teacher(Ho3) 4. Students’ evaluation mean scores would not differ significantly from mean scores of the physics teacher (Ho4) II Literature Review Evaluation in educational practice connotes different things to different scholars. But generally, evaluation connotes the systematic process of gathering, selecting, analyzing and reporting valid information on the attainment of educational goals and objectives in order to facilitate correct adjudication on the effectiveness of teaching method(s) or an educational programme as in reference [11]; reference [12]; reference [13] and reference [14]. In the educational system, evaluation is usually carried out at two major levels - student level and, programme level reference [14]. No place is provided for the evaluation of teacher effectiveness by those who

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are basically involved and in the best position to do so (the student and fellow teachers). To correct this anomaly, reference [15] proposed that evaluation should be conducted to cover three levels of student, teacher and programme. Teacher evaluation according to reference [15] is based on the premise that if teachers should be faced with the realization that their continued employment and promotion would partly be based on the evaluation of their performance by their students (who remain anonymous), and colleagues, then they would be compelled to put in their best in the class. In this regard, two types of evaluation (formative and summative) could be carried out with distinctive roles. While, formative evaluation is undertaken during teaching and learning for the expressed purpose of learning to achieve its objectives, summative evaluation is carried out by the teacher which may be at the end of the term, year or end of a course for the purpose of decision making such as, promotion, demotion, retention or firing. Numerous studies have attempted to measure teacher effectiveness using different methods and on different school subjects/courses. Reference [16] surveyed 480 secondary school teachers from 20 schools and found significant negative attitude to student evaluation of the teacher, irrespective of the use(s) to which the results of such evaluation will be put. Reference [16] using a sample of 2,310 students in 60 secondary schools from 12 states of Nigeria investigated the quality of secondary school teaching in Nigeria from the perspective of the students. The findings indicated that teachers were effective in class attendance; competent in content and pedagogy; and manifested positive relationship with students and disciplinary qualities. The result suggests that students perceived their teacher as efficient in their job performance, hence, student evaluation could be highly effective. Reference [5] compared three instruments for evaluating Biology teacher effectiveness in the instructional process in Edo state of Nigeria. The instruments were; Student Assessment of Teacher Instrument (SATEI), Teacher Assessment of Teacher Effectiveness Instrument (TATEI) and Class Observation. The result of the study showed that there was a strong agreement in the assessment of Biology teachers’ effectiveness between student evaluation and classroom observation by the researcher indicating high degree of objectivity. On the contrary, the Biology teachers’ self-evaluations were biased in their self-assessment of teaching effectiveness. III Methodology The population of the evaluative survey consisted of all the 53 physics teachers in 145 science-oriented senior secondary schools in Nasarawa state, their peers and the senior secondary 2 (SS2) students who offer physics as one of their certificate examination subjects. The sample for the investigation consisted of 9 physics teachers, 18 peers of the physics teacher and 180 physics students selected through multi-stage stratified sampling technique from science-oriented secondary schools located across the ten educational zones in Nasarawa state. Thus, one (1) physics teacher was randomly selected from each of the nine science schools and two colleagues of the target physics teacher were randomly selected from each school using the Hat-and-Draw method. The SS2 students (20 from each of the 9 schools) were selected using the simple random sampling technique. This brought the total research subjects to 207 which were considered representative of the total population. Instrumentation Three types of questionnaires were used for data collection - Student Evaluation of Teacher Effectiveness Instrument (STEV), Peer Evaluation of Teacher Effectiveness Instrument (PEEV) and Self Evaluation of Teacher Effectiveness Instrument (SEEV). Each of the designed questionnaires consisted of two sections: the essential bio-data and 24 items on a 5-point scale ranging from Excellent (5), Good (4), Average (3), Fair (2) and (Poor (1) or (Always (5), Often (4), Sometimes (3), Rarely (2) and Never (1). The scales were used to either elicit the degree of availability or the frequency of the characteristics under assessment. The respondents were expected to indicate their opinions on the effectiveness of the physics teacher by focusing on the extent to which the physics teacher exhibited characteristics/attributes in the areas of preparation of lessons, presentation of lessons, classroom management, communication skills, personality and evaluation of lessons. Validity and Reliability of the Instruments To build validity and reliability into the data collection instruments, efforts was made to relate each item in the questionnaire to a specific variable for assessing physics teacher effectiveness. The items on the three scales were similar and were generated by adapting the Teaching Practice Format used by the Faculty of Education, Nasarawa State University, Keffi. Face validity was sought and obtained by subjecting the instruments to critical appraisal of experts in Measurement and Evaluation. The experts were requested to check for clarity, ambiguity of the items, appropriateness of the items, language use, clarity of purpose and relevance to the issue under investigation using a 5-point rating scale. This enabled the researchers to establish logical validity indices of 0.70, 0.60 and 0.70 for STEV, PEEV and SEEV respectively. The validity indices were considered sufficiently high for use in collecting data for the study.

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Results

Presentation and Analysis of Data Table 2: Comparison of Physics Teachers Assessment by Students, Peers & Self by Schools S/N 1. 2. 3. 4. 5. 6. 7. 8. 9.

NAME OF SCHOOL GSSS ANDAHA GGSSS WAMBA GGSSS DOMA GSSS OBI GSSS KARU GGSSS GARAKU GSSS LAFIA GSSS NASARAWA GSSS NASARAWA EGGON GRAND MEANS

STEV mean scores (%)

PEEV mean scores (%)

SEEV mean scores (%)

56.5 60.5 48.8 51.0 55.8 50.2 53.9 54.7 52.0 53.7

62.5 59.1 47.1 60.9 56.3 56.7 55.4 60.0 57.0 57.2

85.0 85.8 82.5 80.0 82.5 80.8 84.2 77.5 87.5 82.8

Table 2 shows the results for the three methods of assessment for each of the sample schools in Nasarawa state. The study the result shows that the assessment of the physics teacher by students, peers and self-exceeded 50% as bench mark for success. This indicates that generally, the physics teachers were effective in their instructional delivery. . The overall mean assessment scores were STEV = 53.7, PEEV = 57.2 and SEEV = 82.8. The result indicates that there is an agreement between students’ assessment and peer assessment methods. However, the teachers’ self-assessment seemed exaggerated when compared to students’ and peer assessment suggesting a natural tendency of individuals to over-score themselves when given the opportunity for self-evaluation in reference [17]. However, the table also shows the mean assessment of physics teachers’ effectiveness from the three methods of evaluation Table 3: Comparison of Mean Assessment of Physics Teacher Effectiveness by STEV, PEEV and SEEV: Summary of ANOVA. S/N 1. 2. 3.

ASSESSMENT METHOD STEV PEEV SEEV

N 180 18

MEAN 53.70 57.20

S.D 3.43 4.21

S.E 0.25 0.99

82.80

2.94

0.98

SOURCEOF VARIATION

SUM OF SQUARE (SS)

BETWEEN METHODS WITHIN METHODS TOTAL VARIATION

4543.02 360.52 4903.54

MEAN SQUARE (MS) 2271.51 15

SIG. OF F

2 151.4 df (2,24) 3.40 24 26 (α = 0.05) Table 3 shows an F-ratio of 151.4 which is significant beyond 0.05 level of probability with degrees of freedom (2, 24). The null hypothesis (Ho1) was therefore rejected. The result indicates that there is a statistically significant difference between the mean assessment scores on physics teacher effectiveness using the three methods of evaluation (STEV, PEEV and SEEV). Table 4: Multiple Comparisons of Means: Tuckey’s Honestly Significant Difference (HSD) Test. STEV X1 = 53.7 PEEV X2 = 57.2 SEEV X3 = 82.8 1.02 8.48* STEV X1 = 53.7 1.02 6.08* PEEV X2 = 57.2 8.48* 6.08* SEEV X3 = 82.8 * Studentised Mean = 2.98 = Significant df = 17 α = 0.05 The post Hoc Analysis using Tuckey’s Honestly Significant Difference (HSD) test to determine the direction of superiority of means as shown in Table 4 indicates that the ratio of 8.48 (teacher self-assessment) is superior to others. The Studentised mean of 2.98 is greater than the calculated Studentised ratio of 1.02 (df = 17, α = 0.05). This test further indicates that STEV and PEEV were more objective and valid in the assessment of the physics teacher.

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Table 5: Summary of Correlation Coefficient (Pair-Wise) for Three Methods of Evaluation S/N

EVALUATION METHODS

1. 2. 3.

STEV AND PEEV STEV AND SEEV PEEV AND SEEV

CORRELATION COEFFICIENT (r)

REMARK

+ 0.60 +0.29 -0.01

Positive and Strong Positive and Weak Negative and Weak

Table 5 shows the correlation coefficients of the three methods of assessing physics teacher effectiveness. This reveals that students’ assessment and peer assessment (STEV and PEEV) correlated positively and strongly indicating that the two methods were related positively. STEV and SEEV had a weak positive correlation. This implies that the relationship between them was weak while PEEV and SEEV had a negative and weak correlation coefficient. The correlation coefficients obtained in Table 5 were transformed to t-values and tested at a probability level of 0.05. This is to establish whether the relationships were statistically significant. Table 6: Testing the Significance of the Correlation Coefficient (r) S/N

ASSESSMENT (PAIRWISE)

METHODS R

STEV-PEEV

+0.60

Calculated t (tcal) 3.00

2. 3.

STEV-SEEV PEEV-SEEV

+0.29 -0.01

1.35 -0.24

Critical t (ttable)

Remark

2.92

0.05

SIGNIFICANT

2.92 2.92

0.05 0.05

NOT SIGNIFICANT NOT SIGNIFICANT

Table 6 shows the summary of t-test analysis for the pair-wise correlation coefficients of the methods of evaluating physics teacher effectiveness. The result indicates that the relationship between student assessment and peer assessment was significant at 0.05 level of probability. However, the relationship between STEVSEEV and PEEV-SEEV were not significant at 0.05 alpha. Hence, Hypothesis 2 (H 02) was not rejected for STEV and PEEV. V Discussion The results of the study showed that the scores of physics teachers’ self-assessment of instructional effectiveness were higher than the scores of students’ assessment and peer assessment methods (Table 3). However, there was a strong agreement in the assessment of physics teacher effectiveness by students and peers of the physics teacher indicating a high degree of objectivity in their assessment. Based on the great difference between physics teacher self-assessment and the assessments by students and peers of the physics teacher, this study placed more premiums on the assessment done by students and peers of the physics teacher. Hence, selfevaluation of physics teachers in Nasarawa state secondary schools could not be relied upon. A major finding of the study suggests that peer evaluation and student evaluation are valid methods of assessing teacher effectiveness based on their correlation. Furthermore, there was no significant difference between the means obtained from these two methods. This result is in agreement with reference [18] and reference [5] who was strongly opposed to the use of teacher self-evaluation in the assessment of instructional effectiveness because of the tendency to over-score self as found out by earlier researchers like reference [19]; reference [20].Reference [22] equally found out that chemistry teachers were biased in their self-assessment of teaching effectiveness. The result of the study however, is incongruous with reference [17] who argued in favour of the reasonability of teacher self-assessment. The findings of this study are in agreement with reference [21] who argued that student evaluation of teacher effectiveness is one of the several forms of evaluation used to shed light on teaching effectiveness. Evidence from the study further attests to the usefulness and accuracy of students’ evaluation as an index of determining teacher effectiveness. This is in agreement with reference [23] who argued that it is only by the evaluation of our performance by a third person (or persons) that we can ever hope to receive objective feedback as to the quality of our output. Hence, peers of the physics teacher can provide valid assessment of the physics teacher instructional effectiveness. Findings from the study suggest that using a single method to evaluate the teacher’s instructional effectiveness might not be adequate. The practice over the years has been the use of peer evaluation (observation technique) only to assess teacher effectiveness. This study has revealed that incorporating two methods (student evaluation and peer evaluation) for this task offers more valid, efficient and objective means of evaluating the teacher. VI Conclusion Based on the findings of the study, the physics teachers’ performance was satisfactory with overall mean score of 55.5% indicating that they were effective in their instructional delivery. One can therefore

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conclude that they are not responsible for the poor students’ performance in physics at both internal and external examinations. The magnitude of the relationship between student assessment and peer assessment of the physics teacher effectiveness shows a strong positive correlation (+0.60). This implies that high assessment scores from peers of the physics teacher should give a corresponding high assessment score if assessed by students. On the contrary, the use of self-evaluation cannot provide an objective and valid assessment of the physics teacher. Therefore, using the two methods (student and peer) in the evaluation of the physics teacher will check for bias. This will ensure standards and quality assurance. VII Recommendations Based on the findings of this study, the following recommendations were made: 1. Teacher evaluation instrument should incorporate both student and peer assessment of teacher effectiveness in instructional delivery as this will check bias and ensure quality assurance in teacher evaluation. 2. Since student evaluation correlated highly with peer evaluation, school administrators should exercise caution in using only peer assessment for evaluating physics teacher effectiveness and rather prefer to use both peer and student evaluation. 3. Schools in Nasarawa state should ensure frequent inspection of their teachers to monitor their instructional delivery using a combination of the two methods as the results can be used for both formative and summative purposes. It will be unfair to use only STEV or PEEV method as indicators of teachers’ effectiveness; a combination of the three strategies is likely to give a picture of the teacher. 4. Physics teachers in the state need to improve on their effectiveness in terms of instructional delivery as this will improve the overall performance of students in both internal and external examinations. Suggestions for Further Research The comparative analysis of these methods can be carried out in other science subjects such as, Biology, Chemistry and Mathematics. The study could also be carried out in any other subject in Nasarawa state or in other geographical locations in Nigeria to further test the efficacy of these evaluation methods. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

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[15] [16] [17] [18] [19]

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Mass production and formulation of herbicidal metabolites from Phoma herbarum FGCC#54 for management of some prominent weeds of Central India. Sadaf Kalam1, Adarsh Pandey2 and A.K.Pandey3 Mycological Research Laboratory, Dept. of Biological Sciences, R.D. University, Jabalpur, India. 2 Lab of Mycology, Department of Botany, SS (P.G.) College, Shahjahanpur UP,India. 3 Professor, Mycological Research Laboratory, Dept. of Biological Sciences, R.D. University, Jabalpur, India. 1

Abstract: Large scale production of phytotoxins, their long term storage and stability are the important challenges facing the manufacturers of ecofriendly herbicides. Commercialization of phytotoxins as ecofriendly agrochemicals requires low cost, economical feasibility and easy availability of large scale production technology. Submerged fermentation is the process of choice for industrial operations due to the well known engineering aspects such as fermentation modeling, bioreactor design and process control. Biological control especially through microorganisms has shown excellent potential in weed management being cost-effective and environmentally benign method available for the control of deadly weeds. Metabolites from Phoma herbarum FGCC#54, a phytopathogenic fungus were exploited for their herbicidal potential. 16 different agrowaste extracts and weed part decoctions were assessed for biomass and phytotoxin production. Maximum growth was observed in Arhar Bran Extract followed by Malt Extract and Chana Bran Extract. Arhar bran fermented broth exhibited maximum phytotoxicity. pH 4 supported maximum biomass production followed by pH 3.0 and 5.0. Phytotoxin produced at 28±2ºC produced remarkable phytotoxicity. Formulating biopesticides and evaluation of formulation ingredients that are cost effective and promote product stability has progressed with several microorganisms over the past decade. The phytotoxic damage studies clearly show that the most compatible formulant was Tween 80 followed by coconut oil, Triton X-100, Tween-60, Tween-20 and sorbitol. Key words: mass production, formulation, Phoma herbarum, hpt (hours host treatment), agrowastes, phytotoxicity

I. Introduction Fungi have long been recognized as instigators of plant diseases associated with the elaboration of one or more phytotoxins. The detection and study of weed pathogens is important not only for their intrinsic ability to serve as biocontrol agents, but also because of their propensity to produce novel bioactive substances (Strobel, 1991).These substances could become novel herbicides or provide important chemical leads to the herbicide industry (Strobel et al., 1987). Phoma is a well-known phytopathogen responsible for many diseases in plants and is known to produce an array of bioactive extracellular phytotoxic compounds (Chan et al., 2005; Strobel et al., 1991). Large quantities of agricultural wastes (agrowastes) are generated all over the world. The environmental pollution problems associated with conventional disposal methods necessitate the search for alternative, environmentally friendly, benign methods of handling agrowastes. Agrowastes are locally and readily available plant residues which are usually discarded and can be used for mass cultivation of fungi and mass production of its phytotoxic secondary metabolites which possess herbicidal potential. Thus agricultural residues can be considered to be novel and lucrative mines for mass production or large scale production of ecofriendly agrochemicals to combat the hazards of using chemical herbicides. High cost of synthetic chemicals and their toxicity to environment has necessitated the search for ecofriendly alternatives. Easy availability of many agricultural by-products may provide a sustainable way to develop ecofriendly herbicides in economically feasible rates. Thus employing agricultural residues for secondary metabolite production is a value added process to convert these materials which are otherwise considered to be wastes into applicable forms (Akinyele and Adetuyi, 2005).Agrowastes have relatively high nitrogen, phosphorus and potassium contents compared with calcium and magnesium which are low (Akinyele & Akinyosoye, 2005).Development of low-cost methods for large-scale production of metabolites is a critical step in the commercialization of these products (Bowers, 1986). Submerged fermentation of fungi for mass production of secondary metabolites is viewed as a promising alternative for producing valuable, bioactive substances. Liquid

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or submerged fermentation has been the method of choice for large scale production of many products including secondary metabolites. Inspite of the great effort made by the pesticide industry in the last decade, developing new more effective, biodegradable pesticides and producing new types of formulations such as concentrated in suspension, concentrated emulsion-like, granules, soluble liquids and so there are still important problems derived from the immediate release of the active ingredients which compose them (Flores Cespedes et al., 2007). Several types of adjuvants have been screened and exploited for formulation of herbicidal toxins (Nalewaja, 1986; Prasad, 1993). II. MATERIALS AND METHODS MASS PRODUCTION 1. Recovery of strain The strain of the test fungus Phoma herbarum FGCC#54 was obtained from the Fungal Germplasm Collection Center (FGCC), Mycological Research. Laboratory, Department of Biological Sciences, R.D.V.V. Jabalpur (M.P.) India. It was isolated earlier from leaves of the target weed (Parthenium hysterophorus). 2. Screening of agrowastes and host decoctions: Sixteen different agro based waste products were collected from the local market fields while weed parts were collected from weed infested areas, which included: Arhar Bran, Malt, Chana Bran, Paper pulp, Wheat Bran, Wheat straw, Wheat porridge, Pea pod, Parthenium leaf, Parthenium Stem, Lantana leaf, Lantana stem, Hyptis leaf, Hyptis Stem, Sida leaf, Sida stem. Liquid extracts of these agrowastes were prepared for screening out the best agrowaste for mass production. 2. Preparation of agrowaste extracts: (Saxena, 1999) Ten gm of different agro based and vegetable waste products were taken in a vessel and 300 ml distilled water was added, boiled for 60 min, filtered through cheese cloth, then volume was raised upto 500 ml, and autoclaved at 121ºC, 15lbs for 15 min. The filtrate so obtained is the agrowaste extract and decoctions. a. Fermentation: 250ml Erlenmeyer flasks containing 50ml of different extracts were autoclaved at 121ºC and 15 lbs pressure for 15 minutes and then were seeded with 7 days old actively growing culture with the help of 5 mm sterilized cork borer, and incubated in B.O.D. incubator (Remi, India) at different pH and temperatures for 21 days of incubation. b. Extraction of CFCF CFCF was aseptically obtained by filtering the metabolized growth medium through Whatman filter paper No. 1. The supernatant was filtered through the sartorius filter paper 0.45 µm, Minisart (Sartorius Gottingen, Germany) millipore filter under in vacuo conditions (Walker and Templeton, 1978). 3. Effect of pH Similarly the strain of Phoma herbarum FGCC#54 was inoculated in the sterilized extract of Arhar bran maintained at different pH levels with a pH meter (Systronics, India) viz., 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 with the help of 0.1 M HCl and 0.1 M NaOH solution and incubated for 21 days at 28 ±1ºC. Mass production of phytotoxins at different pH was observed and phytotoxic damage rating was assessed by shoot cut bioassay. 4. Effect of Temperature Screened, optimized and sterilized extract of Arhar bran was seeded with 7 days old actively growing culture of Phoma herbarum FGCC#54 and incubated at different temperatures viz., 0, 5, 10, 15, 20, 25, 28, 30, 35, and 40º C in an incubator (Yorco, India) for 21 days. Mass production of secondary metabolites at different temperatures was thus studied and phytotoxicity of the CFCF was determined by shoot cut bioassay. 5. Mass production in a Fermentor 10 liter pilot size fermenter (Scigenics, India) was employed for mass production of secondary metabolites from Phoma herbarum FGCC#54. The medium used for preparation of seed was 5 liter Arhar Bran Extract which was sterilized at 15 psi (121°C) for 20 min and inoculated with 10 ml (3.4 x 10 7 / ml) inoculum and run for 10 days. Harvesting was done after 10 days. Fermentation process was employed for large scale production of secondary metabolites and the parameters were: S.No 1 2 3 4 5 6 7

Parameter Age of seed Inoculum pH Temperature Agitation Harvest Time Biomass

Value 10 days 3.4 x 107 spores/ml 4.00 28±2ºC 100-120 rpm 10 days 12.5gmL-1

Formulation To test the compatibility of the toxin synthesized by the pathogen twelve formulating agents namely, Tween-20, Tween -40, Tween -60, Tween -80, Triton X-100, sucrose, soyabean oil, mustard oil, coconut oil,

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groundnut oil, glycerol, sorbitol were used. Formulating agents were added @ 0.5% to the toxin singly and compatibilty was determined by seedling bioassay. III. RESULTS AND DISCUSSION MASS PRODUCTION 16 different agrowaste extracts and weed part decoctions were inoculated with test fungi and fermented broths were assessed for biomass and phytotoxin production by shootcut bioassay. Data presented in Table 1 reveals significant variation in mycelial biomass caused by cell free culture filtrates obtained by cultivating Phoma herbarum (FGCC#54) on different agrowaste extracts. Extensive mycelial growth was observed in Arhar Bran Extract followed by Malt Extract and Chana Bran Extract. Sugarcane bagasse extract and paper pulp extract yielded negligible mycelial biomass. There occurs a positive drift in final pH from initial pH. As represented in Table 2, of all the spent broths, Arhar bran fermented broth exhibited maximum phytotoxicity. This was followed by Malt Extract Fermented Broth and Chana (Gram) Bran Fermented Broth. With regards to various decoctions prepared from different parts of weeds viz., leaves and stems, less phytotoxin production occurred. But stem decoctions produced more phytotoxins in comparison with leaf decoctions. Wheat Bran Extract Fermented Broth, Wheat Straw Extract Fermented Broth, Wheat Porridge Extract Fermented Broth, exhibited no phytotoxic damage to the weed shoots. Phytotoxin production followed the same trend as biomass production. Shoot cut bioassay was performed with different agrowaste fungal extracts. Maximum phytotoxic damage to Parthenium shoots occurred with Arhar Bran Extracts. The phytotoxicity of these fermented media indicated a maximum foliar damage with fermented media containing Arhar Bran Extract followed by Malt Extract and Chana Bran Extract. Negligible quantities of toxins were produced in sugarcane bagasse extract and paper pulp extract. Numerous advantages can be attributed to the use of liquid culture fermentation or submerged fermentation for the production of herbicides. As liquid or submerged fermentations are relatively low cost, automated processes can be scaled up to very large volumes. Thus, liquid or submerged fermentation has been the method of choice for the large-scale production of phytotoxins. Mass production: For mass production of phytotoxins, a cheaper and economically feasible media is a prerequisite. Results of various agrowastes for mass production of phytotoxin from Phoma herbarum (FGCC#54) clearly indicate that best phytotoxin production was achieved in Arhar Bran Extract. So Arhar Bran Extract was employed as the basal medium. Similarly, Parra et al. (2005), have optimized the composition and concentration of a liquid fermentation medium for the production of phytotoxin from Phoma spp. Effect of pH: The production of fungal secondary metabolites is reportedly dependant on the physico-chemical environment where the mould develops. Hydrogen ion concentration of the growth media thus plays a very critical role in determining phytotoxin production. The effect of different pH on the biomass and toxic metabolite production by the fungi used in this study are shown in Table 3. Biomass production was clearly retarded at higher pH. pH 4 supported maximum biomass production followed by pH 3.0 and 5.0. While higher pH level viz., 6,7 and 8 did not support significant mycelial dry weight. With regards to phytotoxicity Arhar Bran fermented extract at pH 4 showed maximum phytotoxic damage to Parthenium shoots followed by pH 3 and pH 5. Toxin production in Arhar Bran Extract of pH 6, 7 and 8 was negligible and therefore failed to cause significant phytotoxic damage to weed shoots Toxin produced at pH 4 produced maximum damage within 12 hpt, which enhanced till 48 hpt. Similar trend was also recorded by earlier workers (Pandey et al., 2001, 2004).Thus pH 4 was found to be optimum for phytotoxin production where highest quantities of toxic metabolite production was observed. Effect of temperature: The incubation temperature is one of the significant parameter in determining the overall growth and development of any organism. Effect of different incubation temperatures on growth and phytotoxin production by Phoma herbarum (FGCC#54) was studied by shoot cut bioassay on Parthenium shoots and is shown in Table 4. Lower temperature neither favoured fungal growth nor phytotoxin production. Maximum mycelial biomass production was achieved at 28º±2ºC followed by 25º±2ºC and 35º±2ºC. No significant growth and phytotoxin was reported at lower and higher incubation temperatures. Phytotoxin produced at 28±2ºC produced remarkable phytotoxicity. IV.

MASS PRODUCTION OF PHYTOTOXINS THROUGH FERMENTATION IN A FERMENTOR After optimization of pH and temperature values, mass production of the phytotoxin was done on Arhar Bran Extract and phytotoxin production was determined after certain intervals of days. During the process of fermentation in fermentor the fermented broth was eluted with an interval of two days starting from the day of inoculation to the day of harvesting. With increase in days of fermentation phytotoxin production was found to increase with maximum after 10 days of fermentation which exhibited maximum phytotoxicity to weed shoots after 48 hpt. After 10 days of fermentation in the fermentor, fermented broth obtained exhibited maximum

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damage to Parthenium followed by Lantana, Hyptis and Sida as determined by shoot cut bioassay as depicted in Table 5. Least phytotoxin production was oberved after 2 days of fermentation. FORMULATION STUDIES Formulation refers to the process of blending of secondary metabolite or microbe with inert carriers to alter their physical characteristics to enhance its shelf life and field performance. Formulation involves the use of formulants which include: adjuvants, surfactants, wetting agents, spreaders and penetrants. The use of formulatives for biological control is a relatively new application of technology that is well known to the chemical herbicide industry. Adjuvants increase the efficacy of post emergence herbicides by increasing the wettability of the target surface as they reduce surface tension. Adjuvants are also known to enhance penetration (Singh and Mack, 1993). In order to investigate the compatibility of the phytotoxins produced by the test fungus various formulants were tested. Thus, the objective of the present study was to evaluate the compatibility of different formulants as singlets and in combinations against the target weeds. Data represented in Table 6, shows the compatibility testing of various formulants @ 0.5% on different weeds by seedling bioassay. The phytotoxic damage studies clearly show that the most compatible formulant was Tween 80 followed by coconut oil, triton X-100, Tween-60, Tween-20 and sorbitol. Average effect was shown by groundnut oil and sucrose. Arhar Bran fermented broth caused phytotoxic damage to the seedlings but effect was less without adding the formulant. Numerous advantages can be attributed to the use of liquid culture fermentation or submerged fermentation for the production of herbicides. As liquid or submerged fermentations are relatively low cost, automated processes can be scaled up to very large volumes. Thus, liquid or submerged fermentation has been the method of choice for the large-scale production of phytotoxins. Parra et al. (2005), have optimized the composition and concentration of a liquid fermentation medium for the production of phytotoxin from Phoma spp. An optimum pH 4 for phytotoxin production was also recorded by earlier workers (Pandey et al., 2001, 2004). Surfactants play a very important role in improving the performance efficiency of pesticides with the potential to reduce the amount of active required and improve pesticide safety. Tween series of surfactants are nonionic surfactants, each of them ethoxylated sorbital esters of fatty acids and a polyoxyethylene unit 20 repeat groups long on average. They adsorb with the alkyl chain at the hydrophobic surface and the ethylene oxide head group, which is water soluble, protruding into the water solution (Graca et al., 2007). They are generally easily degradable. Oils are used as additives for a variety of reasons such as reducing vapour loss of herbicide, enhancing the performance of herbicides. Traditionally, spray formulations have incorporated petroleum-based oils, but more recently oils extracted from crop seeds such as soybean, sunflower, canola and coconut have been used. Crop oil-based adjuvant i.e. refined or esterified vegetable oils are known to enhance the phytotoxicity of herbicides. Thus formulation with vegetable oils can enhance absorption, translocation and phytotoxicity of herbicides (Gauvrit and Cabanne, 1993). In order to investigate the compatibility of the phytotoxins produced by the test fungus various formulants were tested. On the basis of results obtained above, it can be concluded that the secondary metabolites of Phoma sp. FGCC#54 possess high herbicidal potential and can be developed as potential herbicides for the management of the prominent weeds of Madhya Pradesh. ACKNOWLEDGEMENTS We are thankful to Head, Department of Biological Sciences for laboratory facilities and to Madhya Pradesh Biotechnology Council, Bhopal, India for financial assistance. REFERENCES Akinyele, B.J. and Akinyosoye F.A. (2005). Effect of Volvariella volvacea cultivation on the chemical composition of agrowastes. Afr J Biotechnol, 4 (9), 979-983. Akinyele, B.J. and Adetuyi F.C. (2005). Effect of agrowastes, pH and temperature variation on the growth of Volvariella volvacea. Afr J Biotechnol, 4 (12), 1390-1395. Davis, N.D. and Blevins W.T. (1979). ‘Methods for laboratory fermentations’, in Microbial technology: Microbial processes, eds. H.J. Peppler and D. Perlman) Academic Press, Inc. New York. p.303-329. Fang, Q.H. and Zhong J.J. (2002). Submerged fermentation of higher fungus Ganoderma lucidum for production of valuable bioactive metabolites- ganoderic acid and polysaccharide. Biochem Eng J, 10, 61-66. Gauvrit, C. and Cabanne. F. (1993). Oils for weed control- Uses and Mode of Action. Pest Sci, 37, 147-153. Graca, M., Jeroen H.H.B., Stokes J.R., Granick S. (2007). Friction and adsorption of aqueous polyoxyethylene (Tween) surfactants at Hydrophobic surfaces. J. Colloid Interface Sci, 315, 662–670. Jackson, M.A., Shasha B.S. and Schisler D.A. (1996). Formulation of Colletotrichum truncatum microsclerotia for improved biocontrol of the weed hemp sesbania (Sesbania exaltata). Biol Control, 7, 107-113. Montazeri, M. and Greaves M. P. (2002). Effects of culture age, washing and storage conditions on desiccation tolerance of Colletotrichum conidia. Biocontrol Sci. Techn.12: 95-105. Park, J.P., Kim S. W., Hwang H. J. and Yun J.W. (2001). Optimization of submerged culture conditions for the mycelial growth and exo-biopolymer production by Cordyceps militaris. Lett Appl Microbiol. 33, 76-81. Saxena, S. (1999). Efficacy of microbial metabolites on some fungi to enhance their mycoherbicidal potential for the management of Lantana camara L. Bioscience, Ph.D. Thesis. R.D. University, Jabalpur. Singh, M. and Mack R.E. (1993). Effect of organosilicane- Based adjuvants on herbicide efficacy. Pest Sci,, 38, 219-225. Stowell, L.J. (1991). Submerged fermentation of biological herbicides. In: Microbial control of weeds. (ed D.O. Te Beest) Chapman and Hall, New York. pp. 225-261.

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Sadaf Kalam et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 40-47 Tang, Y.J. and Zhong J.J. (2002). Fed-batch fermentation of Ganoderma lucidum for hyperproduction of polysaccharide and ganoderic acids. Enzyme Microbial Technol, 31: 20-28. Walker, H.L. and Templeton, G. E. (1978), In vitro production of phytotoxic metabolites by Colletotrichum gloeosporioides f sp aeschynomene. Plant Sci Lett, 13, 91-99. Wraight, S.P., Jackson M.A., de Kock S. L. (2001). Production, stabilization and formulation of fungal biocontrol agents. In: Fungi as biocontrol agents. Progess, problems and potential (eds. T.M. Butt, C. Jackson and N. Magan) CABI Publishing, Wallingford. pp. 253287.

S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Table 1: Effect of different agrowastes on phytotoxin and biomass production by Phoma sp. FGCC#54. Agrowaste Extract I. pH F. pH Biomass (gm/L) Arhar Bran Extract 5.60 6.25 10.200.03 Malt Extract 7.04 7.56 8.060.02 Chana Bran Extract 7.44 7.67 7.330.02 Parthenium Leaf Decoction 7.36 7.70 2.800.03 Lantana Leaf Decoction 7.04 7.70 2.130.02 Hyptis Leaf Decoction 7.05 7.52 0.860.02 Sida Leaf Decoction 7.49 7.58 5.000.02 Parthenium Stem Decoction 6.98 7.68 1.460.02 Lantana Stem Decoction 7.83 7.98 1.530.02 Hyptis Stem Decoction 7.37 7.24 6.330.02 Sida Stem Decoction 6.92 8.06 1.220.02 Paper Pulp Extract 6.73 7.64 0.000.00 Wheat Bran Extract 6.48 7.37 0.000.00 Wheat Straw Extract 8.27 7.42 0.000.00 Wheat Porridge Extract 6.37 7.37 0.000.00 Pea Pod Extract 6.66 7.82 0.000.00 SEm± 0.03 LSD5% 0.08

Table 2: Assessment of Phytotoxic damage by phytotoxins produced on different agrowastes by Phoma sp. FGCC#54. (Shoot Cut Bioassay) S.N Agrowaste Parthenium (PDR) Lantana (PDR) Hyptis (PDR) Sida (PDR) o Extract 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 1 Control a 0 0 0 0 0 0 0 0 0 0 0 0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 2 Control b 0 0 0 0 0 0 0 0 0 0 0 0 Arhar 4.310.0 4.500.0 4.720.0 4.270.0 4.490.0 4.710.0 4.160.0 4.190.0 4.250.0 3.790.0 3.910.0 4.070.0 3 Bran 3 4 4 4 3 3 3 3 4 3 3 5 Extract Malt 4.110.0 4.250.0 4.470.0 4.140.0 4.190.0 4.390.0 4.080.0 4.160.0 4.130.0 3.670.0 3.790.0 3.860.0 4 Extract 5 3 3 3 3 5 5 2 2 3 3 3 Chana 4.120.0 4.190.0 4.330.0 4.040.0 4.120.0 4.270.0 4.030.0 4.050.0 4.110.0 3.520.0 3.710.0 3.750.0 5 Bran 3 4 4 3 4 4 2 3 2 2 4 3 Extract Partheniu 3.930.0 4.050.0 4.210.0 3.880.0 4.020.0 4.080.0 3.860.0 3.920.0 4.020.0 3.360.0 3.500.0 3.690.0 6 m Leaf 4 3 3 5 5 4 4 2 4 5 5 3 Decoction Lantana 3.850.0 3.980.0 4.050.0 3.760.0 3.900.0 3.960.0 3.720.0 3.810.0 3.880.0 3.140.0 3.300.0 3.490.0 7 Leaf 4 5 4 4 3 4 4 3 4 2 4 5 Decoction Hyptis 3.800.0 3.920.0 4.010.0 3.650.0 3.740.0 3.800.0 3.680.0 3.750.0 3.820.0 2.810.0 3.050.0 3.310.0 8 Leaf 5 4 4 3 3 3 3 3 3 4 5 4 Decoction Sida Leaf 3.640.0 3.850.0 3.970.0 3.580.0 3.680.0 3.720.0 3.560.0 3.590.0 3.700.0 2.770.0 2.890.0 3.080.0 9 Decoction 3 4 5 4 3 3 4 3 4 3 4 5 Partheniu 3.620.0 3.680.0 3.850.0 3.490.0 3.520.0 3.630.0 3.420.0 3.500.0 3.610.0 2.730.0 2.850.0 2.950.0 10 m Stem 3 4 3 4 4 4 2 4 3 3 3 3 Decoction Lantana 3.580.0 3.600.0 3.770.0 3.350.0 3.500.0 3.590.0 3.320.0 3.410.0 3.460.0 2.500.0 2.630.0 2.750.0 11 Stem 5 4 3 4 4 3 2 3 4 4 3 3 Decoction Hyptis 3.390.0 3.560.0 3.670.0 3.380.0 3.460.0 3.520.0 3.220.0 3.270.0 3.330.0 2.260.0 2.420.0 2.590.0 12 Stem 3 3 4 4 3 3 4 3 3 3 4 5 Decoction Sida Stem 2.350.0 2.460.0 2.650.0 2.070.0 2.220.0 2.290.0 1.800.0 1.890.0 2.010.0 1.790.0 1.840.0 2.000.0 13 Decoction 3 4 3 3 3 3 2 3 5 3 3 4 Paper Pulp 2.270.0 2.430.0 2.600.0 2.070.0 2.180.0 2.280.0 1.760.0 1.820.0 1.890.0 1.690.0 1.850.0 1.920.0 14 Extract 4 5 5 5 2 5 2 2 3 3 4 5 15 Wheat 1.890.0 2.000.0 2.090.0 1.830.0 1.870.0 1.930.0 1.570.0 1.670.0 1.700.0 1.440.0 1.620.0 1.810.0

Page 44

Sadaf Kalam et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 40-47 Bran 4 3 5 3 3 5 2 4 5 3 3 3 Extract Wheat 1.730.0 1.840.0 1.900.0 1.690.0 1.860.0 1.940.0 1.480.0 1.610.0 1.720.0 1.260.0 1.520.0 1.710.0 16 Straw 3 3 3 3 4 4 2 4 4 4 3 5 Extract Wheat 1.300.0 1.500.0 1.590.0 1.270.0 1.390.0 1.620.0 1.120.0 1.260.0 1.290.0 1.090.0 1.320.0 1.540.0 17 Porridge 3 5 4 4 4 4 3 4 4 5 5 3 Extract Pea Pod 0.590.0 0.770.0 0.890.0 0.540.0 0.690.0 0.820.0 0.360.0 0.450.0 0.580.0 0.420.0 0.730.0 0.870.0 18 Extract 5 4 4 3 3 5 3 3 4 5 4 4 SEM± 0.04 0.06 0.05 0.12 0.05 0.04 0.14 0.07 0.06 0.05 0.04 0.04 LSD5% 0.10 0.23 0.14 0.26 0.23 0.19 0.25 0.13 0.25 0.13 0.22 0.29 Values are Means + SEM of three observations Control a- Unmetabolised growth medium Control b- Sterilized Distilled Water Amount of Toxin employed=15 ml/shoot RH-85% PDR - 0-0.99= slight curling & wilting; 1-1.99=slight chlorosis; 2-2.99=marked chlorosis, slight necrosis; 3-3.99=high necrosis and marked chlorosis; 4-4.99=acute necrosis and marked chlorosis; 5=acute chlorosis and acute necrosis leading to death of shoots. Table 3: Mass production of phytotoxins on Arhar Bran Extract at different pH levels by Shoot Cut Bioassay S. No

pH levels

Parthenium (PDR) 12 hpt

24 hpt

48 hpt

Lantana (PDR) 12 hpt

24 hpt

Hyptis (PDR)

48 hpt

12 hpt

24 hpt

Sida (PDR) 48 hpt

12 hpt

24 hpt

48 hpt

Control 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 a

Control 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 0.00±0.00 b

3.32±0.03 3.45±0.04 3.78±0.05 3.29±0.04 3.41±0.04 3.78±0.02 3.17±0.03 3.29±0.04 3.68±0.05 3.06±0.03 3.28±0.04 3.58±0.05

4.49±0.03 4.75±0.04 4.88±0.05 4.37±0.04 4.59±0.05 4.76±0.03 4.08±0.04 4.33±0.05 4.47±0.03 3.84±0.04 4.11±0.04 4.39±0.05

4.28±0.04 4.46±0.03 4.71±0.04 4.29±0.03 4.38±0.04 4.55±0.03 4.20±0.03 4.22±0.03 4.30±0.04 3.70±0.03 3.92±0.03 4.00±0.05

3.94±0.03 4.16±0.05 4.29±0.03 4.23±0.03 4.34±0.03 4.38±0.03 4.06±0.03 4.12±0.05 4.25±0.03 3.59±0.03 3.70±0.03 3.61±0.03

3.64±0.03 3.78±0.05 3.88±0.05 4.15±0.03 4.20±0.03 4.25±0.04 3.93±0.05 4.04±0.04 4.11±0.05 3.34±0.03 3.42±0.04 3.44±0.03

3.58±0.03 3.68±0.04 3.78±0.05 3.60±0.04 3.73±0.04 3.93±0.03 3.45±0.04 3.62±0.04 3.81±0.03 3.04±0.03 3.14±0.03 3.29±0.03

2.35±0.04 2.50±0.03 2.53±0.05 3.21±0.04 3.28±0.04 3.51±0.04 3.18±0.04 3.36±0.03 3.43±0.04 2.06±0.03 2.28±0.03 2.38±0.03

2.13±0.03 2.22±0.04 2.34±0.03 1.20±0.03 1.29±0.03 1.40±0.04 1.13±0.05 1.25±0.03 1.29±0.05 1.23±0.03 1.31±0.04 1.46±0.04

0.63±0.03 0.75±0.03 0.94±0.03 0.71±0.03 0.83±0.03 0.94±0.04 0.66±0.03 0.69±0.04 0.81±0.03 0.31±0.02 0.49±0.03 0.60±0.03

0.39±0.04 0.61±0.04 0.81±0.03 0.38±0.03 0.51±0.03 0.67±0.03 0.40±0.05 0.67±0.04 0.77±0.04 0.14±0.03 0.21±0.04 0.31±0.03

SEM±

0.03

0.06

0.04

0.05

0.03

0.04

0.10

0.05

0.06

0.12

0.18

0.15

LSD5% 0.09 0.10 0.08 0.16 0.34 0.09 0.25 0.19 0.23 0.28 0.54 0.36 Values are Means + SEM of three observations; Control a- Unmetabolised growth medium, Control b- Sterilized Distilled Water; Amount of Toxin employed=15 ml/shoot; RH-85% PDR - 0-0.99= slight curling & wilting; 1-1.99=slight chlorosis; 2-2.99=marked chlorosis, slight necrosis; 3-3.99=high necrosis and marked chlorosis; 4-4.99=acute necrosis and marked chlorosis; 5=acute chlorosis and acute necrosis leading to death of shoots.

