Genotype x Environment Interaction Analysis of IITA Mandate Crops in Sub-Saharan Africa by International Institute of Tropical Agriculture

Genotype x Environment Interaction Analysis of lITA Mandate Crops in Sub-Saharan Africa

Edited by

Indira J. Ekanayake and Rodomiro Ortiz

International Institute of Tropical Agriculture Ibadan, Nigeria

Genotype x Environment Interaction Analysis of lITA Mandate Crops in Sub-Saharan Africa

Edited by Indira J. Ekanayake Consultant Physiologist liTA, Ibadan, Nigeria

and Rodomiro Ortiz Director, Genetic Resources and Enhancement Program ICRISAT, Patencheru, India.

Intemationallnstitute of Tropical Agriculture (liTA)

August 2000

Preface Genotype x Environment Interaction Analysis of IITA Mandate Crops in SubSaharan Africa has been produced from a set of commissioned papers contributed by the scientists (present and past) of Crop Improvement Division of the International Institute of Tropical Agriculture (IITA). As indicated in the introductory chapter: " ... G x E interaction is an important issue in crop improvement efforts . . . Plant breeders need to be constantly aware of it, and to have it quantified, to define strategies and target environments." The book is therefore focused on the research approach followed by !ITA to improve its manciatecrops in Africa and the tools used to achieve this objective. This book has been divided into two sections followed by annexes. The first section, an overview on G x E interactions has seven theme papers. The first paper provides the rationale and think-tank deliberations made at a related workshop regarding the state-of-art in G x E analysis, and advancement in crop improvement activities at IITA. The other six chapters deals with specific topics but from different perspectives regarding 1he tools used and approaches used to manage G x E interaction of mandate crops (for example social sciences, agronomy, physiology, and food science). The second section includes seven papers providing research advances on the subject for UTA mandate crops (plantain and bananas, cassava, yarns, cowpea, maize, and soybean). The maps of the agroecological zones and agroclimatic suitability of nTA mandate crops in sub-Saharan Africa are included in the annexes.

The papers included in this volume provide an update on G x E interaction studies with special emphasis on sub-Saharan Africa and provide examples of some of the most important crops in this region. We hope that the readers fmd this information useful. We thank the contributing authors for investing their work time in this endeavor. Without their commitment this book could not have been possible. We are grateful to those IITA scientists and Dr. F.M. Quin former Director of Crop Improvement Division who assisted in technical editing at various stages, and Ms. A. Moorhead who handled the managing editorial task. We also thank the members of IlTA Information and training program staff for their various contributions to the production of this book.

Rodomiro Ortiz Director Genetic Resources and Enhancement Program ICRISAT Patancheru,lncua

Indira 1. Ekanayake Consultant Physiologist IITA Ibadan Nigeria

iii

List of Contributors M.O. Akoroda, Uruversity ofIbadan, Ibadan, Nigeria (m.akoroda@cgiar.org) M. Bokanga, rITA, Ibadan, Nigeria [m.bokanga@cgiar.org]. RJ. Carsky, IITA Benin Research Station, BP 08-0932,Cotonou, Republique du Benin [r.carsky@cgiar.org]. HK Crouch, llTA High Rainfall Station, Onne, PMB 008, Nchia-Eleme, Port Harcourt, N igeria [present address - crouch@globalnet.co.uk ]. J.H. Crouch, IITA High Rainfall Station, Onne, PMB 008, Nchia-Eleme, Port Harcourt, Nigeria [present address 路 crouch@globalnet.co.uk ].

K.E. Dashiell, UTA, Ibadan, Nigeria [k.dashiell@cgiar.orgJ. 1.1. Ekanayake, I1TA, Ibadan, Nigeria [i.elcanayake@cgiar.org]. CA. Fatokun, IITA, Ibadan, Nigeria [c.fatokun@cgiar .org]. R.S.B. Ferris, lITA Eastern and Southern Africa Regional Center (IITA路ESARC), P.O. Box 7878, KampaJa, Uganda [sferris@imul.com) . S.S. Jagtap, UTA, Ibadan, Nigeria [present address - Department of Engineering, University of Florida, Gainsville , USA]. R. Kapinga, IITA, Ibadan. Nigeria [present address - Ukiriguru Research Institute, Box 1433, Mwanza, Tanzania. Rlcapinga@tanz.healthnet.org]. I.N. Kasele, UTA, Ibadan, Nigeria [present address - USAID/SADC/CIP-SARR..l\lET, c/o INIA, FPLM Malvane, CP 2100, Maputo, Mozambique. J. Kling, UTA, Ibadan, Nigeria [j.lcling@cgiar.org]

V.M. Manyong, IITA, Ibadan, Nigeria. [v.manyong@cgiar.org]. D .H. Mignouna, [ITA, Ibadan, Nigeria. [d.mignmma@cgiar.org]. N.Q. Ng, UTA, Ibadan, Nigeria. [q.ng@Cgiar.org). J.U . Okoro, UTA High Rainfall Station, Onne, PMB 008, Nchia-Eleme, Port Harcourt, Nigeria. D.K. Ojo, rITA, Ibadan, Nigeria.

R. Ortiz, lITA High Rainfall Station, Onne, PMB 008, Ndlia-Eleme, Port Harcourt. Nigeria [present address - International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, Andhra Pradesh, India. r.ortiz@cgiar.org ]. P.O. Oyekan, llTA. Ibadan, Nigeria.

F.M. QuiD, I1TA,lbadan, Nigeria [present address - Flat 1, 8 Woodville Gardens, London W5 2LG, England fmq@fmquin.demon.co.uk).

B.B. Singh, IITA Kana Station, PM 3112, Sabo Bakin Zuwo Road, Kano, Nigeria [iitakano@cgiar.org] M.N. Versteeg, IITA, Abomey-Calavi, Republic of Benin [GTZ project, Serere, Uganda]. D.R. Vuylsteke, IITA-ESARC, P.O. Box 7878, Kampala, Uganda. [dvuylsteke@imul.com).

Contents List of Contributors ....... ....... ........

•

•• •

•

0 ..

........... .

.....

•••

•

••

..... 0

......

Section 1: Overview (Theme) papers 1.

Genotype x Environment Analyses Provide Critical Insights for Improvement afilT A Mandate Crops. , ...... .. ... , F. Margaret Quin 0

....

.. .

.. 0

.. .

. . . . . . . . . . ... ..

.. .

.... ..... .. .. ,

Sotio-economic Heterogeneity, International Testing of Germplasm and Genotype x Environment Interaction .... ....... Victor M. Manyong 0

... 0

....

0 .... 0

.... .

.......

......

Assessment of Genotype x Environment Interaction and Role of Physiological Analyses for Crop Breeding and Yield StabiUty .... ... . , . ..... ... .. .. ..... .... . ... ....... .... .. Rodomiro Ortiz and Indira J Ekanayalce 0

.. .. ..... 0 .... ....

...

••••••

•••

•

••

•

•••

•

•• ••••

•

Genotype x Environment Interactions aod its Analysis in Germplasm Characterization and Evaluation . ................ .. ..... ..... ... ... ......... ... ,.... .. ....

Rodomiro Ortiz and N. Quat Ng 5.

Marker Assisted Selection aDd the Implications of Genotype Interaction ... .. ..... . 0

...................... .... ...

...... ..

.... ....

Environment

...........

.....

.... 0

lonathan H. Crouch. Hitokshi K. Crouch. Christian A. Fatolam. and Jacob DoH. Migouna 6.

Farmer-Managed On-Farm Testing: Approach and Genotype x Environment Considerations .. , ..... ,. Robert J. Carsky and Mark N. Versteeg

0 .... 0

•

0 ......... ..

The Value of Crop Quality Evaluation and End-User Response in Genotype x Environment AnalYltS ................... R.S.B. Ferris. M. Bo/canga, R. Ortiz and Do Vuylsteke 0

.. .......

...

....

0.....

... .. 0

Contents List of Contributun.. .... .. ..... .. .... . ... . .... ..... ... ....... .. .. ... ... ... ... .. .... .. . ...... ....... .

Section 1: Overview (Theme) papers I.

Genotype x Environment Analyses Provide Critical Insi~hts for Improvement of TITA Mandate Crops ... .. .... .. .... ..... . ...... . .... ..... ..... ........... .. .......... .. .... ............. . .. ... .. .. . F. Margaret Quin

SOdo-~onomic

Heterogeneity, International Testing of Germplasm and Genotype x Environment Interaction ....... ,.............. . ........... ........ ...... Victor M Manyong

Assessment of Genotype x Environment Interaction and Role of Physiological Analyses for Crop Breeding and Yield Stability .. .... .. ......... '" . . ... .. ... . . .. .. . . . . . . . ..... . . . .. . . . . ... . . . .. . . .. .. . . . . . . . .. . .. . . . . .. Rodomiro Ortiz and Indira J EkanayaJre Genotype X Environment Interactions and its Analysis in Germplasm Characterization and Evaluation.. ... ..... ..... .. . ...... .. ... .. .... . ....... .. ... .......... Rodomiro Ortiz and N. Quat Ng Marker Assisted Selection and the Implications of Genotype :I Environment Interaction . .... .. ..... . ............ ... ... .... .. . ...... ...... .. .. '" ... '" . .. ... . . . . . . . .. . . . ... Jonathan H. Crouch, Hitolcshi K. Crouch, Christian A. Fatokun. and Jacob DB. Migouna

Farmer-Managed On-Farm Testing: Approach and Genotype x Environment Considerations .... ... .... ........ ... ... ... .. ... .. .. '" Robert J. Carsky and Mark N. Versteeg

The Value of Crop Quality Evaluation and End-User Response in Genotype I Environment Analysis. . .. .... .................................... .......... R.S.B. Ferris, M. Bo/canga, R. Ortiz and D. Vuylsteke

Section II: IITA's Manadate Crops in SSA: Current Status of G x E Approach Stllrchv Creus

8. CuJtivar development, Genotype x Environment Interaction and Multl-site Testing of Improved Plantain and Banana Germplasm In sub-Sabaran Africa .. .... ... ... ... ... . ...... ...... ...... ..... . ... .. . ...... .... .. ...... ...

Rodom iro Ortiz. Jonathan H. Crouch. Dirk R. Vuylsteke. R.Shaun B. Ferris. and Josephin U. a/wro

Environmental CIllllsificatlons and G x E Considerations for Better Adaptation of Cassava 107 Indira J. Ekanayake

10. Implications of G x E Interactions of Cassava under Different Cropping Systems. . .. . . . . ... . . . . .. .. . . . . . . . . . . . . .. . . . . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . .. . . . .

124

Indira 1. Ekanayake, Idumbo N Kasele. and Regina Kap inga

11. First approximation of mapping agriÂŁultural environments for cassava trials. Malaki 0. Akoroda Grpin (Lerum" qnd Cereql)

137

CCODS

12. Evaluation of Genotype x Environment Interactions in Some Soybean Lines.. .. .. .. .. .. .. .. . .. .. .. ... .. .. ....... .. .. .. .... .. .. ........ .......... CA. Fatohn, K.E. Dashiell. P.o. Oyelwn and D.K. 0)0 13. Genotype x Environment Interactions Analysis of Maize at UTA .... ... . ... ..... Jenny G. Kling 14. Breeding Cowpea Varieties for Wide Adaptation by Minimizing Genotype I Environment Interac:tions .. ... .. .. .. .. .... .. .... .. .. .. .. .. ... ... .. .... ....

144

153

173

Annexes I.

The AgroeÂŁological Zones in sub-Saharan Africa.. ..... .. .. .. .. ... .... .. . .. .. .. .. .. .

182

Srikant S. Jagtap

II. List of Abbreviations and Acronyms ........ ...... .. .... .. .. ... , ... . . . . . . . . . .. . . . . . . . . .

194

vii

Section I: Overview (Theme) papers

Quin. 1999. In. G x E analyses of TIT A Mandate Crops

Chapter 1 Genotype x Environment Analyses Provide Critical Insights for Improvement of lITA Mandate Cropsl F. Margaret Quin 1. 1. Introduction 1.2. Various disciplines, test sites and crops 13. Stratified approach 1.4. Sununing'up References

1.1. Introduction Genotype by environment (G x E) interaction is an important issue in the crop improvement efforts for several reasons. Plant breeders need to be constantly aware of it, and to have it quantified, to defme breeding strategies and target environments. Plant physiologists interact with the crop breeders to better understand the G X E interactions.

An understanding of G X E effects could help peasant-subsistence fanners to avoid seasonal variations in yield or output, and the commercial farmers to achieve maximum yields while maintaining the productivity and sustainability of the farming system (Harrison. 1975). As is discussed in more detail below, for various reasons G x E analysis as a research tool has not been fully exploited. The Intemationallmtitute of Tropical Agriculture (UTA) is unique in the Consultative Group for International Agricultural Research (CGIAR) in respect of the number and diversity of crops it covet'S. There are three grain crops: maize (Zea mays L.), cowpea (Vigna unguiculata (L.) Walp .), and soybean (G/ycene max L.), and three vegetatively propagated crops: cassava (Manihot esculenta Crantz) , plantain and banana (Musa spp.), and yam (Dtoscorea spp.). One (plantain and banana) is a perennial crop, while another (cassava) may go through two growing seasons before reaching maturation (this is especially applicable in dry regions and in some high elevation areas). ln addition to individual crops, we have a cropping system mandate for improving system productivity in the sub-Saharan Africa. A workshop on International testing of Gemtplasm and G x E interaction in Sub-Saharan Africa (held in 1994 at UTA) mainly focused on the research approach to improve DTA mandate crops in Africa. Subsequent chapters of this book discuss various too]s used to achieve this objective and advances made in some of the mandate crops. Thus, while procedures for statistical analysis are standard, there is not a single overriding strategy for handling G X E that can be applied across all our mandate crops. The genetics of each crop, its specitic requirements (such as breeding for different agroecologies, cropping systems, and end uses), and decisions on the best means to deliver useful breeding materials to as many clients as possible, especially in Africa, are all factors that influence the strategy followed for each crop. I

A preliminary version of this paper appeared in lITA Research 12:29-32.