Table 4: Mass production of phytotoxins on Arhar Bran Extract at different temperatures by Shoot Cut Bioassay S. Temperatur N Parthenium (PDR) Lantana (PDR) Hyptis (PDR) Sida (PDR) e levels o 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 1 Control a 0 0 0 0 0 0 0 0 0 0 0 0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 2 Control b 0 0 0 0 0 0 0 0 0 0 0 0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 4 5 0 0 0 0 0 0 0 0 0 0 0 0 0.16±0.0 0.31±0.0 0.35±0.0 0.23±0.0 0.28±0.0 0.36±0.0 0.20±0.0 0.26±0.0 0.30±0.0 0.16±0.0 0.17±0.0 0.31±0.0 5 10 4 3 4 4 3 3 4 4 3 3 4 5 0.33±0.0 0.37±0.0 0.48±0.0 0.35±0.0 0.40±0.0 0.52±0.0 0.36±0.0 0.39±0.0 0.45±0.0 0.31±0.0 0.27±0.0 0.39±0.0 6 15 3 3 5 3 3 3 5 3 3 3 4 4 2.55±0.0 2.92±0.0 3.17±0.0 2.66±0.0 2.97±0.0 3.18±0.0 2.46±0.0 2.85±0.0 2.89±0.0 2.35±0.0 2.71±0.0 2.91±0.0 7 20 3 3 3 3 4 3 3 4 5 3 3 3 3.51±0.0 3.76±0.0 3.94±0.0 3.49±0.0 3.64±0.0 3.74±0.0 3.33±0.0 3.51±0.0 3.55±0.0 3.24±0.0 3.42±0.0 3.53±0.0 8 25 4 4 4 4 4 3 3 6 3 3 5 3 4.14±0.0 4.39±0.0 4.85±0.0 4.07±0.0 4.46±0.0 4.76±0.0 3.93±0.0 4.36±0.0 4.54±0.0 3.41±0.0 4.15±0.0 4.36±0.0 9 28 4 5 4 3 3 3 3 4 3 5 3 3

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Sadaf Kalam et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 40-47 4.07±0.0 4.38±0.0 4.58±0.0 4.08±0.0 4.26±0.0 4.58±0.0 3.89±0.0 4.30±0.0 4.40±0.0 3.78±0.0 4.17±0.0 4.30±0.0 5 5 5 4 3 4 5 4 3 5 4 4 4.08±0.0 4.28±0.0 4.53±0.0 3.88±0.0 4.18±0.0 4.37±0.0 3.79±0.0 4.08±0.0 4.19±0.0 3.65±0.0 4.01±0.0 4.14±0.0 11 35 4 4 3 3 3 4 5 5 3 4 6 3 1.78±0.0 1.85±0.0 2.16±0.0 1.69±0.0 1.88±0.0 2.08±0.0 1.53±0.0 1.79±0.0 1.99±0.0 1.38±0.0 1.73±0.0 1.91±0.0 12 40 3 4 3 3 4 5 3 3 3 3 3 4 SEM± 0.03 0.06 0.04 0.03 0.04 0.05 0.03 0.04 0.03 0.10 0.04 0.05 LSD5% 0.09 0.14 0.08 0.08 0.07 0.15 0.08 0.12 0.15 0.31 0.16 0.14 Values are Means + SEM of three observations; Control a- Unmetabolised growth medium Control b- Sterilized Distilled Water; Amount of Toxin employed=15 ml/shoot ; RH-85% PDR- 0-0.99= slight curling & wilting; 1-1.99=slight chlorosis; 2-2.99=marked chlorosis, slight necrosis; 3-3.99=high necrosis and marked chlorosis; 4-4.99=acute necrosis and marked chlorosis; 5=acute chlorosis and acute necrosis leading to death of shoots. 10

Table 5: Effect of different days of fermentation on phytotoxin production in a fermentor (Shoot cut Bioassay). S. Days of Parthenium Lantana Hyptis Sida N fermentaio o n 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 48 hpt 12 hpt 24 hpt 1 Control a 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0 0 0 0 0 0 0 0 0 0 0 2 Control b 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0.00±0.0 0 0 0 0 0 0 0 0 0 0 0 3 2 0.240.0 0.420.0 0.55004 0.250.0 0.350.0 0.440.0 0.160.0 0.250.0 0.420.0 0.110.0 0.150.0 3 2 2 3 2 2 2 2 2 2 4 4 1.080.0 1.190.0 1.300.0 0.660.0 0.910.0 1.170.0 0.610.0 0.810.0 0.800.0 0.270.0 0.450.0 4 3 2 2 2 4 2 2 2 2 2 5 6 1.470.0 1.600.0 1.790.0 1.460.0 1.640.0 1.910.0 1.030.0 1.260.0 1.460.0 0.890.0 1.300.0 2 4 3 4 3 2 3 4 3 3 2 6 8 2.040.0 2.270.0 2.680.0 2.370.0 2.540.0 2.630.0 1.840.0 2.110.0 2.460.0 1.830.0 2.070.0 5 4 4 4 3 3 2 2 2 3 2 7 10 3.380.0 4.150.0 4.810.0 3.570.0 4.260.0 4.610.0 2.930.0 3.280.0 3.470.0 2.420.0 2.650.0 4 4 4 4 3 2 3 4 4 2 3 SEM± 0.03 0.04 0.03 0.04 0.05 0.04 0.06 0.06 0.04 0.05 0.03 LSD5% 0.10 0.13 0.15 0.12 0.11 0.09 0.14 0.18 0.12 0.11 0.08

48 hpt 0.00±0.0 0 0.00±0.0 0 0.220.0 3 0.730.0 2 1.530.0 2 2.260.0 4 2.940.0 3 0.04 0.10

Values are Means + SEM of three observations; Control a- Unmetabolised growth medium Control b- Sterilized Distilled Water; Amount of Toxin employed=15 ml/shoot; RH-85% PDR- 0-0.99= slight curling & wilting; 1-1.99=slight chlorosis; 2-2.99=marked chlorosis, slight necrosis; 3-3.99=high necrosis and marked chlorosis; 4-4.99=acute necrosis and marked chlorosis; 5=acute chlorosis and acute necrosis leading to death of shoots

Table 6: Compatibility of various formulants with phytotoxins from Phoma sp. FGCC#54 by Seedling Bioassay S. Formulant N Parthenium (PDR) Lantana (PDR) Hyptis (PDR) Sida (PDR) s o

Control a

Control b

Control c

4 Tween-80 5 Coconut oil 6

Triton X100

Tween 60

8 Tween-20 9

Sorbitol

Groundnut 10 Oil 11

Sucrose

Soybean Oil

24 hpt

48 hpt

72 hpt

24 hpt

48 hpt

72 hpt

24 hpt

48 hpt

72 hpt

24 hpt

48 hpt

72 hpt

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.270.0 2 3.970.0 3 3.770.0 4 3.650.0 4 3.410.0 2 3.350.0 4 2.560.0 3 2.380.0 2 0.330.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.470.0 2 4.310.0 3 3.810.0 2 3.710.0 2 3.540.0 3 3.470.0 3 2.860.0 3 2.690.0 3 0.670.0 4

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.700.0 2 4.500.0 2 4.080.0 4 3.890.0 3 3.730.0 2 3.540.0 3 2.950.0 4 2.790.0 2 0.860.0 4

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.230.0 4 3.690.0 2 3.470.0 4 3.130.0 5 2.650.0 2 2.230.0 4 1.780.0 5 1.320.0 2 0.890.0 3

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.470.0 3 3.450.0 3 3.270.0 4 2.810.0 4 2.370.0 4 1.830.0 4 1.430.0 2 1.030.0 3 0.540.0 4

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.540.0 3 3.790.0 2 3.350.0 3 2.950.0 3 2.580.0 2 1.940.0 3 1.550.0 2 1.180.0 4 0.720.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 3.680.0 2 3.530.0 3 3.300.0 2 3.090.0 2 2.830.0 3 2.470.0 3 2.000.0 3 1.470.0 2 1.260.0 3

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 3.820.0 3 3.700.0 3 3.500.0 2 3.340.0 3 3.050.0 4 2.730.0 3 2.160.0 3 1.680.0 2 1.530.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 4.050.0 3 3.850.0 2 3.700.0 3 3.530.0 2 3.230.0 2 2.870.0 2 2.310.0 3 1.950.0 2 1.710.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 3.050.0 3 2.780.0 3 2.700.0 3 2.360.0 4 2.280.0 2 1.940.0 3 1.320.0 2 1.250.0 3 1.060.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 3.270.0 4 3.040.0 3 2.850.0 4 2.620.0 3 2.380.0 3 2.100.0 3 1.490.0 3 1.320.0 3 1.200.0 2

0.00±0.0 0 0.00±0.0 0 0.00±0.0 0 3.440.0 3 3.180.0 4 3.050.0 3 2.790.0 3 2.600.0 4 2.260.0 3 1.660.0 3 1.490.0 3 1.380.0 2

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Sadaf Kalam et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 40-47

0.280.0 0.420.0 0.550.0 0.350.0 0.160.0 0.280.0 1.200.0 1.340.0 1.530.0 0.710.0 0.890.0 1.180.0 4 2 3 4 3 2 3 3 3 3 3 4 0.900.0 1.510.0 1.370.0 0.730.0 0.980.0 1.080.0 0.210.0 0.380.0 0.490.0 0.140.0 0.200.0 0.380.0 14 Mustard Oil 3 2 4 4 4 3 3 3 2 2 2 2 4.28±0.0 4.38±0.0 4.48±0.0 4.05±0.0 4.14±0.0 4.17±0.0 3.91±0.0 4.07±0.0 4.14±0.0 3.80±0.0 3.85±0.0 4.01±0.0 15 CFCF alone 4 4 4 3 3 4 5 4 3 5 3 5 13 Glycerol

SEM±

0.03

0.05

0.04

0.14

0.03

0.04

0.05

0.03

0.04

0.05

0.04

LSD5%

0.08

0.11

0.09

0.28

0.09

0.13

0.15

0.14

0.12

0.10

0.19

0.11

Values are Means + SEM of three observations; Control a- Unmetabolised growth medium Control b- Sterilized Distilled Water; Amount of Toxin employed=30 ml/plant; RH-85% PDR - 0-0.99= slight curling & wilting; 1-1.99=slight chlorosis; 2-2.99=marked chlorosis, slight necrosis; 3-3.99=high necrosis and marked chlorosis; 4-4.99=acute necrosis and marked chlorosis; 5=acute chlorosis and acute necrosis leading to death of seedling.

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American International Journal of Research in Formal, Applied & Natural Sciences

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ASSESSMENT OF GROUND WATER QUALITY IN DIFFERENT VILLAGES OF NALDURG, DIST. OSMANABAD (M.S). Imamuddin Ustada & Gulam Farooq Mustafab Dept. of Zoology, Sir Sayyed College, Aurangabad (M.S), INDIA b Dept. of Chemistry, Sir Sayyed College, Aurangabad (M.S), INDIA a

Abstract: The present work has been carried out to create knowledge about quality and potability of the water in the village people. The parameters like pH, TDS, TH, Turbidity, Iron, fluoride, chloride, Sodium, Magnesium, Potassium, Sulphate and Nitrate, Calcium etc. were determined. The results compare with the ISI and WHO. The data revealed that the ground water of some villages is fit for drinking and of some villages can be used but needs some primary treatment. Key words: Physicochemical parameters, Naldurg region, TDS, Fluoride, Chlorides. I. INTRODUCTION Water is an integral part of life on this planet. Water have been studied and managed as separate resources, although they are interrelated. Surface water seeps through the soil and becomes groundwater. Conversely, groundwater can also feed surface water sources. Surface water or groundwater, can contain a range of contaminants that may make the water unsafe to drink or aesthetically unacceptable (e g, bad taste, odor or appearance). Such contaminants include: particles, microbiological contaminants, naturally occurring chemical substances and chemical substances derived from human activities. Treatment for these contaminants is particularly important for surface waters and shallow groundwater that are effect on the human health. To identify the safe drinking water it is necessary to study the Physico-chemical parameter and by comparing the parameter with the standard values. II. MATERIAL AND METHODS There are ten sampling stations were selected in Naldurg region dist. Osmanabad. The water samples were collected from bore well and dug well for the physico chemical analysis. The entire sample collected in the morning into a high grade one litter plastic bottles in the month of July-2013 and brought immediately to the lab for analysis. During the analysis the temperature is kept constant at 270c. Analysis of water sample is done by using standard procedures. For example TDS were measured in lab by using standard procedure of Trivedi and Goel. The pH measure by digital pH meter. Chlorides, calcium, magnesium, sulphate etc. were measured by the standard methods given APHA. Result of the physicochemical parameters of different villages of Naldurg region. All parameters are in mg/L except pH and Turbidity, Turbidity in NTU. TDS

Total H

7.6

643

420

136

0.16

0.5

7.8

367

274

odorless

0.3

8.0

392

274

colorless

odorless

0.2

8.1

413

760

colorless

odorless

0.5

7.9

588

408

349

Vagdari(S6)

colorless

odorless

0.4

7.2

721

392

206

Khudawadi(S7)

colorless

odorless

0.3

8.6

1214

229

Gandhora (S8)

colorless

odorless

0.1

8.8

1175

Chikundra(S9)

colorless

odorless

0.3

867

Villages

Temp

color

odor

Tur.

Murta (S1)

colorless

odorless

0.3

Horti (S2)

colorless

odorless

Jalkot(S3)

colorless

Shapur(S4)

Gujnur (S5)

SO4

NO3

0.36

0.17

0.12

0.17

0.38

0.15

0.25

0.15

0.21

0.31

0.25

130

0.71

0.25

681

168

142

0.38

0.31

564

128

219

112

0.13

0.47

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Imamuddin Ustad et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013February 2014,pp. 48-50

Naldurg (S10) Aliabad (S11)

colorless

odorless

0.4

7.3

876

373

112

310

0.14

0.35

colorless

odorless

0.2

8.2

727

392

203

0.33

0.27

Turbidity

600

500

400

Mg Cl

300

200

100

Fe F

0 S1

S11

SO4 NO3

III. RESULT AND DISCUSSION The collected water sample from different stations was the colorless and odorless and the temperature of the entire water sample is maintained 270c. pH: It is a measure of how acidic/basic water is. The range is from 0 - 14, with 7 being neutral. pH less than 7 indicate acidic, whereas a pH greater than 7 indicates a basic. pH is really a measure of the relative amount of free hydrogen and hydroxyl ions in the water. The standard range pH is 6.5 to 8.5 given by ISI and WHO. In the analysis the pH of Gandhora and Khudawadi water sample has the pH above the standard range (8.8 & 8.6 respectively). Turbidity: turbidity is the measure of relative clarity of a liquid. Clarity is important when producing drinking water for human consumption. Turbidity can provide food and shelter for pathogens. If not removed, turbidity can promote growth of pathogens in the distribution system, leading to waterborne disease outbreaks, which have caused significant cases of gastroenteritis throughout the United States and the world. Although turbidity is not a direct indicator of health risk, numerous studies show a strong relationship between removal of turbidity and removal of protozoa. In the water sample of all stations have the turbidity below the standard range of ISI and WHO. TOTAL HARDNESS: In ground water hardness is mainly due to carbonates, bicarbonates, sulphates, chloride of Ca and Mg. The data of the analysis reveal that the total hardness of Naldurg (876 mg/l), Gandhora (681 mg/l), Sahapur (760 mg/l), are above the standard value of WHO. TOTAL DISSOLVE SOLID (TDS): TDS is directly related to the purity of water. The TDS is the term used to describe the inorganic salts and small amounts of organic matter present in solution in water. The principal constituents are usually calcium, magnesium, sodium, and potassium cations and carbonate, hydrogen carbonate, chloride, sulfate, and nitrate anions. The TDS of water sample of Khudawadi (1214 NTU) and Gandhora (1175 NTU) having the range above the standard values of WHO. CALCIUM: CALCIUM is a mineral that is an essential part of bones and teeth. The heart, nerves, and bloodclotting systems also need calcium to work but higher the amount of calcium causes harmful effects on the health. In the water sample of the many villages of Naldurg region the calcium is present above the range given by WHO. The villages such as Gugnur (349 mg/l), Naldurg (373 mg/l), Vagdari (206 mg/l) and Aliabad (206 mg/l). MAGNESIUM: Hardness of water is directly concern with the magnesium and the sample of the different villages of Naldurg region ranging below the standard value given by the WHO. CHLORIDE: In the investigated water samples in which the water sample of Naldurg (310 mg/l) which were found above the limit of ISI and WHO.

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Imamuddin Ustad et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013February 2014,pp. 48-50

SODIUM: The sodium concentration into the all sample of Naldurg region lower than the prescribed limit by WHO and ISI. POTASSIUM: It is found that the content of potassium is higher in the water sample of Chikundra (112 mg/l) & Naldurg (39 mg/l). IRON: The concentration of Iron in the water sample of Kundawadi(0.71 mg/l) & Gandhora(0.38mg/l) ranging above the standard value given by the WHO and ISI. FLUORIDE: Fluoride can occur naturally in water and the fluoride concentrations above recommended levels, which can have several long term adverse effects, including severe dental fluorosis, skeletal and weakened bones The World Health Organization recommends a guideline maximum fluoride value of 1.5 mg/L as a level at which fluorosis should be minimal. In the analysis of the water sample it is found that the fluoride is below the standard range. SULPHATE: Sulfate is a constituent of TDS and may form salts with sodium, potassium, magnesium, and other cations. Sulfate is commonly found in nature and can be present at concentrations of a few to several hundred milligrams per liter. NITRATE: The nitrate concentration in the water sample of the Khudawadi(49mg/l) and Naldurg (44 mg/l) ranging above the standard limit of ISI. IV. CONCLUSION The physico-chemical analysis of ground water samples in and around Naldurg region reveals that water of all villages is fit for drinking but needs some primary treatment except S10 & S11 because of high TDS & total hardness. V. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

REFERENCES

ICMR, Manual of Standards of Quality for Drinking Water Supplies. Indian Council Medical Research, New Delhi, Special Reports, No.; 1975, (44)27. Jayalakshmi devi, O. and Belagali, S.L.Ramswamy, S.N. and Janardhana, Indian J. Environ & Ecoplan; 2005, 10(2), 45. 14 O. Jayalakshmi devi and Belagali, Nat. Env. & Poll. Tech.; 2006, 5(4), 553 Ayibatele N.B.; First Seosun Environmerital Baseline Survey, In proc. of internal. Conference on water and environ., 1, 4-26, (1992). Mishra K.R., Pradip, Tripathi; S.P, Groundwater Quality of Open Wells and Tube Wells, Acta Ciencia Indica, XXXIIIC, 2, 179 (2002). Tahir M.A., Rasheed H. and Malana A.; Method development for arsenic analysis by modification in spectrphotometric technique, Drik. Water Eng.Sci. Discuss. 1, 135-154 (2008) Raja R E, Lydia Sharmila, Princy Merlin, Chritopher G, Physico-Chemical Analysis of Some Groundwater Samples of Kotputli Town Jaipur, Rajasthan, Indian J Environ Prot., 22(2), 137, (2002). Manivaskam N. Physicochemical examination of water sewage and industrial effluent, 5th Ed. Pragati Prakashan Meerut., (2005) Khan, I.A. and Khan A.A., Physical and chemical condition in Seika Jheelat, Aligarh, Ecol., 3, 269-274 (1985) Sudhir Dahiya and Amarjeet Kaur, physico chemical characteristics of underground water in rural areas of Tosham subdivisions, Bhiwani district, Haryana, J. Environ Poll., 6 (4), 281 (1999) Shrinivasa Rao B and Venkateswaralu P, Physicochemical Analysis of Selected Groundwater Samples, Indian J Environ Prot., 20 (3), 161, (2000) Trivedy R. K. and Goel P. K.; Chemical and Biological methods for water pollution Studies, Environmental Publication, Karad. (1986). Abdul Jameel A, Sirajudeen J (2006) Risk assessment of physicochemical contaminants in groundwater of Pettavaithalai area, Tiruchirappalli, Tamilnadu-India. Environ Monit Assess 123: 299â&#x20AC;&#x201C;312 S.Mumtazuddin, A.K.Azad,M. Kumar and A.K. Gautam; Determination of physico-chemical parameters in some groundwater samples at Muzaffarpur town; Asian J. of Chemical and Envir. Research; 2009, 2 (12), 18.

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EFFECT OF ORGANIC AND INORGANIC ADDITIONS ON PHYSICOCHEMICAL PROPERTIESE IN VERTISOL P.S. Kusro, D.P. Singh, M.S. Paikra and Deepak Kumar Indira Gandhi Krishi Viashwavidyalaya, Raipur (C.G.) India Abstract: The organic carbon, mineralizable nitrogen and NH4+-N showed statistically significant increase over control. Application of recommended dose of NPK+FYM @ 5 ton ha -1 gave highest increase. Inclusion of FYM, Green Manure (GM) and BGA through inorganic source in the treatment increased organic carbon, mineralizable N, NH4+-N and reduced the bulk density. It was concluded that addition of inorganic fertilizer along with organic source improved the soil properties and helped in sustaining soil fertility and productivity. Further the results revealed that balanced NPK through inorganic sources significantly increased the organic carbon, mineralizable nitrogen and NH4 +-N status in soil. Keywords: organic, Inorganic, physico-chemical, vertisol

I. INTRODUCTION Maintaining and improving soil fertility for sustainable agriculture is becoming more crucial due to increasing complexity of the nutritional problems. Organic matter is a substance that has many desirable characteristics, which influence the soil physical, chemical and biological properties. The integrated nutrient management system will have a strong impact on soil fertility and may need to be taken in to consideration in the development of fertilizer commendations. Conjoint use of fertilizers and manures would not only impart sustenance to the production and improve soil health, but also enhance the efficient use of applied nutrients. The integrated plant nutrient supply (IPNS) system appears to be most potential and promising strategy for restoration and sustaining soil health and productivity (Singh, 1998). Such a system certainly actively regulates the geochemistry of metals and some organic compounds in the biosphere. The functions of organic compounds in soils are diverse, at times even contradictory. II.

MATERIALS AND METHODS

Soil samples were collected after wheat crop in 2009 from the experiment on “All India Coordinated long term fertilizer experiment” Project started in 1996 Rabi season. To find out the effect of various treatments and statistically analyzed by Randomized Block Design. For the assessment of the various physico-chemical properties of the soil experimental site, surface soil samples (0-15 cm) were collected and analysis was carried out by Bulk density determinations were made using core sampler method (Blach and Hartge, 1986), Organic Carbon by Walkley and Black rapid titration method as described by Piper (1967) and Mineralizable Nitrogen by potassium chloride and magnesium oxide method, as suggested by Waring and Bermner (1964). The amount of NH4+ -N in the soil before incubation was determined and mineralizable nitrogen was calculated. Experimental site was Rice-wheat cropping system, and initially(1996) the soil was clay loam (Vertisol), Bulk density-1.37 Mgm-1, pH-7.10, EC-0.19dsm-1 at 250 C, Available N-254.41 kg ha-1, Available P-22.09 kg ha1 ,Available K-505.84 kg ha-1. III. RESULTS AND DISCUSSION:Table 1: Effect of Organic and inorganic additions on Bulk density, Organic Carbon, Mineralizable Nitrogen and NH4+-N. Treatments T1 – Control T2 – 50% NPK T3 – 100% NPK T4 – 150% NPK

Bulk Density (Mgm-3) 1.37 1.40 1.38 1.38

Organic Carbon (%) 0.504 0.530 0.568 0.622

Mineralizable Nitrogen (kg ha-1) 38.18 40.76 53.30 54.65

NH4+-N (kg ha-1) 47.48 55.99 60.92 65.40

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P.S. Kusro et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 51-53 T5 –100% NPK + ZnSO4 @ 10 kg ha-1 T6 – 100% NP T7 – 100% N T8 – 100% NPK+FYM @ 5 ton ha-1 (Kharif) 100% NPK (Rabi)

1.39 1.40 1.40 1.29

0.593 0.553 0.544 0.673

50.62 42.33 41.21 76.04

61.03 61.26 55.32 70.77

T9– 50% NPK + BGA (Kharif) 50% NPK (Rabi)

1.30

0.594

64.73

62.26

T10 –50% NPK + GM (Kharif) 50% NPK (Rabi)

1.30

0.643

61.48

63.27

Critical Difference (CD)

0.035

11.28

SEm

0.012

3.86

CV (%)

4.072

14.77

The results revealed that Improved soil physical conditions reflected by lower bulk density of soil, when applied organic and inorganic sources of nutrients continuously for thirteen years. Integration of organic sources with inorganic fertilizer were found more effective as compared to single application in building up fertility and improving physical status of soil. Data indicated that bulk density values were lower in 50% NPK + GM (T10), 50% NPK + BGA (T9) treatments over control (T1). However, 100% NPK + FYM @ 5 ton ha -1 (T8), treatment showed significantly greater reduction in bulk density which may be directly due to by dilution of the soil matrix with greater quantity of less dense incorporated organic or indirectly by improving aggregate stability. Rumpel (1998) observed that the soil bulk density decreased while porosity and water retention increased significantly with FYM treatment. Similar findings were also reported by Patel et al. (1993) and Nimje (1986). Organic and inorganic combination of treatments resulted in significantly higher percentage of organic carbon content over control, due to continuous application of manure and fertilizers. Application of 100% NPK + FYM gave significantly higher percentage of organic carbon content. Marked differences in soil fertility status due to organic and inorganic additions after were noticed. Applications of organic sources with inorganic sources were found more effective in building up soil fertility status as compared to inorganic fertilizer alone. The findings are in conformity with those reported by Singh et al. (1999). This increase in organic carbon content due to use of fertilizers can be attributed to higher contribution of biomass to the soil in the form of crop stubble and residues. However, the difference in the organic carbon content due to application of fertilizers might be the result of differential rate of oxidation of organic matter by microbes (Trehan 1997). Organic carbon plays an important role in maintaining soil health and its increase during the period of experimentation shows that use of fertilizers has contributed in improving the soil health. This also indicates that if fertilizer use is integrated with manure, substantial improvement in soil health can be expected. The data shows that 100% NPK + FYM (T8), 50% NPK + BGA (T9) and 50% NPK + GM (T10) treatments maintained significantly higher mineralizable N status over inorganic treatments. Application of 100% NPK + FYM (T8) showed highest mineralizable N in 0-15 cm depth of soil. Organic and inorganic additions have increased mineralizable nitrogen content in soil relative to never fertilized, due to greater return of organic N to the soil by roots, root exudates and stubbles. However, when the dose was increased from 50 to 150% the mineralizable nitrogen increased with increasing levels of fertilizer dose and use of 100% N alone had resulted in increased mineralizable nitrogen over control. Highest value of mineralizable nitrogen was recorded by integrating the use of recommended dose of fertilizer with FYM. This showed increase over initial value and over control clearly indicating the benefits accruing from integrated use of fertilizers and manure. Vanek Vanek et al. (1996) reported the highest levels of mineralizable N in treatments, which received manure in autumn 1992 followed by fertilizer and Glendining et al. (1991) showed a small increase in total soil N content as a result of long-term (18-135 years) inorganic N application. The content of NH4+-N revealed that the increased doses of NPK fertilizer increased the ammonical nitrogen. Highest concentration of NH4+-N was recorded due to addition of 100% NPK + FYM @ 5 ton ha -1, followed by 50% NPK + BGA and 50% NPK + GM treatments, This may be due to greater return of organic N to the soil by roots, root exudats and stubbles. The fertilizer combinations containing nitrogen (N, NP and NPK) also markedly increased the NH4+-N form of nitrogen in the soil. IV. SUMMERY & CONCLUSION Soil fertility status significantly increased with the application of organic and inorganic combination 100% NPK +FYM integrated treatment from initial to final stage of the soil after thirteen years. Applications of organic sources with inorganic sources were found more effective in building up soil fertility status as compared to inorganic fertilizer alone. These findings indicated that application of integrated use of recommended fertilizer dose along with manure can successfully maintain and improve soil fertility.

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P.S. Kusro et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 51-53

REFERENCES Blach and Hartge. 1986. Methods of soil analysis. Soil Sci. 120. Glendining, M. T. and Powlson, D. S. 1991. The effect of long-term application of inorganic nitrogen fertilizer on soil organic nitrogen. Cambridge, U. K. , Royal Society of chemistry. pp 329-338. Nimje, P. M. and Seth, J. 1986. Effect of phosphorus, form yard manure and nitrogen on some soil properties in a soybean -maize sequence. Journal Agril. Sci. U.K. 103(3): 559-559. Patel, M. L., Gami, R. C. and Patel, P. V. 1993. Effect of farmyard manure and NPK fertilizers on bulk density of deep Black soil under Rice wheat green gram ratation. Gujrat Agri. Univ. Res. Journal 18(2): 109-111. Piper, C. S. 1967. Soil and plant analysis. University of Adelaide, Adelaide, Australia. Rumpel, J. 1998. Effect of Long-term organic and mineral fertilizer on soil properties and development of tomato. Ecological Aspects of nutrition and Alternatives for Herbicides in Horticulture. International seminars warzawa Poland. pp 63-64. Singh, N. P., Sachan, R. S., Pandey, P. C. and Bisth, P.S. 1998. Effect of a decade long terms fertilizer and manure application on soil fertility and productivity of Rice-Wheat system in Mollisol. J. Indian Soc. Soil Sci. 47: 172-80. Trehan, S. P. 1997. A rapid method for the estimation of potential immobilization of N after the addition of cattle slurry to soil. J. Indian Soc. Soil Sci. 45: 14-19. Vanek, V., Najmanova, J. and Peter, J. 1996. Mineral and mineralizable soil nitrogen and yield of cereals. Rostlinna Vyroba. 42(9): 411-416. Waring S. A. and Bermner J. M. 1964. Ammonium production in soil under waterlogged conditions as an index of nitrogen availability. Nature (London) 201: 951-952.

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CONSTRAINTS IN DIVERSIFICATION OF RURAL ECONOMY Mahajan Girish* Extension Specialist (Agricultural Economics), Krishi Vigyan Kendra, Kangra, Himachal Pradesh, India Abstract: The diversification of rural economy is considered essential for solving the problem of rural unemployment, poverty and hunger. This study has been conducted in Kangra district of Himachal Pradesh. Two developed blocks form the developed category and two backward blocks form the backward category were selected on the basis of ranking of infra-structural as well as ranking of agricultural related variables. Two stage stratified random sampling technique with stratification (on the basis of index of crop acreage diversification using Entropy Index) at second stage was employed for the selection of villages and household. Both primary as well as secondary data was used in this study. A sample of 105 and 83 households were selected using simple random sampling technique from the villages, in approximate proportion of the number of households, of developed and backward categories respectively. Averages, percentages and Chi-square test were used for the analysis. The opinion of the household was elicited regarding constraints in introducing new high value cash crops, agricultural related enterprises like rabbitary, beekeeping, poultry, etc. and also problems in switching over to non-farm enterprises. In the opinion of the households, irrespective of the two different categories of the study area and two different pattern of farm diversification, more-diversified farms and less- diversified farms, the constraints like lack of availability of inputs both in adequate amount and at proper time, lack of training and technical know-how, lack of credit facilities, high initial investment, lack of proper marketing facilities etc. were the major constraints in accelerating the process of diversification, both in the farm and non-farm sectors. Keywords: Constraints, rural economy, diversification pattern, crop acreage, chi-square. I.

INTRODUCTION

In poor agrarian economies where credit and insurance markets are not fully developed, diversification of crop enterprises and sources of off-farm income and employment are the most important strategies adopted by the rural households to combat the crop risk and stabilise their income and consumption (Mahajan, 2003). Rural households that are risk averse will diversify their income to reduce overall risk. Diversification is interpreted differently by different scholar; it mean shifting from subsistence farming to commercial farming to some; it implies shifting from low-value food/non-food crops to high-value food/non-food crops to others, and it means switching over from local to high yielding varieties, the integration of animal husbandry, bee-keeping, fisheries, horticulture, mushroom cultivation, etc. to still others. For this economists and researchers both in the country and abroad have strongly advocated for evolving an integrated system approach to the problem of rural diversification for rapid, balanced and egalitarian growth of the rural economy. The diversification of agricultural both by introducing new crops and also of the income sources by introducing non-agricultural enterprises can be instrumental in improving upon the income, consumption and ultimately the living standard of the rural households (Mahajan, 2009). A number of studies are available in the literature on the extent and determinants of diversification (Pope et.al., 1980; Gupta and Tiwari, 1985; Singh et. al.1985; Bhatia and Tiwari, 1991; Chand, 1995; Haque, 1995; Maji and Rahim, 1996; Mahajan, 2003; and Mahajan, 2004). Since crop and income diversification were affected significantly by the factors like age of the head of the family, education status of the head of the family, family size, size of operational holdings, etc. (Mahajan, 2003; Mahajan, 2004). But, in addition to these determinants, there are some constraints like lack of availability of credit, price fluctuation, non-availability of agricultural inputs in time, lack of marketing facilities, very high

Note: This paper is written while the author was working for short-term fellowship at Noragric (Centre for International Environment and Development Studies), Norway and is based on his Ph.D. dissertation, Extension Specialist (Agricultural Economics), Krishi Vigyan Kendra, Kangra, Himachal Pradesh, India

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initial investment, etc. which affect the magnitude of crop and non-crop diversification. It is, therefore, essential to identify the major constraints inhibiting the process of diversification of rural economy both in the agricultural and non-agricultural areas. This type of analysis would have direct implication .It is against this background that an attempt is made to identify the major constraints in diversification of agricultural crops such as vegetable, pulses, oilseeds and horticultural crops; constraints in diversifying to agricultural related enterprises like beekeeping, rabbitary, mushroom cultivation and poultry; and constraints in the adoption of non-agricultural enterprises in farm sector of Kangra district. II. METHODOLOGY Selection of the study area: The study has been conducted in Kangra district of Himachal Pradesh because it is highly advanced from agricultural development as well as from other infra-structural point of view, as compared to other districts of Himachal Pradesh. Two developed blocks (Panchrukhi and Nurpur ) form the developed category and two backward blocks (Lamba-gaon and Nagrota suria ) form the backward category were selected on the basis of the ranking of infra-structural variables like the number of educational institutions, the basic health care units, the number of post offices, the number of fair price shops, the number of financial institutions and the literacy percentage as well as the ranking of variables related to agriculture like the percentage of net irrigated area, fertiliser consumption, seed distribution, and the cropping intensity (Mahajan, 2005). Sampling method: For each of the selected block, a list of all the villages was prepared. These villages were classified into three groups; one completely irrigated; second, completely non-irrigated; and, third partially irrigated. First and the second groups of villages were purposively taken for in-depth analysis. First group of villages was selected for developed category (Nurpur and Panchrukhi); while the second groups of villages was chosen for backward category (Lamba- gaon and Nagrota surian). For the purpose of selection of farm households, two stage stratified random sampling, village as first stage and household as second stage unit, was used for both the groups of villages separately. Selection of villages: From the list of villages of group I and group II, about 5 per cent of the villages ( 12 from group I; 6 from Nurpur and 6 from Panchrukhi; and 13 from group II, 8 from Lamba-gaon and 5 from Nagrota-suria; such that the total is 25 in all ) were selected, using simple random sampling without replacement technique . Selection of households: In the second stage of sampling, a complete list of cultivators from each selected villages was obtained along with their land and operational holding size separately for both the groups. A sample of 105 and 83 households were selected using simple random sampling technique from the villages in approximate proportion of the number of household of group I and group II respectively. Classification of households: The selected farmers from each village were classified into two pattern / strata as: i. More-diversified farms having crop- acreage diversification index more than 0.6393 ii. Less-diversified farms having crop- acreage diversification index less than 0.6393 Using area under a particular crop is taken as the basis of crop- acreage diversification index. The above classification is based on crop- acreage using Entropy index(EI) which is a measure of the index of diversification having logarithmic character. The value of Entropy index varies from 0 to log N. It has the value zero in case of complete specialisation and “log N” in case of perfect diversification i.e. it has direct relationship with diversification (N is the number of enterprises). Total number of different crop enterprises grown in the study area were 19 and the demarcation line came out to be 0.6393 between more-diversified farms and less-diversified farms ( Max value of Entropy index = logN = log 19 / 2 = 1.2787 / 2 = 0.6393 ) . Entropy index (E ) : N E =  Pi Log (1/Pi) i=1 Where, E = Entropy index Pi = Proportion of area under i th crop ( i = 1, 2 , 3 ,…. , N ) N = Number of crop enterprises Both primary and secondary data were used in this study. Depending upon the objective of this study, primary data were collected by survey method on a well structured and pre-tested questionnaire by personally interviewing the cultivators for the agricultural year 1997-98.The data were upgraded again for the year 20102011 on a same questionnaire by personally interviewing the cultivators and found that problems/constraints were more or less the same but their magnitude is different.