Quin. 1999. In. G x E analyses of lIT A Mandate Crops In general, the desire to serve as many clients as possible in an e11icient way tips the balance towards selection for broad adaptation: this means that as materials advance through a breeding scheme, the probability of obtaining a significant G x E interaction decreases. For some situations, however, this strategy is not appropriate, and examining Significant G x E interaction provides a means for local selection to meet specific requirements. In addition, understanding the G X E interaction has the practical advantage of bringing about greater efficiency (time, labor, and costs) in screening and breeding efforts, including rationalization of the number of representative trial sites required to achieve specific breeding targets. lITA has been criticized in the recent past for not paying adequate attention to G x E interactions. N.W. Simmonds in his review of Volume 1 ofmA's 25 years publication wrote that "however, the interaction of varieties with environments (of all kinds) though more or less recognized, were not really assimilated and the phrase 'G x E interaction', universal in technical literature does not, I think, appear." During the 1994 workshop it was clearly shown that we at IITA have rational strategies in place for how we handle G x E, and that we have an appreciable amount of data to support these strategies. This boole also update information on G x E effects since the ' Physiology Program Formulation Workshop' was held at IITA in April 1975 (UTA, 1975). This book attempted to put this information together in the form of position papers and review papers, and discussions thereon. A brief synthesis of all of these chapters is presented below.

1.2. Various Disciplines, Test Sites and Crops 1.2.1 Disciplines involved Our findings were enriched by the fact that crop improvement scientists (in some circles, a synonym for breeders) did not function in isolation. Various disciplines related to the practice of agronomy were represented in the discussions, including plant physiologists, crop protection scientists, socio-economists, modelers, as well as some interesting contrasts among other crops. It is evident from various theme papers presented in this book that the knowledge base for G X E analysis, is rather mighty. Combined with computerization and large data sets available, highly sensitive analysis could be perfonned to make strong decisions on managing G X E effects.

1.2.2. Test sites and crops A perennial crop like plantain (AAB) is known to perfonn well in the lowland wet tropics, while cooking bananas (ABB) show better tolerance to dry environments. Bananas are found not only in the forest but also in the forest-savanna transition zone, and even on alluvial valley soils in the moist savannas. What expectations should we have, then, fOI the performance of tetraploid hybrids (AAAS) in both wet and drier sites such as Ibadan in Nigeria (transition zone) , and Onne in Nigeria or M 'Bahnayo in Cameroon (both in the forest zone, but differing in rainfall, elevation, and soil). Our results showed that yields of the hybrids were superior to those of cooking bananas and the plantain female parent in the forest zone, but the hybrids were less well adapted to the transition zone. At Ibadan, the yield of cooking bananas (ABB) was greater than that of selected hybrids (AAAB) .

Quin. 1999. In. G x E analyses ofIITA Mandate Crops Interestingly, there were no differences between Onne and M'Bahnayo, suggesting that one site could be dropped. This raises an interesting aspect of site rationalization. For reasons of efficiency, we would have a case for dropping M'Balmayo, since IITA's plantain and banana improvement program is based at Onne, but from the standpoint of regional, collaborative work, there is a value in keeping M'Balmayo. How, and at what point, does one trade off between the concerns of scientific efficiehcy and institutional factors?

Our G X E analysis of plantain and banana alSo threw up a challenge. Can we identify, in the future, some plantain hybrids with better adaptation to drier conditions? Now that IITA scientists have more breeding materials and new information on Musa genetics, there is some hope of identifying plantain-like hybrids with bemr adaptation to drier conditions, using sites in both the transition zone and the moist savanna. For cassava, our results indicate a considerable challenge in managing G X E. While cassava is a widely adapted crop, individual clones frequently show only limited adaptation. Thus, setting a target of broad adaptation is not particularly efficient in cassava, and it is better to accommodate and capitalize on a regional approach as with Musa . The regional approach is used here in an institutional context. How best does IITA, as an international center, optimize the management of G x E with respect to linkages with colleagues in the national systems, for germpIasm delivery and testing under local conditions?

1.3. Stratified approach The strategy identified uses a stratified approach, consisting of the following : • •

•

Routine estimation of the stability of a trait across environments, Measuring the stability of leading genotypes so as to identify stable parents across environments, and thereby identifying good parents for use by national breeding programs in the region, Conducting site-specific selection to serve local environments (circumstances, end uses), with the responsibility residing, wherever possible, with nationai programs, and Rationalization of siles wherever possible, to improve efficiency.

For maize, the breeding strategy has historically emphasized broad adaptation. Because it is relatively easy to develop maize populations and test them in various ecologies, broad adaptation is a realizable goal, and our G X E analyses for trials with improved TITA cultivars COnflIlll the success of this approach. Given the ubiquitous nature of the crop (grown from the forest to the Sudan savanna and at varying altitudes), enough heterogeneity exists for further adaptation to local conditions. While temperate germplasm included in our trials has performed well in the savannas, where solar radiation is high, it bas not aqapted so well to the insect and disease pressures of the forest zone or the forest-savanna transition. Overall, the data have helped us to better understand biotic constraints to maize production across agroecoiogies, and enabled the selection of key testing sites. In cowpea, an inbreeding crop, lITA ' s past strong strategy has been to select for wide adaptation, incorporating resistances to biotic and abiotic constraints known to ex.ist in cerealbased cropping systems in three targeted agroecologies: the Sudan savanna, the northern

Quin. 1999. In. G x E analyses ofllTA Mandate Crops Guinea savanna, and the Sahel. To achieve this, early generations (F I to F2) are screened in various ways (in the field., screenhouse, and lab) for lcnown biotic constraints of the targeted agroecologies. Then subsequent generations are evaluated at sites in each agroecology, with a view to identify breeding lines that perfonn weU across all sites . This strategy has since been modified, learning from our G X E analysis, which revealed that while our past strategy was quite efficient for the Sudan savanna, it was less so for the other ecologies, especially the Sahel. We have now begun to screen segregating materials (F2 to Fs) in the Sahel, to improve the levels of adaptation to this zone. We are tin'1s moving toward mOle agroecozone-specific breeding on cowpea. Our experience with soybean (also an inbreeding crop) has been quite different. The G X E analysis showed that IITA has done well in selecting for high and stable yields across environments. Perhaps we have been aided in this, by the less challenging nature of the environment with regard to soybean. It fits nicely into a niche or 'window' in the moist savanna growing season. Donnally calling for wet weather during crop growth and drier conditions near the harvest. Furthermore, by the fact that this crop is relatively new in Africa, which means that the insect pest challenge is low at present and disease pressure is fairly unifolTl1, when improved varieties with resistance to Cercospora leaf spot and bacterial pustule are used. This picture could change if diseases or insect pests offer more of a challenge, and we need to be alert to the situation.

Finally, coming back to a vegetatively propagated starchy crop, yam, UTA's strategy for genetic improvement of this crop targets the main production area, the southern Guinea savanna. The climatic component of the environmental term is, therefore, relatively uniform across testing sites. The G x E analysis revealed that crop husbandry practices were at present greatly contributing to the variations in G x E, and that greater control of this aspect should improve the efficiency of selection and enhance the scope for advances in breeding. Although obviously shorter-teIm analysis of multi-location G x E has been done for yams, and is less than for some world crops, such as ·maize and cassava, is by no means negligible.

1.4. Summing up In swnmary, then, where UTA stands today with regard to each of the crops can be stated as follows: • • • • • •

Plantain. Need for hybrids with greater adaptation to drier conditions. Cassava. Stratified approach to G X E. Maize. Broad adaptation accomplished, and a better understanding of biotic constraints in each zone achieved. Cowpea. Need for better adaptation for the Sahel. Soybean. Good progress so far, but need to be alert to changes in disease or pest situation. Yam. Greater control needed in crop husbandry, to derive greater benefit from G X E analyses .

While recognizing that attaining broad adaptation in our mandate crops is an appropriate breeding strategy, we have used G X E analyses as tools in making some modifications in our research approach to attain greater ecological adaptation. Interestingly, implementing these

Quin. 1999. In. G x E analyses ofIITA Mandate Crops modifications bas reinforced research partnerships between international and national partners, in this continent and elsewhere. In some instances, decisions on site rationalization may need to be flexible, in order to strengthen the institutional links.

References Harrison, M.N. 1975 . Summary of discussion sessions. pp. 108-109. In Proceedings of Physiology Program Formulation Workshop. April 1975, International institute of Tropical Agriculture, Ibadan, Nigeria. lITA. 1975. Proceedings of Physiology Program Formulation Workshop . April 1975, International Institute of Tropical Agriculture, Ibadan, Nigeria. 114 pp. Simmonds, N.W. 1994. The [ITA effort. TAA (Tropical Agriculture Association) Newsletter 14(2): 14-15.

Manyong. 1999. Socio-economic heterogenity & gennplasm testing

Chapter 2 Socio-economic Heterogeneity, International Testing of Germplasm and G x E Interaction Victor M. Manyong 2.1. Introduction 2.2. G x E interaction 2.3. Components of the environment 2.4. Detenninants of the socioeconomic diversity References

2.1. Introduction The process of agricultural research broadly involves four steps as illustrated in Fig. 2.1. Characterization of the environments (1) of which, the two main objectives are the identification of constraints (I-a) and targeting technologies to their more appropriate environment (1-b) . Technology development (2) includes both component studies to understand the basic mechanisms and systems studies when all the components are integrated together. Technology testing (3) has two components on-station testing (OST) and on-farm testing (OFT). which may be with or without farmer's participation. Adoption and impact assessment is at the 4th level. While socio-economic studies are well integrated into the research strategies of the steps I, 3 and 4, the involvement of the social scientists in both components and systems studies conducted by biological scientists, at step 2, is yet to be increased. However, G x E interaction gives opportumtIes for socio-economic studies as an important component of the environment to be integrated in any strategy of technology development.

2.2. G

E interactioD

This is to recognize the reciprocal effect of genotype and environment upon each other. A germplasm with a good genotype can not exhibit its potentials within an unsuitable enviromnent. Inversely, a favorable environment can not make any substantial contrIbution to the achievement of the people 's objectives if the gennpJasm cannot perfonn well. Therefore, early incorporation of the diversity of the environment during the process of technology development is likely to increase the probability of producing technologies well adapted to multiple components of the environment.

Manyong. 1999. Socio-economic heterogenity & germplasm testing

Fig. 2.1. The process of agricultural research pathway involving four steps. lao Characterization of E - Identification of constraints and opportunities, lb. Characterization of E - Technology targeting, 2. Technology development, 3. Technology testing, and 4. Adoption and impact assessment studies.

2.3. Components of the environment Often, one distinguishes two categories of environment, 1) biophysical environment such as soils, vegetation, climate and pests, and b) socio-economic environment whose distinguishing feature is human's involvement in generating the diversity of the systems. Diversity induced by socioeconomic factors is created at three steps of the fanning when the human beings accomplish the following activities. These are a) to supply production factors to the systems such as labor, purchased input and technology, b) to determine the crop management practices of the system such as intercropping versus sole cropping, rows planting versus broad casting, early planting versus late planting, and c) to decide on the acceptability

Manyong. 1999. Socio-economic heterogenity & gerrnplasm testing or on the use of the agricultural product: food crop versus industrial crops, food quality requirements (texture, color, bitterness). Internationa.l testing of germplasm and G x E interaction activities cannot ignore the effects of socioeconomic factors on the variability of the factor E.

2.4. Determinants of the socioeconomic di ersity Behind the various activities, which reflect the socioeconomic heterogeneity of the facto r E, few driving forces make farmers systems to be different from each other. Those are the fo llowing: a) Primarily, access to market that determines the input/output ratio, thus conditions the quantity and quality of both purcbased input and technology available for small-scale farmers. For instance, the accessibility of the rural areas to markets (Fig. 2.2) is often poor in many parts of the bumid and sub-humid lowland zones of fourteen countries of West and Central Africa (Maniyong et aL, 1996a, I 996b). b) The land use intensity reflects the effect of many factors namely land scarcity population density land tenure and agronomic practices. Figure 2.3 gives an example of the land use intensity in west Africa and c) Sociological factors such as education, which determines the management of the system.

IeReg ions with Good Access (m .ha)

Reg ions with Fair/Poor Access (m .ha) 1

175.5

Fig. 2.2. Access to markets in rural areas of 14 countries in West and Central Africa (Manyong et aI., 1996a, I996b).

Manyong. 1999. Socio-economic heterogenity & germplasm testing

s::: Q) (.) Q)

0..

80 70 60 50 40 30 20 10

High

â&#x20AC;˘ Moderate

oLow

0 HF

DS /CS

SGS

NGS

Agroecolog ical Zone Fig. 2.3 . Land-use intensity in rural areas of nine countries in West Africa (Manyong et aI., 1996a). Note; HF - Humid forest, DS/CS - Derived savanna/coastal savanna SGSSouthern Guinea savanna GS - orthem Guinea savanna.

While there is quite a good understanding on the effects of the two first driving forces on the process of agricultural intensification, sociological factors still need some basic research to be integrated in an operational framework to the characterization of the environment. Combining the determinants of the socio-economic diversity with the biophysical factors will help to define homogeneous environments for both international testing of germplasrn and G x E interactions with SSA.

References Manyong V.M. , 1. Smith, G.K. Weber, S.S. Jagtap, and B. Oyewole. 1996a. Macrocharacterization of Agricultural Systems in West Africa: An Overview. Resource and Crop Management research Monograph No. 21 . lITA, Ibadan, igeria. Manyong, V.M., 1. Smith, G.K. Weber, S.S. Jagtap, and B. Oyew01e. 1996a. Macrocharacterization of Agricultural Systems in Central Africa: An Overview. Resource and Crop Management research Monograph o. 22. lITA, Ibadan igeria.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Chapter 3 Assessment of Genotype X Environment Interaction and Role of Physiological Analyses for Crop Breeding Rodomiro Ortiz and Indira J Ekanayake 3.1. Introduction 3.2. Genotype x environment interaction (0 X E) 3.3 . Occurrence of G x E and classification of environments 3.4. How crop improvement programs deal with G x E? 3.5. When and where to do multi-Iocational testing? 3.6. Who are involved in multi-locational testing? 3.7. Future directions References

3.1. Introduction The state of knowledge on what is genotype x environment interaction (G x E)? Why it occurs? How can crop breeding programs deal with G x E'! When and where to do multilocational testing? Whom to involve in multi-Iocational testing? and the components of phenotypic stability is critically reviewed in this document. This analysis is intended to provide appropriate breedlng protocols for the mandate crops of institutions with international mandates to deal with improvement of crops targeted to various agroecological zones, to manage G x E, to have efficient selection schemes, and multi-Iocational testing prior to release of improved cultivars.