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Chi-Square Test: Both the categories of farms faced various problem regarding diversification of their crops and / or other sources of income. These were mainly characterised into three enterprises: 1. Agricultural activities included vegetables, pulses, oilseeds and horticultural crops. 2. Agricultural related enterprises consisted of beekeeping, rabbitary, mushroom cultivation and poultry 3. Non-agricultural enterprises included flour mill, thresher and stone crushing. The percentage of households showing each constraint was tabulated. The problem within a particular category was tested for their differences between two groups a) more and b) less-diversified farms, using Chi-square test. N X2= ď&#x192;Ľ ( O - E )2 i =1 E Where, O = Observed value E = Expected value N = Number of problems / constraints. III. RESULTS AND DISCUSSION CONSTRAINTS IN DIVERSIFYING TO AGRICULTURAL CROPS: (a) Vegetable crops: Table 1 enlists the importance of different constraints in adopting vegetables in terms of per cent of households. Perusal of the table reveals that in developed category, lack of availability of labour was the most important of all constraints; about 70% of the total households reported it to be the major constraint in switching over and / or introducing vegetables along with cereals, dairy and horticultural crops. Price fluctuation / low prices ( 64% ) followed by lack of availability of inputs like seeds, fertilizers, insecticide, etc.in time ( 36%) were other important constraints in augmenting the area under vegetable crops . Between pattern of farm diversification, labour constraint (78%)was found to be more serious in more-diversified farms, while in less-diversified farms price fluctuation was stated to be the important factor by more than three fourth of the households . As compared to this in backward category, which surrogates rain fed agriculture, irrigation was reported to be the most formidable constraint; more than three fourth of all the farmers cited it to be the most important factors in introducing vegetable crops . Lack of timely availability of inputs (52%), non-availability of labour (39%) was also important constraints reported by the farmers in incorporating vegetables along with cereals in their cropping pattern. Similar pattern was discernible in two different patterns of farm diversification, morediversified farms and less-diversified farms, of backward category. Table 1 : Constraints in Increasing Area under Vegetable Crops (Per cent) Constraints

Lack of marketing facilities Inputs are not available in time Lack of labour availability

Diversification pattern More-diversified farms Less-diversified farms (EI> O.6393) (EI<0.6393) Developed category 27.77 11.76 37.04 35.29 77.78 60.78

20.00 36.19 69.52

Lack of capital

5.55

19.61

12.38

Price fluctuation / low prices Other (irrigation )

50.00 1.85

78.43 5.89

63.81 3.81

31.71 56.10 41.46 12.19 21.95 75.61

28.91 51.81 38.55 8.43 25.30 81.93

Lack of marketing facilities Inputs are not available in time Lack of labour availability Lack of capital Price fluctuation / Low prices Others ( Irrigation )

Backward category 26.17 47.62 35.71 4.76 28.57 88.10

Overall farms

Source: Primary Survey (b) Oilseeds: The constraints in undertaking oilseeds cultivation, as reported by the households, are given in Table 2. The table shows that in developed category, lack of marketing facilities, non-availability of inputs in time and price fluctuation were reported to be the important factors in diversifying and expanding area under oilseeds . In more-diversified farms ( EI > O.6393 ) of developed category, nearly 65% of the respondents reported marketing to be an important constraint, while lack of timely availability of inputs were reported to be an

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important factor by 41% of the households in less-diversified farms ( EI < 0.6393 ) . In comparison, in backward category which is a fit case for diversification of oilseeds, The response was fairly good as is evident from the table. Lack of availability of inputs like seeds, fertilisers, etc. was cited to be the major factor by 71% of households. Lack of marketing facilities, non- availability of labour and price fluctuation was reported to be other important constraints by more than fifty per cent of the farmers. The pattern of response was more or less similar in more-diversified farms and less-diversified farms in backward category. Table 2: Constraints in Increasing Area under Oilseeds (Per cent ) Constraints

Lack of marketing facilities Inputs are not available in time Lack of labour availability Lack of capital Price fluctuation / low prices Other (irrigation ) Lack of marketing facilities Inputs are not available in time Lack of labour availability Lack of capital Price fluctuation / Low prices Others ( Irrigation )

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 64.81 19.61 29.63 41.18 7.41 23.53 9.80 22.22 37.25 Backward category 52.38 63.41 64.28 78.05 57.14 48.78 4.76 9.76 45.23 56.10 35.71 41.46

Overall farms 42.86 35.24 15.24 4.76 29.52 57.83 71.08 53.01 7.23 50.60 38.55

Source: Primary Survey (c) Pulses: Table 3 shows the constraints faced by the households in diversifying and expanding area under pulses. In developed category among the constraints, lack of marketing facilities was reported to be important by more than fifty per cent of the households surveyed. Lack of availability of inputs at appropriate time, price fluctuation and non- availability of labour were listed to be other important constraints by more than one fourth of the households. Between two different patterns of farm diversification, the above mentioned factors were more serious in respect of more-diversified farms than in case of less-diversified farms. The table further shows that similar pattern of response was perceptible in backward category. For example, lack of marketing facilities, non-availability of inputs at appropriate time, lack of labour availability, and price fluctuation were listed to be the important factors in enhancing the area under pulses. Between diversification patterns, the response was found to be more acute in more-diversified farms as compared to less-diversified farms. Table 3: Constraints in increasing area under Pulses (Per cent ) Constraints

Diversification pattern More-diversified farms Less-diversified (EI>0.6393) Farms (EI<0.6393) Developed category 70.38 33.33 55.56 27.45 35.19 25.49 38.89 25.49 5.55 Backward category 95.24 68.29 85.71 75.61 59.52 24.39 7.14 7.32 45.24 17.07 -

Overall farms

52.38 41.90 30.48 32.38 2.96 81.93 80.72 42.17 7.23 31.33 -

Source : Primary Survey (d ) Horticultural crops : The opinions of the farmers were also elicited regarding diversification to horticultural crops. The main constraints are presented in Table 4. A glance through the table reveals that in developed category which represents cereals, dairy and horticultural based agriculture, lack of marketing facilities was an important constraint by 63% of the households. The non-availability of inputs in time (50%), price fluctuation (43%), labour problem (40%) and lack of capital (38%) were cited to be other important factors in augmenting area

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under horticultural crops. In more-diversified farms of developed category, 85% of the households stated marketing to be an important constraint followed by non-availability of inputs in time (about 76%), while in less-diversified farms, lack of capital was listed to be important factor by nearly 53% of the sample respondents. There is a vast potential for raising horticultural crops like mango and citrus fruits in backward category, where a significant portion of the land owned was under pastures or waste land or ghasni which can be brought under these fruit crops. Almost all the constraints presented in Table 4 were reported to be the important constraints in raising horticultural crops. Table 4: Constraints in Introducing Horticultural Crops (Per cent) Constraints

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 85.19 39.22 75.92 24.49

59.26 24.04 66.67 Backward category 80.95 78.57 64.29 26.19 69.05 52.38

Overall farms 62.86 51.43

25.49 52.94 17.65 -

40.00 38.10 42.85 -

60.97 73.17 56.10 34.15 43.90 41.46

71.08 75.90 60.24 30.12 56.62 46.99

Source: Primary Survey For example, lack of nursery plants, which is the most important input, was listed to be important constraint by more than 75% of the sample households. Lack of marketing facilities, non-availability of labour, price fluctuation, lack of irrigation water were other important constraints deciding the households to switch over to fruit crops . In more-diversified farms (EI > 0.6303), lack of marketing facilities was cited to be an important constraint by nearly 81% of the farmers followed by non-availability of inputs (about 79%), while 73% of the respondents reported non-availability of inputs specially nursery plants occupied the first place in order of importance followed by lack of marketing facilities in less-diversified farms (EI <0.6393) of backward category. However, it is interesting to note that capital was more serious in less-diversified farms than in case of morediversified farms To conclude, the lack of timely availability of inputs, lack of availability of labour, lack of marketing facilities , price fluctuation, lack of irrigation water ( only on backward category ), lack of capital ( only in less-diversified farms for horticultural crops in both the categories) were reported to be the important constraints in growing new agricultural crops like vegetables, horticulture, pulses, and oilseeds. CONSTRAINTS IN DIVERSIFYING TO AGRICULTURAL RELATED ENTERPRISES In addition to crop diversification by taking up the cultivation of crops like vegetables, horticultural crops etc., a considerable scope exists for introducing agricultural related enterprises like beekeeping ,rabbitary, and poultry, etc. But these have not been adopted in a big way. The opinion of the farmers was sought to know the main constraints in the adoption of these enterprises. The results are given in Table 5 through Table 8 (a ) Beekeeping : The constraints faced by the households in introducing bee-keeping are taken up in Table 5. In overall farms of developed category, about 70% of the households opined that the lack of technical know-how and basic training were the major constraints in introducing beekeeping enterprise. Non-availability of bee- equipments, high initial investment to by boxes for keeping bees and lack of market were other equally important constraints reported by the households. In more-diversified farms (EI >0.6393) of developed category, the lack of technical knowledge and training emerged to be the most important constraints cited by 85% of the households. Very high initial investment, lack of bee-equipments, lack of adequate market and lack of credit appeared other important factors constraining the introduction of this additional enterprise. In comparison, in less-diversified farms (EI < 0.6393), more than three fourth of households reported non-availability of bee- equipments, 73% cited lack of market, around 61% listed high initial cost, 53% lack of technical knowledge and one fifth reported lack of credit as the major constraints in introducing bee-keeping as an additional enterprise to boost their household income. Coming to backward category, the response of all the farmers among all the constraints were very much encouraging when compared to developed category. For example, 84% of the respondents stated that lack of technical know-how and training was the most formidable constraint followed by non-availability of beeequipments (81%). High initial investment (78%), and lack of market (66%) were other important constraints in

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backward category. Between two patterns of farm diversification, lack of technical knowledge and training facilities was most important by 86% of the households in more-diversified farms, where as less-diversified farms, about 88% of the households listed non-availability of bee-equipments followed by lack of technical know-how and training (around 83% ) were the most important factors . Table 5: Constraints in taking up Beekeeping as an Enterprise: (Per cent) Constraints

Lack of technical know-how & training Non availability of bee equipments Lack of market Un remunerative prices Initial investment is very high Lack of credit Others ( Lack of flora and fauna )

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 85.18 52.94 59.26 76.47 51.85 72.55 18.52 27.45 72.22 60.78 25.92 19.61 -

69.52 67.62 61.90 22.86 66.67 22.86 -

85.71 3.81 59.52 11.90 83.33 35.71 47.62

84.34 80.72 65.60 20.48 78.31 39.76 50.60

Overall farms

Backward category Lack of technical know-how & training Non availability of bee equipments Lack of market Un remunerative prices Initial investment is very high Lack of credit Others ( Lack of flora and fauna )

82.93 87.80 70.73 29.27 73.17 43.90 53.66

Source: Primary Survey (b) Rabbitary: The constraints in the adoption of rabbitary as an additional enterprise cited by the households are summarised in Table 6. A perusal of the table reveals that in overall farms of developed category, high initial investment to start rabbit farm was reported as the major constraint by 56% of the households. Lack of technical know-how (50%) and lack of credit (46%) were other important factors stated by the farmers. Between two different pattern of farm diversification , more-diversified farms and less-diversified farms , 57% of the respondents reported lack of technical knowledge and basic training was the most important factor in more-diversified farms, whereas in less- diversified farms very high initial cost was stated to be important factor by 71% of the households. As compared to this in backward category, again the response of the households was much more serious than in developed category. For example, lack of technical knowledge and training was reported to be the most important constraint; more than three fourth of the total households listed it to be the most important factor inhibiting the diversification of rural household economy towards these enterprise. Equally important constraints were the high initial investment, lack of adequate credit and non-availability of quality inputs. In more-diversified farms of backward category, lack of credit was cited to be an important factor by 83% of the households followed by lack of technical know-how and training (around 79%), while 95% Table 6: Constraints in taking up Rabbitary as an Enterprise: ( Per cent) Constraints

Lack of technical know-how & training Non availability of quality inputs Lack of market Un remunerative prices Initial investment is very high Lack of credit Others Hot climate ) Lack of technical know-how & training Non availability of quality inputs Lack of market Un remunerative prices Initial investment is very high Lack of credit Others ( Hot climate )

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 57.41 43.14 24.07 13.73 18.52 23.53 11.11 42.59 70.58 38.89 52.94 40.74 11.76 Backward category 78.57 95.12 40.48 75.61 23.81 31.71 38.10 26.83 69.05 78.05 83.33 43.90 23.81 36.59

Overall farms 50.48 19.05 20.95 5.71 56.19 45.71 26.67 86.75 57.83 27.71 32.53 73.49 63.86 30.12

Source: Primary Survey

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of the respondents reported lack of technical know-how and training facilities occupied the first place in order of importance followed by very high initial investment in less-diversified farms of backward category to start this additional enterprise. (c) Mushroom cultivation: The constraints in introducing mushroom as a supplementary enterprise, as faced by the households, are presented in Table 7. The table shows that the response of households in introducing mushroom as an important cash crop was not so pronounced in the developed as well as in the backward category. Anyway, 35% of the households in developed category cited lack of technical know-how and training in introducing mushroom enterprise as an important constraint. Lack of availability of credit followed by high initial cost had also been reported other factors hindered the rural diversification to introduce mushroom cultivation in their cropping pattern in both the categories. The broad pattern of response was almost similar in more-diversified farms and less-diversified farms of developed as well as in backward categories. Table 7: Constraints in taking up Mushroom cultivation as an Enterprise: ( Per cent) Constraints

Lack of technical know-how & training Non availability of spawn / compost Lack of market Un remunerative prices Initial investment is very high Lack of credit Others Hot climate )

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 40.74 29.41 20.37 13.73 12.96 11.76 25.93 35.29 31.48 37.25 35.19 11.76

35.24 17.14 12.38 30.48 34.29 23.81

73.81 23.81 19.05 11.90 28.57 38.10 30.95

79.52 30.12 13.25 10.84 33.73 39.76 31.32

Overall farms

Backward category Lack of technical know-how & training Non availability of spawn / compost Lack of market Un remunerative prices Initial investment is very high Lack of credit Others Hot climate )

85.37 36.59 7.32 9.76 39.02 41.46 31.71

Source: Primary Survey (d) Poultry: The constraints in the introduction of poultry as an enterprise are summarised in Table 8. A glance at the table reveals that in overall farms of developed category, very high initial investment required to start poultry enterprise was listed to be the most important factor / constraints; around 55% of the households reported it to be the most important constraint. Equally important constraints were the lack of adequate credit, non-availability of good quality poultry birds and feed and lack of knowledge and training. Between diversification pattern, more-diversified farms and less-diversified farms, the high initial investment (59%) and lack of good quality of poultry birds and feed, which is the most important input, were listed to be the important constraint in morediversified farms. In comparison, 61% of the total households stated lack of cheap credit was the most Table 8: Constraints in taking up Poultry as an Enterprise: ( Per cent) Constraints

Lack of technical know-how & training Non availability of quality inputs Lack of market Un remunerative prices Initial investment is very high Lack of credit Others( Disease problem )

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 38.89 50.98 55.56 43.14 12.96 25.49 18.52 13.73 59.26 50.98 48.15 60.78 9.26 17.65 Backward category

Lack of technical know-how & training Non availability of quality inputs Lack of market Un remunerative prices Initial investment is very high Lack of credit Others( Disease problem )

76.19 30.95 14.28 4.76 61.90 50.00 7.14

65.85 43.90 36.59 14.63 70.73 68.29 17.07

Overall farms 44.76 49.52 19.05 16.19 55.24 54.29 13.33 71.08 37.37 25.30 9.64 66.26 59.04 12.05

Source: Primary Survey

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important constraint followed by lack of technical know-how and very high initial investment ( both about 51) in less-diversified farms of developed category . On the other hand in backward category, lack of technical know-how and training was cited to be the most important factor by 71%of the households. Lack of credit, very high initial cost, and lack of quality feed and poultry birds were other important factors in introducing poultry as an additional enterprise. Between pattern of farm diversification , lack of technical know-how and training appeared to be the major constraint by three fourth of the respondents in more-diversified farms , whereas in less-diversified farms , around 71% of the sample households reported very high initial investment as the most important constraint . Lack of credit, lack of technical knowledge and training and lack of good quality of poultry birds and feed were reported to be the other important constraints in less-diversified farms of backward category. Lack of market had also been reported yet another factor inhibiting the diversification of rural economy towards poultry enterprise. On the whole , the introduction of agricultural related enterprises like beekeeping, mushroom cultivation, poultry and rabbitary appeared to be hampered by the lack of technical know-how and training, high initial investment cost, lack of availability of credit facilities, non-availability of quality input and some how lack of market facilities. Thus there is an urgent need for undertaking short duration training programmes, ensure adequate credit facilities, supplying quality inputs related to the enterprise and assuring market facilities to the farmers to promote the introduction of these enterprises . CONSTRAINTS IN THE ADOPTION OF NON-AGRICULTURAL ENTERPRISES The opinion of the sample households was also elicited regarding the major constraints faced by them to diversify towards rural non-farm sector either starting a small scale enterprise such as flour mill and crushers or through buying productive assets like threshers. The results are summarized in Table 9 through Table 11. (a) Flour mill : Table 9 gives the multiple per cent of households reporting different constraints in adopting flour mill as a supplementary enterprise. As may be seen from the table that in developed category, very high initial investment followed by lack of credit and lack of sufficient market were cited to be the most important factors constraining them to start flour mill as an enterprise. The response of the households was also similar between two pattern of farm diversification. However, the response was found to be more pronounced in respect of more-diversified farms (EI>0.6393) than in case of less-diversified farms (EI<0.6393).As compared to this in backward category, lack of credit facilities occupied the first place in order of importance followed by high investment cost and lack of market facilities. Similar pattern of response was observed in more-diversified farms as well as lessdiversified farms of backward category. Table 9: Constraints in Diversifying to Non-agricultural Enterprises : Flour mills ( Per cent ) Constraints

More-diversified farms (EI>0.6393)

Lack of market High initial investment Lack of credit Others (Lack of man-power)

61.11 85.18 75.92 22.22

Lack of market High initial investment Lack of credit Others (Lack of man-power)

50.00 54.76 71.42 19.05

Diversification pattern Less-diversified (EI<0.6393)

farms

Developed category 31.37 56.86 47.06 33.33 Backward category 58.64 65.85 68.29 21.95

Overall farms

46.67 70.48 61.90 27.62 54.22 60.24 69.87 20.48

Source: Primary survey (b)Thresher: The high level of initial investment, seasonal demand and lack of credit facilities were reported to be the important factors which discouraged the households to by productive assets like thresher (Table10) in both the categories. Among these factors, the nature of seasonal demand for thresher emerged to be the most important constraint in developed and backward categories. Yet another important factor was that in a particular village in a particular season, limited number of thresher can do business on a economical viable basis. Table 10: Constraints in Diversifying to Non-agricultural Enterprises : Thresher ( Per cent ) Constraints

Lack of market Initial investment is high

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 40.74 41.18 79.63 66.67

Overall farms

40.95 73.33

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Lack of credit Lack of man power Others (Seasonal demand)

53.70 20.37 72.22

Lack of market Initial investment is high Lack of credit Lack of man power Others (Seasonal demand)

21.43 64.28 71.43 85.71

49.02 17.65 80.39 Backward category 43.90 46.34 51.22 12.19 65.85

51.43 19.05 76.19 32.53 55.42 61.45 6.02 75.90

Source: Primary survey (c) Crusher In so far as starting an enterprise crusher was concerned (Table 11), not many households in Table 11: Constraints in Diversifying to Non-agricultural Enterprises : Crusher ( Per cent ) Constraints

Lack of market Initial investment is high Lack of credit Others (Lack of raw material)

Diversification pattern More-diversified farms Less-diversified farms (EI>0.6393) (EI<0.6393) Developed category 9.26 31.37 38.89 62.74 53.71 50.98 12.96 23.53

Lack of market Initial investment is high Lack of credit

26.19 85.57 64.28

Others (Lack of raw material)

9.52

Backward category 48.78 60.98 95.12 21.95

Overall farms

20.00 50.48 52.38 18.09

37.35 73.49 79.18 15.66

Source: Primary survey developed category were interested in starting crusher as an enterprise. Anyway lack of credit and high initial investment cost were appeared to be the important constraints in both the categories of the study area. Lack of market was yet another important factor only in backward category discouraging the households to adopt crusher as an enterprise. COMPARISON OF PROBLEMS FACED BY THE GROUP: To study whether the problems faced by the group is the same , chi-square test was applied between more-less diversified farms in both the categories of the study area . The results are given in Table 12 through Table 14. (a) Agricultural Enterprises: Table 12 shows the chi-square test for constraints in the adoption of agricultural enterprises. It can be seen from the table that for vegetable, the problem faced by the group was significantly different in developed category. This can be attributed to the fact that less-diversified farms had small size of holding and their problems were inclined towards price fluctuation whereas in more-diversified farms the problem were skewed towards labour availability . In backward category, because of lack of irrigation facilities, vegetable growing had not been taken up at commercial level as yet. The cultivators anticipated the problems and were found to be indifferent between the comparable groups under study. Table 12: Constraints in the Adoption of Agricultural Enterprises: Chi â&#x20AC;&#x201C;square test Category

Developed Backward

Vegetable More-less diversified farms Test value 24.580*** ( 10 ) 6.680 ( 10 )

Oilseeds More-less diversified farms Test value 47.912*** (8) 3.776 ( 10 )

Pulses More-less diversified farms Test value 5.363 (8) 12.376 (8)

Horticultural crops More-less diversified farms Test value 55.536*** (8) 5.949 ( 10 )

Source: Primary Survey Note: Figures in parentheses show degrees of freedom *** Significant at 1 per cent level. In case of pulses, the difference in the problems between more-less diversified farms was not found to be different in both the categories of the study area. Oilseeds occupied important place in the cropping pattern in developed category and the priorities given to various problems faced by more-less diversified farms were also found to be different. It was found that the problems were centred on price fluctuation in more-diversified farms, while labour availability in lessdiversified farms. In backward category, the problems were found to be more or less the same according to their importance.

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In case of horticultural crops, the more-diversified farms had different set of priorities to the problems as compared to less-diversified farms in developed category under study. For example, the most important problem of more-diversified farms was marketing facilities, while adequate capital was found to be important in lessdiversified farms .The cultivators in backward category do not differ significantly in defining their problems about the adoption of horticultural crops. This indicates that the problems were almost similar for the entire area and were related to marketing facilities of inputs and output, price fluctuation, irrigation facilities and labour availability. (b) Agricultural Related Enterprises: The cultivators were asked about the problems faced by them in the adoption of supplementary agricultural enterprises for enhancing their income. These enterprises were beekeeping, rabbitary, poultry and mushroom cultivation. The results are given in Table 13. Table 13: Constraints in taking up agricultural related enterprises: Chi-square test Category Developed Backward

Beekeeping More-less diversified farms Test value 16.747* ( 10 ) 9.158 ( 10 )

Rabbitary More-less diversified farms Test value 40.879*** ( 12 ) 29.537*** ( 12 )

Mushroom More-less diversified farms Test value 14.631 ( 10 ) 9.441 (10 )

Poultry More-less diversified farms Test value 11.301 ( 12 ) 16.388 ( 12 )

Source: Primary Survey Note: Figures in parentheses show degrees of freedom. *** Significant at 1 per cent level. The table shows that in developed category, in case of beekeeping the problem were rated differently by more and less-diversified farms, particularly with respect to lack of technical knowledge and training , nonavailability of bee equipments, and others. In backward category, the importance given to different problems faced by the group, more-diversified farms and less-diversified farms, was found to be more or less the same. This may be because of the fact that beekeeping was not adopted in this area. Rabbitary was not important in any of the categories of the study area at present. The households were asked to specify their problems in general if they had a chance to adopt it. The problems faced by more-diversified and less-diversified farms were different and were related to lack of technical know-how and training, high initial cost, lack of credit and non-availability of input in the developed as well as in the backward categories. Lack of market, which is one of the important constraint, did not figure high in importance in both the categories under study. The poultry was another agricultural related enterprise which was considered in the study area. The problems were found to be same in both the categories. The farmers in more-diversified farms and less-diversified farms were mostly concerned with lack of technical knowledge, high initial cost, lack of adequate credit, and nonavailability of good quality poultry birds and feed. Mushroom was not at all important in developed and in backward categories and the problems faced by them were almost indifferent and was mostly confined to lack of technical knowledge and basic training. (c) Non-Agricultural Enterprises: There are a few enterprises, not related to agriculture, which can be adopted by the cultivatorâ&#x20AC;&#x2122;s for enhancing their income levels. But these have not been adopted due to certain problems faced by the households such as lack of market, high initial cost, etc. The households of two different pattern of farm diversification gave different priorities to the problems faced by them for starting a small scale enterprise such as flour mill and crusher or through buying productive assets like thresher. With the help of Chi-square test, it was estimated whether their problems were different or not. The results are given in Table 14. The results revealed that the priority to various problems for flour mill was found to be different in developed categories, while it was similar in backward category. In case of thresher, more-diversified farms (EI>0.6393) differed significantly from less-diversified farms (EI<0.6393) of backward category whereas response to various problems were not different in developed category. For crusher, the more-diversified farms were found to differ from less-diversified farms in both the categories in giving priorities to problems faced by them. Table 14: Chi- square Test for Constraints in Adoption of Non-agricultural Enterprises Category Developed Backward

Flour mill More-less diversified farms Chi-square value 39.585***(6) 1.047 (6)

Thresher More-less diversified farms Chi-square value 1.732 (8) 27.726***(8)

Crusher More-less diversified farms Chi-square value 10.955*(6) 17.846***(6)

Source: Field survey. Note : Figures in parentheses show degrees of freedom * Significant at 10 per cent level. *** Significant at 1 per cent level.

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IV. CONCLUSION To sum up, the lack of timely availability of inputs, lack of availability of labour, lack of marketing facilities, price fluctuation, lack of irrigation water ( only in backward category ), lack of capital ( only in less-diversified farms for horticultural crops in both the categories ) were reported to be the important constraints in introducing new agricultural crops like vegetables, horticulture, pulses and oilseeds.Second, lack of technical know-how and training, high initial investment, lack of credit facilities coupled with non availability of quality inputs were important constraints in introducing agricultural related enterprises like beekeeping, mushroom cultivation. poultry and rabbitary. For diversifying to non-agricultural enterprises like starting flour mill, crusher and buying productive assets like thresher the lack of credit facilities and very high initial investment were reported to be the important constraints. For buying assets like threshers, the seasonal demand for their services was also an important factor discouraging the household to buy them. Chi-square test for constraints in the adoption of agricultural enterprises suggest that while there were differences in giving priorities to the problems faced by the households in introducing agricultural enterprises in between the group, more-diversified farms and lessdiversified farms only in developed category, in case of agricultural related enterprises and non-agricultural enterprises the problems were also found to be different between the group for their adoption in both the categories under study. Therefore, emphasis on diversification of rural economy shall yield no divident unless efforts are made to improve the productivity of the existing enterprises and also attending to problem like marketing , training, technical know-how, credit and suppling quality inputs related to the enterprises to cultivators REFERENCES: Cheema, S.S. and A.S. Kahlon (1987), “Land Productivity through Crop Diversification.” Fertilizer News, vol. 32 (7): 19-30. Deoghare, P.R., Sharma, B. M. and S. K.Goel (1990), “Impact of Mixed Farming System on Income and Employment on Small Farms in Karnal District of Haryana.” Agricultural Situation in India, vol. XLV (10): 665-670. Ellis Frank (2000), “Rural Livelihood and Diversity in Developing Countries.” Oxford University Press, 1st edn. Gupta R.P. and S. K. Tiwari (1985), “Factors affecting Crop Diversification: An Empirical Analysis.” Indian Journal of Agricultural Economics, Vol.XL (3): 304-307 Haque,T.(1985), “Regional Trends and Pattern of the Rural Economy in India.” Indian Journal of Agricultural Economics, Vol.XL(3): 291297 Haque, T. (1995) “Diversification of Small Farms in India – Problems and Prospects,” Workshop Proceeding, National Centre for Agricultural Economics and Policy Reseach, New Delhi-12. Mani,K. and S. Varadarajan (1985), “Diversification on Farms.” Indian Journal of Agricultural Economics, Vol. 40(3):350-351 Mahajan, G. (2003), “Dimensions and Determinants of Diversification on Kangra Farms of Himachal Pradesh- An Empirical Analysis.” Bangladesh Journal of Agriculture Economics Vol. XXVI (1 & 2): 1 to 22. Mahajan, G.(2004), “Crop Diversification : An Empirical Analysis of Kangra Farms of Himachal Pradesh.” Agricultural Economics Research Review, Vol 17(2):199-217. Mahajan, G.(2005), “Indicators of Development: A block Level Study in Kangra District of Himachal Pradesh.” The Asian Economic Review, Vol. 47(1):137-144. Mahajan, G.(2009), “Diversification of Rural Economy: Effect on Income Consumption and Poverty.” The Asian Economic Review, Vol.51(2): 270-289. Pope, R.D. and Richard, Presscott (1980), “Diversification in Relation to Farm size and other Socio-economic Characteristics.” American Journal of Agricultural Economics, vol. 62 (3): 554-559. Ramesh Chand (1995), “Agricultural Diversification and Small Farm Development in Western Himalayan Region.” Small Farm Diversification: Problems and Prospects, NCAEPR Publication, New Delhi, pp 214 Rao, V. M. ( 1987 ) , “ Changing Villages Structure : Impact of Rural Development Programme .” Economic and Political Weekly, (Review of Agriculture), Vol.XXIII (13): A2 to A5. Reardon, T. (1992), “Determinants and Effects of Income Diversification Amongst Farm Households in Burking Faso.” The Journal of Development Studies, vol. 28 (2). Reardon, T. (I997), “Using Evidence of Household Income Diversification to Inform Study of the Rural Nonfarm Labour Market in Africa.” World Development, vol. 25, No.5, pp 735-747. Saini, A.S. and R.V. Singh (1985), “Impact of Diversification on Income, Employment and Credit Needs of Small Farmers in Panjab.” Indian Journal of Agricultural Economics, vol.40 (3):310-316. Singh, A. J., Jain, K.K. and Inder Sain (1985), “Diversification of Punjab Agriculture: An Econometric Analysis.” Indian journal of Agricultural Economics, vol.40 (3): 298-303. Varadarajan, S. and S. Elangovan (1995), “Scope of Commercialisation of Small Farm Agriculture.” Small Farm Diversification: Problems and Prospects, NCAEPR Publication, New Delhi: 214.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Carbon Sequestration in Tree Plantations at Kurukshetra in Northern India Pooja Arora and Smita Chaudhry* Institute of Environmental Studies Kurukshetra University, Kurukshetra, 136119 Haryana, India Abstract: Plantations of Eucalyptus tereticornes, Tectona grandis, Syzygium cumini were selected in the Kurukshetra University Campus for determining their potential to sequester carbon in vegetation and soil. The plant biomass was 169.44 Mgha-1 in E. tereticornes, 153.31 Mgha-1 in T.grandis and 132.59 Mgha-1 in S.cumini accounting for maximum carbon allocation in E. tereticornes (81.33MgCha -1). The vegetation carbon pool T.grandis and S. Cumini was 73.58MgCha -1 and 63.64 MgCha-1 respectively. Total Soil organic carbon stock (SOC) down to 100 cm depth was maximum in S. cumini (77.72 MgCha -1) followed by E. tereticornes (74.69 MgCha-1) and T. grandis (55.46 MgCha-1). Microaggregates contributed highest to the % weight distribution of soil aggregate size fraction in all the depths and study sites whereas percent organic carbon content was higher in macroaggregates. The soil microbial biomass carbon was found to be maximum in rainy season. Amongst the species, S.cumini had highest amount of soil microbial biomass carbon followed by T. grandis and E. tereticornes. Key words: Biomass, Microbial biomass carbon, Soil Carbon, Soil aggregates, Tree plantations I.

Introduction

Kyoto Protocol has recognized forestry as a sink measure for atmospheric greenhouse gases under the Clean Development Mechanism (CDM) in terms of afforestation and reforestation [1]. Tropical forestry carbon projects are being implemented to reward increased ecosystem carbon (C) sequestration to mitigate anthropogenic emissions ([2], [3]). Tree plantations may contribute in restoring forest ecosystem services and providing economic options to local communities through improved forest management practices ([4], [5], [6]). Tree growth serves as an important means to capture and sequester atmospheric carbon dioxide in vegetation, soils and biomass products [7]. Carbon sequestration can be defined as biotic process whereby the atmospheric CO2 is transferred into a long lived C pool [8]. About two-third of terrestrial carbon is sequestered in the standing forest, forest under storey plant, leaf and forest debris and in forest soils [9]. With the increase of atmospheric CO2 concentration, plantsâ&#x20AC;&#x2122; carbon storage potential could increase due to greater assimilation of carbon through the process of photosynthesis [10]. Carbon sequestration and storage in soils serves as an important means of reducing GHGs in the atmosphere to mitigate predicted climate changes. The level of organic C in a given soil at any one time depends on complex interactions of climate, soil physical, chemical, biological processes ([11], [12]) appropriate forests species and management practices. Further, the quantity and quality of SOC pools are strong determinants of soil quality in terms of biomass productivity and environment moderation capacity ([13], [14]). Estimation of soil C change is essential to determine the amount of carbon contributed by plants to soil C stock [15]. Soil carbon stock usually increases over time after planting trees [16] due to carbon input from litterfall and the turnover of dead roots [17] representing that the higher growth of forest plantation would lead to higher soil carbon accumulation. Soil C sink is being viewed as one that could potentially have a significant impact on sequestering CO2 emissions [18]. Carbon accumulation in soil can be facilitated through the formation of a well soil macro-aggregated structure [19]. Aggregate stability is significantly correlated with SOC due to the binding action of humic substances and other microbial by-products [20]. Forest types also influence soil microbial biomass and activities by determining the quantity and quality of organic matter inputs in to the forest floor [21]. Fluctuations in the size of soil microbial biomass during the growing season are considered an important factor in controlling the turnover of soil carbon and nitrogen [22].

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Understanding the role of secondary and plantation forests as carbon reservoirs is thus, crucial for improving predictions of current and future effects of land use and land cover changes on the global carbon cycle [23]. The present study aims to analyze plant carbon stocks, soil organic carbon stock, soil aggregate carbon and soil microbial biomass carbon in three different tree plantations. II.

Study Site

The study sites are located in the district Kurukshetra of Haryana. It lies between latitude 29°-52' to 30°-12' and longitude 76°-26' to 77°-04' in the North Eastern part of Haryana State and has an area of 1682.53 Sq.Kms. The tree plantations were done in year 2001 by Forest Department of Haryana, under Social Forestry Scheme. The study was conducted in the year 2011-12. The sites include pure plantations of Tectona grandis, Eucalyptus tereticornes and Syzygium cumini. The distance between the rows of trees and between trees in a row was 6.0 m and 6.0 m in T. grandis, 3.5 m and 2.5 m in E. tereticornes and 5.0m and 3.0m in S. cumini. The climate of the study area is of very pronounced character with very hot in summer (up to 45ºC) and very cold in winter (about 3ºC). The maximum and minimum temperature ranged from 18.77 to 45.15 ºC and 5.37 to 32.15 ºC respectively from November, 2011 to December, 2012. III.

Methodology

A. Estimation of Plant Biomass and Carbon Content 20 x 20 m experimental plots were demarcated within the three plantations. Biomass of trees was estimated by dimension analysis of sample trees based on circumference at breast height (cbh) using following volume equations [24]: For Eucalyptus tereticornes: (r2=0.9892) For Tectona grandis: (r2=0.9958) For Syzigium cumini: (r2=0.915) Where ‘V’ is the volume and D is the diameter at breast height. The volume of tree species was multiplied by species specific wood density to calculate the biomass. Carbon content of tree species was calculated by multiplying factor (0.475) with the volume [25]. B. Soil Sampling and Analysis The samples were collected down to one meter depth (0-15cm, 15-30cm, 30-45cm, 45-60cm and 60-100cm) using soil corer from within the sampling plots. Some samples were procured for measurement of bulk density and moisture content and others were air dried, ground and stored for further chemical analysis. Soil moisture was determined using Moisture meter (IR 60, Denver Instruments), bulk density by soil core method [26]. Soil pH was measured in 1:2 ratios with distilled water using Systronics µpH System 361. Soil aggregates were determined by wet sieve method [27]. Organic carbon (%) in soil samples and soil aggregates was analysed by wet digestion method [28]. Soil Organic Carbon stock was estimated from bulk density, soil depth, and organic carbon concentration in soil of the respective soil depth. The microbial biomass from the seasonally collected soil was determined by UV (280nm) absorbance method [29]. IV.

Results and Discussion:

A. Tree Biomass and Carbon Content The total basal area for the tree species calculated was 192.67m2ha-1 with mean cbh 37.95cm for E. tereticornes, 29.89m2ha-1 with mean cbh 55.02cm for T. grandis and 49.5 m2ha-1 with mean cbh 41.85cm for S. cumini. The total above ground biomass was maximum for E. tereticornes (169.44Mgha-1) followed by T. grandis (153.31Mgha-1) and S. cumini (132.59Mgha-1) which accounted for allocation of 81.33MgCha-1, 73.58MgCha-1 and 63.64MgCha-1 respectively. The maximum biomass and carbon content of E. tereticornes was due to highest tree density per hectare whereas the larger values of cbh of trees of T. grandis accounted for its higher biomass and carbon values than S. cumini. Significant positive correlation was observed between basal area of trees and volume, biomass and carbon content of all the three species (Fig 1) B. Soil Analysis Soil samples collected from each site were analyzed for physiochemical properties. The moisture content, pH values and the bulk density in general increased down the depth in the all the three plantations. Maximum soil moisture was observed in S. cumini (7.22%-11%) followed by E. tereticornes (7.73%-9.82%) and T. grandis (4.17%-7.11%). The pH for all soil samples was neutral or slightly alkaline. There were not many variations in the values of bulk density for the soil samples of all the three study sites, yet the maximum were found to be in S. cumini. The organic carbon content decreased down the depth in all the soil samples. The maximum percentage in upper 30 cm of soil depth was observed in E. tereticornes followed by S. cumini and then T. grandis. However, in lower depths, the soil samples from S. cumini had higher percentage of organic carbon content (Table 1). The differences in percent soil organic carbon were significant (p<0.01, 0.05) between the depths but not significant between tree species.

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1 0

8 4 2

2 R² = 0.9934

1 0.5

Biomass (Mg/ha)

0 1 2 Basal Area (m2/ha))

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

R² = 0.9934

1 2 Basal Area (m2/ha))

0.5 1 Basal Area (m2/ha)

1.5

0.5 Basal Area (m2/ha)

2.5

7 6 5 4 3 2 1 0

R² = 0.9943

Carbon (Mg/ha)

R² = 0.9716

0 0

2.5

Basal Area (m2/ha)

1.5

R² = 0.9943

Volume (m3/ha)

0.5 1 Basal Area (m2/ha)

3.5 3 2.5 2 1.5 1 0.5 0

Biomass (Mg/ha)

R² = 0.9934

2 1.5

R² = 0.9716

1 0.5 0

1.5

0.5 Basal Area (m2/ha)

1.2

R² = 0.9943

Carbon (Mg/ha)

12 Volume (m3/ha)

Voulme (m3/ha)

1 0.8 0.6 R² = 0.9716

0.4 0.2 0

0.5

1.5

Basal Area (m2/ha)

0.5

Basal Area (m2/ha)

E. tereticornes

T. grandis

S. cumini

n=162

n=48

n=135

Figure 1: Relationship between basal area and volume, biomass and carbon content for all the three species Table 1: Physiochemical properties of soil samples from different depths and sites. The values after ± represents Standard Error Site

E. tereticornes

T. grandis

S. cumini

Soil Depth (cm) 0-15 15-30 30-45 45-60 60-100 0-15 15-30 30-45 45-60 60-100 0-15 15-30 30-45 45-60 60-100

Moisture Content (%) 7.73±0.12 8.35±0.06 8.69±0.04 9.19±0.07 9.82±0.09 4.17±0.13 5.18±0.11 5.67±0.17 6.10±0.20 7.11±0.09 7.22±0.13 8.97±0.07 9.68±0.05 10.34±0.03 11.00±0.08

pH (1:2) 7.16±0.01 7.25±0.01 7.47±0.01 7.58±0.01 7.79±0.01 6.79±0.01 7.06±0.01 7.16±0.01 7.25±0.01 7.43±0.01 7.15±0.01 7.39±0.01 7.55±0.01 7.68±0.01 7.80±0.01

Bulk Density g/cm3 1.12±0.04 1.18±0.03 1.23±0.04 1.30±0.02 1.41±0.01 1.07±0.02 1.16±0.04 1.2±0.03 1.28±0.01 1.35±0.02 1.14±0.05 1.2±0.03 1.28±0.02 1.34±0.04 1.40±0.01

Organic Carbon (%) 1.196±0.01 0.827±0.01 0.631±0.01 0.46±0.01 0.343±0.01 0.791±0.01 0.634±0.01 0.53±0.01 0.422±0.01 0.261±0.01 0.823±0.01 0.794±0.01 0.647±0.01 0.592±0.01 0.447±0.01

The total soil organic carbon (SOC) stock generally declined with increasing depth. However, in the 60-100 cm depths, the total stock of organic carbon was higher than the upper layers (due to larger volume of soil (40cm)) though the percentage of organic carbon was lower in that depth. The total stock of soil organic carbon in S. cumini was 77.72 MgCha-1 followed by 74.69 MgCha-1 in E. tereticornes and 55.46 MgCha-1 in T. grandis (Fig 2). The maximum amount of SOC in S. cumini might be due to the high soil bulk density.

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SOC (Mg/ha)

30 20 E. tereticornes

T. grandis S. cumini

0 0-15

15-30

30-45

45-60

60-100

Soil Depth (cm)

Figure 2. Soil Organic Carbon Stock (SOC) at different soil depths in study sites. The aggregate size fractions in the soil from different soil depths are given in Table 2. The maximum contribution in weight was observed to be from the microaggregates (250µm-53µm) followed by silt and clay associated fraction (<53µm) and macroaggregates (2mm-250µm) at all the depths and study sites. The percent weight of microaggregate ranged from 55.9-71.9% in E. tereticornes, 66-72.9% in T. grandis and 50.7-72.9% in S. cumini. Table 2. Soil weight (%) distribution in aggregate size classes at different depths in three plantations. The values after ± represents Standard Error. % Weight Study Sites

Soil Depth (cm) 0-15 15-30 30-45 45-60 60-100 0-15 15-30 30-45 45-60 60-100 0-15 15-30 30-45 45-60 60-100

E. tereticornes

T. grandis

S. cumini

2mm-250µm 4.86±0.33 5.56±0.77 5.46±0.34 8.23±0.49 8.57±0.85 7.86±0.08 6.37±0.65 6.09±0.11 7.78±0.76 6.33±0.95 8.67±0.14 13.59±0.34 17.92±0.77 15.42±1.6 21.48±0.60

250µm-53µm 61.45±0.75 71.98±0.93 67.40±0.60 66.28±1.73 55.85±0.98 67.79±0.14 66.00±0.77 72.91±0.98 72.45±0.48 71.28±0.65 72.95±0.48 60.66±0.98 55.90±0.26 57.44±0.11 50.70±0.65

<53µm 18.64±1.66 11.34±0.60 19.99±0.91 20.10±0.99 21.15±0.26 18.64±0.25 18.59±0.76 18.46±0.96 18.05±0.93 20.56±0.65 18.95±0.33 12.62±0.49 23.08±0.26 24.88±1.22 24.20±0.98

E.tereticornes

T.grandis Soil Depth (cm)/ Study Sites

60-100

45-60

30-45

15-30

0-15

60-100

45-60

30-45

15-30

0-15

60-100

45-60

30-45

15-30

0-15

% Organic Carbon

Macroaggregates play important role in storing carbon. The total carbon concentration was greater in macroaggregates (2mm-250µm), followed by microaggregates (250µm-53µm) and then by silt and clay associated soil fraction (<53µm). The trend was same along all the depths and in all the three sites, however, the values of % organic carbon in three sites followed the order: T. grandis > E. tereticornes> S. cumini (Fig 3). 1.4 2mm-250µm 1.2 250µm-53µm 1 <53µm 0.8 0.6 0.4 0.2 0

S. cumini

Figure 3: Organic carbon (%) distribution in aggregate size classes at different soil depths in three sites

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In general, the soil microbial biomass carbon decreased down the depth in all the plantations. It may be due to high organic matter content in upper layers of soil added by leaf litter, plant residues and rhizospheric roots which can be easily decomposed by soil microorganisms. The rainy season accounted for highest amount of microbial biomass carbon due to sufficient amount of moisture and optimum temperature condition necessary for microbial growth. However, amongst the plantations highest microbial biomass carbon was in S. cumini followed by T. grandis and E. tereticornes in winter and summer season, whereas in rainy season maximum microbial biomass carbon was observed in the soil of S. cumini and least in E. tereticornes (Table 3). Table 3: Microbial Biomass Carbon (µg C g-1 of soil) of Three tree plantations at different depths in winter and summer Season. Values after ± represents standard deviation Winter Season

Summer Season

Rainy Season

Depth(cm)

T. grandis

S. cumini

E. tereticornes

0-5cm

182±0.17

179±0.22

104±0.09

5-15cm

129±0.06

101±0.05

78±0.07

15-30cm

87±0.06

58±0.02

32±0.06

0-5cm

88±0.20

70±0.02

62±0.02

5-15cm

74±0.05

54±0.01

28±0.02

15-30cm

59±0.01

43±0.02

16±0.01

0-5cm

514±0.08

818±0.09

401±0.23

5-15cm

277±0.12

401±0.09

261±0.20

15-30cm

156±0.05

269±0.21

105±0.09

Seasonal variations of soil microbial biomass reflect the degree of immobilization and mineralization of soil carbon. A decrease in soil microbial biomass can result in mineralization of nutrients, whereas an increase in microbial biomass may lead to immobilization of nutrients [30]. V.

Conclusion

Forests store large amount of carbon than any other ecosystem. As, carbon sequestration is simply a function of biomass accumulation, the best method to increase carbon stocks is to plant trees [31]. The study reveals that tree plantations have the potential to increase the C pool in the biomass and soil. This sequestration potential plays a significant role in stabilizing the concentration of atmospheric greenhouse gases and thus mitigating climate change. Healthy soil provides a wide range of ecosystem goods and services and sustain biological productivity. The plantations of E.tereticornes, T.grandis and S.cumini have a great potential of carbon sequestration amounting respectively to 81.33MgCha-1, 73.58MgCha-1 and 63.64MgCha-1 as vegetation carbon pool and 74.69 Mg Cha-1, 55.46 Mg Cha-1 and 77.72 MgCha-1 as soil carbon pool. With less tree density per hectare, T. grandis had sufficiently large amount of carbon stored in its biomass representing its higher carbon sequestration potential over other species. Soil carbon is important as it determines ecosystem functions, influencing soil fertility, water-holding capacity and other soil parameters. Soil organic carbon associated with aggregates is an important reservoir of carbon, protected from mineralization and enzymatic degradation [32]. The trends indicated by soil aggregates (% weight distribution) and % organic carbon in aggregate size class in the present study were comparable to other studies ([33], [34]). The higher values of total SOC in water stable aggregate classes are indications of the positive influence of SOC on the stability of these aggregates. Microbial biomass is the most active fraction of soil and its measurement can give an early indication of changes in total soil organic matter. Estimation of soil microbial biomass carbon is of fundamental importance for studying wide range of soil processes involved in nutrient cycling, organic matter decomposition, and soil quality and for monitoring and modelling applications. References [1] [2] [3] [4] [5] [6] [7]

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Singh, V., Tiwari, A., Kushwaha, S.P.S. and Dadhwal, V.K. (2011). Formulating allometric equations for estimating biomass and carbon stock in small diameter trees. Forest Ecology and Management, 261 (11): 1945-1949 Gibbs, H.K., Brown, S., Niles, J.O. and Foley, J.A. (2007). Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2, 045023. doi: 10.1088/1748-9326/2/4/045023 Canadell, J.G. and Raupach, M.R. (2008). Managing forests for climate change mitigation. Science, 320: 1456–1457. Lugo, A.E. (1992). Comparison of tropical tree plantations with secondary forests of similar age. Ecological Monographs, 62: 1– 41. Lamb, D., Erskine, P.D. and Parrotta, J.A. (2005). Restoration of degraded tropical forest landscapes. Science, 310: 1628–1632. Berthrong, S.T., Jobbágy, E.G. and Jackson, R.B. (2009). A global meta-analysis of soil exchangeable cations, pH, carbon, and nitrogen with afforestation. Ecological Applications, 19: 2228– 2241. Mukundi, W.R. and Sathaye, J.A. (2004). GHG mitigation potential Biomass and Carbon Sequestration Potential of PoplarWheat Inter-cropping System in Irrigated Agro-ecosystem in India and cost in tropical forestry-relative role for agroforestry, Environment, Development and Sustainability, 6: 235-260. Lal, R. (2004). Soil carbon sequestration impacts on global climate change and food security. Science, 304: 1623–1627.

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In: Defining Soil Quality for a Sustainable Environment (eds., Doran, J.W., Coleman, D.C., Bezdicek, D.F., and Stewart, B.A.). Soil Sci. Soc. Amer. Special Publ., pp. 3– 21. Bezdicek, D.F., Papendick, R.I. and Lal, R. (1996). Importance of soil health to sustainable land management. In: Methods for Assessing Soil Quality (eds. Doran, J.W., and Jones, A.J). Soil Sci. Soc. Am. Special Publ., pp. 1–8. Toma, Y., Clifton-Brown, J., Sugiyama, S., Nakaboh, M., Hatano, R., Fernandez, F.G., Stewartk, R.J., Nishiwaki, A. and Yamada, T. (2013). Soil carbon stocks and carbon sequestration rates in seminatural grassland in Aso region, Kumamoto, Southern Japan. Global Change Biology, 19: 1676–1687 Sakai, H., Inagaki, M., Noguchi, K., Sakata, T., Yatskov, M. A., Tanouchi, H., and Takahashi, M. (2010). Changes in soil organic carbon and nitrogen in an area of Andisol following afforestation with Japanese cedar and Hinoki cypress. Soil Science and Plant Nutrition, 56: 332–343. Richter, D.D., Markewitz, D., Trumbore, S.E., and Wells, C.G. (1999). Rapid accumulation and turnover of soil carbon in a ReEstablishing forest. Nature, 400: 56-58. Bell, M., and Lawrence, D. (2009). Soil Carbon Sequestration; myths and mysteries. Queensland Government, Department of Primary Industries and Fisheries. Jimenez, J.J., Lal, R., Russo, R.O. and Leblanc, H.A. (2008). The soil organic carbon in particle-size separates under different regrowth forest stands of north eastern Costa Rica. Ecological Engineering, 34: 300-310. Haynes R.J., Swift R.S. and Stephen K.C. (1997). Influence of mixed cropping rotations (pasture-arable) on organic matter content, water-stable aggregation and clod porosity in a group of soils. Soil Tillage Res., 19: 77–81. Hackl, E., Bachmann, G., Zechmeister-Boltenstern, S. (2004). Microbial nitrogen turnover in soils under different types of natural forest. Forest Ecology and Management, 188: 101–12. Yang, Y., Zhu, J., Zhang, M., Yan. Q. and Sun, O.J. (2010). Soil microbial biomass carbon and nitrogen in forest ecosystems of Northeast China: a comparison between natural secondary forest and larch plantation. Journal of Plant Ecology, 3(3): 175–182 Spiotta, E. M. and Shrama, S. (2013). Carbon storage in successional and plantation forest soil: a tropical analysis. Global Ecology and Biogeography, 22: 105-117. Forest Survey of India, 1996. Volume equation for forest of India, Nepal and Bhutan. FSI, Ministry of Environment and Forest, Government of India. Magnussen, S. and Reed, D. (2004). Modelling for estimation and monitoring. FAO-IUFRO. Blake, G.R. and Hartge, K.H. (1986). Bulk Density. Methods of Soil Analysis, Part 1, Soil Sci. Soc. Am: 363-376. Elliott, E.T. (1986). Aggregate structure and carbon, nitrogen, phosphorus in native and cultivated soils. Soil Sci. Soc. Am. J., 50: 627–633. Walkley A. and Black, I.A. (1947). A critical examination of a rapid method for determining organic carbon in soil-effect of variation in digestions and of inorganic soil constituents. Soil Sci., 63: 251-263. Nunan, N., Morgan, M.A., Herlihy, M. (1998). Ultraviolet absorbance of compounds released from soil during the chloroform fumigation as an estimate of microbial biomass. Soil Biol. Biochem. 30 (12): 1599-1603. McGill, M.B., Cannon, K.R., Robertson, J.A., and Cook, F.D. (1986). Dynamics of soil microbial biomass and water soluble organic C in Breton L after 50 years of cropping to two rotation. Can J Soil Sci, 66 (1): 1–19. Godinhor, L., Nacuray, E., Cardinoza, M.M., and Lasco, R.D. (2003). Climate change mitigation through carbon sequestration: the forest ecosystems of Timor Leste. In: Proceedings from the 1st National Workshop on Climate Change, Dili. Trujillo, W., Amezquita, E., Fisher, M.J. and Lal, R. (1997). Soil organic carbon dynamics and land use in the Colombian Savannas: Aggregate size distribution. In: Soil Processes and the Carbon Cycle (eds, Lal, R., Kimble, J.M., Follett, R.F., Stewart, B.A.), CRC/Lewis Press, Boca Raton, Florida, pp. 267-280. Sainju U.M. (2006). Carbon and nitrogen pools in soil aggregates separated by dry and wet sieving methods. Soil Science, 170(12): 937-949. Onweremadu, E.U., Onyia, V.N., and Anikwe M.A.N. (2007). Carbon and nitrogen distribution in water - stable aggregates under two tillage techniques in fluvisols of Owerrr area, Southeastern Nigeria. Soil Till. Res., 97: 195-206.

Acknowledgement The financial assistance in the form of University Research Scholarship to Pooja Arora from Kurukshetra University, Kurukshetra is highly acknowledged.

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MACROFUNGAL WEALTH OF KUSUMHI FOREST OF GORAKHPUR, UP, INDIA Chandrawati, Pooja Singh*, Narendra Kumar, N.N. Tripathi Bacteriology and Natural Pesticide Laboratory Department of Botany, DDU Gorakhpur University, Gorakhpur - 273009 (UP). Abstract: The Kusumhi forest of Gorakhpur is a natural habitat of a number of macrofungi. During present study in the year 2002-05, frequent survey of Kusumhi forest were made for collection of naturally growing macrofungi. A total of 29 macrofungal species belonging to 12 families were observed, Tricholomataceae was predominant. Out of 29 spp. collected 4 were excellently edible, 6 edible, 18 inedible and 1 poisonous. Ganoderma applanatum, Lycoperdon pyriforme, Termitomyces heimii and Tuber aestivum were of medicinal importance, used by local people for wound healing, coagulation of blood, as tonic and in health care respectively. These macrofungi were observed in humid soil, sandy soil, calcareous soil and on wood log, wood, leaf litter, leaf heaps, decaying wood log, troops of rotten wood, Shorea robusta, Tectona grandis as well as on termite nests. 25 species were saprophytic while 2 species were parasitic in nature. Key words: Macrofungi, Diversity, Kusumhi forest. I. INTRODUCTION The Kusumhi forest is located at a distance of 5 km from city head quarter of Gorakhpur and lies in the North East corner of Uttar Pradesh between 2605' and 27029' North latitude and 8304' and 83057' East longitude and covers an area of about 5000 hectares. The average height of the area above sea level is about 107 m. The soil of Gorakhpur which is a part of Trans Sarju - plain comprises Pleistocene alluvial or Gangetic alluvium, brought down by rivers like Ghaghra, Rapti, Rohini and Gandak from Himalayas. Gorakhpur has a monsoonal climate with its characteristics three seasons viz., rainy, winter and summer. Several workers have studied macrofungal diversity of their respective places and recorded their observations in various regions of India viz., Eastern Himalayas, Kashmir valley; Garhwal; South East Maharashtra [1][2][3][4][5]. Kusumhi forest is rich in the resources of edible fungi where no planned efforts have been made so far to collect and conserve their germplasm from this forest. Keeping this in view a systematic and periodic survey was carried during 2002-2005 in order to collect information on diversity of macrofungi, their ecological habitat and nature. II. MATERIALS AND METHODS Survey work was carried out from January to December during 2002-2005. The full bloomed and complete sporocarps of fresh specimens were photographed and collected from their natural habitats. They were kept in sterilized polyethylene packets separately, sealed with staples. Month and locality of fungi collection, nature of substrate, odor, color of fresh specimen and nature of latex, if present were noted. Their ecological habitats and occurrence in solitary or groups were recorded. The specimens were brought to the laboratory for further observations and identification. The morphological investigation of macrofungi fructification were done visually with respect to size, diameter and length of cap (pileus)/ stalk (stipe), colour and its variation during maturity, color of gills and its mode of attachment to stem and other details of ascocarp/basidiocarp. Thin sections of gill/hymenium layer was cut, stained in cottonblue and mounted in lactophenol for microscopic studies of collected specimens. Microscopic observations included size, color & nature of basidium / ascus, size, color & number of spores, color of spore mass, presence/absence of cystidia. Finally the specimens were preserved separately in solution of distilled water (70): alcohol (25): formaldehyde (5). On basis of morphological and microscopic studies macrofungal species were identified with the help of literature [6][7][8][9]. III. RESULTS A total of 29 macrofungi belonging to 12 families were observed. Tricholomataceae was predominant having maximum number (10) of species. Agaricaceae and Coriolaceae were represented by 3 and 4 species respectively. Coprinaceae, Cortinariaceae, Geastraceae, Phallaceae, Polyporaceae and Tuberaceae were represented by one species each (Table 1). The order Aphyllophorales was represented by 3 families viz., Coriolaceae, Ganodermataceae and Polyporaceae (Figure1). Agaricales was represented by 4 families viz., Agaricaceae, Coprinaceae, Cortinariaceae and Tricholomotaceae (Figure 2). Fructification details of some edible and medicinal mushrooms of Kusumhi forest-

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Agaricus augustus Fr: Up to 10-19 cm dia, pileus: 10-19 cm tall and 2-4 cm dia, flesh whitish, firm and thick, soon breaking up into fibrous scales in more or less concentric rings against a yellow-tinged background, gills: pallid pink becoming chocolate-brown or blackish at maturity; stipe: 5-8 x 1.5 cm whitish, solid tapering slightly upwards, ring white broad, superior, flesh whitish, tinged pink in the stem base, stuffed, sometimes becoming hollow; basidiospore: 7-10 x 4.5-5.5 µm, basidia: 4 spored, gill edge. Cystidia formed of subspherical units arranged in chains, strong odour, edible and good. Coprinus comatus (Mull.: Fr.) S F Gray: Cap 6-16 cm tall, variable dia, pileus: 9-29 cm tall x 1.5-2.5 cm dia, white, at first ovoid; cylindrical, pressed to the stalk at the margin, becoming campanulate-conical, decorated with feathery, overlapping, partly reflexed, velar remnants, white when young or stained brownish, flesh white, soon discolouring, fleshy in relation to cap size, fragile auto-digesting, then cap appear blackish-violet; gills: white at first, rapidly becoming pinkish, grey-brown and finally blackish-violet, free crowded, stipe: 10-19 cm tall, 1.5-2.0 cm wide, white, smooth, slightly swollen at the base and rooting, ring white, thin, loose and often slipping down, stem towards the base, flesh white, medium hollow and fragile; spores: 10-12 x 6-7 µm, date-brown, smooth, almond shaped; basidia: 30-31 x 6-8 µm, clavate, 4 spored, sterigmata 6-8 µm. Cystidia not distinctive, edible . Ganoderma applanatum (Pers: Wall) Pat: Grayish-brown (cocoa-brown), more or less flattened, radially wavy or wrinkled and concentrically grooved and zoned, broadly attached sessile, flesh cinnamon-brown, thinner than the tube region, very tough and fibrous; pores: white, bruising brown, circular, 4-5 per mm tube brown, 7-10 mm deep, spores: brown, warty, broadly ellipsoid, flattened at one end with hyaline germ pore, non-amyloid, 7-9 x 4.5-6 µm, basidia: 4 spored. Cystidia absent, inedible and medicinal. G. lucidum (Curt.: Fr.): 7-15 x 11-20 cm, woody to corky with lateral stem, reniform and directly attached to substrate; pileus: surface brilliantly laccate, radially rugose, dark reddish to brown, yellowish towards the margin brittle, soft margin blunt, context: light brown, 0.9-10 cm wide, without horny deposits, tube layer somewhat cinnamon to almost concolorous with the context 0.7-1 mm long, not-stratified; hymenophores: white, turning brown when bruising and with the age, 4-5 pore per mm stipe: lateral up to 8 cm long, 1-1.5 cm wide, concolorous and laccate as pileus; dermis of the hymenodermis vera type, thick walled elements, some of them diverticulated at the base; spore: 9-10 x 6-5 µm, yellowish brown. Grifola frondosa (Dicks.: Fr.) S F Gray: Tongue like, individual, caps 4-9 cm dia, 0.5-1 cm thick, arising in clumps from a common repeatedly branching stem creating the appearance of an irregular rosette, annual; parasitic on wood of broad leaf trees, usually arising from the base of the trunk, favouring beech but also on oak. The fungus generates a destructive white rot. Upper surface tan, with olivaceous tinge several or many ligulate caps arising from a central branched stem; each cap thick and leathery with undulating, sometime split margin zoned concentrically or wrinkled radially stem pallid cream or grayish laterally compressed, 2-5 cm long, flesh white, soft and fibrous; pores: whitish-cream, angular or rounded, 2-3 per mm, tubes white decurrent, 2-3 mm deep; Spores: hyaline, smooth, broadly ellipsoid, non-amyloid, with droplets, 5-7 x 3.4-4 µm, basidia: 4 spored. Cystidia absent, edible. Lepista nuda (Bull.: Fr.) Cke: Cap 6-11 cm dia, at first lilaceous, becoming brownish and drying more pallid at first convex and slightly umbonate becoming flattened and finally shallowly depressed and wavy, smooth, flesh bluish-lilac and thick; gills: lilaceous, fading to buff or brownish with age emarginate fairly narrow, crowded; stipe: 5-8 cm tall, 1.5-2.4 cm dia, concolorous with cap more or less equal, fibrillose and often slightly thickened at the base, ring absent, flesh bluish-lilac thick, firm full; spores: pink minutely roughened, ellipsoid, non-amyloid 6-8 x 4-5 µm, basidia: 4 spored. Cystidia absent, edible. Marasmius oreades (Bott.:Fr.) Fr. : Cap 2-4.5 cm dia, smallish, pale tan, fleshy agaric, with blunt umbo and tough rooting, tan, hygrophanous, drying to buff but retaining tan tinge at centre, at first convex, then flattened and broadly umbonate, smooth but striate at the margin; flesh whitish buff thick at the centre, otherwise thin; gills: whitish becoming ochraceous cream, adnexed or free, farily broad distant; stipe: concolorous with cap, smooth or finely scurfy, slender, more or less equal, whitish downy at the base and slightly rooting, stiff; ring absent, flesh whitish buff and tough; spores: hyaline, smooth, ellipsoid, non-amyloid, occasional droplets 7-10 x 4-6 µm; basidia: 4 spored. Cystidia absent, edible. Tricholoma giganteum Mass : Up to 30 cm in height; pileus: 9-25 cm in dia, convex, spherical to conical, becoming flattened with broad umbo, smooth, dry, flesh white becoming yellowish with age and thick, gills: white crowded, free edge entire, thick; stipe: 7-30 cm long, 2-5 cm wide, white, cylindrical smooth, tapering slightly upwards, ring absent, flesh white, stuffed; spores: 5-6 x 3-35 cm subspherical, non-amyloid; basidia: 32-38 x 7-8 µm hyaline, clavate, 4 spored. Cystidia absent; hymenophoral trama: regular homiomerus, hyphae septate, hyaline, excellently edible. Termitomycetes heimii Heim : Up to 22 cm height; pileus: 3-5 cm in dia and extended up to 10 cm on full maturity, plane to convex with prominent umbo, white umbo, brown, margin inflexed to incised, surface smooth, viscid when moist; gills: crowded, free, white margin serrulate brittle, stipe: 12-20 cm long 1-2 cm wide, cylindrical, tapering downwards white to creamnish white solid, fleshy anulate, pseudorrhiza long; annulus: single layered thick, white superior; spores: 5-8 x 4-7 µm, ovate to ellipsoid, whitish to pinkish, in-amyloid; basidia: 3034 x 8-11 µm, clariform and 4 spored, sterigmata up to 4 µm long, cystidia: 30-34 x 6-8 µm, clavate hyaline;

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hymenophoral trama: subregular divergent, hyphae hyaline, septate, clamp connection present excellently edible and medicinal. Tuber aestivum Vitt : 3-7 cm dia, subterranean dark brown warty ball, typically under beech but also other broad leaf trees on calcareous soil; apothecium blackish-brown, irregularly subspherical, covered in 5-6 sided pyramidal warts, flesh at first whitish, becoming grey brown with white marbled veins; asci: sub-spherical, 60-90 x 50-60 µm; spores (2-5) at first hyaline, becoming yellowish brown, reticulated, broadly ellipsoid or sub-spherical, nonseptate, irregularly arranged, 25-50 x 17-35 µm, paraphyses absent, excellently edible and medicinal, can be eaten raw. Table 1: Diversity of macrofungi in Kusumhi forest Name of macrofungi Agaricus angustus Fr. Collybia fusipes (Bull.: Fr.) Quel Coprinus comatus (Mull.: Fr.) S F Gray Coriolus versicolor (Li.: Fr.)Quel Ganoderma applanatum (Pers: Wall) Pat G. lucidum (Curt.: Fr.) Geastrum rufescens Pers. Grifola frondosa (Dicks.: Fr.) S F Gray Heterobasidion annosum (Fr.) Bref Inocybe fastigiata (Schaeft:Fr.) Quel Ischnoderma benzonium (Wahl.:Fr.) Karst Lepiota naucina (Fries) P. Kummer Lepista nuda (Bull.: Fr.) Cke Leucocoprinus cepestipes (Saw. Fr) Pat. Lycoperdon pyriformae Schaeff. L. spadiceum Pers. Marasmius oreades (Bott.:Fr.) Fr. M. rotula (Scob.:Fr.) Fr. Mutinus caninus (Huds.: Pers.) Fr. Mycena alcalina (Fr.) Kummer M. inclinata (Fr.) Quel M. pearsoniana Dennis ex Sing. Polyporus umbrellatus Fr. Termitomyces giganteum Mass T. heimii Heim T. robustus (Beeli) Heim Tuber aestivum Vitt Xylaria corpophila (Pers: Fr.) X. hypoxylon (Li: Fr.) Grev.

Collection number/ collection date DDUNPL52 14/7/04 & 18/7/05 DDUNPL32 20/8/04 & 21/7/05 DDUNPL58 28/7/04 & 21/7/05 DDUNPL15 21/7/05 DDUNPL25 18/8/03 DDUNPL26 18/7/03 & 20/9/05 DDUNPL74 18/8/04, 20/8/05 DDUNPL17 20/7/04 & 29/7/05 DDUNPL18 2/8/04 & 21/7/05 DDUNPL64 21/7/05 DDUNPL19 21/7/05 DDUNPL54 21/7/05 DDUNPL35 11/8/04 & 18/7/05 DDUNPL56 21/7/05 DDUNPL71 18/8/04 & 19/7/05 DDUNPL72 19/7/04 DDUNPL36 20/8/04 & 18/7/05 DDUNPL38 10.7.05 DDUNPL 19/7/04 DDUNPL40 2/8/04 & 4/7/05 DDUNPL42 21/7/05 DDUNPL43 2/8/03 & 21/7/05 DDUNPL14 21/8/03 & 28/9/05 DDUNPL48 21/7/05 DDUNPL45 5/9/02, 15/7/03 DDUNPL46 8/9/04 & 22/9/05 DDUNPL5 10/7/03,20/8/04 & 18/7/05 DDUNPL2 2/7/05 DDUNPL3 28/8/04 & 20/7/05

Family

Property

Agaricaceae

edible and good

Tricholomataceae

Inedible

Coprinaceae

Edible

Coriolaceae

Inedible

Ganodermataceae

Inedible, used in wound healing

Ganodermataceae

Inedible, immunomodulating

Geastraceae

Inedible

Coriolaceae

Edible

Coriolaceae

Inedible

Cortinariaceae

Poisonous

Coriolaceae

Inedible

Agaricaceae

Inedible

Tricholomataceae

Edible

Agaricaceae

Inedible

Lycoperdaceae Lycoperdaceae

Edible, used for blood coagulation Inedible

Tricholomataceae

Edible

Tricholomataceae

Inedible

Phallaceae

Inedible

Tricholomataceae

Inedible

Tricholomataceae

Inedible

Tricholomataceae

Inedible

Polyporaceae

Inedible

Tricholomataceae

Excellently edible,

Tricholomataceae

Excellently edible, medicinal

Tricholomataceae

Excellently edible, medicinal

Tuberaceae

Excellently edible, medicinal

Xylariaceae

Inedible

Xylariaceae

Inedible

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Table 2: Monthly distribution of macrofungal species in Kusumhi forest Year

No of species August September

May

June

July

October

November

December

2002

2003

2004

2005

Table 3: Habitat diversity of wild macrofungi in Kusumhi forest Major type

Ecological habitat

Saprophytic

Calcareous soil

Inocybe fastigiata

Humid soil

Agaricus augustus, Heterobasidion annosum, Leucocoprinus cepestipes, Mutinus caninus, Xylaria hypoxylon

Sandy soil

Geastrum rufescens, Lepista nuda, Tuber aestivum

Parasitic Other

Macrofungi

Wood

Grifola frondosa, Xylaria corpophila

Wood log

Ischnoderma benzonium, Collybia fusipes, Coriolus versicolor, Marasmius oreades, M. rotula, Mycena alcalina, Polyporus umbrellatus

Leaf litter

Coprinus comatus, Termitomyces giganteum

Leaf heap Decaying wood log Rotten wood Shorea robusta stem Tectona grandis stem Termite nest

Mycena inclinata, M. pearsoniana Lepiota naucina Lycoperdon pyriforme, L. spadiceum Ganoderma applanatum Ganoderma lucidum Termitomyces heimii, T. robustus

IV. DISCUSSION The collected species were grouped into excellently edible, edible, inedible, poisonous and medicinal species. Four macrofungi viz., Termitomyces giganteus, T. heimii, T. robustus and Tuber aestivum were excellently edible; Agaricus augustus, Coprinus comatus, Grifola frondosa, Lepista nuda, Lycoperdon pyriforme, Marasmius oreades were edible species while 18 species were inedible (Table 1). Ganoderma applanatum, Lycoperdon pyriforme, Termitomyces heimii and Tuber aestivum were medicinal as reported in earlier literature [10]. From time to time different workers have studied macrofungi diversity of their respective places in India but Kushmhi forest of Gorakhpur division (UP) is least explored with respect to macrofungal diversity. Abraham and Kaul [2] recorded 5 species viz., Coprinus insignus, Lactarius fuliginosus, Leucoagricus holoseciens, Leucopaxillus paradoxus and Tricholoma terrum from Kashmir valley, while Bhatt et. al. [11][3] observed 4 species of Russula, 6 species of Amanita and 9 species of Lentinus from Garhwal Himalayas. Das [12] collected 70 edible mushrooms from Himachal Pradesh. Vrinda et al. [13] collected excellent edible fungus Termitomyces umkowaanii from Western Ghats. Chandrawati et al. [14] surveyed 7 forests of Gorakhpur division and reported presence of 69 mushroom species belonging to 28 families, out of which 15 genera viz., Agaricus, Calvatia, Clitocybe, Coprinus, Geastrum, Lentinus, Lycoperdon, Morchella, Pleurotus, Russula, Scleroderma, Termitomyces, Tricholoma, Tuber, and Volvariella were edible. Chauhan et al. [15] reported 6 species of edible mushroom from Gwalior division. The water content of substrate and relative humidity of atmosphere are of prime importance in controlling the growth and reproduction of fungi. The 70% relative humidity is probably lowest limit for initiating fruit body formation and 80% relative humidity is needed for further development. Atmospheric humidity above 90% is detrimental for fruiting of mushroom species. Temperature has profound effect on growth and reproduction of fungi, the temp between 25-35oC or below is suitable for fruit body formation in several mushrooms. During present survey the months of July, August and September (monsoon season) showed more fructifications of macrofungi in Kusumhi forest. Singh and Prasad [16] recorded number of mushroom spp. during 1997-98 and 1998-1999 and observed greater number of species in the year 1997-98. The occurrence of species was found to be greater in September during 1997-98 and in August in 1998-99, while July and October exhibited lesser number of macrofungi. However, in present investigation July month of each year (2002-05) showed greater number of macrofungal species. There was complete absence of fungal species in Jan, Feb, Mar, and Apr during each year. Moderate

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numbers of species were observed during Aug, Sept of each year. Lesser number of species were noticed in Oct, while only one species was recorded during Nov and Dec (Table 2). Varying number of macrofungi of different species in different months may be due to low relative humidity, low temperature range, very little and no rainfall. As evident from Table 3, 25 species of macrofungi were saprophytic, 2 were parasitic, while 2 species were growing on termite nest. Wood log was the most favored substrate for macrofungal growth (7 spp.) followed by humid soil (5) supporting moderate number of macrofungi. Thus present work reveals few uncommon but edible fungi found native to the region of survey; further work viz., other bioprospects of macrofungal species and attempts of its commercial cultivation needs to be worked out.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

TN Kaul (1978) . “Nutritive value of some edible Morchellaceae”. Indian J. Mush., 4, 26-34. SP Abraham and TN Kaul (1985). “Larger fungi from Kashmir- III”. Kavaka,13, 77-81. VK Bhatt, RP Bhatt and RD Gaur (2000). “Mushrooms of Garhwal Himalayas, the genus Lactarius Pers. Ex. S.F. Gray”. Mush. Res. 9,11-18. MS Patil,BD Kundalkar and SD Nanaware (2003). “Studies on jelly fungi Auriculariales”. Indian Phytopath., 56, 43-49. SQA Kumar and YP Sharma (2007). “A contribution to potential edible macrofungi of Doda district, Jammu and Kashmir”. Indian J. Mycol. Pl. Pathol., 37, 647. GF Atkinson (1961) . “Studies on American fungi- II Edition”. Hafner Publishing Company, New York, USA. K Natarajan (1978).” South Indian Agaricales- IV”. Kavaka, 6: 65-70. NS Atri and SS Saini (1989). “Family Russulaceae Roze- A Review, In: Plant science research in India- Present status and further challenges”; (M.L.Trivedi, B.S. Gill and S.S. Saini Eds.). Today and Tomorrow’s Printer and Publisher, New Delhi, pp. 115-128. M Jordan (1995). “The encyclopedia of fungi of Britain and Europe” (John Taylor Book Venture Ltd. Edition), David and CharlesBrunel House, Newton Abbot, Devon, UK. DK Rahi (2001). “Studies on edible tribal mushrooms of Madhya Pradesh and development of technology for large scale production”. Ph.D. Thesis, R.D. University Jabalpur (MP), India. RP Bhatt, VK Bhatt and RD Gaur (1995).”Fleshy fungi of Garhwal Himalayas- The genus Russula”. Indian Phytopath., 48, 402411. K Das, JR Sharma and RP Bhatt (2002).” Russula flavida Frost & Peck- An addition to Indian ectomycorrhizic fungi”. Mush. Res 11: 9-10. VB Vrinda , CK Pradeep and TK Abhraham (2002). “Termitomyces umkowaanii (Cook and Mass) Reid.- an edible Mushroom from Western Ghat”s. Mush. Res., 11: 7-8. Chandrawati, Narendra Kumar and NN Tripathi (2006) . “Diversity of wild mushrooms in forest of Gorakhpur region”. National Symposium on Microbial Diversity and Plant Health Problems, Dec. 18-19, Gorakhpur, pp. 31. RKS Chauhan , S Chauhan and HS Chaubey (2007). “Wild edible mushrooms from Gwalior Division”. XXX All India Botanical Conference, Nov. 28-30, 2007, Gwalior, pp. 213. CS Singh and AB Prasad (2003). “Diversity of fleshy fungi in Eastern Uttar Pradesh. In: Frontiers of fungal diversity in India” (Prof Kamal Festschrift, Rao et al. Eds.). International Book distributing Co., Lucknow, pp. 327-350.