3.2. Genotype x environment interaction (G x E) The description of Bailey (1983) as, " In some experiments or processes yield measured is affected by two or more factors. If yield is not the sum of separate effects, factors are said to interact Such an interaction of factors could be called as G x E." This statement also describes the narure of a phenotype (P) . P defmed in a single environment using an additive linear model is as follows : P=G+E, G and E are genotype and environment respectively. In this model, G x E is nested within E. Testing of genotypes in different environments or multi-locational testing is needed to separate E from G x E . Therefore,

p :: G + E+ [G x E]

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

The G x E effects, which occur between genes and their environments can thus be regarded as an example of non-additivity of main factors, G and E. G x E is the relative differences in performance across environments for individual cultivars. It may result in selected genotypes perfonning well in one environment but not in another. This phenomenon forces crop breeders to consider adaptation and stability of a given genotype. In the absence of G x E, cultivar perfonnance (yield) across environments can be predicted with (G+ E) - I measurements, i.e., testing all genotypes in a single environment and a single genotype in all environments. This type of analysis is possible where the ranking of genotypes do not change across environments. In this manner a "super genotype", which is high yielding and stable across a large number of environments could be potentially selected. In practice however selection of such a "super genotype" is scanty.

3.2.1. Number and types of GxE The number of possible G x E interactions are {[(GE)!] / [G! E!]}. Irrespective of this high number, types ofG x E can be grouped into two classes: cross order and nOD-cross order interactions. Cross order or qualitative interactions are defmed as those with a change in ranks of genotypes across environments. In non-cross order or quantitative interactions ranking of genotypes across environments do not change. However the differences in magnitude among genotypes could occur. The former type complicates identification of superior genotypes in field, their selection and use in fanner's fields. The latter group influenced by the testing scale does not offer difficulties for breeding. Fig.3.1 illustrates examples of non-cross order and cross order interactions. Conclusions on cross order interactions need caution, unless the effect of 'noise' is considered in the statistical model.

3.3. Occurrence of G x E and classification of environments Environment as defmed by the geneticist is comprised of all external conditions that are related to development of a genotype. Components of environments are therefore, genetic, i.e., residual genotype at a particular locus or interactions among loci, and non-genetic, i.e. due to environmental variations. Nyquist (1991) stated that "genetic analysis is not satisfactory either from a practical or a theoretical point of view whenever a character is strongly influenced by the environment. It is true only if it includes a concrete specification of precise features of that environment affecting that character, and their quantitative importance in relation to different genotypes". Such an analysis is posslble where the relevant variable is observed and studied, and when not treated as an error to be randomized. Environments are of two types: predictable and unpredictable (Table 3.1). Predictable environments are those with permanent attributes of a location which fluctuates in a systematic manner. Unpredictable environments are those with non-systematic fluctuations and may include variations in cultural practices as those in inefficient agricultural systems. Year-to-year fluctuations are considered as unpredictable environmental variations since advanced prediction is not feasible .

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

90.-----------------------------0-

•

--•= Q.

PITA 1 Agbagba

... _--

60~----~------------~----~ Environment Farmer o NPK

110.---------------------------~

E u

I!I

•

100

PITA 1 Agbagba

90 80

70~-----T------------~------J Environment 400 NPK Farmer 120.-------------------------~

110

•

PITA 1 Agb.gb.

100

90 80 70~----~~----------~------~

Farmer

800 NPK

Environment

Fig. 3.1 . Interactions for plant height at 4 months after planting improved plantain bybrid PITA-l across 4 management practices: non-cross order (top graphs)and cross order (bottom graphs).

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

From a statistical point of view, unpredictable or uncontrolled environmental features are considered as random factors and may be assessed by testing across different years and testing sites. Predictable or controlled features of the environment are presented fixed effects in additive linear models. Therefore, plant breeders always work with mixed models because the environment has specific combinations of predictable and Wlpredictable features. All environmental variations have macro- and micro-envirorunental causes. Macroenvironments are composed of populations of micro-environments in an experimental field in a defmed period of time usually one growing season. Thus, locations and years are samples of a very large number of macro-environments within a specific agro-ecological region. While, micro-environments are all those factors other than the genotype that affect plant development. They are physical and chemical soil attributes in which the plant grows (e.g. moisture, aeration and availability of mineral nutrients) and other biological organiSms (fungi, insects, nematodes, viruses, mammals, birds, weeds, inter-plant and intra-cultivar competition etc.J to which the plant is exposed during its life cycle. Blocking is an important option to remedy problems associated with micro-environmental effects. In addition, in vegetatively propagated crops the common origin of planting materials is crucial to have uniform trials. Tissue culture derived plantlets are promising planting materials to achieve "seed unifonnity" in these crops.

Table 3.1. Major features of predictable and unpredictable environments (Allard and Bradshaw, 1964)

Predictllble Environments a)

Pennanent features : climate, soil type (pH, texture, mineral elements, etc.), day length

(Spatial variation) b) Predictable and fluctuation features : Man determined interventions such as planting date, sowing density. tillage methods, fertilizer levels and applications, irrigation, harvest methods, pesticides etc. (Cultural practices)

Unpredictable environments a)

Weather patterns: amount and distribution of rainfall and temperature (Temporal

Biotic stresses: pests and diseases (Parasites and predators), weeds, crop density

variation) (Cropping systems)

3.3.1 Assessment of G x E, site-selection, and breeding strategies Measuring G x E is important to determine the most appropriate breeding strategy to develop genotypes for target environments. When G x E interactions are not important breeding materials may be safely tested in the most convenient environment. Otherwise, breeding materials need on-farm testing in.specific environments. Several problems may arise when genotypes are selected in an environment different to that of the targeted agroecological zones. Most important factor is sensitivity of genotypes to environmental changes (Table 3.2). For example, breeders at the Centro Intemacional de

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Agricultural Tropical (CIAT) have shown that location specific selections of beans yielded higher at their respective location site (Singh et aI., 1992). However, these location specific selection were lower yielding than the across selection lines when compared over locations.

Table 3.2 . Types of selection of genotypes for above or below average enviromnents

Environments

SeledioD upwards Genotypes with high sensitivity Genotypes with 16w sensitivity

Above-average Below-average

downwards Genotypes with low sensitivity Genotypes with high sensitivity

Moreover, the selection gains may decrease concomitantly with an increase in the level of environmental stress when selection was not carried against that specific stress. Mean yield increases when genotypes are grown in stressed environments where they were selected for cultivation. Otherwise, yield may decrease when genotypes are grown in non-stressed environments unless there was a high genetic variance for yield in the stressed environment during the selection process. However low genetic variances are characteristics of stressed environments (Table 3.3). Hence, selection when stress occurs may result in low yields in favorable environments. This has been called the "cost for additional traits"; e.g:, resistance to specific biotic and abiotic stress factor. Table 3.3 . Genetic parameters in tomato marketable yields tested in different environments (Ortiz and Izquierdo, 1993)

Environment Above

ere;

cTr;]I

cr.

200±lOS*

322±7S·

179

average Average

9+7

17+9·

Below

23j:12

28±10·

W(%) 68.5

CV(%) 19.2

46.2 67.0

37.2 39.4

average

-2+1 25+4· 18 48.8 the variances due to genotypes, genotype x environment interactions, and error, respectively. H2 is broad sense heritability (or the fraction of phenotypic variance caused by differences in heredity), CV is coefficient of variation. • indicates that somce of variation was significant at P '" 0.05.

Poor

crG ,crG£.ale are

In developed countries, breeding for adverse conditions has not been considered a practical proposal unless the aim was to detect resistant cultivars to biotic stresses. Indeed, most of the US commercial cultivars have been selected for their ability to respond to high input (fertilizers, pesticides, and supplemental irrigation) under optimal or acceptable environmental conditions. Otherwise, selection for average productivity has been advocated to allow an increase in yield under both stressed and non-stressed conditions in developing countries. However, for stressed environments more than one breeding operation for cultivar development should be carried out ' in specific sites to match specific genotypes to their respective environments. In subsistence agriculture, maximum yield itself is not as important

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

as achieving a certain minimum (but economical) yield leve1. Herein, breeders must select high yielding cultivars that also decrease the risk of poor yields under adverse environments. Decision making methods under risk as developed by economists may also be applied to the problem of simultaneous selection for yield and stability. Therefore, International Agricultural Research Centers (lARCs) such as nTA should select genotypes resulting from a compromise between yield response to favorable environments and its variability across environments.

3.3.2 Site selection for a breeding program. Environment plays an important role in identification of promising genotypes adapted to specific stresses (Table 3.4). For example, selection for N-uptake should take place in envirorunents with low to medium N input in which clear phenotypic differences between In optimum or favorable efficient and non-efficient genotypes may be measured. envirorunents, genetic differences still exist but are irrelevant. In contrast, in poor environments, true genetic differences may be hidden because all entries tested will have their phenotypes similarly affected by stress.

Table 3.4. Expected phenotypes of contrasting genotypes under different environments Genotype

Tolerant Susc~!ible

Optimum (nonstress) Normal Nanna!

Environment (stress level) Poor (stress Breeding site >farmers threshold) (stress) Nanna) Diseased Diseased Diseased

Ideally, the environment where breeders select genotypes are those where phenotypes match with genotypes, i.e., to do visual "genotyping". This sometimes depends on the specific trait that is being subjected for improvement. FOI example, selection for black sigatoka resistance in plantains, based on measurement of youngest leaf spotted, is more efficient in the rainy than in the dry season (Vuylsteke et al., 1993). However, phenotypic differences between resistant and susceptible genotypes are observed in the dry season (Table 3.5), regardless of the fungus, which prefers the rainy season for its development in Musa leaves. There was no G x E interaction, i.e., no change in ranking order of differences in magnitude, in the host response to black sigatoka when evaluated in different seasons (Table 3.5).

Table 3.5. Host-response to black sigatoka, as measured by the youngest leaf spotted, of resistant tetraploid hybrids (BSRTII) and their susceptible plantain parent (AAB) at Nyombe, Cameroon (1992-93). (Vuylsteke et aI., 1993). Clones

Dry season

BSRTH AAB

12 + 0.5

Rainy season ILl + 0.5

9.2 3.3

3.5

Difference

7.6

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

3.4. How crop improvement programs deal with GxE? "The phenotype of a variety, actual physical outcome, is the resultant ofgenetic, environmental, and interactive effects. Breeders manipulate G, agronomists (and crop protection specialists) manipulates E, and a/l are concerned with GxE. These interactions are crucial and it points to the need/or joint research and development ofG and E, i.e. , breeding crop husbandry and crop protection together" (Simmonds, 1993). Indeed the interaction of breeding with other disciplines is a special case of G x E. Multilocational trials provide data to decide which cultivar developed at a breeding site may be extended to another site. In addition, information gathered during multi-site evaluation may help agronomists and crop protectionists to determine factors involved in adaptation to specific biotic and abiotic stresses. This could then assist breeders to develop cultivars with specific attributes that may enhance yield stability and/or crop adaptation.

3.4.1 Statistical assessment of G x E Several methods ha,'e been proposed for the statistical analysis of G x E and for the determination of phenotypic response to various changes in the environments (Table 3.6). The simplest is combined analysis of variance (ANOVA) of testing entries over environments (namely, locations, years, and seasons). This analysis provides means not only to determine when and where to do multi-Ioeational testing but also how to allocate resources for proper testing of breeding materials.

3.4.2 Components of variance and resource aUoeation The effectiveness of the testing system can be improved by determining the components of variances from the ANOVA for locations (or sites), years, replications, genotypes, and their respective interactions and by targeting these components (Nevado and Ortiz, 1985). Formulas have been developed and potential solutions have been proposed for djverse situations (Sprague and Federer, 1951) and to compare breeding stra1egies (Ortiz et al., 1991). For example, fewer replication and locations are required to detect significant genetic variation, as measured by broad sense heritability, in potato progenies derived from 4x-2x matiDp than in those from the conventional4x-4x breeding approach (Fig.3.2).

HERITABILITY

---------.-

----

0.85 0.65

4X-2X;101B'S 4X-2X; SlIPS

4X-2X;4PEPS 4X-2X; 3flSlS 4X-2X;2~

4X-1X; 10fElS --e- 4X-2X; 1F£P --+- 4X4X;5fEPS

0.45

--6--0-

0.25

---0-

--+-

0.05

4HX;4t£PS 4X4X;3t£PS 4X4X;2F£PS 4X4X;lPfP

+-#---~----r-----r-----'

10 LOCATIONS

Fig_ 3_2. Resources in testing of 4x-2x VS. 4x-4x approach in potato breeding, as determined by broad-sense heritability at varying number of locations and replications (Source: Ortiz etal. , 1991).

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Testing of improved gennplasm is a three-fold game of nwnbers. According to an experienced breeder, to increase the probability of selecting potentially suitable cultivars following measures can be made. These are, a) to test several genotypes to increase the chance of including exceptional genotypes; b) to have large number of replications to increase the experimental accuracy, and c) plant trials in many sites-year environments to minimize unpleasant surprises and to specify a cultivar for a recommended growing region. The logic behind bas been" the more data collected, the faster the progress". However, the number of plots (equals genotypes x locations x years x replications) is a function of operational budget (i.e., "the budget game"). Hence, the number of locations, years, seasons, replications, and testing genotypes ()r treatments depends on the aim of trial. Large number of replications may be required for hypothesis testing when small differences between treatments are expected. Otherwise, at least two replications may suffice to provide recommendations about cultivar release when tested over several environments in the same agroecological zone. Also , the genetic structure of accessions included in an experiment influences testing. For example, testing hybrid and open pollinated tomato genotypes in common trials seems to be more efficient than doing individual trials according to cultivar type (Ortiz, 1991).

Table 3.6. Evolution of additive linear stochastic models to explain G x E Analysis of variance (Fisher, 1918) and pair-wise ANOVA (Plaisted and Peterson, 1959) YIOf = 1.1 + a, + 13e + 9~ + E,er Regression (Mooers, 1921; Yates and Cochran, 1938; Finlay and Wilkinson, 1963 ; Eberhardt and Russel, 1966) y p = ~ + a, + Pc + ~8PC + Pat + Ep Joint regression (Tukey, 1949; Wright, 1971) Y ger = ~ + ex, + P. +Ka.~t + PIC + E.... Complete equation for linear regression (Gauch, 1992) y .... = ~ + a, + P. + ~.P. + ~ca, + ~a.Pc + PIC + &.... Principal component analysis (PCA) (PearsQll, 1901)

Y.... = I.l + a, + 1:,,1..1). ~,"ll ... + Pee + E.... Shifted multiplicative model (SHMM) (Seyedsadr and Cornelius, 1989) y .... = \If + E."-. ~1'\.., + P~ + E.... Additive multiplicative model interactions (Fisher and Mackenzie, 1923; Gauch and Zobel, 1988; Gauch, 1992) Ya", = I..l + a. + Pc + l:nAn ~'"o + P", + &,er where the eigen vectors (unitless) are scaled as unit vectors, i.e., tar,. ~ 82• = 1. The expected values are Y IC = I..l + a. + 13. + taM ~'" B.., with n = 1, n .. The AMMI I model is y .... = Il + t. + 1t. + P,,°'y,) + (1,,0·'5.)+ P,t + E....

...

where Ylet = yield of genotype g in replication r of environment e. )l = grand mean. a. '" genotype deviations , i.e. mean minus grand means, Pc = environmental deviations, el • =

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

interactions. E,er = error term or fiPldom variation, ~ = genotype slope, 0.., = environment eigen vector, P,~ = residual (not explained by regression model) or the residual in the peA or AMMI models when all axes are nor used, 1( = joint regression constant estimated by ~1~gct8 ! ~,ctl •• ~. = regression of each environment's interactions on the genotype deviations, i.e., :E.(8••a.)/ ~.a2a:' A,. = singular value for PCA axis n (which has unit of yield in AMMI), "A.0~ = convenient scaling for multiplicative parameters, Y", = genotype eigen vector for axis n, and !t. = environmental mean

The historical trend in most breeding operations has been to use several sites and few replications for testing. It is important when G x E is significant, because increasing replications within environments does not control variation. Moreover, G x E may cause unpleasant surprises during cultivar development when entries are tested in too few site-years environments. Unexpected G x E affects prediction of cultivar performance and may erode farmer's confidence in the breeding program, thereby jeopardizing the acceptance of further cultivar releases. Breeders try to overcome time constraint for testing and releasing potential cultivars by increasing the testing sites. within~ site

It is clear that proper aids (e.g. statistics), to decision making process during selection of best cultivars at a minimum cost, may enhance the efficiency of testing and of breeding operation as a whole. At the end, assessment of breeding work will be measured by its progress (i.e., cultivars grown by fanners) per research dollar invested in a respective crop improvement program.

3.S. When and where to do multi-Ioeational testing? Breeding materials need evaluation in the specific environments where fanners may grow them. In this regard, cultivars require adaptation to the various stresses that may occur in the targeted region. In addition, testing may take place across diverse environments as feasible but within the specific agroecological zone in which the release of potential cultivar may occur. This implies an eco-regional approach for multi~site testing of promising breeding materials, i.e .. dividing targeted environments into more homogeneous subsets in relation to the types of stress and develop different cultivars for diverse environments. Therefore, breeders need to test breeding materials (clones, lines or advanced populations) in specific areas rather than for global scale adaptation at one site or one agroecological zone.

3.S.1 Statistical meaning of GxE in ANOV A A significant genotype x location interaction suggests that testing sites belong to different environments (spatial variation). Specific adaptation is achieved by subdividing targeted region into homogenous agroecological zones that minimize the GxE within this ecogeographical area. Similarly, a significant genotype x treatment (or cropping system) interaction implies a special environment created by specific cultural practices for each of test genotypes. In both cases, breeders need to develop more than one cultivar for each targeted ecological zone or cropping system. A complex problem may arise when the ANOVA shows a significant genotype x year interaction, because there are no specific solutions to this interaction arising from temporal

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

vanatlOn. Only genotype testing over many years (or cycles in perennial crops) could remedy this problem. Some breeders have suggested that testing over a geographical range may substitute temporal variation with spatial variation in the development of buffering capacity of the germplasm to environmental change. This implies that buffering to environmental change (due to either spatial or temporal variation) rely on the same genetic mechanisms(s). Second order interactions (e.g. genotype x location x year [G x L x V]) are difficult to interpret and seldom have a major importance. Nevertheless, a significant second order interaction is a warning signal of interactions between complex main factors. Testing genotypes over a representative range of conditions is the most appropriate strategy to rectify the situation. Moreover, a significant G x L x Y interaction is not to be completely ignored and wide adaptability of breeding materials is not appropriate. Most of the multi-environmental trails have statistically significant interactions. However, assessing the relative importance of GxE may help to make some inference about main factor effect on the target trait. Percentage of the total variation, as measured by sum of squares, corresponds to the GxE source of variation. Additionally, interpretation of GxE needs to consider the attributes of both genotypes and environments (Table 3.7).

Table 3.7. Magnitude of GxE according to specific genotypes tested across specific environmental situations. Genotypes Similar elite genotypes Diverse genotypes

Environments Uniform and highly productive Contrasting environments

Mamitude of G x E Small Large

3.5.2 Correlated responses across environments Assessment of breeding materials in the environments where fanners may grow them is needed, however budget constraints determine the location( s) of selection. Breeders have two choices: to select in the environment where the potential cultivar may be grown or in another environment which allow the optimum phenotypic expression of desirable trait. An alternative is to consider performance of genotypes across two environments as two different but correlated traits . Where selection in one environment is efficient for development of cultivars adapted to other environments (i.e., correlated response across two environments). It is dependent on square root of heritability of the trait under selection in each environment (i.e., H. and Hy), and genetic correlation between the two performances (rd. Correlated response to selection (CRy) is i.H.Hyl'oCTpy , where ix :: intensity of selection in alternative environment X = phenotypiC standard deviation of the trait under selection in alternative environment X

a py

Direct response to selection in targeted environment VeRy) is iyH\, where iy and HZ y are intensity of selection and heritability of the trait under selection in target environment Y, respectively. Efficiency of selection in alternative environment X is measured by the ratio CRJRr, i.e., ro(ixH.)/iyHy). For example, yield potential (t ha路 1 year路 l ) measured in Musa germplasm

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

(landraces and hybrids) in three IITA stations in humid forest (anne and M'Balmayo) and in transition zone (Ibadan) of sub-Saharan Africa and genetic parameters (Ortiz et a1., 1994) are given in Table 8. Selection at anne is effective for M ' Balmayo only when the intensity of selection at Onne is 10% higher than at M'Balmayo because CR,. and Ry are 0.80 and 0.89, respectively. Otherwise, selection of genotypes targeted for M'Balmayo should be carried out at M'Balmayo. Similarly, selections from Gnne or M'Balmayo would not express their potential at Ibadan due to poor correlated responses (0.40 and 0.13 for Onne and M'Balmayo, respectively). Hence, selection in breeding stations even in the same agroecological zone (e.g., Gnne and M'Balmayo in the humid forest) may lead to development of different cultivars or populations. Indeed plant breeders need to restrict their selection operations to their specific environments. In this regard, clustering of similar environments may assist for recommendations about cultivar release across si.milar environments. When selecting for multiple traits, breeders need to select in the environment where greatest heritability for all traits occur, or by independent culling in the optimal environment for each trait. A tandem selection scheme is pursued when there are different priority rankings among traits. Table 8. Yield potential (t ha路1 year路 l ) measured in Musa germplasm (Jandraces and hybrids) in three UTA stations in humid forest (Onne and M'Balrnayo) and -in transition zone (lbadan) of subSaharan Africa and genetic parameters

Ecological '(.onel Location

alG

Onne M'Balmayo

13.43 13.84

1.80 3.31

Ibadan

3.86 M'Balmayo

cr.

HZ(%)

14.34 15 .50

93.71 89.29

4.18 Ibadan

92.30

Humid forest

. Genetic correlations Onne M'Balmayo

0.80

Transition zone 0.64

0.40 0.13

3_6. Who are involved in multi-Iocational testing? Collaboration between lARCs and NARES is a prerequisite in multi-Iocational testing, which is a part of the whole breeding operation and not only as its adaptive component. Multi-disciplinary teams (breeders, agronomists, crop protectionists, and post-harvest specialists as well as socio-economists) of the IARC in benchmark sites for each agroecological zone needs to select together the most promising genotypes in the breeding station. "Representative" site of an agroecological zone in terms of natural (climate, soil, vegetation, stress press~res, etc.} and socio-economic (either market or population driven) environments can be used. Multi-Iocational testing of these selections are done for the identification of potential cultivars for the different targeted areas with NARES collaborators. After advanced trials in NARS stations, the materials are selected for on-farm trials in consultation with progressive fanners and considering consumer' s preferences. On-farm testing provides information about cultural practices that may enhance cultivar productivity

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

under fanner conditions while developing a cultivar profile. This increases the chances for farmer's adoption of new cultivars.

3.6.1 Multi-site trials: Breeders aims and farmer's needs Fanners do not always share the objectives of breeders in cultivar development. Breeders aim for high reliability (or accuracy) and relevance in a specified geographical area when selecting promising genotypes for release to the farming community. The former is achieved by increasing replications while latter is accomplished by testing at many sites. However, in either case research costs are limiting leading to breeders ' paradox; to release cultivars that should have an excellent reception across growing regions but without resources and time to give to each farmer his own custom-bred cultivar. Indeed fanners want cultivars with excellent growth in their fanns and sometimes in each of their specific fields in a farm. Their interest is micro-environmental. Conversely, farmers ' perspective focuses on genotype yield variation from year to year (or season to season) at a given location (i.e. , farm) . Subdividing the entire growing region into several-targeted agroecological zones, which allow farmers to obtain optimal economic yields, could reach a compromise between farmers and breeders. The relative disadvantage of general to specific adaptation in a breeding program may be measured by the ratio {crd[a'- a+crGE-O')/II2]}, where 0' is the variance of phenotypic standard deviation. A ratio of unity suggests that general adaptation is possible because GE is almost zero. Theoretically, this hypothetical general adaptation is achieved by selection in a single environment In contrast, general adaptation is achieved by selection in a single environment. In contrast, general adaptation, which focuses on genotype's yield variation across locations, is not achieved when this is zero. For this environmental stratification is the best alternative to minimize G x E in predictable environments or selection fo r yield stability in unpredictable environments should be pursued. However, selection for stability influences the mean performance because selection is biased toward the poorest environments. In contrast, nonstable genotypes with higher yield potential than stable genotypes have their specific adaptation directed towards favorable environments.

3.7. What are the components ofpbenotypic stability? Stability implies that both yield and quality depend on holding in steady state some aspects of morphology and physiology of the crop in question allowing other factors to change. Phenotypic stability is clearly under certain genetic control with variable gene systems leading to unifonn productivity, implying high and stable economic returns across yeats in specific locations. Hence, it refers to fluctuations in phenotype while genotype of the cultivar or population remains stable. Finally, phenotypic stability is defmed in relation to the target population of environments where stability parameter is applied, because breeders aim to select cultivars, with desirable and stable phenotypes in the environments characteristic of the region. where these cultivars are required by farmers.

3.7.1 Adaptation, stability, canalization, plasticity, and flexibility Any change in the phenotype (function or structure) which allows the plant to better cope with the environment is adaptation. In this regard, certain characters, that are advantageous for the individual (or population) to perform well under specified environmental conditions, increase the adaptive value or fitness of the individual (or populations) to this environment.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Consequently, phenotypic adaptation, i.e., the adjustment of the phenotype to those prevalent and natural environmental conditions, may be achieved. Phenotypic specialization leads to genetic adaptation because the best fit genotypic variant has a new phenotype with better survival ability, where original form failed. In the genetic sense, adaptation implies a combination of genes built up and preserved together (i.e., a "linkat") by natural selection. Adaptation is also defmed in the context of spatial variation while stability concept, means a phenotypic response to a specific site across years and/or cultural practices. In this context, stability is the differential expression of genetic systems across environments. Selection for phenotypic stability is complex and is probably partially achieved. Moreover, phenotypic stability is a trait with a low heritability, which slows progress through selection of parents based on their stability per se. Probably the best alternative is to apply indirect selection for trait components insensitive to environmental fluctuations. Another path to achieve phenotypic stability is hybridization of parents with diverse patterns of GxE to obtain progenies with potential superior phenotypic stability. Non-parametric methods to assess phenotypic stability, based on rankings within each environment, are regarded in tenns of spatial variation (in the homeostatic sense) and not of phenotypic response to environmental change (i.e., the dynamic or agronomic stability). The latter is determined by regression methods that also measure the deviation of observed from predicted yields (Table 3.6). Therefore, non-parametric methods for stability assessment by genotypic variance across environments are associated with the static or biological concept of stability, i.e., genotypes possess unchanged phenotypes regardless of any environmental variation. This static concept is applied on disease resistance and quality, because these traits are essential in modem agriculture. Otherwise, the dynamic concept of stability, i.e., the stable cultivar does not have deviation from its ex.pected response to environments. The property of developmental pathways to achieve a pennanent phenotype regardless of environmental dis turbances has been called canalization. These are traits not affected by environmental stresses, i.e., the same adaptive phenotype is produced in different environments because the genotype of this developmentally flexible organism allows this to occur. In contrast, phenotypic plasticity involves ability of the plant for developmental change, in response to change in environmental factors . However, non-inherited phenotypic plasticity is genetically determined irrespective of the adaptive value of the change occurring. In general, phenotypic plasticity is specific to the character and to the environmental influences, and although in its direction, it can be radically altered by selection. Genetic flexibility is dermed as the ability of the genotype to vary and adapt to a changing environment. This occurs as a result of cryptic potential genetic variability. Similarly. ability of the individual to remain functional in diverse environmental conditions is called phenotypic flexibility. While behavioral flexibility refers to the faculty of an individual to adapt reversibly (elastic response), to temporal or spatial variation. Environmental conditions and selection intensity determine the different adaptive responses of the individual (or populations) (Table 3.9).