ACKNOWLEDGEMENTS Authors are thankful to the Head, Department of Botany, DDU Gorakhpur University, Gorakhpur for providing laboratory facilities and Prof. Kamal for identification of fungal species

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Short term Forecasting model of Area Production and Productivity on Lathyrus in Bastar District D.P. Singh, Deepak Kumar, Deo Shankar and P.S. Kusro Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), INDIA Abstract: Lathyrus sativus locally called ‘Khesari’, ‘Teora’, ‘Lakh’/’Lakhdi’ is an important post man soon crop of the Bastar District of Chhattisgarh. The present study is based on secondary data for the last 11 year. i.e., from 2002 to 2012. In the present study, short term forecasting model for next two year of area, production and productivity for the lathyrus were estimated. The best fitted trend value for the area among the year 2002 to 2012 has recorded in the year 2008 which is 2.22 thousand hectare close to actual value, production has recorded in 2005 which is 0.69 thousand tones and best fitted trend value for productivity in 2010 which is 613.69 Kg/ha was recorded. The estimated area forecast for next two year has recorded 4.06 and 4.43 thousand hectare, estimated production for next two year 2.91 and 3.18 thousand tones. Productivity has estimated for next two year which is 576.65 and 564.30 Kg/ha. Keywords: Forecasting, area, production, productivity, Lathyrus, Bastar I. INTRODUCTION The major grass pea growing states in India are Madhya Pradesh, Maharashtra, Bihar, Odissa, West Bengal and Eastern Part of Uttar Pradesh. Pulses play an important role in the rain-fed cropping system of India. Chhattisgarh, the 26th state of the Indian Union came into existence on November 1, 2000. The state is geographically situated between 17046'N and 2405 North Latitude and 80015'E and 84020' East Longitude. The total geographical area is around 135 lakh ha. of which cultivable land area is 58.81 lakh ha & forest land area is 60.76 lakh ha with more than 2.07 crore population (Directorates of Economics and Statistics, Govt. of Chhattisgarh, 2011). About 80 percent of the population in the state is engaged in agriculture and 43 percent of the entire arable land is under cultivation. Paddy is the principal crop and the central plains of Chhattisgarh are known as rice bowl of central India. Other major crops are coarse grains, wheat, maize, groundnut, pulses and oilseeds. It is estimated that approximately 43 lakh hectares can be potentially irrigated covering 75 percent of the entire cropped area in the state (Department of Agriculture, Govt. of Chhattisgarh). Lathyrus sativus locally called ‘Khesari’, ‘Teora’, ‘Lakh’/’Lakhdi’ is an important post –mansoon crop of the Bastar region of Chhattisgarh, relay cropped by broadcasting seeds 15-20 days prior to harvest of rice crop at seed rates of 80-100kg /ha, while about 40kg /ha would suffice by drilling. It is used as traditional dal, bread and besan (decuticled Lathyrus flour) for human consumption as well as livestock feed concentrate. Association of neurotoxin causing of lathyrism in mid-seventies, created lot of hue and cry, and brought the crop under discard. Even legislative measures were passed to stop its cultivation. But it has successfully withstood the pressures. Lathyrus is showing decadal linear growth rate of area production and productivity owing to emergence of better options like chickpea where protective irrigation is there. The short term forecasting trend of area, production and productivity of Lathyrus is worked out from the secondary data received from 2002 – 2012 (Department of Agriculture, Govt. of Chhattisgarh). In the present study, short term forecasting model for next two year of area, production and productivity for the lathyrus were estimated. Time sires analysis method such as regression models are often used to predict the trend and outputs. II. MATERIALS AND METHODS The study mainly confined to Bastar District of Chhattisgarh. The inception of the new state of Chhattisgarh new district was formed by splicing up the original district. The secondary data of area, production and productivity on Lathyrus of 11 year were collected for period 2002-2012 i.e. from the emergence of new state. Data collected in Department of Agriculture, Government of Chhattisgarh for period 2002-2012 were subjected to analyze through least square technique. The data are analyzed by using software like MS-EXCEL and SAS. Least square technique was adopted to observed the short term forecasting model, the model used was A. Linear regression model

Yˆ   0  1 X  e Where Yˆ = Estimated value of Y. X = Time variable,  0 = Intercept ,  1 = Regression coefficient , e = Error term

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For testing of significance of regression coefficient, ‘t’ test was carried out using the following formula: t=

1 S .E. 1 

with n-2 degree of freedom

Where ˆ1 = Estimated value of ˆ1 ,

S .E.( ˆ1 ) = Standard error of ˆ1 , n = Number of observations

III. RESULTS AND DISCUSSION This chapter contains the results on trend and short term forecasting analysis carried out for area, production and productivity on lathyrus for Bastar district of Chhattisgarh. Trend analysis for area, production and productivity for Bastar District The result of trend analysis for lathyrus under area, production and productivity of Bastar district are presented in table 1 which shows the value of regression coefficient along with its test of significance and R 2 value. Regression parameter for estimating area, production and productivity of lathyrus under Bastar district from Table 1 showed that highest R2 was obtained in area (83.82 %) followed by production (74.67 %) while lowest R2 (44.72 %) was obtained productivity. The regression coefficient was highly significant (P<0.01) in area, production and productivity were significant at 5 % level. Table 1: Regression factor for estimating area, production and productivity under Bastar District Intercept

Regression coefficients

S.E.

t cal

Area

555.447

0.368**

0.053

6.94

83.82 %

Production

302.327

0.277**

0.053

5.22

74.67 %

Productivity

2541.71

-12.34*

4.75

2.70

44.72 %

Regression factor

Note: ** Indicates Significant at 1 percent and *indicates significant at 5 percent R2 for the estimated equation show the closeness of the estimates to the actual value. Estimated forecast area (‘000 ha) for Bastar district is presented in figure 1, production (‘000 tones) for figure 2 and productivity (Kg/ha) figure 3. Following are the short term forecast of area, production and productivity for next two years Year

2013

2014

Area (‘000 ha)

4.06

4.43

Production (‘000 tones)

2.91

3.18

576.64

564.30

Productivity (Kg/ha)

Figure 1: Actual and forecasted area (‘000 ha) for Bastar district 5 4.5

Area (000,ha)

4 3.5 3 2.5 2 1.5 1 0.5 0 2000

2002

2004

2006

2008

2010

2012

2014

year Actual

Estimated

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D.P. Singh et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 76-78

4.5

Production (000,tones )

4 3.5 3 2.5 2 1.5 1 0.5 0 2000

2002

2004

2006

2008

2010

2012

2014

Year Actual

Estimated

Figure 2: Actual and forecasted production (‘000 tones) for Bastar district

750

Productivity (kg/ha)

700 650 600 550 500 2000

2002

2004

2006

2008

2010

2012

2014

Year Actual

Estimated

Figure 3: Actual and forecasted productivity (Kg/ha) for Bastar district REFERENCES [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8].

Annual Report (2001- 02 to 2005-06). Rabi Progress Report, Bastar, Govt. of Chhattisgarh, India Annual Report (2006- 07 to 2011-12). Rabi Progress Report, Bastar, Govt. of Chhattisgarh, India Directorates of Economics and Statistics, 2011, Govt. of Chhattisgarh, India Arya. S. L and Rawat. R. K. P., 1990, Agricultural growth in Haryana - A district wise analysis. Agric. Sit. India, 45(2):121-125. Billore, S. D. and Joshi, O. P., 1998, Growth in area, production and productivity of soybean in India. Agric. Sit. India, 55(8): 495-499. Dhindsa. K. K. and Sharma. A., 1996, Growth of agricultural production and productivity in Punjab 1970-73 through 1990-93 - a component analysis. Anvesak. 26(1): 51-61. Singh, D.P. 2006, Price fluctuation study of timber in Chhattisgarh, M. Sc (Agri) Thesis. Indira Gandhi Agricultural University, Raipur (C.G.). Sahu, P.K. and Das A.K., 2009, A text book on “Agriculture and Applied Statistics II” : 66-70.

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Bio-efficacy of root extract of Boerhaavia diffusa on yellow disease of ginger A. K. Pandey1, L. P. Awasthi2, V. P. Pandey3, N. K. Sharma3, A. Kumar3,SK Singh2 and SK Pandey2 1 IIVR. Varanasi, U.P., India 2 N.D. University of Agriculture and Technology Narendra Nagar (Kumarganj), Faizabad, U.P. 224 229 3 Krishi Vigyan Kendra, Kaushambi, U.P., India. I. INTRODUCTION Research for environmentally sound pesticides received an impetus following the publication of silent spring numerous higher plants and their constituents have shown success in plant disease control and proved to be harmless and phytotoxic unlike chemical fungicide (1,2,3,8,9). The application of extract of green plants for the control of diseased caused by various fungi had been reported (4, 5, 6). In the pre cent communication root extracts from Boerhaavia diffusa for their fungitoxicity against Fusarium oxysporum f. sp. zingiberi.the causal organism of yellow disease of ginger (Zingiber officinale rose L.). Thus plant product using (Boerhaavia diffusa root extract) may offer a practical and economical alternative for management of this disease. Boerhaavia diffusa root extract have been reported to be highly effective against yellow disease of ginger caused by Fusarium oxysporum f. sp. zingiberi. II. MATERIALS AND METHODS The present research work entitled, “Management of rhizome rots of ginger through Boerhaavia diffusa root Extract” was carried out at Main Experiment Station, Department of Vegetable Sciences, Narendra Deva University of Agriculture and Technology, Narendra Nagar, Kumarganj, Faizabad (U.P.) India during Kharif, 2009. The rhizome was planted in field 25 x 15 cm spacing with three replication in randomized block design. The six treatments were applied in treated plot along with control. Preparation of plant root extract: The extracts from different plant will be prepared by the method as described earlier (Wyatt and Shephard, 1971; Tangnchi, 1976 and Verma and Awasthi, 1979), root of B. diffusa (B.D.) was cut into small pieces and allowed to dry under shade at room temperature. Roots was powdered in a grinder and stored at low temperature for further use. The crude extract was prepared by making the suspension of root powder in sterile water @ 1 g/10 ml (w/v). The pulp was squzed through two folds of chase cloth @ the homogenate was clarified by centrifugation at 3000 rpm for 15 minuets. The supernatant was used for seed treatment and foliar sprays. Rhizome treatment: Rhizome of ginger was soaked in B. diffusa root extract (10%) for over night. In control, rhizomes was soaked in water intend of B. diffusa inhibitor for over night. The treated and untreated rhizomes were sown separately on field. Sowing of rhizomes: Treated and Untreated rhizomes were sown on natural field condition as described earlier. Spraying of Botanicals: For sprays of B.D. root extract was given at weekly intervals starting from 3-4 leaf stages of seedling. In control beds, water alone was sprayed instead of the botanicals. Symptomatology: Diseases symptom was observed by visual method, when diseases symptom appeared on plant, like leaf, stem and rhizome. Disease incidence: The No. of plant was affected with pathogen (fungi) out total no. of plant in a plot were recorded. Per cent disease incidence and per cent diseases control were calculated by using the following formula: Disease incidence (%) 

No of infected plants per plot

 100

Total plant per plot % Disesese control 

C-T

 100

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A. K. Pandey et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 79-80

Where C = Per cent disease incidence in untreated T = Per cent disease plot incidence in treated plot III. RESULTS AND DISCUSSION Results presented in Table-1 indicated a gradual decrease in disease incidence with the corresponding increase in number of sprays and rhizomes treatments with B. diffusa root extract. Minimum disease incidence of 34.65 per cent was recorded in rhizome treatment + 3 foliar sprays with (10%) B. diffusa root extracts followed by rhizomes treatments + 3 foliar sprays with B. diffusa @ 5% (35.66%), rhizomes treatment with 10% B. diffusa root Extract (39.42%), three foliar spray with B.D.@10% (47.10%), three foliar spray with B.D.@5%(48.64%). Table-1: Effect of rhizome treatment and foliar spray with B. diffusa root extract on yellow disease of ginger S. No.

Treatments

Disease incidence (%)

Disease reduction (%)

T1-Rhizome treatment with B.D. root extract @ 10%

40.33 (39.42)

56.78 (48.90)

T2-Foliar spray of B.D. @ 5%

56.33 (48.64)

39.34 (38.84)

T3- Foliar spray of B.D. @ 10%

53.66 (47.10)

42.49 (40.68)

T4-( T1+T2)

34.00 (35.66)

63.57 (52.88)

T5-(T1+T3)

32.33 (34.65)

65.35 (53.96)

T6-Control (Water spray)

93.33 (75.19)

0.00

SEmÂą

1.019

1.049

CD at 5%

2.912

2.998

3.773

4.578

On the other hand, maximum (53.96) reduction in disease incidence was recorded in plots with rhizomes treatment + 3 foliar spray with B.D. root extract @ 10% followed by rhizomes treatment + 3 foliar spray with B.D. root extract @ 5% (52.88%), rhizomes treatment with B.D. root extract @ 10% (48.90%), three foliar spray with B.D. root extract@10% (40.68%), and three foliar spray with B.D. root extract @5% (38.84%). The first appeases of disease in plant the spraying of B.D. root extract the falling pattern of disease plant in decreases. Because, the B.D. root extract induces the resistance against the fungal and viral diseases. Awasthi, and Singh (2007), reported the management of papaya disease through botanicals treated with B. diffusa root extract (BD) + three foliar sprays with B.D. which was found most effective, and exhibited minimum disease incidence and maximum plant height and no. of fruits LITERATURE CITED Awasthi, L.P. and Shyam, Singh (2007). Important viral diseases of papaya and their eco-friendly management. Nati. Sy. on Adv. Fron. of Plant Disease Management, November, 2007. pp.14. Crainage, M.D. and S.M. Severer (1987). Antibacterial and antifungal activity of Artabotrys hexapetalous leaf extract. Int. J. Disease 5: 173-179. Chandra, H. and Dikshit, A. (1981). Volatile fungi toxicant from the leaves of Ageratum conyzoides against Colletotrichum capsici and Penicillum italicum. J.Indian Bot. Soc. (Suppl) 60: 13. Dubey, N.K., Tripathi, N.N. and S.N. Dixit (1984). Higher plants a promoting source of antifungal constituents. In Sinha, R.P.(ed). Roy commemoration fund. Recent Trends in Bot Res.pp.210-228. Kishore, N., Dubey, N.K., Tripathi, R.D. and S.K. Singh (1982). Fungi toxic activity of leaves of some higher plants. Nat. Acad. Sci. Letters 5: 9-10. Mishra, A.K., Mishra, D.N. and N.N. Tripathi (1988). Mycotoxic evalution of some higher plants. Nat. Acad. Sci. Letters 11 (1): 5-6. Pandey, D.K., R.N. Tripathi N.N. Tripathi and R.D., Tripathi, (1988). Antifungal activity of some seed extracts. Enviroment India 4: 83-85. Sharma, N. (1994) Fungi toxic properties of plant latex against some post-harvest disease. J. Bioved 5: 84-85. Sharma, N. (1998). Control of post-harvest disease with natural plant product. Post-harvest disease of horticultural Perishable 245-262. Taniguchii, T., and Goto, T. (1976). Purification of an inhibitor of plant virus infection occurring in leaves of Chenopodium amaranticolar. Ann. Phytopathol. Soc. Jpn. 42: 42-45. Verma, H. N. and Awasthi, L.P. (1979). Antiviral activity of B. diffusa root extract and the physical properties of the virus inhibitor. Can. J. Bot., 57: 926-032. Wyatt, S.D., and Shephard, R.J. (1971). Isolation and characterization of a virus inhibitor from Phytolacca Americana. Phytopath., 59: 1787-1794.

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RESPONSE OF POTASSIUM IN INCEPTISOL UNDER CROPPING PATTERN SYSTEM AFTER FOURTH CROP CYCLE P.S. Kusro, D.P. Singh, Deepak Kumar and Manish Arya Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.) India Abstract: An experiment was conducted to study of crop response to k application in vertisol in maizesunflower cropping system, at Bastanar block of Bastar District of (C.G.) India. The treatment consisted of five level of fertilizer K application (0, 50, 100, 150, 200, kg K 2O/ha) in four replication using RBD and no crop responses were observed to K fertilizer in terms of yield and K uptake of maize and sunflower crops. This indicates that soil under study is sufficient to meet the K requirement of the crops. Key words: Maize, sunflower, potassium. I. Introduction Potassium (K) is the third major essential plant nutrient along with N and P for successful crop production. As a nutrient element it play vital and crucial physiological roles in plant growth but its importance in crop production is often under estimate because it does not produce rapid growth like N. The essentiality of K to plant growth has been known since the work of Von Liebig published in 1840 (Spark, 2000).Application and importance of K is ignored by the Indian farmers due to its adequate soil supply. Potassium is removed by the crops i.e. cotton, sugarcane, maize, sunflower etc. in large quantities and removal often exceeds that of Nitrogen. Different type of crops removes large amount of potash (408-625 kg K2O/ha) under intensive cropping system every season. Hence, the present investigation was undertaken to study of application of potassium in Inceptisol under cropping pattern system after fourth crop cycle. II. Materials and Methods A field experiment was conducted during 2008-12 at Bastanar Block of Bastar District (C.G.) under National Agricultural Innovative Project, Component窶的II, using two popular crops maize ( var.4640 pro-agro) in Kharif season (2010) and Sunflower (var.Jwalamukhi) in Rabi season (2010-11). This experiment was initially started during Kharif season (2008) with rice and Rabi season (2008-09) with wheat, which was first crop cycle. The experimental soil was sandy loam in texture, pH 6.40, Organic carbon 0.45 % and low N 210 kg /ha, medium P 13.60 kg/ha and high K 512 kg/ha, in soil available nutrient status. The statistical model carried out in a Randomized Block Design with five treatments, each treatment replicated four times. T1- 0 kg K2O/ha, T2- 50 kg K2O/ha, T3- 100 kg K2O/ha, T4- 150 kg K2O/ha, T5- 200 kg K2O/ha, were applied through Murate of Potash, Nitrogen @ 120 kg/ha as urea and Phosphorus @ 80 kg/ha as SSP were applied uniformly. III. Result and Discussion Table 1: Effect fertilizer k application on yield of maize and sunflower Treatment level Kg K2O/ha Control

Maize Yield q/ha Grain 34.33

Straw 50.31

Sunflower Yield q/ha Grain Straw 23.49 48.25

50 100

36.13 36.28

58.43 55.17

24.51 23.61

49.20 47.52

150 200

36.80 34.70

58.52 55.66

23.34 23.11

41.93 46.93

Mean CD at 5%

3470 NS

55.62 NS

23.61 NS

46.66 NS

Note: NS indicates Non significance at 0.05 percent. The grain and straw yield of maize and sunflower did not show any significant variation due to different doses of fertilizer K (Table 1). This indicates that soil under study has sufficient level of K. Non significant result on grain and straw yield of two different major crops due to graded doses of fertilizer K application in vertisol.

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Table 2. Effect fertilizer k application on content and uptake of K by maize Treatment level Kg K2O/ha Control 50 100 150 200 Mean CD at 5%

Mean k content % Grain 0.59 0.58 0.66 0.60 0.57 0.60 0.06*

Straw 1.75 1.84 1.81 1.81 1.77 1.80 NS

Mean k uptake Kg/ha Grain 20.1 20.8 23.9 22.0 19.5 21.3 6.75*

Straw 88.3 107.9 100.0 105.5 98.4 100.0 NS

Note: * Indicates significant and NS Indicates Non significance at 0.05 percent. Table 2 represents the K content and uptake of k by maize crop was significant in grain. However, there straw did not have significant variation due to different doses of fertilizer K. Table 3. Effect fertilizer k application on content and uptake of K by sunflower Treatment level Kg K2O/ha Control 50 100 150 200 Mean CD at 5%

Mean k content % Grain 0.75 0.80 0.81 0.77 0.79 0.78 0.04*

Straw 2.76 2.86 2.98 3.25 2.96 2.96 NS

Mean k uptake Kg/ha Grain 17.5 19.6 19.1 18.0 18.3 18.5 NS

Straw 133.2 140.8 141.6 136.7 137.1 137.9 NS

Note: * Indicates significant and NS Indicates Non significance at 0.05 percent. Table 3 represent the K content in sunflower crop was significant in grain but uptake of K by sunflower was not significant in their grain and straw. Similar to maize and sunflower yields, their K content and uptake did not influence with different doses of K fertilizer. Potassium uptake was higher in straw than that in grain mass and little amount was translated for grain formulation K is absorbed by the crop in excess of their need under sufficient large quantity of soils K is present. This is termed as luxury consumption (Singh et al 1999). References Kalita, U., Ojha, N.J. and Talukar, M.C. 1995. Effect of levels and time of potassium application on yield and yield attributes of upland rice. J. Potassium Res. 11(2): 203-206. Key, L.M. 1995. Response of rice to potassium application in Kharpona soil series of West Bengal. J. Potassium Res. 10(3): 216-222. Kulkarni, R.V., Marathe, A.B. and Patil, A.P. 2005. Response of application of Potassium fertilizers on yield uptake of potassium by wheat on K deficient soil. J. Soils and Crops. 15(1): 57-59. Prasad, B. and Prasad, J. 1997. Response of rice to potassium application in calcarious soils. J. Potassium Res. 13(1): 50-57. Ranshur, N.J., Sonar, K.R. and Todmal, S.M. 2007. Effect of potash application on yield of sorghum and forms of potassium in soil. J. Soils and Crops. 17(2): 255-257.

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Single and Combined Effect of Garlic and Carbon Tetrachloride on Serum and Brain Acetylcholinesterase Activity in Rat Kazi Layla Khaled, Ira Ghosh Department of Home Science University of Calcutta Kolkata-700027, INDIA Abstract: Present experiment is aimed to study the effect of garlic on acetylcholinesterase activity in serum and brain of normal and carbon tetrachloride intoxicated rat. Twenty adult male rats of Wistar strain were divided into four groups, with five rats in each group. One group was left as control and to the second group only a single dose of 5 ml CCl4 solution/kg body weight [intraperitoneal injection of 20% CCl4 (v/v) solution in olive oil] was given and then left untreated. To the third group only garlic was fed orally for 15 days at the dosage of 4g/kg body weight. The fourth group was also treated with a single dose of 5 ml CCl 4 solution/kg body weight and from the next day garlic was administered orally at the dose of 4g/kg body weight for 15 days. The result revealed that CCl4 had insignificant inhibitory effect on serum and brain AChE level and garlic significantly accelerated the enzyme activity both in serum and brain of normal and carbon tetrachloride intoxicated rat. Therefore carbon tetrachloride showed mild inhibitory effect which is insignificant and garlic singly or in combination significantly enhanced AChE activity both in serum and brain. Key words: Allium sativum , acetylcholinesterase, carbon tetrachloride, brain, serum. I. Introduction The enzyme acetylcholinesterase (AChE), rapidly split acetylcholine (ACh), the neurotransmitter at the synapses of all pre and post ganglionic fibres of parasympathetic and few post ganglionic fibres of sympathetic nerves and at the neuromuscular junction into choline and acidic component [1] thereby rapidly terminating the action .The enzyme is present in the motor end plate, synaptic junction, brain, spinal cord, red blood corpuscles and blood serum. Anything which interferes with the action of AChE causes serious disturbance of neurojunctional and neuromuscular activities. Exposure to certain toxic agents leads to convulsion, paralysis and perhaps death [2],[3]. The enzyme acetylcholinesterase had been estimated by Sastry and Murty and were they showed undernutrition had lowering effect and rehabilitation caused elevation of enzyme activity in rat brain[4]. Ellis et al. [5] administered carbon tetrachloride (CCl4), a hepatotoxic substance in rabbit which did not show any change in plasma and liver AChE activity, whereas rat plasma and liver showed a decrease. Various workers have done experimental studies on the inhibitory effect of organophosphate, lead acetate, carbamate, etc on the AChE activities [6], [7]. Yassin had shown the efficacy of garlic on the enzyme activity in lead intoxicated rabbit[8]. Saluya and Kumar [9] studied on the inhibitory chronic effect of copper sulphate on AChE in rat stomach. However, practically no attempt has been made to study the effect of garlic (Allium sativum Linn.) on AChE activity in the brain and serum of carbon tetrachloride intoxicated rat. Keeping this into our consideration an experimental design has been made to investigate the effect of garlic on AChE level of brain and serum in normal and carbon tetrachloride intoxicated rats under hepatotoxic stress. II. Materials and Methods Healthy male albino rats of Wistar strain of an average body weight 90g have been selected for experimentation. Animals were kept in clean polypropylene cages covered with chromate plate grill and maintained in normal husbandry conditions for 7 days with stock diet and water ad libitum for acclimatization. After 7 days animals were divided into four groups with 5 rats in each group. Throughout the experimental period animals were given stock diet [10] and water ad libitum. A. Preparation of garlic paste and mode of feeding Garlic was purchased from the local market and the cloves were cleaned, peeled and macerated to homogenious mass in a motor and pestle. Required amount was prepared freshly every day, mixed with known quantity of

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water and given orally by a feeding needle. The dosage was 4g/kg body weight /day for 15 days time period [11]. B. Administration of CCl4 A single dose of 5 ml CCl4 solution/kg body weight (intaperitoneal injection of 20% CCl 4 (v/v) solution in olive oil) was given before commencement of the experiment [12]. Treated rats were given 4g garlic /kg body weight per day for 15 days. Treatment is expressed against each group as follows: Group I- Untreated, control(C) Group II- CCl4 injected on first day, then left untreated for 15 days (CCl 4) Group III-Garlic treatment from 2nd day for 15 days (G) Group IV- CCl4 treatment on day 1, then fed garlic from day 2 for 15 days (GCCl 4) All the animals were handled with utmost human care and autopsied on the 17 th day, after 12 hours of fasting with ether anesthesia. Immediately after autopsy blood was collected directly from the heart and serum was separated by standard method .Brain was dissected out carefully and the whole brain was homogenized in ice cold normal saline in Potter Elvehjem homogenizer to obtain a 10% homogenate. It was centrifuged and the supernatant was used as the enzyme source. Both serum and brain AChE was determined by the method of Huns and Robert [13]. III. Statistical Analysis Data were analyzed by Analysis of Variance (ANOVA). Statistical analysis was also done by student’s “t” test [14]. IV. Results Weekly body weight records of all the experimental animals (Table-1) were made. No behavioral changes in the animals were seen throughout the experimental period. Weekly record of the body weight showed that the body weight gain was normal. There was significant weight increment in all the animals which showed that there was a normal weight gain both in control and treated animals. Table 1: Initial and final body weight of experimental animals Groups

Experiment

Body weights(g) Initial

Final

P-value

(Mean ± SE)

(5)

90.00 ± 2.89

116.60 ± 1.87

< .001

(5)

CCl4

90.50 ± 1.75

120.00 ± 2.50

<.001

III (5)

91.60 ± 1.66

123.60 ± 3.85

<.001

GCCl4

90.00 ± 2.01

125.00 ± 2.89

<.001

(5)

Figure in the parenthesis indicates number of animals. SE- standard error, C-control, CCl4-carbon tetrachloride treated, G-Garlic treated, GCCl4- Garlic treatment in CCl4 intoxicated rats. Findings of Table 2. on AChE level of brain and serum show a similar propensity of responsiveness towards (CCl4) or garlic or CCl4 and garlic together. Garlic increased the enzyme level of both brain and serum in rat significantly. The mild and insignificant inhibitory effect of CCl4 is counteracted by garlic in both brain and serum of rats. The brain AChE activity showed more responsiveness to the action of garlic. Table 2: Acetylcholinesterase activity in serum and brain of experimental animals Groups

(5)

III (5) IV

(5)

AChE activity in serum

AChE activity in brain

Mean ± SE (units/ml)

Mean ± SE (units/mg tissue)

1585.36 ± 3.40

79.20 ± 0.501

1571.00 ± 7.09 NS 1711.85 ± 0.80 P < .001 P < .001a 1605.17± 2.08 P < .05 P < .001b P < .001c

78.00 ± 0.346 NS 85.07 ± 0.256 P < .001 P < .001a 83.80 ± 0.282 P < .001 P < .001b P < .05c

Experiment C

CCl4 G GCCl4

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Figure in the parenthesis indicates number of animals in each group. C-control, CCl4-carbon tetrachloride treated, G-Garlic treated, GCCl4- Garlic treatment in CCl4 intoxicated rats, NS-not significant, SE-standard error, a CCl4 vs G, b CCl4 vs GCCl4, c G vs GCCl4.. V. Discussion In the present study we find out the effect of garlic homogenate at the dosage of 4g/kg body weight per day for 15 days on serum and brain AChE activity in normal and carbon tetrachloride treated rats. It has been observed that the enzyme activity has increased significantly (P< .001) in garlic treated rats. Only CCl 4 treatment decreased the enzyme level insignificantly. However, the combined effect of CCl 4 and garlic showed an increased AChE value which is also significantly raised but lower compared to that of only garlic fed rats. Carbontetrachloride is a strong hepatotoxic compound due to the formation of tricholro methyl free radicals (CCl3−) that bind covalently to the neighboring lipid, initiating lipid peroxidation that leads to severe membrane damage. But in the present study CCl4 practically did not alter the enzyme concentration which is partly in conformity with the observation of Ellis et al. [5] who administrated CCl4 in rabbit which did not show any change in plasma and liver AChE activity but in their study they found that rat plasma and liver showed a decrease. They attributed it to the species specific difference of the ester hydrolyzing enzyme in liver and plasma of these two animal species. Various scientist have studied on the inhibitory action of organophosphate compounds, lead acetate, carbamate, etc, on AChE activity [6]. Heavy metals induced central cholinergic system and their possible mechanism have been dealt by Saxena et al.[15]. The effectiveness of garlic on the AChE activity of central nervous system has studied by Yassin [8] in lead intoxicated rabbits. Widespread use of AChE inhibitor specially pesticide produce large number of human poisoning events worldwide. The main known neurotoxic effect of these substances are AChE inhibition which causes cholinergic over stimulation that results in neuro psychological sequeale as both short and long term effects [16],[17]. Therefore, our finding on acceleration of AChE activity as a result of garlic treatment alone or in combination or with CCl4 intoxication can be an effective remedy in case of enzyme inhibition or neuropsychological deficit observed in old age. Also there may be an enormous scope for further study on the efficacy of garlic in counteracting the effect of pesticide and other poisoning that cause enzyme inhibition. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

Rosenberry TL(1975): Acetylcholinesterase.Adv.Enzymol.Relat.Areas.Mol.Biol.43:103-218. Goodman and Gillman’s (1990): The pharmacological basis of therapeutics. (Eds) A. Goodman Gillman, T.W. Rall, A.S. Nies. P. Taylor.8th edition. Pargamon Press. New York. 96 - 127. Auletta JT, Johnson JL, Rosenberry TL,2010: Molecular basis of inhibition of substrate hydrolysis by a ligand bound to the peripheral site of acetylcholinesterase. Chem Biol Interact. Sep6:187(1-3):135-41. Sastry PS and Murthy PSVR. (1984): Nutritional effects on the biochemistry of developing brain. In: Nutrition and brain. Golden Jubilee Publication. Status Report Series.(Eds) P.N. Tandon and Gomathy Gopinath . Indian National Science Academy, New Delhi, India. pp 38-57. Ellis S. Sanders S. Bodansky O. (1947): Effect of carbon tetrachloride liver damage in rabbit and rat on Acetylcholine Esterase Activity .J Pharmacol Exp. Ther. 91, 225-262. Heath DF. (1996): Organophosphorous poisons: Acetylcholinesterase and related compounds.International Series of Monographs on Pure and Applied Biology. Pergamon Press, New York.13, 8. Gur ,M. and Kumar, S.(1993): Effect of an organophosphorus Pesticide on AChE enzyme Kinetics in normal and injured myocardium of the heart of Channa punctatus , Biomedical . Res .4(2), 171-179. Yassin Maged M. (2005): Prophylactic efficacy of garlic lobes, black seed or olive oils cholinesterase activity in central nervous system parts and serum of lead intoxicated rabbits. Turk J Biol.29, 173-180 Saluya U. , Kumar S. (2005): Inhibitory chronic effect of copper sulphate on acetylcholinesterase activity and enzyme kinetics with its subsequent reactivation in the stomach of Rattus norevegicus. Asian.J.Exp.Sci. 19(1), 65-71. Mathew BC. Prasad NV. Prabodh R. (2004): Cholesterol lowering effect of organosulpher compounds from garlic: a possible mechanism of action. Kathmandu Univ.Med J. (KUMJ). 2 (2), 100-102. Chen L. Hong JY. Hussain AH. Cheng WF. Yang CS. (1999): Decrease in hepatic catalase level by treatment with diallyl sulphide and garlic homogenate in rat and mice. J Biochem Mol Toxicol.13 (3-4), 127-134. Dutta GK. Chakraborty M. Ghosh S. Debnath PK. (2002): Hepatoprotective effect of Desmotrichum fimbriatum BI. in mice with Carbontetrachloride –induced liver damage. Biomedical Research. 13(2/3), 81-84. Huns Bockendahl and Robert Ammon. (1963): Cholinesterase. In methods of enzymatic analysis. (Eds) Bergmeyer HU. Academic Press. New York. pp 771-774. Sendecor GW and Cochran WG.(1967): Statistical Methods. 6 thEdition. Iowa State University Press. Ames, Iowa Saxena ,G.,Dube,S.N. and Flora,SJS.(2005): Heavy metals induced central cholinergic system disorders and their possible mechanism. In: Recent Trends in the Acetylcholinesterase system.(Eds) : ) Parveen Mahira and Kumar Santosh (IOS Press, Amsterdam),chapter 3, 23-42. Roldon-Tapia L. Leyva A. Laynez F. Santed FS. (2005): Environmental Health Perspective. 113(6), 1-6. Parveen Mahira and Kumar Santosh(2005): Acetylcholinesterase and acetylcholine in cardic tissue and cardiomyopathy. In: Recent Trend in the Acetylcholinesterase System, (Eds) Parveen Mahira and Kumar Santosh (IOS Press, Amsterdam),171-179.

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Screening of suitable grains substrates for Spawn development, growth and yield of Pleurotus eous Santosh K Sahu, D.P. Singh, Rakesh Patel and G.K. Awadhiya Indira Gandhi Krishi Vishwavidhyalaya Raipur (C.G.), INDIA Abstract: Screening of suitable grains substrates for spawn development, growth and yield of Pleurotus eous was studied in Mushroom Research Laboratory Department of Plant Pathology, College of Agriculture, IGKV, Raipur at 2011-12. Spawn development and yield of P. eous was also studied among cereal grains, sorghum.(7.33 days), paddy grain (8.66 days) and maize grains (9 days) took significantly less time for spawn development and maximum yield was recorded on maize grain (560.03g) with BE (83.96 %) compared to other grains. Keywords: Pleurotus eous pink oyster mushroom cereal grain spawn development yield.

I. INTRODUCTION The mushroom is a form of plant life without leaves, buds, flowers, and is recognized as fleshy macro-fungi, a group of achlorophyllous organisms. These are sometimes tough and umbrella like sporophore (fruiting body) with spores, naturally grown in fields, forests, on manure heaps, water channels and hilly areas, mostly during and just after rains. Since earliest time, the mushrooms have been treated as special kinds of food. They are considered as one of the four major edible mushrooms cultivated in different countries for human consumption. Pleurotus with its great variety of species constitute a cost effective means of both supplementing the nutrition to human kind through the production of edible mushrooms and alleviating the suffering caused by certain kinds of illnesses through the use of medicinal mushrooms and their derivatives as nutriceuticals and even as pharmaceuticals. The protein contents of the food stuffs like, vegetables and cereals etc. is low as compared to mushroom (Hayes and Haddad, 1976; Jandaik and Kapoor, 1975. Bano et. al. 1980, and vitamins (Kazeli and Dzabaridee, 1994). For overall nutrition mushroom falls between the best vegetables and animal protein sources (Benjamin, 1995). Unlike the animals, most Fungi are stationary and can't pursue their food. (Kendrick 1985; Alexopolus and Mims, 1996). Mushroom has a lot of production potential and due to its rapid growth it gives so large amount of crop which could not be compared with any other crop (Robinson and Davidson, 1959). II. MATERIAL AND METHODS Different types of cereals grains were evaluated to see their effect on spawn development of P. eous. Spawn was prepared in glucose bottles using cereals grains (sorghum, lathyrus, soybean, wheat, maize, and paddy) as substrates. The grains were processed and filled in glucose bottles (250 g) than sterilized at 20 Ibs PSI for 2 hrs. Thereafter, these bottles were inoculated with equal sized mycelial bit of pure culture. Inoculated bottles were incubated at 25 ± 2C and observations were recorded when the mycelium covered the entire grains in any treatment. Three replications were kept in each treatment for observing spawn run and to know the yield effect of different grains (sorghum, lathyrus, soybean, wheat, maize, paddy) spawn of P. eous for this the developed or prepared different grains spawn added to the wheat straw at the rate of 4% and spawning carried out . Three replications were kept in each treatment. III. RESULT AND DISCUSSION Different grains substrates were studied for spawn development of P. eous and subsequently data is presented in table (Fig and Plate). There was significant difference in spawn development of P. eous on different grains. Among the tested grains, minimum (7.33 days) period for spawn development of P. eous was recorded in sorghum grains with mycelial character whitish pink compact mycelial growth all grains were completely covered by mycelium and tightly held with each other followed by paddy grain (8.66 days) with mycelial character whitish pink thread like mycelium covered by mycelium and tightly held with each other, maize grain (9 days) with mycelial character pink cottony compact mycelial growth all grains were completely covered by mycelium and tightly held with each other. however, lathyrus grains took maximum (20.33 days) with mycelial character mycelial growth was poor and grains were not fully covered time for spawn development of P. eous and followed by soybean (15.33 days) with mycelial growth was poor and grains were not fully covered. The present findings are very close to the results obtained by Munjal, 1973, Sing et al., 1986 and Suman, 1990 and Khatri and Agrawal, 2002 who reported early spawn development in sorghum grains. Ratainh and Surargiary

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(1994) reported Sorghum, wheat and paddy grains were to be most suitable for spawn development of P. sajorcaju. Mathew et al. (1996), Chaurasia (1997) reported spawn development of P. columbinus on bajra and sorghum grain to be superior to other grains. Hafeez et al. (2000) reported that spawn production on sorghum grains was significantly higher than pearl millet, maize and wheat grains. Shukla (2003) also used different grains for spawn development of Calocybe indica and found early spawn development on maize grains. Sharma (2003) found that kutki grains shortest period for spawn development (8 days) and the overall biological efficiency (52.0%) was highest on wheat grain spawn. Saayir and Yildiz (2004), tested barley, sorghum and wheat grains for grew determining the mycelial growth of Pleurotus spp. Pleurotus spp. grew better on the sorghum grains than the barley and wheat grains. Chandrawanshi (2007) also used different grains for spawn development of H. ulmarius and found early spawn development on maize grains. Asghar et al. (2007) found full mycelial growth was obtained in 7.83 days on sorghum grains followed by 11.83 days on wheat grains and 13.167 days on oat grains for full mycelial growth. Spawn run period minimum days required wheat grain (9.66 days) required followed by paddy (11 days). and sorghum (11 days) grains and maximum days required maize grain (11.33 days) and primodial initiation period minimum days required paddy grains (13 days) required followed by wheat grains (13.33 days) and maximum days required maize grains (15.55 days) followed by sorghum (14.66 days). Fruit body size of P. eous on maize grains (pileus 8.17 mm and stipe 0.70 mm) was highest, followed by wheat grains (pileus 6.55 mm and stipe 1.71 mm), paddy grains (pileus 7.65 mm and stipe 1.19 mm) and sorghum grains (pileus 7.85 mm and stipe 1.25 mm). The highest yield recorded on maize grains (560.03g) with BE (83.96 %) followed by wheat grain (525.55g) with BE (78.79 %) and minimum yield recorded was paddy (474.23g) with BE (70.09 %) followed by sorghum grains (479.73g) with BE (71.92 %) and no spawn run on lathyrus and soybean grains. Very close to the results obtained by Chaurasia (1997) who reported that bajra and sorghum grains are suitable for early spawn development of P. columbinus, but higher yield was obtained from maize grains. Shah et al., (1999) found that the time required for the complete spawn run was 11 days on Trifolium followed by wheat grain (17 days). Sharma (2003) found that the overall biological efficiency (52.0%) was highest on wheat grain spawn. Pal et al., (2008) found that wild Pleurotus in wheat grains required the lowest number of days to spawn run (9.50). The highest fresh yield of P. eous was recorded on wheat and sorghum grains as substrates (490.0 and 397.5 g/500 g dry substrate, respectively). In P. eous, the number of days to spawn run was lowest on wheat grain substrate (10.75).The highest wheat grain substrate (375.0 g/500 g dry substrate). Tripathy et al., (2009) Cultivation on Bajra resulted in significantly faster mycelial growth as compared to other substrates followed by Jowar. Table - Evaluation of different grain substrate for spawn development of Pleurotus eous S.No. 1. 2. 3. 4. 5. 6. SEmÂą CD

Grain Spawn development Mycelial Character substrats (days)* 10.33 White cottony thread like mycelium covered all grains but grains were tightly held Wheat with each other. 8.66 Whitish pink thread like mycelium covered by mycelium and tightly held with each Paddy other. 9.00 Pink cottony compact mycelial growth all grains were completely covered by Maize mycelium and tightly held with each other. 7.33 Whitish pink compact mycelial growth all grains were completely covered by orghum mycelium and tightly held with each other. oybean athyrus

15.33 20.33 0.51 1.57

Mycelial growth was poor and grains were not fully covered. Mycelial growth was poor and grains were not fully covered.