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crl?P breeding

Table 3.9. Environment, selection, adaptive response~ outcomes. and outputs Environments: Examples: Selection: Adeptive response:

Uniform and Stable Breeding station Slight Genetic and phenotypic fleXibility

Outcome: Output:

? Heterogeneous popUlation for further testing

Diverse and unstable Multi-site trials Weak Stron~ flexibility Phenotypic Genetic Elasticity (Flow of flexibili ty Plasticity variability) Adaptive flexibility and Developmental canalization Stability Adaptation â&#x20AC;˘ Stable cultivar in Eco-cline b specific agroecological zone

'Fitness achieved as a consequence of changes in gene frequency and potential allele fixation. b Gradient of genotypes with respective phenotypes in different locations in the ecoregion.

3~7.2

Genetic base and phenotypic stability

Stability may be achieved by either developing cultivars made of a mixture of different genotypes with similar phenotypes (e.g., multi-lines) but each adapted to somewhat different range of environments; or by selecting well buffered (or homeostatic) individuals where each member of the population is well adapted to a range of environments (Allard and Bradshaw, 1964). When developing stable cultivars both the genetic structure of materials and their breeding behavior need consideration to achieve phenotypic stability. Individual buffering to environmental change plays a major role in homogeneous populations that achieve their stability through individual or population buffering to environmental change. Outcrossing species might have their individual buffering associated with heterozygosity. while in an inbreeded speciestbis individual buffering seems to be a property of the genotype which may not be heterozygous (Allard, 1961). Indeed, it has been observed that either hybrid or open pollinated cultivars achieved yield stability in tomato (Ortiz and Izquierdo, 1994). Nevertheless as pointed out y Allard (1961) " Under optimal conditions homozygotes and heterozygotes differ little in fitness but as conditions deviate more and more widely from optimum, the advantages of heterozygotes increases progressively". The advantages of heterozygosity in yield across environments were demonstrated for tetraploid potato by Amoros and Mendoza (1979). On the other hand, population bUffering, which is beyond that of different co-existing individuals of the population, arises through interactions among constituent genotypes. The relationship between genetic structure of the cultivar or population and stability is established in Table 3.10.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Table 3.10. Genetic structure and stability Stabili!Y High

Inbreeders

Outcrossers

Bulks

Racial composites Open pollinated landraces Synthetics Double cross hybrids Thee-wIlY cross hybrids Single cross hybrids Inbredparents

Mixtures

Multilines

Low

Pure lines

Clearly, genetic diversity in either heterogeneous mixtures or heterozygosity leads to Hence, heterozygous-heterogeneous phenotypic stability in diverse environments. populations are a source in development of cultivars with low G x E due to their genetic flexibility and elasticity. In self pollinated crops (e.g. cowpea, rice, soybean,), the utilization of diallel selective mating scheme along with bulk methods allow the development of wellbuffering populations from which potential stable cultivars are released. In this modified recurrent selection scheme, F IS are used as parents to increase the genetic base of the population. Recombination process and environmental selection on a segregating popUlation under different conditions could generate new genotypes, which may have improved phenotypic response to environmental change. Value of outcrossing is demonstrated in small natural populations, which provide parents for further development of stable and high yielding hybrids . In vegetatively propagated polysomic crops (banana and plantain, potato, sweetpotato, yams, polyploid cassava) phenotypic stability and high yields are achieved in highly heterozygous cultivars (Peloquin and Ortiz, 1992).

3.7.3 Crop domestication and physiological factors "Reluctant hunters and gatherers. with knowledge ofplants, took the step of domesticating plants whenever they were forced to and with whatever suitable plants were at hand to get food,fiber. and medicine (Evans, 1993)". It has been suggested that introduction of new crops could give better results than trying to modify those indigenous to a region. Expansion of cassava in Africa is an example. Moreover, most of crops, which were domesticated in specific geographical areas (Table 3.11). Have become popular in very distant areas from center of origin, or even show more variability in secondary centers of diversification as a consequence of crop redistribution (e.g. plantains in West and Central Africa or highland cooking-beer bananas in East Africa (see Evans, 1993). Hence, an evolutionary approach to a crop improvement requires the understanding of genetic and physiological events leading onto crop domestication. Certainly, a knowledge about determinants of crop domestication .....i ll enhance the utilization of statistical procedures to measure phenotypic stability (Table 3.9), because stability parameters do not provide insight into factors affecting the phenotype of a specific crop. Yield of domesticated plants is achieved by different physiological and genetic factors which (a) influence adaptation of wild forms to agriculture , (b) spread the crop beyond the center of origin, and ( c) raise yield potential in each specific location. Several changes associated with domestication of wild species are seed retention, modification of glumes and spines in cereals, reduced seed coat thickness, greater size of harvested organs, correlated

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

changes in size, polyploidy and DNA content, change of form (allometry and condensation), rapid and unifoIlU germination, synchronization of flowering and maturation, life cycle and breeding (mating systems), loss of bitter and toxic substances and enhancement of others, and adaptation to cultivation (i.e., separation between crops and weeds). Genetic changes required to change from wild to cultivated plants occurred mainly between 11,000 B.C. to 3,000 B.C. in a relatively rapid and straight forward sequence of events and probably by unconscious man selection. Table 3.11 . Center of domestication of selected crops. Relion . Southeast Asia and Melanesia China

Crop Piper, Ricinus, Mungifera, rice, Job's tears, yarns taro (Colocasia), sugar cane bananas fox-tail, millet, broom com, hemp, japonica

rice soybean Indica rice, arboreum cotton, diploid wheat, mungbean Near East (West Asia) and Mediterranean 2- and 6-row barley, hexaploid bread wheat, Europe date palm, chick pea, einkom and emmer wheat pea lentil, vetch flax Ensete, froger and pearl millet, seSanle, Africa sorghum, African rice, cowpea, Bambara ~oundnut, oil palm yams Amaranth, quinua, lupin, common bean, South America Lima bean, tomato, hot pepper, potato, sweetpotato, cassava, peanut, tobacco, barbadense cotton strawb~f!Y Central America squash, chili and sweet pepper, maize, upland (hirsatum) cotton cucumber, squash, sunflower, blackberry, North America blueberry, cranberry, sumpweed Indian sub-continent

Crops were able to grow beyond the center of origin through modifications in their adaptive responses to new environments. For example, crops exhibit shortened or enlarged growing season as a consequence of their response to different factors such as day length, temperature, irradiance (and tolerance to shade), and water stress. Therefore, the effect of latitude, solar radiation, temperature and rainfall patterns on growth and development, yield need to be fully understood for the holistic betterment of a crop. Knowledge in plant physiology may be needed to achieve the yield potential in each environment. Crop physiology uses the comparative approach as an experimental tool to elucidate physiological background of a genotype. For example, investigations on sources and sinks, photosynthesis, growth rates, translocation and partitioning, and harvest index are among the most important topics to understand yield (Table 12).

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Table 3.12 . Researchable issues in yield physiology (summarized from Evans, 1993) Research topic Sources and sinks Crop photosynthesis

Growth rates

Translocation and partitioning

Harvest index (HQ

Issues Regulation of photosynthesis by sink activity, sink capacity and limitations 1. Photosynthetic rate comparisons as affected by irradiance, temperature, day length, acclimation responses , leaf rank, leaf age, demand for assimi1ates, nutrition and levels of mineral elements 2. Assessment of single leaf photosynthetic rate studies: relation between carbon exchange rate (CER) of single leaf photosynthetic rate studies, relation between CER and leaf area, relation between photosynthetic capacity and duration 3. CER in optimal and sub-optimal conditions 4. Some component processes: adaptive changes 5. Rubisco and Electron transport 6. Canopy photosynthesis and plant of . canopy architecture :. d~atio~ photosyntheSlS; lIght mterception and plant architecture Relative growth rate (RGR), Crop growth rate (CGR): data quality, dependence on leaf area index (LAl), envirorunental dependence, photosynthetic pathways, inter-specific differences, and shoot weight, and CGR in relation to yield Export from leaves, phloem capacity, the partitioning of assimilates: geotropic and hormonal factors, functional equilibrium and developmental trends Developmental and environmental effects Comparison between different crops: the rise in ill, source of the rise in HI, selection for Hl.

Also physiological studies focus on how reduced investment in other organs (e.g., plant height, stem or root growth, branching and tillering) or other processes (e.g., changes in reserves balance in life cycle) affect yield and its components. Utilization of external inputs (fertilizers, artificial irrigation, pesticides, etc.) and efficient use of these resources are a part of the agenda. In collaboration with socia-economists and engineers, energy return ratios of machines and fuels, fertilizer and pesticide production, labor, and animal power involved in current agricultural practices are investigated with the aim to determine how sustainable are the marginal returns for specific inputs.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

3.7.4 Ideotype breeding: the marriage between breeding and physiology The statistical approach to study G x E relates the observed genotypic response to a sample of environments where entries were tested. Otherwise, in the analytical approach, biotic and abiotic stresses defme the environments and needed phenotypes. Moreover, in the latter approach, individual components of complex phenotypic traits are investigated, aiming to simplify the analysis and to provide tools for the prediction and assessment of environmental effects on the phenotype. The empirical approach for genetic betterment of a crop consists of "defect elimination" and "selection for yield". In contrast, in the analytical approach a model (or ideotype) is established for each specified environment, aiming to increase the productivity in that specific situation. Ideotype is often determined with inputs from crop morpho-physiology. The goal of ideotype breeding is "to define a plant type which should be theoretically efficient and then breed for this", i.e., breeders select for and not against specific phenotypes. A major benefit of ideotypc breeding is that breeders are forced to formulate in advance a description of what to develop. High yielding locally adapted landraces are the initial source of parental material in ideotype breeding, because chances of developing high yielding cultivars with exotic or wild germplasm alone are very small. Specific issues to be solved before ideotype breeding is, identification of phenotype enhancing traits and their inter-relationships, and establishment of a single optimal genotype-phenotype combination.. Availability of suitable genetic variants and population size required to recover the desired phenotype (or the tyranny of numbers) are two most important practical problems in ideotype breeding.

3.8. Future directions Sometimes, statements such as a new cultivar must not be introduced until all potential hazards have been tested and removed, are heard in scientific discussions. Although breeding programs operate within time frames of decades, it may not necessarily lead to cultivar release. A delay of several years could occur in providing farmers with improved germplasm, which fit their needs. Nevertheless, agricultural scientists should be able to improve their current skills and knowledge to develop cultivars characterized for having maximum yield potential in the targeted environment but with maximum phenotypic stability.

3.8.1 Selection for low inputs Selection for high average yield and low GxE has been suggested to achieve acceptable yields across environments.. However this breeding approach has been unsuccessful in increasing yield in low input agriculture. Moreover, genotypes with above average yield and low GxE are adapted to high yielding environments in which normally artificial irrigation and synthetic fertilizers are provided.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Genetic progress through selection in low or no input agriculture requires a good characterization of the targeted environment, proper choice of selection and testing environments, increased genetic diverSity, and breeding for component traits which are less affected by GxE and may enhance adaptation to a specific stress. Hence, genetic information regarding (a) the nature of variation for adaptation, and (b) inheritance of component traits, required to achieve economic yields under stress, are crucial to develop the breeding strategy. Challenge is to develop cultivars with sustainable productivity with few or even none external inputs.

3.8.2 Search for durable resistance There are two concepts, resistance and tolerance, which should be clearly interpreted in breeding for defense mechanisms in plants. Resistance is the ability of the plant to reduce growth or development of the parasite 'after' contact has been initiated or established, while tolerance is determined by those mechanisms by which the plant reduces the extent of damage per unit parasite present. A tolerant (but susceptible) cultivar supports high popUlation levels of the pest but suffers relatively little damage in terms of yield reduction. This clearly shows the danger of releasing tolerant cultivars with stable yields in agricultural systems, because tolerance does not reduce the levels of infection in contrast with resistance. Genetic improvement for durable resistance seems to be a major concern in current breeding programs~ However, durable resistance is that which remains effective in a cultivar that is widely grown for a long period in an environment favorable for the disease. Hence, breeding for durable resistance seems to be complex because the durability of resistance is always defmed a posteriori. Unquestionably, breeders cannot establish the durability of resistance in their materials by testing their behavior in breeding plots at the experimental station. Moreover, potential durability of any resistant germplasm cannot be established by multi-Ioeational testing alone. Multi-site trials only provide means to determine whether resistance is at an adequate level across diverse environments. Only widespread cultivation of a specific resistant cultivar is acceptable as a proof of durability of resistance. An insurance against resistance breakdown may be obtained by keeping genetic diversity in farmer's field. Either cultivar mixtures or multi-lines seem adequate to achieve durability of resistance.

3.8.3 Crop Modeling "Modeling is basically a synonym/or understanding (Gauch, 1992) " Crop-growth simulation models are developed to understand cultivar response to environmental fluctuations and to predict its future performance in a changing environment. These models aim to optimize agronomic practices for a given class of genotypes. Major features of crop simulation models are related to plant growth and development, e,g., rate of organ initiation, rate of progress between key growth stages, canopy development, initiation and filling of harvestable organs, rate of photosynthesis on dry matter accumulation, etc. Such growth models are useful to defme ideotypes in breeding for stress resistance. The value of a specific phenotypic trait in the hypothetical cultivar to be selected is determined by simulating its absence; presence 路 or certain level of expression in a specified number of cropping seasons. Alternative ideotypes are tested and narrowed down to numbers that may be manipulated in a breeding program. In modeling, the entire trial is considered as a single entity to be studied as a whole. Therefore, modeling based on data from multi-site trials aim

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

to establish relationships between genotypes and environments, and when possible determine the causal factors affecting the phenotypic response. The latter is achieved by combining multi-site data with weather databases provided by geographic infonnation systems (GIS). While GIS also help to predict changes in the ecosystem, this may also defme megaenvironments for complex and sizable G x E.