*Average of three replication

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Table - Studies on different grains raised spawn on spawn run period and yield of P. eous Treatments Spawn run (different grain) (days)**

Wheat

9.66

Primordial Initiation (days)** 13.33

Paddy

7.65

1.19

8.0

474.23

70.09

Maize

11.33

15.55

8.17

0.70

10.5

560.03

83.96

Sorghum

14.66

7.85

1.25

9.2

479.73

71.92

Soybean

Lathyrus

SEm± CD

Yield

600

525.55

Yield (g)

Average weight of Basidiocarp*

Yield (g)**

BE (%)

8.5

525.55

78.79

0.38 1.19

0.47 1.45

25.98 80.06

Spawn run days 560.03

Primodial initiation (days) 18 16

479.73

474.23

500

Basidiocarp size (mm)* pileus stipe 6.55 1.71

400

12 10

300

200

Days

S.No.

6 4

100 0.00

0.00

2 0

wheat

Paddy

Maize Sorghum Soybean Grain substrate

Lathyrus

Fig.4.8 Studies on different grains raised spawn on spawn run period and yield of Pleurotus eous

REFERENCES Akyuz, M., and Kirbag, S. 2010. Determination of culture process for obtaining basidiocarp of Pleurotus eryngii (DC. ex Fr.) Quel. var. ferulae Lanzi a speciality mushroom. Indian J. Hortic.. 67(1): 73-75.

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Santosh K Sahu et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 86-89 Asghar, R., Tariq, M. and Rehman, T. 2007. Propagation of Pleurotus sajor-caju (oyster mushroom) through tissue culture. Pak. J. Bot., 39(4): 1383-1386. Awasthi, S.K. and Pandey, N. 1989. Spawn making and effect of spawn made up on various substrates on yield of Pleurotus sajor-caju (Fr.) Sing., an edible mushroom. Natl. Acad. Sci. Lett. 12(8): 271-273. Aysha-Rafique. 1998. Preparation and evaluation of spawn for the cultivation of Pleurotus in Gujarat. J. Scientific Industrial Res., 57(3): 143-147. Bano, Z. 1967. Studies on mushroom with particulars references to cultivation and submerged propagation biological nature and cultivation method. (Eds. S.T. Change and T.H., Quinio). The Chinese Uni. Press, Hong Kong. pp. 363-382. Chandrawanshi, P. 2007. Study on blue oyster mushroom Hypsizygas ulmarius, Bull.ex Fx.) in Chhattisgarh. M. Sc (Ag.) Thesis I.G.K.V. Raipur (C.G.). Chaurasia, V.K. 1997. Studies on production technology of Pleurotus columbines at Raipur. M.Sc. Thesis Submitted to I.G.K.V., Raipur: 95. Elhami, B. and Ansari, N. A. 2008. Effect of Substrates of Spawn Production on Mycelium Growth of Oyster Mushroom Species. J. Biologic. Sci. 8(2):474-477.

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ORGANIC AMENDMENTS INFLUENCING GROWTH, HEAD YIELD AND NITROGEN USE EFFICIENCY IN CABBAGE (Brassica oleracea var. capitata L.) Ranjit Chatterjee1*, S. Bandhopadhyay2 and J. C. Jana1 1 Department of Vegetable and Spice Crops 2 Department of Agronomy Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar-736165(W.B), India Abstract: Cabbage is heavy feeder of nutrients and demands adequate nitrogen for biomass production. Amount of dry matter produced per unit of nitrogen applied or absorbed can be judged by estimating the nitrogen use efficiency of different nutrient sources used for cabbage cultivation. Field experiments were conducted at UBKV, Pundibari, West Bengal, India to access the influence of different nutrient source on growth, head yield and nitrogen use efficiency in cabbage. The experiment comprised of 15 different nutrients source combining inorganic fertilizers, organic manures (farmyard manure and vermicompost) and Azophos biofertilizers were laid out in RBD with 3 replications. Growth and head attributes of cabbage were significantly influenced by different nutrient combination and vermicompost emerged as better organic nutrient source over farmyard manure. Inoculation with biofertilizers exerted more positive result over uninoculated treatments. The nutrient schedule comprising of higher amount of vermicompost (5 t/ha) along with 75% of recommended inorganic fertilizers in presence of biofertilizes inoculation emerged as potential nutrient source and resulted in many fold improvement in the form of vigorous growth, advanced head maturity, maximum curding percent and highest head yield as compared other nutrient combination. The different parameters of nitrogen use efficiency (PFP, AE, PUE and AR) were markedly enhanced by the same nutrient combination. Keywords: Organic amendments; inorganic fertilizers; nitrogen use efficiency; cabbage growth and head yield. I. Introduction Cabbage (Brassica oleracea var.capitata L.) is an important winter season vegetable grown throughout the country. The marketable head is an excellent source of vitamins, minerals and dietary fibers and consumed fresh as salad and cooked as vegetable or utilized as processed product. Cabbage is a heavy feeder of nutrients and to increase the head yield farmers are indiscriminately using the inorganic source of nitrogenous fertilizers. Though excess nitrogen increases the total dry biomass but it adversely affects the head quality by producing coarse and loose head, reduces keeping quality, enhances the nitrate nitrogen content of head and above all deteriorates the soil health (Chatterjee, 2009). Therefore, nitrogen management have significant influence on crop growth, head yield and soil health. Organic manures act as a store house of plant nutrients. They played direct role in supplying macro and micro nutrients and indirectly in improving the physical, chemical and biological properties of soil (Palaniappan and Siddeswaran, 1994). The incorporation of organic source of nutrients in the form of vermicompost, farmyard manure and biofertilizes is known to influence the availability and uptake of nitrogen thereby enhances the crop growth and yield (Bahadur et al., 2003; Chatterjee et al., 2006; Sharma et al., 2012). Vermicompost is the product of ingested biomass by earthworm after undergoing physical, chemical and microbial transformations and available in the form of cast. Besides macro and micronutrients it also contains humic acids, plant growth promoting substances like auxins, gibberellins, and cytokinins (Krishnamoorthy and Vajrabhiah, 1986), N-fixing and P-solubilizing bacteria, enzymes and vitamins (Ismail, 1997). Application of farmyard manure also influenced the nitrogen availability and crop growth. Again azotobacter and phosphate solubilizing bacteria containing biofertilizess harbor beneficial micro organism and mobilizes nutritive elements from insoluble to soluble form (Bhattacharya et al., 2000). Estimation of nitrogen use efficiency of different nutrient sources will help to judge the amount of dry matter produced per unit of nitrogen applied or absorbed, which will not only augment the efficiency of different nutrient sources but will also minimize the ill effect of over use of chemicals. However under acid soil of eastern Himalayan region the use efficiency of applied nitrogen is very low due to over use of inorganic nitrogen source and poor activity of beneficial soil microbes in adverse situation. Keeping the above in view the present work was formulated to

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determine the effect of diverse nutrient source on growth, head yield, nitrogen use efficiency and soil nitrogen balance of cabbage cultivation. II. Materials and Methods The field experiment was conducted at the Instructional Farm of UBKV, Pundibari, Coochbehar, West Bengal, India during winter season (November to February) of 2005-06 and 2006-07. The site is located at 89o23′53′′ E longitude and 26o19′86′′ N latitude and at 43 m above mean sea level. The area is situated at 26o19'86" N latitude and 89o23'53" E longitude, at an elevation of 43 meter above mean sea level. The area is characterized by high relative humidity and a prolonged winter with high residual soil moisture. The temperature range of this area varies from minimum of 7-8oC to maximum of 24-33.2oC. The annual rainfall ranges between 2100 to 3300 mm, 80% of which is received through south-west monsoon during JulySeptember. The soil was a sandy loam (61, 20, 18% sand, silt and clay respectively) with pH 5.71. The initial soil organic carbon was 0.83% and available N, P and K contents were 154.28, 21.17 and 124.48 kg ha-1 respectively. The treatment consisted of 15 combinations of different nutrient sources and was laid out in randomized block design with three replications. The treatments were selected for sole and combined application of varied levels of vermicompost and farmyard manure (FYM) along with 100% and 75% of recommended dose of inorganic fertilizers in presence and absence of biofertilizes along with a control (no manure or fertilizer). The combinations were T1-Control ; T2-100% Recommended Fertilizer Dose (R.F.D) (150N:80P:75K kg/ha) ; T3-100% R.F.D + 8 Mt/ha FYM + biofertilizes ; T4-100% R.F.D+2.5 Mt/ha vermicompost +biofertilizes ; T5 -100% R.F.D + 4 Mt/ha FYM +1.25 Mt/ha vermicompost + biofertilizes ; T6 75% R.F.D + 8 Mt/ha FYM ; T7-75% R.F.D + 8 Mt/ha FYM + biofertilizes ; T8-75% R.F.D + 2.5 Mt/ha vermicompost ; T9-75% R.F.D + 2.5 Mt/ha vermicompost + biofertilizes ; T10-75% R.F.D + 4 Mt/ha FYM + 1.25 Mt/ha vermicompost + biofertilizes ; T11 -75% R.F.D + 16 Mt/ha FYM ; T12 -75% R.F.D +16 Mt/ha FYM + biofertilizes ; T13 -75% R.F.D + 5 Mt/ha vermicompost ; T14-75% R.F.D + 5 Mt/ha vermicompost + biofertilizes and T15-75% R.F.D + 8 Mt/ha FYM + 2.5 Mt/ha vermicompost + biofertilizes. The field was ploughed thoroughly to get fine tilth of the soil and the recommended dose of inorganic fertilizers was applied in the form of urea (N-46%), single super phosphate (P-16%) and muriate of potash (K-60%). Full dose of P and K along with half N were applied as basal and rest N was top dressed at two equal splits of 30 and 45 days after transplanting. Vermicompost and farmyard manure were applied to the respective plots at the time of transplanting. The biofertilizes Azophos containing Azotobacter chroococcum and Phosphate Solubilizing Bacteria (Acinetobacter sp) with standard microbial population (× 108) was applied as seedling root dipping (250g/litre water) just before transplanting using rice gruel as an adhesive. Cabbage (cv. Golden Acre) seeds were sown in the nursery beds during first week of October and healthy seedlings of four weeks old were transplanted in the main field during first fortnight of November for both the years in 3 m x 3 m plots with a spacing of 60 cm within and between rows. The crop was raised adopting standard cultural practices. The observations were recorded on ten randomly selected plants from each plot on different growth attributes and yield characters (Table 1). The use efficiency of applied nitrogen was worked out in terms of partial factor of productivity (PFP), agronomic efficiency (AE), physiological use efficiency (PUE) and apparent recovery (AR) by employing the following formula used by Dua et al.(2009) and Sharma & Banik (2012). PFP (kg dry head/kg N applied) = Yf/Na AE (kg dry head/kg N applied) = (Yf – Yc)/Na PUE (kg dry head/kg N applied) = (Yf – Yc)/(Nupf – Nupc) Nupf – Nupc AR (%) = × 100 Na Where Yf = dry head yield (kg/ha) from fertilized plot ; Y c = dry head yield (kg/ha) from control plot ; Na= amount of N applied (kg/ha) ; Nupf = nitrogen uptake (kg/ha) in fertilized plot and Nupc = nitrogen uptake (kg/ha) in control plot. While computing the above indices the amount of nitrogen added through fertilizers as well as through farmyard manure and vermicompost were considered. The mean N (on dry matter basis) and dry matter content of farmyard manure was 0.78% and 34.20% where as for vermicompost 2.12% and 42.40% respectively. The initial surface (20 cm) soil samples were collected prior to the layout of the treatments to access the initial fertility status of the soil. Soil samples were also drawn after harvest for studying the post harvest fertility of soil. The collected soil samples were dried, powered and sieved for chemical analysis. The available nitrogen in the soil was estimated by modified Macro Kjeldahl method (Jackson, 1967). The total uptake of nitrogen was worked out from the dry matter production and estimated nitrogen content of dry fruits and plant residues (Tandon, 1999). Nitrogen balance sheet was worked out by comparing the applied nitrogen and total removal of nitrogen by the different treatment combination.

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III. Results and Discussion A. Growth attributes The observation recorded on plant height at 30 days after transplanting (DAT) revealed that the treatment containing sole inorganic fertilizers recorded maximum plant height (10.67 cm). In contrast, the treatments involving combination of inorganic and organic sources of nutrients recorded lower plant height at this stage. However at head maturity stage, unlike 30 DAT the plant height showed significant differences and the treatments, combining inorganic and organic sources of nutrients recorded significantly higher plant height compared to control and sole inorganic fertilizers. The enhancement of plant height with 100% inorganic fertilizers at 30 DAT may be due to the direct effect of higher amount of inorganic nitrogen, which is an integral part of protein and chlorophyll molecules. The data on days to head maturity showed significant differences among control, sole inorganic fertilizers and combined use of different source of nutrients. Integration of different nutrients source significantly reduced the maturity days compared to control or N 150P80K60. The earliest maturity of head (73 days) was recorded for the plants received 75% of recommended inorganic fertilizers along with vermicompost (5 t/ha) and seedling root inoculation with biofertilizes which was 12% and 10% advancement over the control and N150P80K60 respectively. Earliness in head maturity in vermicompost loaded treatments could be attributed to enhanced vegetative growth coupled with adequate reserved food material which facilitated early differentiation of vegetative buds and subsequently early maturity of marketable head. The result clearly showed that organic manures along with reduced level of inorganic fertilizers performed better over individual application of 100% inorganic fertilizers for all the growth attributes. Again, among the organic manures, vermicompost emerged as better growth medium over farmyard manure. Addition of biofertilizes under reduced inorganic fertilizers and higher organic manures showed significant positive results over uninoculated treatments. Significant positive influence of vermicompost on cabbage growth attributes was previously reported by Zhenyu and Yongliang (2012) and Ghuje et al., (2007). Table 1: EFFECT OF DIFFERENT SOURCES OF NUTRIENTS ON GROWTH AND YIELD ATTRIBUTES OF CABBAGE (POOLED MEAN OF 2 YEARS) Treatments*

Plant height (cm)

Days to head maturity

Percent marketable head

Head weight (g)

Head yield (t/ha)

Harvest Index (%)

30 At head DAT maturity T1 7.51 14.34 82.00 42.38 687.18 7.13 44.31 T2 10.67 16.18 80.00 56.28 912.26 11.88 52.27 T3 9.81 19.21 79.00 62.53 1383.37 20.65 61.23 T4 10.20 20.46 78.00 67.34 1417.28 21.38 64.88 T5 9.94 19.89 79.00 64.21 1394.48 21.18 63.11 T6 8.21 17.22 78.00 61.04 1166.43 16.39 57.13 T7 8.43 17.36 77.00 64.11 1231.44 17.60 57.92 T8 8.75 17.72 77.00 65.73 1281.44 19.33 59.45 T9 8.83 18.58 76.00 68.43 1357.32 21.54 61.41 T10 8.87 18.12 77.00 65.58 1301.22 19.75 64.68 T11 8.92 18.84 76.00 69.23 1442.62 23.74 69.84 T12 8.98 19.61 76.00 71.72 1487.36 25.57 72.42 T13 9.13 21.11 74.00 72.21 1470.76 24.04 70.14 T14 9.37 23.39 73.00 79.43 1547.34 27.86 77.41 T15 9.24 21.97 74.00 76.26 1503.44 26.46 74.68 0.18 0.98 1.49 1.98 66.42 0.85 2.21 SEm (ď&#x201A;ą) CD (P=0.05) 0.52 2.78 4.23 5.60 188.47 2.41 6.23 *Treatment details are given in the text. RFD: Recommended fertilizer dose;VC: vermicompost; FYM: farmyard manure; S.EmStandard error of the mean; CD-Critical difference

B. Yield attributes and yield The yield attributing characters were significantly influenced by combined application of inorganic and organic sources of nutrients. The results (Table 1) indicated that 75% of recommended inorganic fertilizers along with higher amount of organic manures have exerted positive influence and surpassed the treatments N150P80K60 and control. Application of 75% of recommended inorganic fertilizers along with vermicompost (5 t/ha) and seedling inoculation with biofertilizes resulted in maximum percent of marketable head (79.43%), head weight (1547.34 g), head yield (27.86 ton /ha) and harvest index (77.41%).This treatment recorded 44% and 26% more marketable head, 56% and 41% higher head weight and 74% and 57% greater head yield over control and N100P60K60 respectively. The findings suggested that irrespective of treatments, reduction of 25% of recommended inorganic fertilizers is possible only when higher amount of organic manures and biofertilizes were combined. The results further revealed that among the 75% inorganic fertilizers treatment combination,

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yield attributes were significantly influenced by the form and level of the organic manures and higher levels of vermicompost emerged as better growth medium over higher levels of farmyard manure. As the treatment T14 recorded 4% higher head weight and 8% greater head yield over the treatment T142. This could be attributed to increased availability of nutrients in the soil that might lead to synthesis and accumulation of more photosynthetes and subsequently higher head weight and more head yield in cabbage (Ghuje et al., 2007). C. Nitrogen use efficiency Partial factor of productivity The pooled results showed that presence of higher amount of organic manures gradually increases the partial factor of productivity of applied nitrogen (Fig. 1A).The highest value (20.98 kg dry head /kg N applied) was observed for the treatment containing 75% of recommended inorganic fertilizer along with vermicompost (5 t/ha) and seedling inoculation with biofertilizes. The result indicated that use of higher amount of organic manures can transform the applied nitrogen into economic yield more efficiently compared to the plots received sole inorganic fertilizers. It further showed that partial factor of productivity does not increased much when 25% nitrogen was reduced with same amount of organic manures but showed distinct difference when levels of organic manures were doubled. Again seedling dipping with biofertilizes had enhanced the partial factor of productivity when higher amount of vermicompost was used instead of farmyard manure. Agronomic efficiency The agronomic efficiency of different nutrient combination (Fig. 1B) showed an increasing trend with increased level of organic manures and the highest value (17.46 kg dry head/kg N applied) was recorded when plants were treated with 75% of recommended inorganic fertilizers along with vermicompost (5 t/ha) and seedling inoculation with biofertilizes. This might be due to better availability of nitrogen as per crop requirement and reduced loss of nitrogen leading efficient uptake and utilization of applied nitrogen. In contrast the lowest agronomic efficiency (1.91 kg dry head/kg N applied) by sole inorganic fertilizers indicated decreased response of applied nitrogen towards dry head yield of cabbage. The result further indicated that reduced rate of inorganic nitrogen along with lower level of organic manures failed to improve the agronomic efficiency, but increased level of organic manures enhanced the agronomic efficiency under reduced level of inorganic nitrogen. This could be due to the fact that added organic manures not only acted as source of nutrients but also had influenced their availability slowly and steadily throughout the crop growth as per crop demand and reduced N loss leading to efficient uptake and utilization of applied nitrogen (Singh et al., 2009). Among different organic manures, biofertilizes inoculation with vermicompost had marked influence over farmyard manure on agronomic efficiency. Under 75% of inorganic fertilizers treatment combination, biofertilizes inoculation with highest level of vermicompost (T14) recorded 21% improvement in agronomic efficiency over the highest level farmyard manure (T12). This could be attributed to the fact that presence of biofertilizess under vermicompost based medium might have enhanced the uptake of applied nitrogen by contributing the growth hormone like auxins and cytokinins besides fixing the atmospheric nitrogen and mobilizing the phosphorus of the soil better than FYM, which results in higher absorption of nitrogen (Chatterjee, 2009) Physiological use efficiency The physiological use efficiency (Fig. 1C) of different nutrient combination showed gradual increase with increased level of nitrogen absorbed. Higher physiological use efficiency was observed where higher amount of vermicompost was used over higher amount of farmyard manure, irrespective of biofertilizes inoculation. The highest value of PUE (29.65 kg dry head/kg nitrogen) was recorded for the treatment containing 75% of recommended inorganic fertilizer along with higher level of vermicompost (5 t/ha) and seedling inoculation with biofertilizes, which was 71% higher over sole application of chemical fertilizers (N150P80K60). The superior value of physiological use efficiency under higher organic manure combination could be the result of higher yield under higher organic manure containing treatments, which reflected the better conversion of source to sink by these treatments. Apparent recovery The apparent recovery (Fig. 1D) showed an increasing trend with the increased level of organic manure application and the highest apparent recovery (58.88%) was recorded for the treatment containing 75% of recommended inorganic fertilizers along with vermicompost (5 t/ha) and seedling root inoculation with biofertilizes. The increased apparent recovery is the expression of nitrogen uptake by the fertilized plants rather than the amount of nitrogen applied and the addition of more amount of organic manure not only acted as source of nitrogen but also influenced their availability. The result further showed that under reduced inorganic fertilizers treatment combination where nitrogen was substituted through higher amount of vermicompost recorded the higher values of apparent recovery compared to the treatments where nitrogen was substituted through higher amount of farmyard manure. Addition of higher amount of vermicompost might have converted

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the applied nitrogen to economic yield attributes much better compared to sole application of inorganic fertilizers and farmyard manure as source of organic manures.

Fig.1A Partial factor of productivity (kg dry head/kg of N applied)

Fig.1C Physiological use efficiency (kg dry head/kg of N applied)

Fig.1B Agronomy efficiency (kg dry head/kg of N applied)

Fig.1D Apparent recovery (%) (kg dry head/kg of N applied)

Fig. 1(A-D) Nutrient us efficiency of cabbage as influence by different nutrient sources (treatment details are given in the text). Nitrogen balance sheet The nitrogen balance sheet was worked out (Table 2) by comparing the applied nitrogen and total removal of nitrogen by the different treatment combination. The result showed that with the increasing levels of organic manures either vermicompost or farmyard manure the removal of nitrogen was increased and application of highest level of vermicompost (5t/ha) and 75% of recommended inorganic fertilizers in presence of seedling inoculation with biofertilizes registered the highest nitrogen removal (173.14 kg /ha) where as control plots recorded the lowest removal of nitrogen (73.07 kg/ha). The increased availability of nitrogen under combined nutrient sources could be attributed to favourable soil environment which enhanced the process of mineralization resulted in higher uptake of nitrogen by the plants (Barani and Anburani 2004). The nitrogen balance (applied â&#x20AC;&#x201C; uptake) was found positive for all the nutrient combinations. The highest actual and apparent nitrogen gain for combined application of higher level of vermicompost and reduced level of inorganic fertilizers in presence of biofertilizes inoculation showed the soil enriching effect of applied nitrogen through vermicompost which gradually improved the available status of nitrogen due to higher rate of mineralization of vermicompost. The control plot and sole chemical fertilizers (N150P80K60) recorded maximum actual loss and apparent loss of nitrogen respectively. The findings established that diverse nutrients source combining inorganic and organic sources of nutrients in presence of biofertilizes will help to enrich the soil and will enhance the availability of soil nitrogen for sustainable production of cabbage in eastern Himalayan region. IV. Conclusion The experimental results suggested that nutrient combination from different source have significant role on crop growth and head yield of cabbage. Judicious combination of higher amount of vermicompost and seedling inoculation with biofertilizes in presence of reduced level of inorganic fertilizer can enhance the nitrogen use efficiency of applied nutrients. Substitution of 25% recommended inorganic fertilizers dose is possible when higher amount of organic manures and biofertilizes were combined together. The nutrient schedule comprising of 75% recommended inorganic fertilizers and vermicompost (5 t/ha) along with seedling

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root dipping of biofertilizes may be practiced to achieve desired yield, nutrient use efficiency and sustainability of the production system. TABLE 2: SOIL NITROGEN BALANCE AS INFLUENCE BY DIFFERENT NUTRIENT SOURCES Treatments* A B C D E= (A+B)-C F= D-A G= D-E T1 154.28 0.00 73.07 71.48 81.21 -82.80 -9.73 T2 154.28 150.00 106.84 151.32 197.44 -2.96 -46.12 T3 154.28 171.36 151.24 161.19 174.40 6.91 -13.21 T4 154.28 172.48 154.17 166.79 172.59 12.51 -5.80 T5 154.28 171.92 152.31 164.38 173.89 10.10 -9.51 T6 154.28 146.36 143.22 157.41 157.42 3.13 -0.01 T7 154.28 146.36 146.10 163.34 154.54 9.06 8.80 T8 154.28 147.48 155.18 164.29 146.58 10.01 17.71 T9 154.28 147.48 156.40 170.32 145.36 16.04 24.96 T10 154.28 146.92 154.23 168.53 146.97 14.25 21.56 T11 154.28 167.72 158.05 172.28 163.95 18.00 8.33 T12 154.28 167.72 161.19 173.12 160.81 18.84 12.31 T13 154.28 169.95 164.32 173.53 159.91 19.25 13.62 T14 154.28 169.95 173.14 178.42 151.09 24.14 27.33 T15 154.28 168.84 168.27 175.23 154.85 20.95 20.38 *Treatment details are given in the text. A: Initial soil N (kg/ha); B: Applied N (kg/ha); C: N Uptake by plants (kg/ha); D: Available soil N after tomato harvest; E: Expected soil N balance (kg/ha); F: Actual soil N gain/loss (kg/ha);G: Apparent soil N gain/loss(kg/ha).

References [1]

Bahadur A, J Singh, A K Upadhyay, K P. Singh. 2003. Effect of organic manures and biofertilizers on growth, yield and quality attributes of broccoli. Veg. Sci., 30 (2): 192-194.

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Barani P and A A Anburani. 2004. Influence of vermicomposting on major nutrients in bhendi var. Arka Anamika, South Ind. Hort., 52 (1/6): 170-174.

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Bhattacharya P, R K Jain and M K Paliwal. 2000. Biofertilizers for vegetables. Indian Hort., (July-September issue). ICAR publication. New Delhi. pp 31-33.

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Chatterjee R, P Choudhari and S Bandhopadhayay. 2006. Influence of integrated use of organic and inorganic sources of nutrients on green yield of palak cv Pusa Bharati in terai zone of West Bengal. Env. & Eco., 24(S):722-723.

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Chatterjee R. 2009. Production of vermicompost from vegetable wastes and its effect on integrated nutrient management of vegetable production, Ph.D. Thesis, Uttar Banga Krishi Viswavidyalaya, Pundibari, Coochbehar, West Bengal, India.

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Dua V K, S S Lal and P M Govindakrishnan. 2009. Nutrient requirement of french bean (Phaseolus vulgaris) in potato (Solanum tuberosum)-french bean intercropping. Indian J. Agril. Sci., 79 (5): 329-333.

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Ghuje T D, A K Gore and S B Jadhav. 2007. Effect of organic and inorganic nutrient sources on growth yield and quality of cabbage. J Soils & Crops., 17(1):89-92.

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Gomez A K and A A Gomez. 1984. Statistical procedures for agricultural research, A Wiley inter-science publication, John Wiley and sons, New York.

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Ismail S A. 1997. Vermicology the biology of earthworms. Orient Longman, Hyderabad, India, pp 92.

[10] Jackson M L. 1967. Soil chemical analysis, Prentice Hall of India Pvt. Ltd, New Delhi, India [11] Krishnamoorthy R V, and S N Vajrabhiah. 1986. Biological activity of earthworm casts, an assessment of plant growth promoter levels in casts, Proceedings of the Indian Academy of Science (Animal Science) 95, 341â&#x20AC;&#x201C;351. [12] Palaniappan S P and K Siddeswaran. 1994. Integrated nutrients management in rice based cropping systems. Proceedings of the XIII National Symposium on integrated input management for efficient crop production, February 22-25, 1994. TNAU, Coimbatore, India, pp 41-53. [13] Panse G and P V Sukhatme. 2000. Statistical methods for agricultural workers, ICAR Publications, New Delhi, India. [14] Sharma, J. P, P Rattan and K Sanjeev. 2012. Response of vegetable crops to use of integrated nutrient management practices. J Food & Agril. Sci., 2(1): 15-19. [15] Sharma R C and P B Banik. 2012. Effect of integrated nutrient management on baby corn-rice cropping system: economic yield, system productivity, nutrient use efficiency and soil nutrient balance, Indian J Agril. Sci., 82 (3): 220-224. [16] Singh D K, J K Singh and Lal singh. 2009. Real time nitrogen management for higher N use efficiency in transplanted rice under temperate Kashmir conditions. Indian J. Agril. Sci., 79 (10): 772-775. [17] Tandon H L S. 1999. Methods of Analysis of Soil, Plants, Waters and Fertilizers, Fertiliser Development and Consultation Organization, New Delhi, India. [18] Zhenyu Lu and Yongliang Ma. 2012. Effect of vermicompost on soil fertility and growth, quality of cabbage.Chinese Agricultural Science Bulletin http://en.cnki.com.cn/Journal_en/D-D000-ZNTB-2005-12.htm. Accessed 25 October, 2013.

Acknowledgments All the authors duly acknowledge the technical and financial support from the University Uttar Banga Krishi Viswavidyalaya.

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Available online at http://www.iasir.net

PERFORMANCE of CARNATION (DIANTHUS CARYOPHYLLUS L.) GENOTYPES for QUALITATIVE and QUANTITATIVE PARAMETERS to ASSESS GENETIC VARIABILITY among GENOTYPES MS. TARANNUM II Ph.D. (Hort.), Department of Floriculture and Landscape Architecture, University of Horticultural Sciences, Bagalkot. College of Horticulture, UHS campus, GKVK (P), Bangalore-560 065, Karnataka, INDIA B. HEMLA NAIK 2 Professor (Hort.) cum Coordinator, Project Planning & Monitoring Cell, University of Agricultural and Horticultural Sciences, Savalanga Road, SHIMOGA-577 204, Karnataka, INDIA College of Horticulture, UHS campus, GKVK (P), Bangalore-560 065, Karnataka, INDIA 1

Abstract: Eight genotypes of Carnation were evaluated for growth, flowering, flower yield, flower quality and vase life parameters to assess spectrum of genetic variability between these characters under NVPH. Genotypes Soto, Dona and White Dona proved to have better vegetative growth whereas, for flowering parameters Soto, Golem and White Dona were early to initiate flower buds thereby earlier to reach the peak flowering. Flower quality parameters like flower stalk length was highest in Soto, Big Mama and Harish, White Dona and Dona while, maximum stalk girth was recorded in White Dona, Soto and Dona. Other quality parameters like flower diameter, flower stalk weight found best in Soto, White Dona, Dona, Big Mama and Harish. Maximum vase life and highest yield per plant were recorded in Cv. Soto followed by Dona and White Dona. Correlation between flower yield v/s 23 quantitative traits, showed higher genotypic to phenotypic correlation coefficients for most of the characters studied. Flower yield per square meter showed highly significant association with number of branches, nodes per stalk and nodes per plant, stem girth, number of leaves, leaf area, total dry matter and duration of flowering, and significant association with plant spread, girth of flower and flower length and negative correlation with days taken to flower bud initiation, first harvest and peak flowering at genotypic level. Whereas, nodes per plant and duration of flowering exhibited positive and highly significant correlation with yield, however, significant with plant spread, number of branches, nodes per stalk, stem girth, number of leaves and vase life at phenotypic level. Thus, these traits may serve as an effective selection parameter of carnation. Key words: Carnation, NVPH, Crop improvement, Genotypic, Phenotypic, Correlation. I. Introduction Carnation (Dianthus caryophyllus) is an important flower crop in Caryophyllaceae family having great commercial value as a cut flower due to its excellent keeping quality, wide array of colour and forms, also ability to withstand long distance transportation and remarkable ability to rehydrate after continuous shipping. Carnations are preferred to roses and Chrysanthemums, in several exporting countries as cut flower can also become useful in gardening for bedding, edging, borders, pots, and rock gardens. From medicinal point of view, the Carnation flowers are considered to be cardiotonic, diaphoretic and alexiteric [15]. In this modern era, with the increasing demand, development of Carnation cultivars with more desirable floral characteristics and higher productivity are found to be very important. The crop is need to be grown under protected structure as performance of varieties varies with region, season, genotypes and growing environment. In India, there is wide fluctuations exist with respect to temperature, light intensity and humidity which not only affect the yield and quality of flowers but also limit their availability for a particular period of a year. Hence it is necessary to grow Carnation under polyhouse condition for obtaining good quality flowers. Superiority of Carnation as a cut flower is judged based on its quality which plays a vital role in the International cut flower trade. Hence testing of the available varieties for suitability and adaptability with respect to flowering, flower quality, and yield parameters are of prime importance. It is essential for plant breeders to estimate the type of variation available in the germplasm also, clear assessment of association exist

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between the flower yield and various quantitative traits is of at most importance to understand the association between the characters and their relative contribution to the flower yield to bring about rational improvement in carnation. Further, there is a need of superior varieties suitable to our conditions. Hence, knowledge of the nature and the extent of variability present between the yield and yield contributing characters are considered to be of greater importance in selection of superior variety for planning an efficient breeding programme and to produce the desired quantity and quality of flowers for domestic as well as export market to meet the International demand. Keeping all these point in view the present investigations was undertaken to study the various biometrical parameters and to ascertain the nature and extent of correlation present in vegetative and flowering eight genotypes to identify the elite genotype to be used in the hybridization programme to bring the desired improvement for cut flower yield in this crop. II. Materials and methods The present investigation was carried out at Department of Floriculture and Landscape Architecture, College of Horticulture, Mudigere from July 2011 to June 2012, to evaluate the performance of eight different genotypes of Carnation viz., Dona, White Dona, Harish, Big Mama, Soto, Liber, Golem and Big Net procured in pro-trays with coco peat media from M/S Florence Flora Ltd. Bengaluru grown under naturally ventilated polyhouse. The design followed was Randomized Complete Block Design (RCBD) with eight treatments and three replications. Raised beds of 30 cm height were prepared having one meter width with a row spacing of 20 cm and 15 cm between plants. Rooted cuttings of Carnation plants were planted and grown by following standard cultivation practices like soil sterilization, irrigation, nutrition, pinching, netting, disbudding, harvesting etc. as per the standard package of practice. The data collected from five randomly selected tagged plants 30 days after pinching from each replication and analyzed as described by [12]. Vase life data analyzed for each treatment following completely randomized design [20]. Simple correlation coefficients pertaining to the phenotypic and genotypic variations for various characters of carnation genotypes were computed as per [17]. The values of correlation coefficient (r) were calculated and the test of significance was applied as per the procedure outlined by [6]. The observations on growth, flowering flower quality, flower yield, vase life and genotypic and phenotypic correlation between those characters in different genotypes of carnation are presented in the Table 1, 2, 3 & 4. III. Results and discussion Flower yield is an important parameter which decides the significance of suitability of the particular genotypes for commercial cultivation, which ultimately reflects on cost of cultivation. Maximum number of flowers per plant was recorded in the cultivars Soto, Dona and White Dona whereas, it was registered minimum in Big Net and Big Mama. The study revealed that, greater leaf area, more number of leaves and branches per plant as well as plant spread, dry matter accumulation resulted in the accumulation of maximum photosynthates. All these parameters contributed for production of more number bigger sized of flowers. The results are in consonance with the findings of [15], [7]. Flower quality parameter decides the significance of suitability and economic value of the particular genotypes in the International cut flower trade. Flower stalk length is very important quality trait which decides the quality of Carnation cut flowers and also plays an important role in vase life extension by improving their post-harvest life. Maximum flower stalk length was observed in Soto followed by Big Mama, Harish, Dona, and White Dona (Table 1) however, Liber had shorter stalk. The difference in stalk length among the genotypes may be attributed to the inherent genetic character associated with the genotypes, also due to the growing environmental conditions as reported by [13], [5] and [7]. Girth of flower stalk also plays vital role in making the flower for standard cut flower. Cultivar White Dona, Soto, Dona and Harish had better and strong flower stalks whereas, Big Mama had weak flower stalks by having lesser stalk girth might be due to the genetical constitution of the genotypes [13], [7]. Being a genetically controlled character, flower diameter was found superior in cultivars Soto, Big Mama, Harish and White Dona might be attributed to the presence of more number of petals per flower whereas, cultivar Liber produced small sized flowers due to the less number of petals in its flower bud [18]. Cultivar Soto found superior in terms of flower weight, followed by White Dona while, it was less in cultivar Liber. Variation in flower weight could be expected among the genotypes as the attribute is generally a genetic character [7]. The variations with respect to flower weight among varieties might also be the result of higher water and carbohydrates level in the flower. Water plays a very important role to maintain flower turgidity, freshness and petal orientation. Similarly, carbohydrates serve as energy source for growing bud, flower opening and longevity. The ultimate effect of all these factors resulted into strong and long flower stalks, large sized buds or flower and finally increases in flower weight. This variation might be due to the varietal characters as reported by [8]. Similar variations were also observed previously in Carnation by [18]. Vase life is one of the important criteria which decide the economic value of flower. Cultivars Soto (10.00 days), Dona (9.50 days) and White Dona (9.33 days) recorded higher vase life however, Big Mama registered

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minimum (6.17 days). These variations in vase life among the genotypes might be the result of increased accumulation of carbohydrates since, these cultivars could produce more number of leaves and higher chlorophyll content, which might have led to increased photosynthesis and increased carbohydrates. Variation in vase life could also be attributed to fact that, the variation in ability to produce ethylene and sensitivity to it among the different genotypes. Similar variations for vase life were also observed previously in Carnation by [13] and [7]. Significant differences were observed among different Carnation genotypes with respect to flowering parameters (Table 1). The cultivar Soto was first to show its visible flower bud by taking 95.16 days after planting followed by Golem whereas, Big mama (135.34 days) and Big Net (130.01 days) were very late to initiate variable buds. Similarly Soto was the earliest to reach peak flowering (143.30 days). Other cultivars like Dona (152.12 days), Golem (153.12 days) and Liber (156.08 days) were moderately early whereas, cultivars Big Mama (191.71 days) and Big Net (187.36 days) initially exhibited slow growth and reached their peak flowering late in the season this resulted in late flowering. These variations might be attributed to genetical make up and physiological difference among the genotypes as reported by [4] & [7]. Vegetative growth is measured in a better way in terms of plant height, number of shoots, number of leaves per plant, total dry matter accumulation etc (Table 1). These factors are significantly important as they play a key role in deciding the ultimate crop yield. Cultivars Soto, Big Mama, Harish, Dona and White Dona were vigorous in their growth whereas, Big Net and Golem were medium whereas, cultivar Liber was dwarf, recording minimum plant height (Table-1). Genetical make-up of the genotype, growing environmental conditions, production technology and cultural practices followed might have resulted in such variations with respect to plant height among the genotypes. This was in accordance with the reports of [7]. Number of shoots production is greatly influenced by apical dominance. It was registered maximum (6.80) Cv. Soto followed by White Dona (6.07), Big Mama (5.87), Dona (5.73) and Harish (5.60) which exhibited lesser apical dominance as compared to Cv. Big Net (4.60). Leaves are the prime important functional units for photosynthesis, which greatly influence the growth and flower yield of any crop. Significantly maximum number of leaves per plant (149.73) was recorded in cultivar Soto throughout the growing period followed by Dona (110.00), White Dona (105.20) and Harish (104.00) however, minimum was recorded in Big Net (79.07).The production of more number of leaves per plant in these genotypes was due to increased plant height, number of nodes and branches per plant. Similar results were obtained by [7]. Physiological parameters like leaf area, and total dry matter (TDM) production varied significantly within the genotypes. Genotype Soto has recorded maximum (2279.88 cm2) leaf area per plant whereas, it was least in Golem (903.91 cm2). Similarly, Soto has recorded maximum (89.12 g/plant) TDM followed by White Dona, Harish and Dona whereas, it was registered minimum in cultivar Big Net. These variations might be due to increased number of leaves, shoots and plant spread which in turn helped in maintaining higher leaf area which ultimately might have increased the dry matter production per plant in such superior genotypes. The results are in agreement with the findings of [11] in Carnation. Variability in the population is a prerequisite especially for characters where improvement is required. Success of plant breeding programmes largely depend on the amount of genetic variability present in a given crop species for the character under improvement. Phenotypic and genotypic correlation coefficients were computed between character pairs for all the twenty three parameters studied i.e., flower yield v/s ten vegetative, eight qualitative and four flowering traits of eight carnation genotypes are presented in Table 2, 3 and 4, respectively. A positive correlation between desirable characters is favourable to the plant breeder because it helps in simultaneous improvement of both the characters. Correlation coefficient analysis measures the mutual relationship between various plant characters and determines the component characters on which the selection is based for genetic improvement for a particular character [14]. Generally genotypic correlation coefficient were high in magnitude than the corresponding phenotypic correlation coefficients for all the attributes under study, indicating a strong inherent association between various characters and was masked by environmental component with regard to phenotypic expression as reported by [9]. In many cases genotypic and phenotypic correlations were very close indicating less environmental influence. The genotypic correlation of flower yield per meter square exhibited positive and highly significant correlation with number of branches, nodes per stalk and nodes per plant, stem girth, number of leaves, leaf area, total dry matter and duration of flowering and significant association with plant spread, girth of flower and flower length at genotypic level. Whereas, at phenotypic level nodes per plant and duration of flowering exhibited positive and highly significant association with yield and significant association with plant spread, number of branches, nodes per stalk, stem girth, number of leaves and vase life at phenotypic level. Similar results were also observed by [10] in roses for flower diameter and by [19] for vase life in Chrysanthemum. Hence, selection on the basis of these characters may not be effective as they are controlled by non-additive gene action. With respect to qualitative parameters length of flowers stalk exhibited positive and highly significant correlation with flower diameter number of petals and flower length and significant association with flower bud

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diameter and flower weight at genotypic level. However, had positive and highly significant association with flower diameter and number of petals and showed significant correlation with flower length and weight at phenotypic level. Girth of flower stalk had positive and significant association with flower length, vase life and yield at genotypic level whereas, significant association was observed with vase life at phenotypic level. Flower bud diameter exhibited positive and highly significant correlation with flower weight and significant with flower diameter, number of petals and flower length at genotypic level whereas, significant association was observed with flower weight at phenotypic level. Diameter of flower had positive and highly significant association with number of petals, flower length and flower weight at genotypic level, and the same character showed significant correlation with above parameter at phenotypic level. Number of petals per flower exhibited positive and highly significant association with flower length and flower weight at genotypic level. Whereas, had positive and significant association with flower length and flower weight at phenotypic level. Flower length showed positive and highly significant association with flower weight and significant with yield at genotypic level while, significant association found with flower weight at phenotypic level. None of the characters showed significant association with flower weight at both genotypic and phenotypic level. Vase life exhibited positive and significant correlation with yield at phenotypic level. These results are in line with the findings of [16] in dahlia. Days to flower bud emergence exhibited positive and highly significant association with days to flower opening and days to peak flowering at genotypic and phenotypic level, respectively. Days to flower opening had positive and highly significant association with days to peak flowering at both genotypic and phenotypic level respectively. Duration of flowering showed positive and highly significant association with yield both at genotypic and phenotypic level respectively. None of the characters showed significant association with days taken for peak flowering at both genotypic and phenotypic level. These results are consonance with [1] in gerbera. Vegetative parameters like plant height exhibited positive and highly significant association with plant spread, nodes per stalk and per plant, number of leaves, leaf area and total dry matter, however, exhibit significant association with number of branches and internodal length at genotypic level. Whereas, plant spread showed positive and highly significant association with number of branches, nodes per plant, number of leaves, leaf area, total dry matter and significant association with stem girth, internodal length and yield at genotypic level. In case of number of branches exhibited positive and highly significant association with nodes per plant, internodal length, number of leaves, leaf area, total dry matter and yield whereas, significantly associated with stem girth. Similar heritability estimates were reported by [3] in Chrysanthemum. Nodes per stalk exhibited positive and highly significant correlation with nodes per plant, number of leaves, leaf area, total dry matter and yield while correlated significantly with stem girth at genotypic level. Nodes per plant exhibited positive and highly significant correlation with number of leaves, leaf area, total dry matter and yield at both genotypic and phenotypic level and significant correlation with stem girth at genotypic level. Stem girth showed positive and highly significant association with leaf yield and significantly correlated with leaf area and total dry matter at genotypic level. Internodal length exhibited positive and significant correlation with number of leaves, leaf area and total dry matter. Number of leaves showed positive and highly significant association with leaf area, total dry matter and yield at genotypic level. Leaf area exhibited positive and highly significant association with yield at genotypic level. However, total dry matter showed highly significant relationship with yield at genotypic level. The results are supported by [11] in Carnation. This reveals that indirect selection of any one of these characters shall lead to concomitant increase in cut flower yield. Flower yield per square meter exhibited positive and highly significant correlation for most of the characters both at phenotypic and genotypic levels. It has got interdependent relationship with the vegetative parameters like number of branches, nodes per stalk and per plant, stem girth, number of leaves, leaf area, total dry matter production which might have resulted in the production of flower quality parameters like flower length, flower girth thereby bud and flower diameter and number of petals per flower and number of flowers per plant due to maximum duration of flowering. Due to all these positive and significant inter relationship the flower yield per square meter was increased. It clearly indicated that, all the above characters were interrelated and interdependent for enhancing the cut flower yield of carnation as it was evidenced by highly positive and significant correlation observed at phenotypic and genotypic levels (Table 2, 3 and 4). The results were corroborated with the findings of [2] in Marigold. IV. Conclusion Based on present findings, it can be concluded that cultivars viz., Soto, White Dona and Dona have emerged as promising genotypes with respect to growth, earliness in flowering, flower yield and quality parameters during the entire period of its growth. Correlation studies revealed that, flower yield has got inter-dependent relationship with the vegetative parameters like, number of branches, nodes per stalk and nodes per plant, stem girth, number of leaves, leaf area and total dry matter production which might have resulted in the production of flower quality parameters like flower length, flower girth thereby bud and flower diameter and number of petals

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per flower and ultimately increased the number of flowers per plant. Hence, selection of the above stable characters in the promising genotypes will helpful in improving the flower yield and these characters should be given prime emphasis during selection for improvement of carnation. Table 1: Vegetative growth, flowering, Flower quality and yield parameters in different genotypes of Carnation grown under protected cultivation

Sl. Genotypes No.

Dona

Plant No. of No. of height leaves/ Shoots/plant (cm) plant

Length Total Days to Girth of Flower Flower Days to of Vase Dry flower Flower Flower stalk Yield/ peak Flower life matter bud stalk dia. (cm) weight plant flowering stalk (days) (TDM) initiation (mm) (g) (No’s) (cm)

97.63

5.73

110.00

56.75

110.04

152.12

84.50

4.60

5.43

39.35

10.00

9.50

White Dona 96.73

6.07

105.20

76.93

106.19

161.55

84.27

5.70

5.62

52.12

9.50

9.33

3. 4. 5. 6. 7. 8.

Harish 93.27 Big Mama 91.53 Soto 111.13 Liber 56.96 Golem 63.59 Big Net 76.73 S. Em± 2.33 CD @ 5% 7.03

5.60 5.87 6.80 5.60 5.40 4.60 0.13 0.39

104.00 95.47 149.73 81.60 89.87 79.07 7.51 22.77

62.50 48.33 89.12 34.18 30.20 24.67 0.71 2.14

123.09 135.34 95.16 112.85 102.34 130.01 1.01 3.07

175.31 191.71 143.30 156.08 153.12 187.36 1.61 4.87

86.20 88.33 93.57 51.37 57.57 77.32 0.75 2.28

4.23 3.67 4.99 4.36 4.22 4.39 0.12 0.38

5.69 5.69 5.86 4.70 5.20 5.03 0.09 0.26

36.69 48.00 53.37 26.44 35.56 35.46 1.30 3.93

7.70 7.00 11.67 8.50 7.80 6.33 0.24 0.74

7.33 6.17 10.00 8.67 9.17 7.33 0.27 0.80

Table 2: Genotypic and phenotypic correlation between vegetative and flower yield parameters in different genotypes of carnation Genotypes 1. Plant height (cm) 2. Plant spread (cm) 3. Number of branches 4. Nodes/stalk 5. Nodes/plant 6. Stem girth (mm) 7. Internodal length (cm) 8. Number of leaves 9. Leaf area (cm2) 10. Total dry matter (g/plant) 11. Flower yield/m2

G P G P G P G P G P G P G P G P G P G P G P

1 1 1

2 0.84** 0.70* 1 1

3 0.67* 0.59 0.94** 0.79* 1 1

4 0.83** 0.69* 0.61 0.52 0.58 0.44 1 1

5 0.83** 0.76* 0.81** 0.67* 0.85** 0.74* 0.91** 0.74* 1 1

6 0.41 0.36 0.66* 0.55 0.64* 0.56 0.66* 0.59 0.64* 0.59 1 1

7 0.67* 0.63* 0.75* 0.64* 0.80** 0.73* 0.24 0.24 0.56 0.45 0.09 0.12 1 1

8 0.88** 0.73* 0.98** 0.65* 0.90** 0.78** 0.84** 0.54 1.02** 0.85** 0.61 0.55 0.71* 0.59 1 1

9 0.94** 0.71* 1.06** 0.67* 0.95** 0.76* 0.94** 0.55 1.10** 0.79** 0.64* 0.56 0.66* 0.54 0.99** 0.96** 1 1

10 0.87** 0.85** 0.90** 0.79* 0.89** 0.82** 0.79** 0.70* 0.91** 0.85** 0.72* 0.66* 0.66* 0.64* 0.94** 0.80** 1.00 0.78* 1.00 1.00

11 0.58 0.56 0.75* 0.65* 0.87** 0.77* 0.80** 0.72* 0.90** 0.84** 0.82** 0.72* 0.48 0.42 0.94** 0.72* 0.97** 0.65 0.79** 0.77 1.00 1.00

*Significant @ 5 %, ** Significant @ 1 % Table 3: Genotypic and phenotypic correlation between qualitative and flower yield parameters in different genotypes of carnation Genotypic Length of flower stalk (cm) Girth of flower stalk (mm) Flower bud diameter (cm) Flower diameter (cm) Number of petals/flower Flower Length (cm) Flower weight (cm) Vase life (days)

G P G P G P G P G P G P G P G

1 1 1

2 0.23 0.22 1 1

3 0.63* 0.57 0.11 0.05 1 1

4 0.91** 0.84** 0.25 0.17 0.78* 0.63 1 1

5 1.04** 0.81** 0.29 0.25 0.74* 0.47 0.95** 0.74* 1 1

6 0.87** 0.72* 0.65* 0.56 0.62* 0.47 0.90** 0.72* 0.95** 0.62* 1 1

7 0.77* 0.75* 0.52 0.48 0.85** 0.72* 0.86** 0.78* 0.94** 0.68* 0.84** 0.68* 1 1

8 -0.11 -0.11 0.75* 0.64* 0.12 0.09 0.02 0.02 -0.21 -0.16 0.41 0.35 0.14 0.18 1

9 0.32 0.32 0.67* 0.62 0.32 0.28 0.42 0.41 0.27 0.11 0.77* 0.59 0.46 0.44 0.88

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Tarannum et al., American International Journal of Research in Formal, Applied & Natural Sciences, 5(1), December 2013- February 2014,pp. 96-101 P G Flower yield/m P *Significant @ 5 %, ** Significant @ 1 %

0.75* 1 1

Table.4: Genotypic and phenotypic correlation between flowering and flower yield parameters in different genotypes of carnation Genotypic Days taken to bud initiation Days taken to flower opening Duration of flowering (days) Days taken for peak flowering Flower yield/m2

G P G P G P G P G P

1 1 1

2 0.99** 0.98** 1 1

3 -0.63 -0.61 -0.67 -0.66 1 1

4 0.97** 0.95** 0.99** 0.93** -0.64 -0.64 1 1

5 -0.83 -0.81 -0.85 -0.81 0.94** 0.91** -0.85 -0.8 1 1

*Significant @ 5 %, ** Significant @ 1 %

References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16]. [17]. [18]. [19]. [20].

Anuradha, S. and Narayana Gowda, J. V. 2002. Inter-relationship between growth and yield parameters with flower yield in Gerbera. Journal of Ornamental Horticulture, New Series, 5(1): 35-37. Banupratap, Tewari, G.N. and Mishra, L.N. 1999. Correlation studies in Marigold. Journal of Ornamental Horticulture, New Series, 2(2): 84-88. Barigidad, H., Patil, A.A. and Nalawadi, U.G. 1992. Variability studies in Chrysanthemum. Progressive Horticulture, 24(1-2): 55-59. Bhautkar, M. Y., 1994, Carnation cultivation in greenhouse under Mahabaleshwar condition. J. Maharastra Agric. Univ., 19(2): 292-293. Dalal, S.R., Wankar, A.M. and Somavanshi, A.V., 2009, Performance of Carnation cultivars under Polyhouse condition. The Asian J. Hort., 4(1):225-226. Fischer, R. A. and Yates, F. 1963. Statistical tables for Biological, Agricultural and Medical Research, Oliver and Boyd Ltd., Edinburgh. Gharge, C.P., Angadi, S.G., Biradar, M.S. and More, S.A., 2009, Evaluation of Standard carnation (Dianthus caryophyllus Linn.) cultivars under naturally ventilated Polyhouse conditions. J. Orn. Hort., 12(4): 256-260. Halvey, A.H. and Mayak, S., 1979, Senescence and post-harvest physiology of cut flowers. Horticulture Reviews Part II., 3: 59143. Johnson, H.W., Robinson, H.F. and Comstock, R.E. 1955. Genotypic and phenotypic correlation in Soyabean and their implications in selection. Agronomy Journal, 47: 477-483. Lal, S.D., Seth, J.N., Yadav, J.P. and Danu, N.S., 1982. Genetic variability and correlation studies in Rose. Progressive Horticulture, 14(4): 234-236. Mahesh, K. 1996. Variability studies in Carnation (Dianthus caryophyllus L.). M.Sc. Thesis, University of Agricultural Sciences, Bangalore. Panse, V. G. and Sukhatme, 1967. Statistical Methods for Agricultural Workers. Indian Council of Agriculture Research, New Delhi, pp. 100-174. Patil, R. T., 2001, Evaluation of standard carnation (Dianthus caryophyllus L.) cultivars under protected cultivation. M. Sc. Thesis, University of Agriculture Sciences, Dharwad. Robinson, H.F., Comstock, R.E. and Harvey, P.H. 1949. Estimates of heritability and degree of dominance in Corn. Agronomy Journal, 41: 353-359. Shiragur, M., Shirol, A. M., Reddy, B.S. and Kulkarni, B. S., 2004b, Performance of standard Carnation (Dianthus caryophyllus L.) cultivars under protected cultivation for vegetative characters. J. Orn. Hort., 7(3-4): 212-216. Shyamal, M.M. and Kumar, R. 2002. Genetic variability and correlation studies in Dahlia. Journal of Ornamental Horticulture, New Series, 5(1): 40-42. Singh, R.H. and Choudhary, B.D. 1979. Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi, pp. 50-61. Singh, K. P. and Sangama, 2003, Evaluation of post-harvest quality of some cultivars of Carnation flowers grown in greenhouse. J. Orn. Hort., 6(3): 274-276. Sirohi, P.S. and Behera, T.K., 2000, Genetic variability in Chrysanthemum. Journal of Ornamental Horticulture, 3(1): 34-36. Sundar Raj, N., Nagaraju, S., Venkataram, M. N. and Jagannath, M. K. 1972. Design and analysis of field experiments. UAS, Bangalore.

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DEVELOPING STATISTICAL MODELS TO STUDY THE GROWTH RATES OF PADDY CROPS IN DIFFERENT DISTRICTS OF CHHATTISGARH D.P. Singh, Deepak Kumar, M.S. Paikra and P.S. Kusro Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), INDIA. Abstract: Chhattisgarh has a tremendous agricultural potential with a diversity of soil and climate, mountains, plateau, rivers, natural vegetation and forest. It has no seas and no connection with Himalaya and yet it has hilly and mountains with big rivers. The rainfall ranges from 800 mm to 1700 mm in different years. Paddy is the major crop of the region.According to a government estimate, net sown area of the state is 4.828 million hectares and the gross sown area is 5.788 million hectares. Chhattisgarh is also called the "Paddy bowl of central India". Different regression equations such as linear, logarithmic, inverse, quadratic etc. are fitted for Paddy crop with respect to yield (KG/ha) for 16 districts of Chhattisgarh to study the growth rates. Keywords: Statistical Model, Growth, yield, regression, Paddy I. INTRODUCTION The Chhattisgarh, 26th state of India, was carved out of Madhya Pradesh on November 2000. It covers about one-third of geographical area of undivided Madhya Pradesh. The Chhattisgarh extends south east of Madhya Pradesh from 17046’N to 2405’ N latitude and from 80015’ E to84020’ E longitude. Chhattisgarh has a tremendous agricultural potential with a diversity of soil and climate, mountains, plateau, rivers, natural vegetation and forest. It has no seas and no connection with Himalaya and yet it has hilly and mountains with big rivers. The rainfall ranges from 800 mm to 1700 mm in different years. Diversified crops and cropping systems are the typical characteristics of Chhattisgarh. Paddy is the major crop of the region, on the other hand kharif potato are being grown in plateau area of northern hills, while in Bastar plateau, crops like coconut, coffee and wide range of tuber crops, spices and medicinal plants are being grown. In Chhattisgarh, Paddy, the main crop, is grown on about 77% of the net sown area. Only about 20% of the area is under irrigation; the rest depends on rain. In the mid-1990s, most of Chhattisgarh was still a mono crop belt. Chhattisgarh is also called the "Paddy bowl of central India". Paddy is an important crop grown in nearly 44 million ha of land in the country with the productivity of 2.2 t/ha which is less than the productivity of many countries. Annual population growth rate of the country is nearly 1.8 % and if per capita consumption of Paddy is expected to be 400 gm of Paddy per day then the demand for Paddy in 2025 will be 130 m. tonnes. In Chhattisgarh, Paddy occupies average of 3.6 million ha. With the productivity of the state ranging between 1.2 to 1.6 t/ha depending upon the rainfall. Different regression equations such as Semi log polynomial, linear etc. are fitted for Paddy crop with respect to area, production and yield for 16 districts of Chhattisgarh to study the growth rates. The best fit models (regression equation) which are chosen for estimating the growth pattern is based on the R2 value is considered as the best model. II. MATERIALS AND METHODS The study mainly confined to sixteen district of Chhattisgarh. The inception of the new state of Chhattisgarh new district was formed by splicing up the original district. The secondary data of area (‘000 ha), production (‘000 mt) and yield (Kg/ha) on Paddy of 15 year were collected for period 1997-98 to 2011-12. Data collected in Department of Agriculture, Government of Chhattisgarh for period 1997-98 to 2011-12 were subjected to analyze through different regression equation. The data are analyzed by using software like MS-EXCEL, Statistical Analysis System (SAS) and Statistical Package for Social Science (SPSS). The used models are given in table 1. III. RESULTS AND DISCUSSION Keeping in view the specific objective of the study, the data collected on the yield of Paddy for different district of Chhattisgarh have been subjected to various statistical methods. A. Developing statistical models to study the growth rates of paddy yield

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The results of different growth models estimated for the yield of Paddy crop in all districts of Chhattisgarh for 15 year (1997-98 – 2011-12) are presented in table 2. A1. 1.1 Paddy Yield The results of different growth models estimated for yield of the paddy crop in 16 Districts of Chhattisgarh are presented in the Table 2. Table 1: The used models S. No.

Equation

Description

Model Name Linear

Yi= b0 + b1 ti

Logarithmic

Yi= b0 + b1 ln (t)i

Inverse

Yi= b0 + b1 / ti

Quadratic

Yi= b0 + b1 ti + b2 t2i

Cubic

Yi= b0 + b1 ti + b2 t2i + b3 t3i

Power

Compound

Yi= b0 tbi or ln(Y) = ln(b0)+b1 ln(t) Yi= b0 b1t or ln(Y) = ln(b0) + ln(b1)t

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated. Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated and ln is Natural Log. Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated. Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated. The quadratic model can be used to model a series that takes off" or a series that dampens. Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated. Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated and ln is Natural Log

S-Curve

Y = e (b0 + b1/t) or ln(Y) = (b0) + (b1/t)

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated, ln is Natural Log and “e” is the exponential function.

Logistic

Y = 1 / (1/u + b0 + b1t) or ln(Y) = (b0) + (b1 * t)

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated, ln is Natural Log and u is the upper boundary value.

Growth

Y = e (b0 + ( b1 * t )) or ln(Y) = (b0) + (b1 * t)

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated, ln is Natural Log and “e” is the exponential function.

Exponential

Y = b0 * e ( b1 * t ) or ln (Y) = (b0) + (b1/t)

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated, ln is Natural Log and “e” is the exponential function.

Y and ti’s are yield and time period respectively.b0 and bi’s are constants to be estimated and ln is Natural Log

Table2: District wise trend using best fit models for yield from 1997-98 to 2010-11 in Paddy crop Districts Raipur Mahasamund Dhamtari Durg Rajnandgaon Kawardha Bilaspur

Janjgir Korba Raigarh Jashpur

Equation Linear Logarithmic Linear Logarithmic Linear Logarithmic Linear Logarithmic Linear Quadratic Cubic Linear Quadratic Cubic Linear Logarithmic Inverse Growth Linear Logarithmic Linear Logarithmic Quadratic

Parameter Estimates Constant b1 -81288.824 41.187 -626566.506 82575.883 -96313.121 48.648 -740086.706 97497.670 -223761.402 112.492** -1712665.3 225484.865** -107235.191 54.145 -823893.025 108532.887 -69284.053 35.158 -45533.830 .000** -29993.533 .000** -98470.745 49.807* -48563.315 .000* -31927.510 .000* -271419.848 136.259** -2074577.2 273082.84** 274745.8 -5.473E8** -37.614 .022 -74360.086 37.686* -573021.226 75521.634* -73047.571 37.000* -562362.941 74111.654* -35991.745 .000*

R2 b2 .012** .000** .012* .000* .009

b3 3.863E-6** 4.13* -

0.26 0.26 0.26 0.26 0.68 0.68 0.23 0.23 0.21 0.36 0.36 0.29 0.29 0.29 0.79 0.79 0.79 0.14 0.32 0.32 0.32 0.32 0.32

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Linear Linear Linear Logarithmic Linear Quadratic Cubic Logarithmic Inverse

-38111.090 2661.255 -59114.633 -457846.028 -71927.818 -35412.482 -23240.705 -865887.338 115571.291

19.556 -.765 30.113 60382.000 36.453 .000 .000 114078.019* -2.287E8*

.009 .000 -

3.02 -

0.11 0.01 0.14 0.14 0.18 0.18 0.18 0.27 0.27

IV. CONCLUSION Regression parameter for estimating yield of paddy under different district of Chhattisgarh. In our study the developed regression model for paddy cultivated was found to be best fitted Linear and logarithmic regression model respectively. Highest R2 was obtained in Janjgir district which is (79 %) followed by Dhamtari district (68 %) was obtained while lowest R2 obtained Koriya ditrict which is (10 %). The results highlight shows overall increasing trend of paddy yield in Chhattisgarh. The validity of the forecasted value can be checked when the data for the lead periods become available. R2 for the estimated equation show the closeness of the estimates to the actual value. REFERENCES Achoth Lalith., Nagraj N., Reddy Keshava., Rebello N. S. P. and Ramanna R., 1988, A study of growth and variability of pulse production in Karnataka. Asian Econ. Rev. 30 (2) 274-287. Addisu Tadege Anmaw, 1997, Growth and Instability of Oil seed production in Karnatka, India M.Sc. (agril) Thesis. Univ. Agric. Sci., Dharwad. Chengappa, P.G., 1981, Growth rates of area, production and productivity of coffee. Indian J. Coffee Res., 11 (2) : 19-26 Jain R.K., Ghuraiya R.S. and Pathak K.N., 1994, Growth of oilseed production in Bundelkhand zone of Madhya Pradesh: progress and prospect. Crop Res. Hisar. 8(2) :225-232. Joshi B. Deepak, 2009, M.Sc. Thesis, To study the growth and instability in oilseed production in Karnataka. Kaushik. K. K., 1993, Growth and instability of oilseed production in India, Indian J.Agric. Econ.. 48 (3): 334-338. Mundimani. S. M., Sastry. K. N. R and Murthy. J. N. V., 1995, Growth performance of oilseed in Karnataka, Agric. Sit. India, 52(7), 451454. Patel. G. N and Agarwal. N. L, 1994, Growth and instability in production of groundnut in Saurashtra region of Gujarat, Agric. Sit. India. 49(1), 171-174. Rath. N., 1980, A note on Agricultural Production in India during 1955-78, Indian J.Agric. Econ., 35 (2): 94-103. Singh. A. J. and Kaur. P., 1993, Growth and instability in oilseeds in India. Agriculture Situation in India, 48(1): 9-11. Tripathy. S. Y. and Mishra. S. N., 1997, Growth and instability of Ragi production in Orissa. Agric. Sit. India, 54(1): 77-79.

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A Study of Processing Model and Different Application Aspects of Agricultural Image Processing 1

Jyoti Saini, 2Amit Kumar, 3Shrinath Tailor 1,2

M.Tech. Student, 3Assistant Professor Department of Computer Science and Applications Pratap University, Chandwaji, Jaipur, INDIA Abstract: Image processing has involved its contribution to almost all area of real time applications. One of such fastest growing research area is the agricultural image processing. This kind of processing includes the work on different agricultural objects and products such as flowers, fruits, leaves etc. In this paper, these all application aspects are explored. These aspects are also defined along with the individual aspect exploration and the associated challenges. The paper has also defined a standard agricultural processing model. This model is based on the feature point analysis to perform the image recognition and classification. Keywords: Fruit, Flower, crop, Leaf, Feature Extraction I. AGRICULTURE IMAGE PROCESSING Image processing is having its importance in different application areas such as medical images, real time images, industrial image processing, texture classification, object identification etc. One of the growing research areas for image processing in the agricultural field. Agricultural Image processing is not limited to single application [4][5]6], instead, it is itself having number of correlated image processing areas shown in figure 1. Crop recognition and Classification

Agricultural Land Identification

Agricultural Image Processing

Fruit or Leaf recognition and Classification

Disease Identification in Crop, Fruits, Land

Figure 1: Agricultural Image Processing Applications As shown in the figure, the agricultural having four kind of object for the image processing. These applications objects include land, crop, leaves and fruits. One of the major challenges in agricultural images the image acquisition. Different kind of agricultural Images and relative issues are defined in this section A) Land Image Processing Such as to perform the agricultural land processing, the images are captured using the satellite or the distance vision cameras. Such distance vision images are captured using aircraft. To perform the accurate image processing in such images, high resolution and high quality images are required. These images are called SAR images or remote sensing images. SAR image processing is one of the most challenging research areas. These SAR images are used to identify the agricultural land area identification as well as estimation of the area so that the GIS based decision can be taken. This analysis work includes the geospatial and geographical information access to take the effective decision [7][8][9]. B) Crop Image Processing Crop image processing is another research area for identification of the crop as well as the identification of the crop diseases. The disease here includes the detection of the pest on crops. The challenge in this area is vast number of available crops as well as their internal grading. The crop identification is having number of

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challenges shown in figure 2. Image quality is one of the major issues in such images because these kind of images sometimes having a small variation as well as to explore the image features, high quality images are required. Another issue in image acquisition is the image part. Each crop has the different feature set for that it is required to retrieve the image where maximum features will be explored. Once the images are acquired, even the classification can be difficult because of the thousands of available crops. As the number of crop types in the dataset increases, it becomes difficult to classify the images. Another issue in classification process is the internal grading of images. A particular crop type further contains number of quality grades that are having a negligible visible difference. Because of this, it becomes very difficult to classify the crops under the quality grade. While working with crop disease identification, a crop can have number of disease issues. It is difficult to identify the crop specific disease by performing the feature analysis [11][12]. Image Acquisition and Quality

Variety of Crops

Crop Processing Challenges

Identification of Crop Specific Disease

Internal Quality Grades of Crops

Figure 2: Crop Image Processing C) Leaf Image Processing Leaf image processing is also an important sub domain of agricultural processing. Most of the trees or plants are recognized by analyzing the leaf. The leaf recognition is used to identify the fruit plants, vegetable plants, trees etc. To perform this recognition multifactor analysis is performed [13]. There are number of feature factors that are used to perform the identification and classification of different kind of plants, tree and flower. Flower Identification Vegetable Identification

Leaf Processing

Fruit Plant Identification

Tree Identification

Figure 3: Leaf Image Processing The leaf image processing is defined under different vectors such as leaf shape, size, color etc. Based on the intensity and color analysis leaf disease, pest etc. can also be identified. D) Fruit Image Processing Fruits are the most important agricultural objects as well as the consumable agricultural products. Fruits are the special processing objects that are used in different domains such as fruit type identification, fruit storage age identification, fruit quality identification, fruit disease identification and classification[14]. In this paper, a study of the image processing in the area of agriculture is defined. This paper has defined the basic processing on different kind of agricultural objects or products. In this section, the exploration of aspects

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and challenges associated with different agricultural areas is defined. In section II, the work done by the earlier authors in the agricultural image processing is defined. In section III, some of the effective image segmentation process with relative aspects is explored. In section IV, the conclusion driven from the work is discussed. II. RELATED WORK Image processing is having its valuable importance in the agricultural applications. These applications include the crop identification and classification, fruit classification, fruit and crop disease identification, land identification and classification etc. The work already done by different researchers in this area is discussed in this section. Parveiz Zeaiean has defined work on crop acreage estimation by performing the supervised learning approach. Author presented the scene analysis to identify and estimate the crop area and its classification. Author has used the maximum likelihood classification algorithm along with parallelepiped algorithm to estimate the crop area. Author performed the accurate size and shape identification of different crop areas [1]. Another work on the crop land identification and classification was performed by Wilbert long. Author defined the work on satellite captured images and performed the structural analysis. Author identified the vegetationa and manmade structures to identify different land areas. Author defined a parametric learning under supervised learning approach for the classification process. The obtained results are showing the effective classification of the land area [2]. A work on the remote sensing images for the land classification was defined by Jinguo Yuan. Author performed the main work on preprocessing stage to improve the land areas by performing the atmospheric correction and the reformation of satellite images so that Hyperion image can be obtained. Author has defined the unsupervised learning approach to improve the classification accuracy and to perform the effective image classification. Author obtained the accuracy level up 99.3%. In this work, author performs the hybrid classification using the biophysical characteristics along with PCA approach [3].A multiscale analysis along with improved classification and segmentation was proposed by Y.Lanthier. Author defined the pixel oriented as well as object centric approach for the classification. Author integrated the maximum likelihood algorithm with hierarchical segmentation to identify the cluster members. Author defined the ground area based analysis approach for the identification of the similar land areas. The analysis was performed under multiple vectors such as size, shape, color etc [4]. Heather McNairn has defined a crop classification approach using multipolarization and polarimetric data. Author defined the phased array based analysis using L-band polarization method so that different kind of crops would be identified. The classification is here defined to achieve the 70% accuracy rate. Author defined the decomposition using three main approaches called Cloude-Pottier, FreemanDurden and Krogager approach. This work is based on the linear polarization along with parametric analysis to perform the crop classification to improve the diverse capability so that crop classification. The satellite captured images were processed to identify different crop types. The analysis was done using the intensity diversity analysis. The obtained result shows the crop classification increasing viable [5]. A study oriented work based on multisensor fusion for crop classification was defined by Zhengwei Yang. This paper includes the study on different image fusion approaches such as PCA (Principal Component Analysis), Intensity Hue Saturation (IHS) and image band stacking (IBS). Author performed the spectral information decomposition along with multispectral transformation for fusion process. Author improved the classification assessment along with temporal combination analysis to improve the classification results [6]. Jianhoung Liu also work on the multi-spectral analysis based image classification for cropland parcels. This paper involves the texture analysis along with neighbor pixel scaling and analysis to perform the classification. Author defined the spectral variation analysis under different noising so that image observation will be done effectively and the inner boundary based crop differentiation will be obtained. A work on the agricultural land cover classification was defined by B. Erdenee to perform the land cover classification so that effective area segmentation will be done. Author improved the work by performing the vector analysis on agricultural land to handle the ground truth data with supervised learning analysis. Author defined the reflective methods to perform the agricultural data monitoring and the ground checking of the images [7]. Multi-temporal adaptive work on agricultural land classification was proposed by Giuseppe Satalino. Author defined the work by performing the activity analysis along with image polarization and low incidence angle acquisition. Author has used the maximum likelihood algorithm along with temporal dataset processing to improve the work accuracy. Author has improved the work based on reference map to improve the spot data based classification [8]. A statistical method was adapted by Umberto Amato to perform the agricultural land classification. Author used the discriminant analysis approach to perform the analysis on intensity diversity. The method adapted by the author combined the spectral band analysis along with segmentation and classification process. Author defined a multiclass SVM to derive the effective results. The presented work obtained the accuracy level more than 95% [9]. A study on application of remote sensing in agricultural images was presented by Mustafa Teke. Author defined the hyper spectral analysis approach to perform the classification. Author used the observation to maximize the resource usage and to handle the farming practices. Author defined the plant disease classification and identification of pest in crop

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[10]. Fruit image segmentation is one of the growing research area in agricultural image processing. A work on fruit segmentation using multi-spectral feature analysis was presented by Calvin Hung. Author defined the conditional analysis along with pervised feature learning. Author defined the variance analysis using caopy tree to improve robustness and accuracy [11]. III. ASPECTS OF AGRICULTRE IMAGES PROCESSING APPROACHES To perform the agriculture image processing, the foremost task is the extraction of the image features. Image segmentation is about to extract the image features. To perform this feature extraction, the image analysis is been done under different aspects. These aspects include the change detection, intensity analysis, color intensity analysis etc. In case of agricultural processing such as leaf recognition or the fruit identification, the shape and size of leaf and fruits is performed. To perform this kind of recognition, the corner detection is performed. These corner points are used to determine the contour characteristics identification from the image. To perform the effective feature identification, the feature extraction and recognition approaches are divided in different vectors. These classes are shown in figure 4. Agriculture Image Feature Detection

Region Feature Extraction

Grain Feature Extraction

Point Feature Extraction Feature Matching

Area Based Matching

Feature Based Matching

Transform Model Estimation

Image Resampling

Figure 4: Agricultural Image Segmentation Process A)

Agricultural Image Feature Extraction

Feature extraction is about to identify the valuable information from the image based on which the decision regarding the image processing can be performed. This feature extraction can be performed in terms of extraction of image shape, intersection points, edge extraction etc. This feature extraction process includes the identification of grain ending points, distinctive point extractive, center of gravity point extraction etc. The feature extraction approaches are: i) Region Features Region features are defined as the closed boundary features that are defined with appropriate size specification such as leaf shape, grain shape etc. The regions are also defined respective to view point, rotation angle etc. The accurate area identification is the tune up process under the segmentation parameters. ii) Grain Features Grain feature are used to extract the contour feature extraction, identification of disease impact, pest detection etc. Edge detection analysis is also used to perform the structure detection of the leaf or the fruit. iii) Point Feature Point feature is the lowest level of feature extraction. It includes the extraction of intersection point extraction, centralized point extraction, corner point identification. The curvature analysis along with discontinuities detection comes under this kind of detection.

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B) Feature Matching Feature matching is about to match the input image with referenced database image. The feature detection is here been performed to match the intensity value along with neighbor pixel comparison. This feature matching process is defined under different methods given as under i) Area Based Matching The region is the identification and matching of the input object image with the referenced image. This work includes the matching of distinctive information along with local image shape and structure. The leaf shape and size identification is such kind of recognition process to identify the plant or tree type. The similarity analysis between source and the referenced image is performed to identify the similarity level. ii) Feature Based Matching The second aspect to perform the effective recognition is the structural local feature based matching. This matching includes the intensity analysis. The matching based on the feature descriptors is used to optimize the matching process. The feature vectors includes the centroid matching, corner point matching, edge based matching etc. Feature extraction is defined the matching based on the image sample instead of whole image. This image part based recognition is effective to perform the effective recognition. iii) Transform Model Estimation Another mapping function definition and extraction on the agriculture source image relative to the referenced image is performed. Here the mapping is performed based on the function based analysis. The transformation to the input image to result image is performed. The image pair or the distance analysis employs the effective model generation and evaluation. Better the mapping, more effective the results will be. The classification of the agricultural images is done using the model estimation. iv) Image Resampling The resampling is the conversion of the image form to other. This transformation process is performed to map the image to referenced image. This approach includes the interpolation process along with regular grid based analysis so that the effective image mapping will be performed. The disease identification and classification are comes under this vector. It also includes the image repairing to resolve the image problems. VI. CONCLUSION In this paper, a study of the agriculture image processing is defined. This paper has explored the different agriculture objects under different image processing aspects and the application aspects. The paper also explored the feature extraction processing model on these agricultural images. REFERENCES [1] [2] [3] [4] [5]

[6] [7] [8] [9] [10]

[11] [12] [13] [14]

Parviz Zeaiean Firouzabadi, Performance Evaluation of Supervised Classification of Remotely Sensed Data for Crop Acreage Estimation, pp 2718-2720, 2001 Wilbert Long, III and Shobha Srihar.n, Land Cover Classification of SSC Image: Unsupervised and Supervised Classification Using ERDAS Imagine, pp 2707-2712, 2004 Jinguo Yuan and Zheng Niu, Classification Using EO-1 Hyperion Hyperspectral and ETM Data, International Conference on Fuzzy Systems and Knowledge Discovery, pp 1-5, 2007 Y. Lanthier, A. Bannari, D. Haboudane, J. R. Miller and N. Tremblay, HYPERSPECTRAL DATA SEGMENTATION AND CLASSIFICATION IN PRECISION AGRICULTURE: A MULTI-SCALE ANALYSIS, IGARSS, pp 585-589, 2008 Heather McNairn, Jiali Shang, Xianfeng Jiao, and Catherine Champagne, The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 12, pp 3981-3992, 2009 Zhengwei Yang, Yangrong Ling and Claire Boryan, A Study of MODIS and AWiFS Multisensor Fusion for Crop Classification Enhancement, pp 1-6, 2009 Jianhong Liu, Wenquan Zhu, Minjie Mou and Lingli Wang, Cropland Parcels Extraction Based on Texture Analysis and Multispectral Image Classification, pp 1-4, 2010 B. Erdenee, Tateishi Ryutaro and Gegen Tana, PARTICULAR AGRICULTURAL LAND COVER CLASSIFICATION CASE STUDY OF TSAGAANNUUR, MONGOLIA, IGARSS, pp 3194-3197, 2010 Giuseppe Satalino, Donato Impedovo, Anna Balenzano and Francesco Mattia, LAND COVER CLASSIFICATION BY USING MULTI-TEMPORAL COSMO-SKYMED DATA, MultiTemp 2011, pp 17-20, 2011 Umberto Amato, Anestis Antoniadis, Maria Francesca Carfora, Paolo Colandrea, Vincenzo Cuomo, Monica Franzese, Stefano Pignatti, and Carmine Serio, Statistical Classification for Assessing PRISMA Hyperspectral Potential for Agricultural Land Use, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, pp 615-625, APRIL 2013 Mustafa Teke, Hüsne Seda Deveci, Onur Haliloğlu, Sevgi Zübeyde Gürbüz and Ufuk Sakarya, A Short Survey of Hyperspectral Remote Sensing Applications in Agriculture, pp 171-175, 2013 Mattia Marconcini, Diego Fernández-Prieto, and Tim Buchholz, Targeted Land-Cover Classification, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, pp 1-21, 2013 Calvin Hung, Juan Nieto, Zachary Taylor, James Underwood and Salah Sukkarieh, Orchard Fruit Segmentation using Multispectral Feature Learning, RSJ International Conference on Intelligent Robots and Systems, pp 5314-5320, 2013 Andreas Merentitis, Christian Debes and Roel Heremans, Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff, IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, pp 1-14, 2014.