3.8.4 Biotechnology and G x E Current developments in biotechnology (in-vitro screening, genetic engineering, and marker-assisted selection and introgression) are adding more tools for crop improvement. However, these tools do not replace the need for multi-Iocational testing across years (or seasons). They however offer opportunities to provide more insight about crop G x E. For example, molecular markers provide means to investigate the interaction of specific quantitative trait loci (QTL) with the enviromnent whereas conventional ANOVA procedures detect significant marker locus x environment interactions. In addition, it is accomplished by comparing patterns of significant QTL-marker association in different environments. This enhance the manipulation of plant genome and its improvement because QTLs that are insensitive to environmental fluctuations can be identified and may be incorporated in a desirable genotype through marker-assisted selection or introgression.

3.8.S Holistic approach to manage GxE "Prodigious improvements are possible when G and E are exploited together, independent manipulation of GxE may be nearly helpless". High, stable and sustainable productivity is the fmal goal in crop improvement. Genetic bettennent of the crop alone is not sufficient. Although the most important components of crop improvement, do n01 prOVide the answer to this challenge, plant health and crop management as well as resource base management are needed for achieving this objective. Consumer preference, enhanced utilization, and other socia-economic aspects are important features to be considered in the development of an improved genotype based technological package. Their integration provides the means to develop an environmentally sound sustainable production system. Indeed, the combination of good plant breeding and high husbandry inputs was instrumental for the green revolu.tion of wheat and rice, and is still important in the high productivity of plantation crops such as su.gar cane, oil palm, and rubber. Similarly, environmentally sustainable integrated pest management by combining cultivars with partial resistance and biological control of pests may be the answer to achieve durable resistance, because the pest is exposed to the plant host response and to its natural enemies, parasites, and predators . In addition, sustainable and stable crop production in a changing environment requires a close interaction between different research fields to develop new cultivars to meet this new challenge. Genotypes with acceptable yields .under increased COl> temperature, drought, cloud cover, and climatic variability are required in the agricultural systems of the 211t century.

References Allard, R.W. 1961. Relationships between genetic diversity and consistency of performance in different environments. Crop Science 1: 127-133.

Ortiz and Elcanayake. 1999. G x E and physiological analyses for crop breeding '

Allard, R. W. and A.D. Bradshaw. 1964. Implications of genotype-environmental interactions in applied plant breeding. Crop Science 4:503-507. Amoros, W.A. and H.A. Mendoza. 1979. Relationship between heterozygosity and yield in autotetraploid potatoes. American Potato J. 45: 455. Bailey, R.A. 1983. Interaction ..

pp.

176-181. In S. Kotz and N.L. Johnson (eds.),

Encyclopedia o/Statistical Sciences. John Wiley, New York. Eberhart, S.A. and W.A. Russell. 1966. Stability parameters for comparing varieties . Crop Science b:36-40. Evans, L.T. 1993. Crop Evolution, Adaptation and Yield. Cambridge University Press, Cambridge. Finlay, K.W. and G .N. Wilkinson. 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14:742-754. Fisher, RA. 1918. As cited by R.A. Fisher. 1942. The Design of Experiments. Third edition, Edinburgh: OJiver and Boyd. Fisher, R.A. and WA Mackenzie. 1923. Studies in crop variation. II. The manurial response of different potato varieties. Journal ofAgricultural Sciences (Cambridge) 23 :311-320. Gauch, H.G . 1992 . Statistical Analysis Designs. Elselvier, Amsterdam.

0/ Regional Yield Trials: AMMI Analysis of Factorial

Gauch, H.G., and R .W . Zobel. 1988. Predictive and postdictive success of statistical analyses of yield trials. Theoretical and Applied Genetics 76 : 1-10. Mooers, C.A. 1921. The agronomic placement of varieties. Journal of American Society of

Agronomy 13 :337-352. Nevado, M. and R. Ortiz. 1985. Pruebas de hipotesis enseries de ensayos. Agro-Ciencia 1:路23-

37: Nyquist, W.E. 1991. Estimation of heritability and prediction of selection response in plant populations. Critical Review in Plant Science 10:235-322. Ortiz, R. , 1991.Una methodologia de seleccion multiple por productividad y estabilidad para cutivares de tomate. Agro-Ciencia 7: 135-142. Ortiz, R. and 1. Izquierdo. 1993. La interacci6n genotipo por ambiente en el rendimiento comercial del tomate en Am6rica Latina y el Caribe.Turria/ba 42:492-499. Ortiz, R. and 1. Izquierdo. 1994. Yield stability of hybrid and open pollinated tomato cultivars in Latin America and the Caribbean. HortScience 29 : 1175-1177. Ortiz, R., SJ. Peloquin, R. Freyre, M. Iwanaga, and SJ. Peloquin. 1991. Efficiency of 4x x 2x breeding scheme in potato for multi trait selection and progeny testing. Theoretical and Applied Genetics 82 : 602-608.

Ortiz and Ekanayake. 1999. G x E and physiological analyses for crop breeding

Ortiz, R., D. Vuylsteke, 1. OkOfO, C. Pasberg-Gauhl, and F. Gauhl. 1994. MET-l : Multisite evaluation of hybrid Musa germplasm UTA stations. MusAfrica 4:6-7.

Pearson, K. 1901. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, flh Series 2:559-572. Peloquin, SJ. and R. Ortiz. 1992. Techniques from introgressing unadapted germp1asm to breeding populations. pp. 485-507. In H.T. Stalker and 1.P. Murphy (eds.). Plant Breeding in the 1990s. CAB International, Wallingford, Oxon, UK. Plaistead, R.L. and L.C. Peterson. 1959. A technique for evaluating the ability of selections to yield consistently in different locations or seasons. American Potato lournaI43 :535-541. Seyedsadr, M. and P.L. Cornelius. 1989. Estimation of Parameters of Shifted Multiplicative Moder for a Two-way Table. Dept. of Statistics, University of Kentucky, Lexington, Technical Report 278. Simmonds, N.W. 1993. Tropical plant breeding: success, failure or a bit of each? Tropical Agricultural Association Newsletter December 13(4):3-5. Singh, S.P., lA. Gutierrez, C.A. Urrea, A. Molina, and C. Cajiao. 1992. Location-specific and across-location selections for seed yield in populations of common bean, Phaseolus vulgan's L. Plant Breeding 9:320-328 . Sprague, G.F. and W.T. Federer. 1951. A comparison of variance components in corn yield trials. II. Error, year x variety. location x variety, and variety components. Agronomy lournaI43:535-541 . Tukey, lW. 1949. One degree of freedom for non-additivity. Annals of Mathematical Statistics 33:1-67. Vuylsteke, D., E. Foure, and R. Ortiz. 1993. Genotype - by- environment interaction and black sigatoka resistance in the humid forest of West and CeolTal Africa. MusAfrica 2:6-

8. Wright, J.W. 1971 . The analysis of prediction of some two factor interactions in grass breeding. Journal ofAgricultural Science (Cambridge) 76:301-306. Yates, F. and W.G. Cochran. 1938. The analysis of group of experiments. Journal of Agricultural Sciences (Cambridge) 28:556-580.

Ortiz and Ng. 1999. GxE and germplasm issues.

Chapter 4 G x E interactions and its analysis in germ plasm characterization and evaluation Rodomiro Ortiz and .1V-Quat Ng 4.1. Introduction 4.2. Statistical analyses tools for germplasm characterization and evaluation 4.3. Augmented designs 4.4 . Nearest neighbor analysis 4.5. Partitioning of phenotypic variation 4.6. Heritability and Repeatability Reference

4.1.Introduction Peop]e have an interest in the conservation of genetic resources because of the potential uses of this germplasm. Hence, gene-banks, as biodiversity reservoirs, are sources of alleles for sustainable crop breeding. The utilization of genetic resources from a species in crop breeding requires knowledge about the extent of phenotypic and genetic diversity. This knowledge al10ws a proper germplasm organization and further development of improved parents and new cultivars. Hence, germplasm evaluation bas the following objectives: â&#x20AC;˘ characterization of germplasm according to morphological characters, â&#x20AC;˘ determination of the agronomic value of the germplasm for its improvement, and â&#x20AC;˘ classification and establishment of relationships between and within species. The fITst step in biodiversity research consists in the collection of useful germplasm, which has been often followed by the utilization of proper descriptors to analyze the number and types of useful polymorphism. Descriptors are those characteristics that measure germplasm variation. The identification of specific but variable descriptors allows the evaluation of gennplasm diversity. The data recorded should be analyzed with proper statistical tools to select the genetic material according to the breeding goals.

4.2. Statistical analyses tools for germplasm characterization and evaluation The extent of research for germplasm characterization and evaluation depends on the program objectives, financial support, available data on genetic diversity (and pedigree information), and knowledge and importance of characteristics being investigated. The importance given to this activity in a gene bank may be determined by the resources and time invested. Irrespective of this investment, researchers MUST design and analyze their research for germplasm characterization and evaluation with appropriate statistical tools. These statistical procedures permit to determine the source and extent of variation.

Ortiz and Ng. 1999. GxE and germplasm issues.

4.2.1 Use of regular cbecks Three points are to be considered when including regular check accessions in an unreplicated set of accessions. These are: inclusion of two or three different checks in the field at the rate of one check for every 20 test accessions, adjustment of the performance of each test accession, and comparison of the accessions based on the adjusted performance.

4.2.2 Measurement of adjusted performance The measurement of adjusted performance is the percentage or proportion of the measures of the performance of the checks in the same sector. It is derived by subtracting or adding the deviation of the mean perfonnance of the nearest full set of checks from the mean of the performance of all the checks. For example, the number of seeds per pod of cowpea is given below. Mean number of seeds per pod of the nearest full set of checks = 9.5 Mean for al1 checks = 9.0 No. of seeds per pod for each testing accession nearest to the checks should be adjusted downward, by 9.5 - 9 = 0.5 .

4.3.Augmented designs Augmented designs consist of unreplicated but randomized testing accessions, which are grouped in blocks of convenient size (i.e., SO accessions). In the design two or three checks are included, which are also randomized within each block. The check perfonnance is analyzed in the fonn of a complete randomized block. The estimate of error from the checks is then used to compare the performance of the testing accessions.

4.3.1 Augmented design analysis There are two standard errors for comparing a pair of lines in augmented design analysis. a)

S..J2 for the two accessions placed within a block

V2..J(S2 + Sb 2) for two accessions placed in different blocks, where

Sb 2 = Block MS - Error MS No. of check lines

An example of calculations is given below.

Ortiz and Ng. 1999. GxE and gennplasm issues.

Example 1 . Twenty accessions, divided into four groups of five, and A and B check lines Block 1

Block 2 Block 3 Block 4

2 9(B)

6 6(B)

8(A)

9(A)

19 1

7(A) 7

7(B)

8 13 11

7(B)

20 18 7(A)

5 14

The analysis of variance table for the above example is as follows. Source of variation Block Check Error Total

Degrees of freedom

Sums of squares

Mean squares

0.5

7 0.5 0.5

2.333 0.1667

Grand mean of A and B = 7.5 MeanA =7.75 MeanB =7.25 Replication = 4 Standard errors,

"(0.1667/4) = 0.204 LSD within block t.os "2 x S = 0.92 2 LSD between blocks t.os "2 " (S2 + Sb ) = 4.77

4.4. Nearest neighbor analysis The procedure for this nearest neighbor analysis consists of following sequential steps: • • • • • • • •

Compute the deviations of each plot from the mean of all plots of that treatment Compute the mean deviations eX) of the neighbors of each plot Compute the treatment totals and means for the X values Compute an analysis of covariance of the actual values (Y) on eX) Compute regression (b) ofY onX Adjust the Y value for each plot by subtracting b (X - x) Iterate the above steps until the adjustments are the same Compute the ANOVA of the adjusted values.

Example 2. Nearest neighbor using completely randomized design, with 7 treatments (V .. V 2•

.. , V 7 )

9 V2 10 V2 9V2 9 Vs

12 VI 7Vs 7 V7 18V 1

18 V6 4 V. 18 V3 17 V7

10 V.. 10 V3 30V 1 19V6

24 V6 21 Vs 18 V3 32 VI

17 Vi 24 VI 16 Vi 5 V.

30 Vs

29V I 16 V2 26V 1

16 V3 12 V6 4 V. 4 V2

Ortiz and Ng . 1999. GxE and gennplasm issues.

V I has 8 replicatesiplot V2 to V7 each has 4 repllcates. Treatment totals

IVI

I isl

Treatment means

Calculations Four steps involved in the calculations are given below.

Step 1. Computation for the deviations of each plot from the mean of all plots of that treatment -0.5 V z -12.63 V~

-0.5 V 2 -7.75 Vs

-10.63 VI -9.73 V5 -7.25 V7 -4.63VI

-0.25 V6 -1.75 Vi -2.5 V)

4.25 V.

2.75 V7

5.75 V6

2.75 V 7

13.25

0.5 V3

-5.5 V)

4.25 Vs

1.37 VI

6.37 V,

-6.25 V6

7.37VI

2.5 V3

l.75 V 7

6.5 V 2

-1.75 V 4

0. 75V6

9.37 V,

-0.75 V.

3.37 VI

-5.5 . V 2

Example of the calculation of dellialion Deviation ofV2 (Plot I, Y = 9) from V2 treatment mean (9.50) = 9 -9.5 = -0.5

Ortiz and Ng. 1999. GxE and germplasm issues.

Step 2. Computation for the mean deviation (X) of the neighbors of each plot -11.63

-3.50

-2 .71

-3.58

-8 .07

-3.25

-9.21

-3.10

0 .28 V)

-2 .57

-4.08

OV.

3.75 V5

6.79 V7

3.21 Vs

3.5 V3

3.53 V3

1.03 Vs

3.78 VI

3.72 VI

1.71 V6

0.06 VI

5.69 V3

2.41 V7

2.44 V 2

-1.75

V. 0.46 V 7

6.5 V6

0.83 VI

4.83 V.

0.08 VI

0.81 V z

Example ofthe calculation ofmean ofdeviations for neighboring plots

V 2 (Plot 1):; -0.5 -[-0.5 - (-10.63) -f (-0.5) - (-12 .63)]/2 = -0.5 - 11.13 :; -11.63

V 2 (plot 2) = -10.63 -[-10.63 - (-0 .25) + (-10.63) - (-9.73) + -10.63 - (-0.50)]/3 :; -1063 - (-7.13) =-3.5 Step 3. Computation for treatment totals and means for the X values.

V1 V~

V. Vs V6

Treatment total

Treatment mean

-2.69 -6.46 -17.59

-0.336 -1.6 -4.398 2.398

9.25 13 6.56 -0.17 1.96

3.25 1.64 -0.043 0.061 = General mean (x)

Step 4. Compute an analysis of covariance of the actual values (Y) on X, and the regression (b) ofYonX For treatment VI, b = 0.845 Adjusted value for each V 1 plot For example,

=actual y-b (X - x)

(plot 2) = 12 - 0 .845(-0.336 - 0.061) = 12 - 0.845 X (-0.397)

= 12 -0.34 = 12.34

Ortiz and Ng. 1999. GxE and gennplasm issues .

4.5. Partitioning of phenotypic variation The variation recorded in quantitative descriptor may be split into different sources according to specific additive linear models. Some of these models in gennplasm research are indicated below with their respective tables for the statistical analysis of variance.

4.5.1. Analysis of variance for each trait as determined per plot mean

over seasons and years In this ANOVA, the model can be described as follows :

where: j.1 = general mean e = experimental error Y, S, rand G are year, season, replication and accession effects. A combination of two or more capital letters indicate respective interactions. Analysis of variance is shown in Table 4.1. Table 4.1 . Analysis of variance of genotypes over years and planting seasons (based on plot means)

Source of variation Years (Y) Seasons (Sl YxS RepslYx S Accessions (G) GxY GxS GxYxS

Degrees of freedom y-l s-1 ys- y- s + 1 ysr- ys g-1 gy- g y + 1 gs - g - 5+ 1 gys - gs - gy - ys +~ +y +5-1 gysr - gys ysr + 1 Pooled error where y, s, g and r are the number of years,

Mean square

F-tests Random model

(Ml+M8)/(M3+~)

(M;t+Ma)1(M3+M 7) (M3+M9)/(M.&+Mg) MJ M 9 (Ms+Ms)/(M(i+M7) MJM8 M7!Ms

Me Ms_ M6 M7 Ms

Ma/ Mg

F-tests Fixed model M1/ M9

M:JM9 MJM~

MJM9 Msl M9

Md M9 M7 /M 9 Msl M9

M9 seasons, accessIOns and rephcatIons respectIvely.

4.5.2. Analysis of variance for each trait as determined per plot mean for accessions across environments The model used for this analysis is as follows.

Ortiz and Ng. 1999. GxE and gennplasm issues.

where: 1.1 ,., general mean e = experimental error E, r and G are environment, replication and accession GE is accession by environment interaction. Analysis of variance is shown in Table 4.2. Table 4.2. Analysis of variance of genotypes over environments (based on plot means) Source of variation

Degrees of freedom

Mean sqllare

F -tests Random model

F-tests Fixed

model Environments (E) e-l (M1+Ms)l(M2+~) Ml/ M2 Ml er-e Relllications M2 M1+Ms Mli'Ms Accessions (G) g-I Ml M~+~ M3i'M~_ ge-g-e +1 GxE MJMs M.JMs ~ gre- ge~e Pooled error Ms where e, g and r are the number of environments, accessions and replications respectively.

4.5.3. Analysis of variance for each trait recorded in individual plants The model is as follows .

where: J..I. = general mean

e = experimental error (plot to plot) d = sampling error (plant to plant, within plot) E, G, R are environment, accession. replication, and GE is the accession by environment interaction. Analysis of variance is shown in Table 4.3 . Table 4.3. Analysis of variance among races (R) and accessions (G) within races over environments (based on individual plants) Source of variation Environments (E) Replications IE Accessions_CG) Among races (Rj) GIRl ... G~

GxE RxE GIRl xE ... G~xE

Pooled error Sampling error

Degrees of freedoDl e- 1 er- e

Mean

F-tests Random

square Ml

model (M l+Ms)!(Ml+~)

M~s)

~-I

MJ/Mg Mi M5 MiM,

c-l gj- 1 &i- 1 ge - g - e+ 1 re-r-e+l gje - gj- e + 1 gje-gl-e+l gre - ge - er + e grep -gre

M30

Mw'M40 MllJM.1

Mw'M5 M31/M 5

M3l

F-tests Fixed model

M3.1~n

M3a1M~

MJ M5

MJMs

M.o

M.tlMs

M41

Mfl/Ms

~Ms ~JMs

M... Ms

M.JMs

~jMs

MsI~

Ms/ M6

Ortiz and Ng. 1999. GxE and germplasm issues.

where e, t , c, g and p are the number of environments, replications, races, accessions and plants per plot, respectively.

4.6. Heritability and Repeatability Quantitative descriptors showing continuous variation are often used in a natural system of classification, even when the environment or the genotype x environment interaction significantly affect their phenotypic expression. As earlier indicated by others, the environmental effect and the genotype x environment interaction may be lessen in germplasm characterization by â&#x20AC;˘ assessing the germplasm in several environments and using the mean values â&#x20AC;˘ evaluating the gennplasm in several environments and defming similar phenotypic responses in each specific environment, and â&#x20AC;˘ comparing only those traits which are not affected by the environment. Based on their broad sense heritability (H), or the ratio between accession (~G) and phenotypic variance (02p), descriptors are recommended for grouping germplasm. Phenotypic 2 variance has been defmed as a e + a 2GE IE + ~c I(Er), where E and r are environments and replications, and is the error variance. Heritability may be useful for selection of descriptors for racial classification but H has no value in germplasm characterization because its calculation does not include the environmental variance (a 2E)' Repeatability values (re> is the ratio of variance components due to differences between accessions (0 2G) and the sum of the corresponding components due to differences among environments (~E) and accession by environment interactions (0 2GE), i.e. , re = oLo( o~

a1rn. This statistics (rd has been suggested for the basis of choosing descriptors

with

quantitative trait variation. Therefore, the best descriptors for germplasm catalogs are those easy to score and which have a constant phenotypic expression in all environments, i.e., high repeatability due to low or nil environmental influence. Hence, descriptors for germplasm characterization should not be biased by the environment. Meanwhile, descriptors with low or nil accession by environment interaction (i.e., high heritability), although they may be affected by the environment, are more important for agronomic evaluation or selection, and for classification. Also descriptors should be accurate (i. e., unbiased) and precise (or with minimum or nil error measurements), i.e. low coefficient of variation. Table 4.3 lists heritability, repeatibility and the coefficient of variation of descriptors of Peruvian maize. Ear and kernel characteristics are the best descriptors for assessing quantitative variation in this germplasm.

In summary, this brief overview provides examples on how statistical tools allow the proper assessment of genetic resources variation within and across environments. Methods, therefore, are available to manage macro- and micro-environmental influence in phenotypes for an appropriate evaluation of plant genetic resources.

Ortiz and Ng. 1999. GxE and germplasrn issues.

Table 4.3. Heritability (H). repeatability (R) and coefficient of variation (CV, %) of selected quantitative descriptors of Peruvian maize (Ortiz and Sevilla, 1997)

Trait

CV(%)

Plant height (em} Ear hei~ht (em) Number of leaves Branched part length(cm) Number of kernel rows Ear length (em)

0.78 0.88 0.89 0.82 0.89 0.80 0.87 0.96

1.03

-6.1

1.49 1.63 11.61 8.92 2 .09 1.08 1.18

11.7 6.4 16.4 8.4 8.3 6.5 4.4

Ear diameter {em) Kernel width (cm)

Reference Ortiz R. and R. Sevilla. 1997. Quantitative descriptors for classification and characterization of highland Peruvian maize. Plant Genetic Resources Newsletter Il0:49-52.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

Chapter 5 Marker-assisted selection and the implications of genotype x environment interaction Jonathan H. Crouch, Hitokshi K. Crouch, Christian A. Fatokun and Jacob D.H. Mignouna 5.1. Introduction 5.2. The basis of molecular genetic marker-assisted selection techniques 5.3. Methods of identifying molecular markel8 associated with agronomic traits 5.4. Limitations of models for identifying markers to quantitative trait loci (QTL) 5.5. The relevance ofG x E interaction to marker-assisted selection References

5.1. Introduction Genetic markers and maps have been used in plant breeding for almost a century (Morgan, 1911) and have proven to be powerful tools for selection, identification and the study of the organization of plant genotnes. The basis of all genetic maps (and the markel8 on which they are based) lies on the theory that Mendelian genetic factors, which exist close together on the same chromosome, have a high probability of being co-transmitted from the parent to progeny (Morgan, 1911). MOIphological characters such as flower color, leaf shape or male sterility are an extremely convenient type of marker but are relatively rare. In addition, dominance, late expression, deleterious effects, pleiotropy and epistasis frequently reduce the usefulness of such markm. Biochemical markers such as isozymes are rarely associated with undesirable phenotypic effects and are more abundant than morphological markers (Stuber, 1992). However, of the 3000 or so plant enzymes known, less than 60 have been assayed for isozyme polymorphism and only ten to twenty isozyme loci are commonly found to be polymorphic in most breeding populations (Vallejos, 1983). In contrast, molecular markers have no phenotypic effect, are not affected by the presence or absence of other loci and a large number can readily be detected in most breeding populations (Arus and Moreno-Gonzalez, 1993). DNA markers can be developed through the detection of Restriction Fragment Length Polymorphism (RFLP) or through a variety of methods based upon the Polymerase Chain Reaction (PCR) such as Random Amplified Polymorphic DNA (RAPD), Variable Number of Tandem Repeats (VNTR) and Amplified Fragment Length Polymorphism (AFLP). The concept of genetic maps based on molecular genetic markers was initiated by Botstein et a1. (1980) with reference to the human genome. Since that, time similar maps have been developed for over twenty plant species (O'Brien, 1993). However, it is clear that a genetic map is not a vital precursor to the development of an efficient marker assisted selection program (Stuber et aI., 1992). Yet the potentially rapid development of this aid to selection in plant breeding programs has often overridden practical considerations concerning the increasingly wide-ranging extrapolation of this technique. It is in this way that the effects of G x E interaction have been somewhat neglected by many molecular biologists operating in this field . In particular, we are concerned here with how G

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

x E interactions might confound the efficiency of marker assisted selection programs. In order to appreciate this we must first develop a precise understanding of the technologies being used and how they are applied in the generation of molecular markers.

S.2. Tbe basis of molecular genetic marker-assisted selection techniques S.2.1 Restriction Fragment Length Polymorphism (RFLP) RFLP technology is based on the use of random fragments of DNA which have been doned from a plant within the species, genus or family of interest. These DNA fragments are then used as probes to screen plant genomes for polyrnorpbisms at the DNA level (Helen~aris et a!., 1985). Genomic DNA is extracted from the plants of a segregating population, enzymatically restricted and the fragments separated according to size by agarose gel-electrophoresis. DNA fragments are then transferred to a membrane and hybridized to a radioactive-labeled probe (techniques reviewed by Sambrook et al., 1989). X-ray film is exposed to the membrane bound labeled probe in order to identify the fragments to which the probe has hybridized. Polyrnorphisms are detected as variation in the length of genomic DNA fragments. which hybridize to the probe DNA (Fig 5.1). Such hybridization is only possible when there is a region of sequence homology between sample and probe DNA. These differences in fragment length can be caused by single base-pair substitutions at a restriction enzyme recognition site or by more dramatic deletion/insertion events. However, this analysis can not directly identify the nature or extent of such differences (Watson et al ., 1987) . RFLP patterns are then compared with the phenotypic segregation of an important character until a closely linked DNA marker is found. Concomitantly, the RFLP patterns of various marker loci can be comparatively analyzed in order to build up a linlcage map. This type of analysis allows one to estimate the genetic distance from marker to gene of interest or from market to marker. However, distances between loci on molecular genetic maps are a reflection of the frequency of recombination events and may vary considerably between populations derived from different parents. Furthennore, the actual physical distance from marker to gene may be very large despite a very small genetic distance.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

. .

Genomic DNA

Restriction enzyme digest of genomic DNA, e.g. cut with the enzyme EcoRl at specific recognition sites (E).

E.... _

Restricted fragments separated according to molecular weight by agarose gel electrophoresis

Molecular size marker (kb) 23.0 9.4 6.6 4.3 2.3 2.0

PI P2 Fl

Direction of separation

--- -- -- - -r::-: .. .. r-:::

... .. ........ ...

., .. ... ·....·..-. ·...... ·... .... .. .. ... ·... .. ... .·.,.. ··... ..... ... .. '

0.5

F2 progeny

... ....

.. ... ....... ..... .. ... .. ... ....... ..... . .. ........_. ..

r::.. . .. -:-:-

.... .... .... ....... ............ ...... .. ·..·.··..·...-. ..·.·.-.. ... ·;.:.

.. .. ... .. ... ....... ... .. ... .. .. ... .. ... .. ... .. ... .. ... .. ... ....... ..... ';':

... .... .... ... ....... ..... ... -.. .... .. ... .... . ....... ..... ... .. ... .. ... ..

":":'"

-::.... ...... ...

...... .... ·... ·...

..... ··..-. ...... ·..... .... ...... .. ....

c.:.:. ..:.:.

Separated fragments are transferred on to a membrane filter and probed with a radio-labelled DNA fragment. Autoradiograph of probed filter

PI P2 Segregation locus

Monomorphic locus Multiple allele locus

F2 progeny

- -------- ------------- ----

Figure 5.1. Diagrammatic representation of the processes involved in RFLP analysis.

Crouch, Crouch, Fatolrun and Mignouna. 1999. MAS & G x E implications

A large proportion of the genetic changes which result in RFLP occur without any detectable phenotypic effect. For this reason, moltfular markers are extremely useful due to their immunity to bias through dominance, late expression, deleterious effects, pleiotropy, and epistasis. In addition, physical distances have little significance in breeding although they may have serious impJications for marker directed gene-cloning programs. However, the main limiting factor in the production and utilization of marker assisted selection procedures based on RFLP is the level of variability present in the genome of the species being Studied. So little molecular variation was found in cultivated tomato that inter-specific Closses had to be utilized in initial studies (Bematszlcy and T~ley, 1986b). Similar problems have been experienced in a number of other crop specid. In addition, RFLP analysis has the disadvantage of being expensive and time consuming. Nevertheless, the co-dominant nature of RFLP loci can generate more detailed information than analyses based on RAPD or AFLP markers.