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Investigation of Physical, Mechanical and Biodegradation Properties of Nitrile Butadiene Rubber by Natural Polymers and Nano- Silica Particles a

M. Mousavi, bO. Arjmand, cH., Mostajabi, dH., Shooli Department of Industrial Polymer Engineering, Science and Research Yazd Branch, Islamic Azad University, Yazd, Iran. b Nourabad Mamasani Branch, Islamic Azad University, Nourabad , Iran. Department of Industrial Polymer Engineering , Shiraz Branch, Islamic Azad University, Shiraz, Iran. d Department of Chemical engineering , Chemical Industries, Islamic Azad University, Omidiyeh Branch, Omidiyeh, Iran a

Abstract: Nitrile butadiene rubber (NBR) as a high consumption of plastic is widely utilized in the plastics packaging industries. This research concerns physical, mechanical properties and also biodegradation of Nitrile Butadiene Rubber, which to be composed with the natural polymers such as starch and glycerol. Silica nano-particles were used as filler to improve the physical and mechanical properties of NBR. NBR is a complex of Acrylonitrile and Butadiene copolymer that is produced by the emulsion method. To evaluating its physical and mechanical properties, stretching and bending and impact tests were used and the results show some improvement in these properties. Our observations show that temperature plays an important role as main factor in order to improve the mechanical properties of the nano-composites. Obtained results indicate that mixing arrangement does not influence on the mechanical properties of the nano-composites. XRD x-ray diffraction was performed and nano-composites morphology by scanning electron microscopy (SEM) was carried out. Keywords: Acrylonitrile butadiene, Nano-silica, Starch, Morphology, Composites. I. Introduction Economy and rising fuel require in various fields, has increased demand for lightweight materials such as polymers. But due to the lower strength of polymers compared to metals, it seems necessary to strengthen them. Reinforced polymers with common materials is caused harm into two main features of polymer, hence lightness and ease process ability. Therefore, in recent researches small amounts of nano-particles (less than 10% by weight) are used as reinforce of the polymers. Nylon 6 was the first polymer that was used for the preparation of nano-composites by Toyota in 1990. Nowadays, thermoset polymers such as epoxy, poly amid and thermoplastic polymers such as polypropylene, polystyrene, acrylonitrile Styrene are used as the context substance of this composite material. Reinforcing phase is used in nano-composites, including nano-particles, nano-sheets, nanofibers and nano-tubes. Nano-particles have the most usage as reinforcing materials in nano-composites. Clay (Nano-clay) is a nano-particle which is often used in preparation of nano-composites. But recently, other nanoparticles are also used such as silica, metallic nano-particles and organic and inorganic particles. In the development of multi-component materials in nano or micro scale three independent topics should be considered: components selection, production, processing and performance. In general there are three methods to produce polymer matrix nano-composites. These methods include direct mixing, processing solution, and situ polymerization [1,2]. One of the main aspects in preparation of different nano-composites to achieve high quality is in dispersion of the fillers of them which causes physical and mechanical properties increases somewhat and this topic verifies for many thermoplastic and thermosetting polymers [3,4]. Nitrile butadiene rubber in an emulsion process while it is combined with liquid contains nonpolar solvents such as mineral oil or oil, grease, tar, bitumen, gasoline hydrocarbons, etc it will have great swelling. Silica is used as important filler for rubber, this substance is used to reinforce tires. Nitrile butadiene rubber is a holistic, affordable and flexible rubber in all aspects and also nowadays are widely used in wastewater application programs according to the resistance against non-polar solvents. [5,6,7] Yoon et al. investigated the effect of treating layered silicate fillers with a surfactant to obtain an exfoliated structure and so to improve the mechanical properties of nitrile rubber (NBR) vulcanizates. They considered the modification of MMt with amine derivatives and Kim et al. Also, they analyzed the behavior of three amino surface modifiers in NBRâ&#x20AC;&#x201C;MMt composites, concluding that the insertion of the modifier with the longest chain (octadecylamine) gives a greater increase in the interlayer distance, plus better mechanical properties [8-9]. Chakraborty et al. remarked that Nitrile rubber (NBR) is a special purpose rubber and can be used in several applications that require oil resistance. There are several research papers that have reported enhanced mechanical properties for NBR nano composites [10]. Nah et al. reported that

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mechanical properties of NBR could be improved using long chain surface modified montmorillonite [11] The transport coefficients for the nano composites are considerably lower than those of the unfilled NBR. The diffusion of the penetrant solvent depends on the concentration of free space available in the IER matrix to accommodate the penetrate molecule [12]. The main objective of this research work is to evaluate of Physical, Mechanical and Biodegradation properties of Nitrile Butadiene Rubber using natural polymer and this study addresses nano silica particle as a filler in order to improve it s properties. II. Martial and Method Nano-composites of acrylonitrile butadiene rubber / starch / silica nano-particles including : 1- chamber's temperature )542 ° C) , 2- rotating round (80 rpm) , 3- N1's mix order were put in the dryer for 12h at 80 °C before mixing silica nano-particles. Then, according to mentioned mix order and designed formulations, obtained samples were processed in internal mixer. Then required Fragments were prepared for pressure molding process after cooling. Obtained fragments from the process stage were dried for 12h at 80 °C to absorb moisture and then were prepared for the pressing in pressure molding machine. Terms of pressure molding machine was done at 230°C and 25MPa. The samples were obtained for the various tests in order. This test was performed according to ASTM D638 standard and with strain rate of 20 mm per minute. From this test Young's modulus (E), tensile strength ( σb), length increase until rupture (εb) were obtained. This test is used in order to assess the amount of energy absorbed in effect of fracture. This test is done in two ways Isod and Charpy. In this research, in order to evaluation of fracture energy, impact isod test according to ASTM D638 standard was used. To evaluate how to place nano silica plates in polymer matrix X-ray diffraction test was used. Mobility rate was 0.2 degree per minute that was performed in the range of 2θ equals to 1 to 10 degrees of centigrade.

Cloisite 30B S1 S2 S3 S4 S5 S6 S7 S8 S9

Figure 1: XRD curve of pure nano-silica and NBR and produced starch containing 3 weight percent of silica nano-particles. Table 1: The results of XRD test 4

2θ , °

Sample ID

5ө

4.61

4.53

4.49

4.39

4.01

3.84

4.21

3.53

3.17

d001

18.1

19.49

18.72

52.11

55.25

52.1

52.82

52.1

52.98

III. Results and Discussion A. Impact Resistance Tensile test based on ASTM D638 standard by using ZWICK 1458 puller under environmental conditions with speed of 10 mm per min and Isod impact test on samples without a notch based on ASTM 256 standard by using Toyoseiki Impact machine under room temperature were performed. The results of impact resistance test of alloyed samples are presented in Table 3 and Figure 2. As are shown with the increase of NBR’s percentage, impact resistance will be increased. Also the results of Impact Resistance test by adding starch to the alloyed samples are provided in table 3 and figure 2. Table 2: Impact resistance properties of the alloys Samples's Number

Impact Resistance (J/m2)

129832

185835

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527824

551817

522878

260 250 240 230 Impact 220 Resistance (J/m2) 210 200 190 180 170 0

Samples number

Figure 2: The effect of NBR percentage on sample's impact resistance As is seen in figure 2 with NBR increasing, impact resistance has increased and gained greater flexibility. Table 3: Impact resistance properties of the alloys Samples's Number

Impact Resistance (J/m2)

572891

177852

22829

285 260 235 210 Impact 185 Resistance (J/m2) 160 135 110 85 60 0

Samples Number

Figure 3: The effect of Starch percentage on sample's impact resistance As is shown in figure 3 with starch increasing, impact resistance has decreased and gained less flexibility then it will be broken quickly. Table 4: Impact resistance properties of the alloys by adding silica nano-particles Samples's number

Impact Resistance (J/m2)

341855

432893

753842

B. Tensile Properties The obtained results of tensile test are presented in Table 4 and Figures 3 to 4 and the achieved results after adding starch into alloyed samples are provided in Table 5 and Figures 5 to 6.

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Table 5: Tensile properties alloyed samples Samples's number

Tensile strength to break (Mpa)

Elongation at break (%)

Elastic Modulus (Mpa)

1 5 3 4

1318277 1798429 1228823 1828377

582 485 382 782

5248927 4728223 5428825 358722

1328392

482

88925

640 600 560 520 Impact Resistance 480 (J/m2) 440 400 360 320 0

2 Samples number

Figure 4: Effect of silica's nano-particles percentage on the elongation 200 190 180 170

Tensile strength 160 (MPa)

150 140 130 120 0

3 4 Samples number

Figure 5: Effect of silica's nano-particles percentage on the module. As are shown in figures 4 and 5 with increasing percentage of silica nano-particles into rubber made composites' modulus is reduced , impact resistance and also elongation at breaking point are increased. Table 5: Tensile properties alloyed samples Samples's Number

Tensile strength to break (Mpa)

Elongation at break (%)

Elastic Modulus (Mpa)

1228154

482

4258832

1478222

282

848993

1478525

389

5228228

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500 450 400 350 Elastic 300 Modulus 250 (MPa) 200 150 100 50 0 0

Samples number

Figure 6: Effect of starch's percentage on the samples's modulus 7 6 Elongation 5 at Breaking point (%) 4 3 2 0

3 4 Samples number

Figure 7: Effect of starch's percentage on the elongation of samples As are shown in figures 6 and 7 with increasing percentage of starch made composites' modulus has greatly reduced and modulus has increased by increasing the starch amount and impact resistance and also elongation at breaking point have decreased. C. Bending Test Bending test is useful for quality control and specification determine. The bending properties of polymers are especially important for both designers and parts manufacturers. Made stresses in the sample mixture caused by pressure and tensile stresses. In thick samples cutting forces are also producing. The bending properties determine according to the stresses and strains that is produce in the outer surface of a sample. One of the bending test advantages over other tests (stretching), is high deformation at low stress levels and easier strain measuring. In figure 8, the maximum bending is 2.921 MPa and this amount of bending in the subsequent figures according to their combination percentage changes and gradually becomes more. In figure 9, the maximum bending reaches to 2.921 MPa and carefully can be realized that in this figure the elastic module increases to a remarkable extent. In Figures 10 and 11 the maximum bending reaches into 3.829 MPa and 4.460 MPa respectively. D. Tensile Test Tensile test was performed based on ASTM D638 standard by using ZWICK 1458 tensile apparatus under ambient conditions with a speed of 10 mm per min under room temperature. E. Impact Test Isod impact test was performed over samples without a notch based on ASTM 256 standard by using Toyoseiki impact apparatus under room temperature.

Figure 8: Bending test sample 5

Figure 9: Bending test sample 6 with 1% nano silica

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Figure 10: Bending test sample 7 with 2% nano-silica F.

Figure 11: Bending test sample 8 with 3% nanosilica

Tensile Test

Figure 12: Tensile Test sample 5

Figure 13: Tensile test sample 6 with 1wt% of nano silica

Figure 14: Tensile test sample with 2wt% of nano-silica Figure 15: Tensile test sample 8 with 3wt% of nano-silica Table 6: Tensile Test, starch and NBR glycerol and nano-silica Sample

Maximum tensile (MPa)

Breaking (mm)

Speed (mm/min)

Elastic modulus (MPa)

yield point (mm)

Maximum yield point (MPa)

Energy (J)

38.312

1.982

259.223

1.711

132.822

1.434

22.942

5.159

472.223

1.951

179.429

5.253

24.272

5.823

477.423

1.273

512.885

3.223

73.512

3.933

25.158

3.239

522.279

5.198

G. Test results of electron microscope (SEM) In order to investigate the level and microstructure of nano-composites that containing nano-silica and starch natural polymer the samples surfaces was imaged by electron microscope. Images show the lesions and rupture of the polymer phases caused by separation of chain by increasing percentage of starch. In general, polymer chains are also torn apart by separation of starch from polymer matrix that this phenomena is due to the separation of starch from polymers.

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Figure 16: Image of SEM for sample containing 5 wt% of starch and 18.7 wt% of NBR

Figure 17: image of SEM for sample containing 5 wt% of starch and 17.4 wt% and 2 wt% of NBR nano-silica

Figure 18: Image of NBR for sample containing 5 wt% of starch and 3 wt% of nano-silica, 14.8 wt% and 3 wt% of NBR nano-silica IV. Conclusion By increasing percentage of starch, made composite's module has decreased highly and again by increasing the amount of starch, module has increased and impact resistance and also elongation at break point have decreased. But by adding silica nano-particles, physical and mechanical properties are improved slightly and the best situation is when 3 wt% of nano-silica is added to the composite as a filler. In this case, the tensile-strength properties and impact is increased to 3 weight percentage of nano-silica. By increasing low percentage of starch this elastomer which has a wide application in the automotive industry can be converted to biodegradable polymer that in the following investigation this case will be checked. Electron microscopic examinations somewhat show that this composite, while starch and silica nano-particles were added to the sample, also can has biodegradation properties. V. References [1] [2] [3]

A.Khazrai., Working mixture in the polymer industry, ) 2009( M., Edrisi ., Three thousand and sixty method of making nano-materials and chemical product (2010 ) K.T. Gillen, R. Bernstein and M.H. Wilson, Polymer. Degrad. & Stability J., (87) 2005

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Effect of fenoxycarb, a juvenile hormone analogue, administration to the second instar larvae of rice moth, Corcyra cephalonica Staint. (Lepidoptera: Pyralidae) Akansha Singh and S. K. Tiwari* Department of Zoology, D.D.U. Gorakhpur University Gorakhpur- 273009, U.P. (India) Abstract: The 2nd instar larvae of Corcyra cephalonica were exposed to 0.001, 0.005, 0.01, 0.05, 0.10, 0.50 and 1.00 ppm concentrations of fenoxycarb and their insecticidal activities were evaluated. The higher concentrations of this compound severely disrupted the metamorphosis of C. cephalonica. The significant difference in larval mortality, pupation, pupal mortality and adult emergence in comparison to their control were observed. At 0.50 and 1.00 ppm concentrations of fenoxycarb there was 100% suppression of adult emergence. Thus, fenoxycarb at these higher concentrations behaves as insecticides that severely hamper the normal growth, development and metamorphosis of C. cephalonica. This juvenile hormone analogue may be used for the effective control of this pest in particular and Lepidopterous pests in general. Key Words: Second instar larvae, metamorphosis, stored cereal pest, fenoxycarb I. Introduction There are various species of Lepidoptera known to infest the stored cereal commodities. The rice moth, Corcyra cephalonica (Staint.) is one of the major pests of stored cereals and cereal commodities in Asia, Africa, North America, Europe and other tropical and subtropical regions of the world. This moth was first identified and reported by Stainton [1], who named it Melissoblaptes cephalonica. Later, Ragonot [2] gave it the generic name Corcyra. The only recognized species of this genus is cephalonica. Ayyar [3] made the first record of Corcyra cephalonica. This moth is believed to be of eastern origin but has become a cosmopolitan species. Its larval stages cause serious damage to rice, gram, sorghum, maize, groundnut, cotton seeds, peanuts, linseeds, raisins, nutmeg, currants, chocolates, army biscuits and milled products [4], [3], [5], [6], [7], [8], [9], [10]. The damage to the stored products could cause weight loss, detriment in quality and reputation. Being a r-selected species, insects are capable of establishing large population within very short time which may manifest the damage more. The loss of weight due to a single larva may be small, only a few milligrams, but with populations measured in millions this would be a remarkable amount. The loss of quality, however, is very important. Therefore it becomes essential to prevent/control the insectâ&#x20AC;&#x2122;s infestation from the very beginning. Ordinarily, the control measures in stores are based on fumigation with chemicals like hydrogen phosphate. Residues and insect resistance are reasons for potentially limiting the use of fumigation with chemicals in the near future [11]. Now a days, alternative methods are being appreciated. One of the alternatives may be the inclusion of insect growth regulators (IGRs). These compounds are highly effective against various insects attacking stored products and other pests that have become resistant to organic insecticide. There has been a renewed interest in IGRs usage, specifically in the capacity as grain protectant treatment, surface treatments, as well as aerosol and fogging treatments in the interior of food storage structures [12]. There are three types of IGRs: juvenile hormone analogue (agonists), ecdysteroid agonists and chitin synthesis inhibitors [13]. IGRs with juvenile hormone activity also known as juvenile hormone analogues (JHAs), are nonpoisonous and do not bioaccumulate, therefore they generally do not persist for prolonged periods in the environment. IGRs have been shown to generally have a good margin of safety for most non-target biota, as they display a very low toxicity for humans and other mammals, are readily biodegradable (i.e., very low persistence in the environment), highly toxic to target insects, and leave no hazardous residues, making JHAs very useful in food preservation and storage [14]. Fenoxycarb (a juvenile hormone analogue) is a non neurotoxic carbamate, which was discovered in 1981 and was introduced by R Maag AG [15]. It was the first JHA compound introduced to control agricultural pests [16]. It has shown JHA activities against insects in several orders including Lepidoptera, Coleoptera,

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Homoptera, Dictyoptera, Diptera, and Orthoptera [17] (Grenier and Grenier 1993), but also exhibits some non JHAspecific effects on many insects [18] (Retnakaran et al. 1985). An experiment was conducted to evaluate the effects of fenoxycarb on the life cycle stages of rice moth, C. cephalonica when treated as 2nd instar larvae. The objective of this study was to determine the lethal and sublethal effects on survival and development of larvae, pupae and the effects on the growth duration and longevity of adults under laboratory conditions. Such knowledge may devise ways and means for the effective control of C. cephalonica in particular and lepidopterous pests in general. II. Materials and methods Corcyra cephalonica (Staint.) adults were obtained from already existing laboratory stock culture maintained on normal dietary medium composed of coarsely ground jowar (Sorghum vulgare) mixed with 5% (w/w) powdered yeast inside large glass containers (150 mm diameter, 200 mm height) at temperature 26 ± 10C, relative humidity (RH) 93 ± 5% and a light regime of 12 hrs light and 12 hrs darkness. Such standard culture was maintained throughout the year. From this culture whenever needed, newly emerged males and females were transferred to oviposition glass chambers (35 mm diameter, 200 mm height). Since, C. cephalonica individuals do not feed during their adult stage, no food was provided to them during their confinement in these vessels. Eggs laid by the females were collected and then placed in glass chambers for hatching.Fenoxycarb (C17H19NO4), Ethyl N-[2-(4- phenoxy phenoxy) ethyl] carbamate, a non terpenoid juvenile hormone analogue, P-686N, used throughout the experiment was obtained from AccuStandard, New Haven, CT 06513, USA. For the preparation of different concentrations of fenoxycarb in dietary media, a stock solution of known concentration of JHA was prepared by dissolving it in acetone and then adjusted via serial dilutions to achieve its required concentrations. Now required volume of different concentrations of fenoxycarb was thoroughly mixed with the required quantity of normal food (roughly ground jowar mixed with 5% w/w yeast powder) to get different desired concenrations i.e. 0.001, 0.005, 0.01, 0.05, 0.10, 0.50 and 1.00 ppm. This treated food was then air dried to eliminate completely the acetone. For control purposes, the normal food were mixed with a definite volume of acetone similar to that of JHA mixed experimental solution and then air dried in the same way. To evaluate the toxicity of fenoxycarb on the ontogeny of C. cephalonica when exposed as 2nd instar larvae, freshly hatched larvae of C. cephalonica were allowed to feed on normal dietary medium (kept inside 250 ml beakers) for exactly 9 days. On 10th day 25, 2nd instar larvae were kept in each beaker containing 50 grams of dietary medium mixed and treated separately with different known concentration of fenoxycarb. Experiment was conducted on seven different concentrations of fenoxycarb (0.001, 0.005, 0.01, 0.05, 0.10, 0.50 and 1.00 ppm). Twenty five 2 nd instar larvae were also kept on normal dietary medium. All sets of experiments were kept at the temperature, relative humidity and photophase, as mentioned earlier. After completion of developmental cycle, the percent adult emergence and percent pupal mortality was observed and on that basis percent pupation and percent larval mortality was calculated. The developmental course and external morphology of larvae, pupae and adults were also observed. Adult mortality was also noted up to 24 hrs of emergence. The corrected total mortality was calculated by Abbott’s formula [19]:% experimental mortality - % control mortality Corrected total mortality = 100 X 100 - % control mortality Experiments were replicated six times and values have been expressed as mean ± SEM. Student t-test was applied to observe the significance of difference from their control. III. Results Table 1 represents the toxicodynamic effects of fenoxycarb on the ontogeny of rice moth, Corcyra cephalonica when exposed at 2nd instar larval stage. A significant larval mortality was obtained with the increase of fenoxycarb concentration in the diet. At 0.001 ppm concentration of fenoxycarb larval mortality was 4.00 ± 1.46% while 100% larval mortality was recorded at 1.00 ppm concentration of this compound. As the fenoxycarb concentration increases in the diet, a significant enhancement in pupal mortality occurs. 96.00 ± 1.46% pupation was recorded at 0.001 ppm concentration which decreased to 32.00 ± 4.62 % at 0.50 ppm concentration of fenoxycarb. At the same time 5.56 ± 0.89% pupal mortality was recorded at 0.001 ppm concentration of fenoxycarb, which increased to 100% at 0.50ppm concentration of this compound. A significant reduction in adult emergence was recorded following of increased concentration of fenoxycarb. At 0.001 ppm concentration of fenoxycarb 90.67 ± 1.69% adult emergence was recorded that decreased to 34.00 ± 2.68% at 0.10 ppm concentration of fenoxycarb. Along with above mentioned facts we also found that there were delayed emergences of adults as the concentrations of fenoxycarb increased in diet. At 0.05 and 0.10 ppm many of emerged adults were abnormal.

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Degree of abnormalities ranges from no morphological distinguishing clue for male and female, unfolded or twisted wings, twisted legs and abnormally long abdomen in males and too much swollen abdomen in females. Majority of abnormal adults were died within 24 hours of their emergence. However, the normal and quite healthy adults were also emerged along with abnormal ones at all concentrations where adult emergence occurred but their percentages were decreased with increased concentrations (not shown in table). It is noteworthy that females had slightly prolonged growth duration than males in control as well as in treated groups. In addition, the higher concentrations of fenoxycarb i.e. 0.01, 0.05, 0.10, 0.50 and 1 ppm produced giant larvae, supernumerary larvae, larval-pupal intermediates and abnormal pupae. We have considered those larvae as larval-pupal intermediate that were able to form cocoon but failed to form pupae inside cocoon. The abnormal larvae after a variable period of time stopped feeding and eventually died. IV. Discussion In the present investigation fenoxycarb, a non terpenoid juvenile hormone analogue, caused a significantly dose dependent enhancement in larval and pupal mortality and a similar associated dose dependent reduction in pupation and adult emergence of C. cephalonica when treated at the later period of 2nd instar larvae in the dietary media. Recently similar findings have reported by Chandra and Tiwari [20] against 3rd instar larvae of Ephestia cautella with methoprene at relatively higher concentration. Fenoxycarb being a carbamate caused a very different influence on the life-span of C. cephalonica. It showed the potential for prolonging the larval stages and formation of supernumerary larvae or larval-pupal intermediates which were also achieved by Moreno et al. [21] when 0.1, 1 and 10 ppm of fenoxycarb was applied topically to Ephestia kuehniella Zell.Similarly Kostyukovsky et al. [22] reported that 0.1, 0.5, 1, and 2 ppm of pyriproxyfen (a fenoxycarb derivative) caused 100% larval mortality and prolongation of life-span of insecticide susceptible and actellic resistant strain of Tribolium castaneum when treated in food medium from egg laying. Extension of life stages of C. cephalonica, in the present investigation, corresponds to the results of [23] with Shydroprene on oriental cockroach, [24] with R334 on Bombyx mori, [25] with pyriproxyfen on Plodia interpunctella and [26] with pyriproxyfen on Plutella xylostella. In holometabolous insects, the developmental switch between juvenile and adult forms depends on juvenile hormone (JH), a sesquiterpenoid produced by the corpora allata gland [27]. The presence of JH in pre-final larval instars ensures that the next molt, promoted by ecdysteroids, produces another, only a larger larva [28]. Parthasarathy and Palli [29] also reported that the presence of JHA during the penultimate or final instar larvae of T. castaneum blocked larval- pupal metamorphosis and induced supernumerary larval molts. At an appropriate stage, a natural drop of JH secretion permits metamorphosis. At this critical time if excess of JHA is provided to insect, it may disrupt normal developmental pathway and cause repetition of larval or pupal instars respectively [30], [31] and [32]. Due to increased percentage of larval mortality and prolongation of larval period, there were decreased percentages of pupation with increase in fenoxycarb concentration. In Lepidoptera the low concentration of juvenile hormone coupled with 20-hydroxyecdysone titers promotes larva to pupal moults [33]. Due to excess of JHA in insect body only a small percentage of larvae were able to metabolize this unusual high concentration of JH in its body and got success to reach at pupal stage in dose dependent manner. Decreased pupation along with increased pupal mortality, with increase in concentration of fenoxycarb was also achieved by Moreno et al. [21] on E. kuehniella and Liu and Chen [34] on Chrysoperla rufilabris. Application of IGRs often results in pupal mortality either by direct treatment reported by Soltani et al. [35] or by larval treatment. At the beginning of the pupal stage of holometabolous insects, there is an additional JH-sensitive period for pupal versus adult determination that JH must be absent in epidermal cell obligated to adult development [28]. Hence, the presence of JHA at this critical time, resulted in the production of deformed pupae and adults of C. cephalonica. We found the reduced percentage of emerged adults with increased concentration of fenoxycarb. Fenoxycarb also caused abnormalities in adults such as formation of larvoid adults or adults with twisted wings and twisted legs. However the normal and quite healthy adults were also emerged along with abnormal ones at all concentrations where adult emergence occurred but there percentages were decreased with increased concentrations. Abnormalities in adults and decreased adult longevity due to treatment with JHA were also reported by Ghasemi et al. [25] for P. interpunctella. At 0.50 and 1.00 ppm concentrations there was 100% reduction of adult emergence of C. cephalonica. Thind and Edwards [36] also achieved 100% reduction of adult emergence of insecticide susceptible and resistant strains of T. castaneum, Cryptolestes ferrugineus and Oryzaephilus surinamensis at 1 ppm dose level of fenoxycarb, when treated as 24 hrs old larvae. The larvae of C. cephalonica is the most important stage in damaging commodities (as it is the only feeding stage of C. cephalonica and the extension of its development and production of giant larvae would certainly result in more food being consumed. But, at 0.50 and 1 ppm concentration fenoxycarb caused 100% inhibition of the occurrence of adults of this pest. These findings suggest

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that fenoxycarb may be considered as a leading compound for the control of rice moth, C. cephalonica in particular and Lepidoptera pest in general. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

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H. T. Stainton, Description of a New Species of the Family Galleridae, Entomologist’s Monthly Magazine, Vol. 2, 1866, pp. 172-173. E. L. Ragonot, Revision of the British Species of Phycitidae and Galleridae, Entomologist’s Monthly Magazine, Vol. 22, 1885, pp. 17-32. T. V. R. Ayyar, Some Insects Recently Noticed as Injurious in South India, (Ed.) T.B. Fletcher In Report of the Proceedings of the Third Entomological Meeting, Pusa, Superintendent Government Printing, Calcutta, Vol. 1, Feb. 1919, pp. 314-328. F. H. Chhittenden, The Rice Moth, U. S. Department of Agricultural, Bulletin No. 783, July 1919, pp. 1-30. J. W. Munro and W. S. Thompson, Report on Insect Infestation of Stored Cacao. pp: 24. Empire Marketing Board, London, Vol. 24, 1929, pp. 1-40. O. W. Richards and G. V. B. Herford, Insects Found Associated with Cacao, Spices and Dried Fruits in London Warehouses, Annals of Applied Biology, Vol. 17, May 1930, pp. 367-395. W. M. Noyes, Moth Pests in Cocoa and Confectionery, Bulletin of Entomological Research, Vol. 21, Mar. 1930, pp 77-121 G. V. B. Herford, The More Important Pests of Cacao, Tobacco and Dried Fruit. Great Britain Imperial Institute Bulletin, Vol. 31, 1933, pp. 39-55. A. S. Atwal, Agricultural Pests of India and South-East Asia, Kalyani Publishers, Delhi, India, 1976, pp. 502. H. Piltz, Corcyra cephalonica (Staint.), J. Kranz, H. Schmutterer and W. Koch, Disease Pests and Weeds Tropical Crops, Eds. Verlag Paul Parey, Berlin and Hamburg, 1977, pp. 439-440. WMO (World Meterological Organization), Scientific Assessment of Ozone Depletion: Proceeding World Meterological Organization Global Ozone Research and Monitoring Project. Report N. 37. WMO, Geneva, 1994. E. L. Nino, C. E. Sorenson, S. P. Washburn and D.W. Watson, Effects of the Insect Growth Regulator, Methoprene, on Onthophagus taurus (Coleoptera: Scarabaeidae). Environmental Entomology, Vol. 38, 2009, pp. 493-498. H. Oberlander, D. L. Silhacek, E. Shaaya, and I. Ishaaya, Current Status and Future Perspectives of the Use of Insect Growth Regulators or the Control of Stored Product Insects, Journal of Stored Product Research, Vol. 33, 1997, pp. 1-6 H. Tunaz and N. Uygun, Insect Growth Regulators for Insect Pest Control. Turkish Journal of Agricultural Forestry, Vol. 28, 2004, pp. 377-387. C. R. Worthing, Ed., The Pesticide Manual: A World Compendium, Eighth ed., The British Crop Procetion council, Croydon, England, 1987. J. Miyamoto, M. Hirano, Y. Takimoto and M. Hatakoshi, Insect Growth Regulators for Pest Control, with Emphasis on Juvenile Hormone Analogs: Present Status and Future Prospects. In: Pest Control with Enhanced Environmental Safety. Eds. Duke S, Menn J, Plimmer J. ACS Symposium Series, American Chemical Society, Washington, DC, No. 524, 1993, pp. 144-168. S. Grenier and A. M. Grenier, Fenoxycarb, a Fairly New Insect Growth Regulator: A Review of Its Effects on Insects. Ann Appl Biol, Vol. 122, 1993, pp 369–403. A. Retnarkaran, J. Granett and T. Ennis, Insect Growth Regulators, Eds. G. A. Kerkut and L. I. Gilbert, Comprehensive Insect Physiology, Biochemistry, and Pharmacology, Pergamon Press, New York, Vol. 12, 1985, pp. 529-601. W. S. Abbott, A Method of Computing the Effectiveness of an Insecticide. Journal of Economic Entomology, Vol. 18, 1925, pp. 265267. A. Chandra and S. K. Tiwari, Insecticidal Effect of Methoprene on the Pre-Adult Stages of Almond Moth, Ephestia cautella Walker (Lepidoptera: Pyralidae), Journal of Biology and Erath Science, Vol. 3, 2013, pp. 269-274. J. Moreno, N. Hawlitzky and R. Jimenez, Effect of the Juvenile Hormone Analog Fenoxycarb on the Last Larval Instar of Ephestia kuehniella Zell. (Lep., Pyralidae). Journal of Applied Entomology, Vol. 114, 1992, pp. 118-123. M. Kostyukovsky, B. Chen, S. Atsmi and E. Shaaya, Biological Activity of Two Juvenoids and Two Ecdysteroids Against Three Stored Product Insects. Insect Biochemistry and Molecular Biology, Vol. 30, 2000, pp. 891-897. J. P. Edwards, H. F. Corbit, J. E. McArdle, J. E. Short and R. J. Weaver, Elimination of Population of the Oriental Cockroach (Dictyoptera: Blatellidae) in a Simulated Domestic Environment with the Insect Juvenile Hormone Analogue (S)-Hydroprene, Journal of Economic Entomology, Vol. 86, 1995, pp. 436-443. N. K. Sashindran, J. N. Sashindran, and V. A. Vijayan, Alteration in the Primary Metabolites in Three Different Tissues of Silkworm, Bombyx mori L. Under the Influence of a Juvenoid, R394. Caspian Journal of Environmental Science, Vol. 5, 2007, pp. 27–33. A. Ghasemi, J. J. Sendi and M. Ghadamyari, Physiological and Biochemical Effect of Pyriproxyfen on Indian Meal Moth Plodia interpunctella (Hubner) (Lepidoptera: Pyralidae). Journal of Plant Protection Research, Vol. 50, 2010, pp. 416-422. M. Alizadeh, J. Karimzadeh, G. R. Rassoulian, H. Farazmand, V. Hoseini-Naveh and H. R. Pourian, Sublethal Effects of Pyriproxyfen, a Juvenile Hormone Analogue, on Plutella xylostella (Lepidoptera: Plutellidae): Life Table Study. Archives Phytopathology and Plant Protectection, Vol. 45, 2012, pp. 1741-1763. L. I. Gilbert, N. A. Granger and R. M. Roe, The Juvenile Hormones: Historical Facts and Speculations on Future Research Directions. Insect Biochemistry and Molecular Biology, Vol. 30, 2000, pp. 617-644. H. Nijhout, Insect Hormones, Princeton University Press, Princeton NJ, 1994, pp. 267. R. Parthasarathy and S. R. Palli, Molecular Analysis of Juvenile Hormone Analogue Action in Controlling the Metamorphosis of the Red Beetle, Tribolium castaneum, Archives of Insect Biochemistry and Physiology, Vol. 70, Jan. 2009, pp. 57-70. S. Fukuda, The Hormonal Mechanism of Larval Molting and Metamorphosis in the Silkworm. Journal of the Faculty of Science, Section IV. Vol. 6, 1944, pp. 477-532. V. Wigglesworth, The Physiology of Insect Metamorphosis, Cambridge University Press, Cambridge, 1954, pp. 152. C. M. Williams, The Juvenile Hormone, II, Its Role in the Endocrine Control of Moulting Pupation and Adult Development in the Cecropia Silkworm. Biology Bulletin, Vol. 121, 1961, pp. 572-585. T. S. Dhadialla, A. Retnakaran and G. Smagghe, Insect Growth and Development-Disrupting Insecticides, Eds. L. I. Gilbert, I. Kostas and S. S. Gill, Comprehensive Insect Molecular Science, Pergamon, Elsevier, Oxford, UK, 2005, pp. 55-115. T. X. Liu and T. Y. Chen, Effects of the Insect Growth Regulator Fenoxycarb on Immature Chryroperla rufilabris (Neuroptera: Chrysopidae). Florida Entomologist, Vol. 84, 2001, pp. 628-633.

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N. Soltani, S. Chebira, J. P. Delbecque and J. Delachambre, Biological Activity of Flucycloxuron, a Novel Benzoylphenylurea Derivative on Tenebrio molitor: Comparison with Diflubenzuron and Triflumuron, Experientia, Vol. 49, 1993, pp. 1088-1091. B. B. Thind and J. P. Edwards, Laboratory Evaluation of the Juvenile Hormone Analogue Fenoxycarb Against some InsecticideSusceptible and Resistant Stored Products Beetles. Journal of Stored Product Research, Vol. 22, 1986, pp. 235-241.

Acknowledgements The authors thank Prof. R. Singh, Head, Department of Zoology, D.D.U. Gorakhpur University Gorakhpur, for providing laboratory facilities, Mr. Awanish Chandra and Priyanka Tripathi for their kind help during the entire tenure of the work and AccuStandard, New Haven,CT 06513, USA for providing fenoxycarb, P-686N.

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Table 1: Effect of fenoxycarb on the ontogeny of rice moth, Corcyra cephalonica exposed as 2 nd instar larvae Fenoxycarb concentratio n (ppm)

Percent# larval mortality

Percent# pupation

Percent# pupal mortality

Percent# adult emergence

Percent# adult mortality

Percent# total mortality

Corrected#* total mortality

Control

2.67 ± 1.33

97.33 ± 0.93

1.37 ± 0.84

95.33 ± 1.23

4.00 ± 1.03

0.001

4.00 ± 1.46

96.00 ± 1.46

5.56 ± 0.89b

90.67 ± 1.69c

9.33 ± 1.69

5.56 ± 1.76

0.005

6.00 ± 0.89d

94.00 ± 0.89c

13.48 ± 1.74a

81.33 ± 1.98a

18.67 ± 1.98

15.28 ± 2.06

0.01

14.67 ± 1.98a

85.33 ± 1.98a

24.99 ± 2.67a

64.00 ± 2.30a

36.00 ± 2.30

33.33 ± 2.41

0.05

22.00 ± 2.00a

78.00 ± 1.98a

27.35 ± 2.81a

56.67 ± 2.81a

17.40 ± 2.62

53.19 ± 2.23

51.23 ± 2.32

0.10

43.33 ± 3.78a

56.67 ± 3.78a

40.00 ± 3.38a

34.00 ± 2.68a

21.44 ± 4.25

73.29 ± 2.46

72.17 ± 2.56

0.50

68.00 ± 4.62a

32.00 ± 4.62a

100a

100

1.00

100a

100

# Values have been expressed as mean ± SEM of six replicates. a, b, c and d, significantly different (p < 0.001, p < 0.01, p < 0.05 and p < 0.1 respectively) from control, when ttest was applied. Total mortality includes larval mortality, pupal mortality and adult mortality. *Corrected total mortality was calculated by Abbott’s formula (1925).

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