5.2.2 Assays Based on the Polymerase Chain Reaction (peR) PCR relies 00 the use of a pair of DNA primtrs (usually 20 oligonucleotides in length) which are designed to hybridize to opposite DNA strands, flanking a region of interest (Saiki et a1. 1985). DNA synthesis is carried out by the enzyme DNA polymerase, derived from the thermophilic bacterium 1'hermus aquaticus (Taq). The region of DNA is amplified exponentially through a series of temperature cycles, which allow for the denaturation of DNA strands, primer annealing and new DNA strand synthesis (Fig. 5.2). The amplified DNA fragment is visualized directly by agarose gelioelectrophoresis, thereby providing a quick and cheap technique (reviewed by Amheim and Erlich, 1992). PCR technology now offers the potential for developing a marker system whereby small leaf samples can be placed directly into a PCR mixture (Oeragon and Landry 1992) and the reaction is of sufficient specificity (Harcourt and Gale, 1991; Higuchi et aI., 1992) to allow fluorescent analysis and thereby selection (Fregeau and Fourney, 1993) within the Eppendorf tube. Based on such a system, marker-assisted selection may now become routinely applicable to breeding programs.

5.2.2.1 Randomly Amplified Polymorphic DNA Rapid fmgerprinting of genomes through RAPD analysis arose from PCR technology. The RAPD technique involves the use of relatively short primers of arbitrary sequences. RAPD analysis has proven to be a versatile method of detecting polymorphisms for genetic mapping. However, genomic fingerprints are generated under conditions where the primer will initiate synthesis on the DNA template even when the match with the template DNA is imperfect. During the amplification reaction, only the most efficient primer-template interactions produce prominent PCR products leading to a fingerprint of a few to more than 100 bands. Unfortunately, RAPD analysis has been known to suffer from a lack of consistency within and between laboratories (Penner et at, 1993; Jones et ai., 1997). The pattern of fragments amplified largely depends on the availability of template sites to which the arbitrary primer can anneal. Thus, differential competition within the peR can be a major source of errors between samples (Hallden et al., 1996). Differences between the methodology of DNA isolation may also be a source of inconsistency (Micheli et aI., 1994). Whilst spurious

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

5' ~----

Primer a

DNA polymerase I

1st Amplification cycle

dNTP's ++

- - - Primer b

5' Each cycle consists of denaturation of template DNA at 94't followed by primer annealing and new DNA strand synthesis at lower temperatures (50-72 't)

t I

2nd Amplification cycle

Nth Amplification cycle

3' Accumulation of major peR products

Figure 5.2. Schematic representation of the processes involved in the PCR.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

annealing of primers also leads to multifarious artifacts but can be minimized by the use of nested primers (Amheim and Erlich, 1992) or mor~ elegant adaptations (Shakey et ai., 1994). Other artifacts in the banding pattern can be overcome by optimizing, for each primer, the PCR reaction parameters: template DNA and magnesium concentrations, and ratio of primer to template DNA concentrations (Innes et at., 1990). Finally, considerable stringency must be applied whilst scoring amplification patterns (Shroch and Nienhuis, 1995). An alternative approach to alleviating the repbatability problems associated with RAPD analysis is the development of SCAR (Sequence Characterized Amplified Regions) markers. This technique is based on the use of RAPD markers to eventually generate sequence specific primers for PCR amplification of a single locus. SCAR markers have now been successfully developed in several systems including from RAPD markers linked to downy mildew resistance genes in lettuce (Paren and Michelmore, 1993).

5.2.2.2 Variable Number of Tandem Repeats (VNTR) Advances in PCR-based marker technologies include the construction of primers from genomic regions flanking microsatellite DNA sequences. These DNA fragments are termed variable number tandem repeats (VNTR), short t~ndem repeats (STR) or simple sequence repeats (SSR) according to their size and nature tJeffreys et at, 1985; Wolff et aI., 1988; Weber and May 1989). Simple sequence repeats (SSR) are regions of short tandemly repeated DNA motifs (generally less than or equal to 4 bp) with an overall length in the order of tens of base pairs. Simple sequence repeat length polymorphisms (SSRLP) are generated by highly specific peR amplification and, therefore, should not suffer from the reproducibility problems experienced with RAPD analysis. SSR have been reported to be highly abundant and randomly dispersed throughout the genomes of many plant species (Powell et aI., 1996). Variation in the number of times the motif is repeated is thought to arise through slippage errors during DNA repJication. Thus, VNTR may occur even between closely related individuals. Microsatellite markers have been used in plants for fmgeIprinting , mapping, and genetic analysis. However, the isolation of micro satellites is time consuming and expensive. Yet with the availability of automated DNA sequencing facilities, improved techniques for the construction of genomic libraries enriched for SSR and improved techniques for the screening of appropriate clones, micro satellite isolation is likely to become increasingly routine. Microsatellite markers are particularly useful in genetic analysis and plant breeding as, in contrast to other PCR-based assays, they provide co-dominant information,

5.2.2.3 Amplified Fragment Length Polymorphism (AFLP) More recently the technique of amplified fragment length polymorphism (AFLPTM)(Zabeau and Vos, 1993) has been successfully applied in a variety of systems. AFLP assays have been demonstrated to have a very high multiplex ratio (average number of alleles detected per assay) in a number of systems including potato (van Eck et al., 1995), rice (Cho et aI., 1996) and soybean (Keirn et a1., 1997). In addition, AFLP analysis requires no prior knowledge of the genome. Unfortunately, the information content of these banding patterns is restricted as, at present, they must be treated as dominant markers. However, when AFLP analysis is applied to large populations. circumstantial allelic relationships may be sufficient for practical purposes. Software has been developed to distinguish homozygotes and heterozygotes on the basis of band intensity. Yet, such an approach may be frequently

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

confounded by the presence of bands of intennediate intensity. AFLP assays are also technically demanding and expensive in that they require a number of DNA manipulations and a complex visualization procedure. In addition, they require relatively large amounts of reasonably higbquality DNA. The use of poor quality DNA may lead to incomplete digestion, which can result in artificial polymOIphisms. However, there is rapid progress in the further development of this type of analysis. In particular, the conversion of AFLP markers to allele-specific PCR assays now appears to be routine in a number of diploid crops (Cho et aI., 1996; Hutton et al. , 1998; Shan et aI., 1998). This offers the possibility of using AFLP technology for the rapid and efficient identification of markers for important agronomic characters. A specific marker may then be converted into a simple PCR assay for easy routine screening within the breeding station. However, it remains to be seen if a similar approach is also effective in complex polyploid crops.

5.3. Methods of jd~ntifying molecular markers associated with agronomic traits

5.3.1 Single marker models Many statistical methods for calculating linkage between markers and the genetic components of a phenotype have been described (Darvasi and Weller, 1992; Haley and Knott, 1992; Martinez and Curnow, 1992). However, these methods are shrouded in complex mathematics and are difficult to conceptualize. Consequently, their relative sensitivities and limitations are frequently overlooked. Alternatively, more robust and routine statistics can be used to calculate the probability that a certain DNA fragment is linked to a region of the genome contributing to the character of interest. Such a method is relevant whether that DNA fragment has been identified by hybridization to a probe fragment or amplified though peR.

An important quality of this approach is that it can be operated in the absence of a dense genetic linkage map. The initial step in such an analysis is to calculate the mean ofphenofYPe scores from segregants possessing a particular DNA fragment (x) and the mean of scores from plants without that fragment (y). One can then test the hypotheSis that x and y are not significantly different from each other using a Students's t-test. If a DNA fragment is closely linked to a region of the . genome contributing to the character of interest, x will be significantly different from y. If there is no linkage, the banding patterns will be randomly distributed in comparison with the phenotype, and x will not be significantly different from y. Such a method is particularly powerful since it can be used to identify markers to both monogenic and polygenic traits . Single point analysis of this type has been described in studies on the genetics of aliphatic glucosinolates and disease resistance in Brassica (Margrath et al., 1994)., flowering time in Arabidopsis rhalitma (Clarke, 1993) and heterosis in maize (Stuber et aI., 1992). These studies report that the fmdings of single point analysis closely compare to those of interval analysis.

5.3.2 Interval mapping Many models have been defmed to estimate the contribution of marker-linked loci to the quantitative trait of interest (reviewed by Arus and Moreno-Gonzalez, 1993). The maximum likelihood method described by Morton (1955 ) for single marker data was further developed

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

by Lander et al. (1987) in order to utilize flanking markers within RFLP maps to mediate 'interval mapping'. This analysis calculates an 'odds ratio' of the chance that the data would arise from a locus with the defined effect divided by the chance that those data would arise given no linked locus. Log lD of the odds ratio is then presented as the LOD ('log-odds') score, which is essentially a measure of the reliability of the hypothesis in the same way as Student's t-test. Tb.~, by scanning the interval between two flanking markers it is possible to identify the most likely position of the locus as corresponding to the site of maximum LOD score (Lander and Botstein. 1989). In this way, entire chromosomes can be scanned for loci contributing to the qualitative and quantitative character of interest (Fig. 5.3). The mathematical basis of these calculations has been reviewed by Luo and Kearsey (1992). The LOD threshold value for statistical significance depends on the size of the genome and the density of the genetic markers mapped. For example, a LOD threshold of around 2.4 avoids false positives with 95% probability when 60 flanking markers in 1200 centimorgans (cM) are tested (Lander and Botstein, 1989). An advantage of this approach is that flanking markers can be identified which are closely linked upstream anddowristream to the locus of interest. This is preferable to a single marker as the probability of two simultaneous crossover events within such a small distance is extremely low.

5.4. Limitations of models for identifying markers to qnantitative trait loci (QTL) A primary problem of single marker models is that they can not distinguish between a marker locus closely linked to a QTL having a small cffectand a marker locus distantly linked to a QTL having a large effect. In the development of a marker assisted selection procedure this can be a serious limitation. However, these models are highly robust to variable data sets. On the other hand, interval-mapping models are not highly robust and demand normally distributed phenotype data exhibiting no dominance component together with Mendelian segregation of DNA banding patterns. If any of these criteria is lacking, aberrant LOD score profiles can rcsult leading to the generation of spurious markers. Furthermore,accurate LOD score profiles are notoriously difficult to interpret in detail (Luo and Kearsey, 1992). For example, a number of QTL may be sufficiently close together to produce a single mode LOD curve (see [b) in Fig. 5.3), although in this situation there is frequtntly a reinforcing effect on the LODscore maxima, In contrast, mUltiple mode LOD curves in close proximity do not necess~y predict the presence of multiple QTL (see [aJ in Fig. 3). In addition, both single marker analysis and interval mapping have serous limitations in their ability to detect QTL of small effect (see [c] in Fig.5.3). Qther limitations can result from the type of molecular genetic technique used. For example, if heterozygous individuals can not be identified from their phenotypes then dominant marker systems (RAPD and AFLP) may become confounded. This is not a problem associated with co-dominant marker systems (RFLP and VNTR). The maximum potential benefit of marker assisted selection procedures is based on the ideal but rare condition of zero recombination between marker locus and the QTL. Even if this is achieved, there remains the possibility of recombination in populations derived "from differing parent genotypes. It is, therefore, crucial that marker assisted selection procedures be developed using populations closely related to those to which it is expected to be applied.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

[b]

[a} 4 Q) L-

a u

CJ)

o....J

-------------------

[c] ----~--~---

i pw \ a~

pO~a

pNeÂŤk

i p07e"

pN404A

pO"'.

Linkage Group Markers Figure 3. Graph of LD scores (calculated by MAPMAKER-QTL) indicating the likelihood of the presence of QTL contnbuting to the phenotype in question at every 2 cM along the specified linkage group. (aJ bimodal LOD peak, [b] single mode LOD peak,and [cJ LOD peak not significant at the threshold LOD score of 2 (from Couch, 1994).

5.S. The relevance of G x E interaction to marker-assisted selection The majority of important agronomic traits in all crop species vary in a quantitative manner. Whether this infers a polygenic basis or a low heritability does not detract from the inevitable importance of G x E interaction in influencing the final phenotype. Furthermore, a high narrow sense heritability does not necessarily mean that a trait is unaffected by its envirorunent, for heritability is not the opposite of phenotypic plasticity. Thus, even with

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

traits of high heritability there still remains the possibility of substantial changes resulting from environmental variation (Suzuki et aI., 1989) . At this point, it is important to clarify the defmition of an environment. In a biometrical perspective, there are both temporal and spatial aspects of an environment. That is to say that both location and season constitute different environments. In addition, there are both biotic and abiotic detenninants of different environments in the same location and season. The potential for identifying markers to individual genes contributing to quantitative characters was demonstrated long before the advent of molecular marker technology (Breese and Mather, 1957; Law, 1967). Yet the major influence of the environment on the expression of such characters has generally restricted the study of quantitative trait loci to the statistical techniques of biometrical genetics (Falconer, 19~9; Kearsey and Pooni, 1996). However, with the development of RFLP and PeR-based mapping techniques there now exists the potential to resolve the genetic basis of QTL in a systematic manner. Unfortunately, the intricate nature of these techniques may imbue a false sense of accuracy. Marker assisted selection procedures generally rely upon reference to the plant phenotype and for this reason G x E effects remain of crucial importance. Indeed, the accuracy of a genetic marker system may be entirely limited by the accuracy of the phenotypic data on which it was originally based. The issue of G x E interaction is particularly relevant in the quest for durable disease resistance. It is an obvious case that different locations and seasons may harbor different strains of a pathogen. It is now well accepted that genes conferring race--specific resistance (and the markers of them) are of limited value even if we are aware of their nature. However, the issue is considerably broader since apparently similar strains of a pathogen may interact differentially with the same host in different environments. Thus, markers for genes effective against a specific strain in one location may again be of little value in another area. It is crucial, therefore, that the development of such selection procedures takes full account of the multifarious interactions within the host-environment-pathogen triangle. However, this is not a unique situation, the same argument applies to the whole spectrum of QTL breeding. G x E interaction may be simply defmed as differential genotypic expression across environments (Romagosa and Fox, 1993). Genetic variation in quantitative characters is generally caused by a few loci with relatively large effects and many loci having progressively smaller effects (Thompson, 1975; Paterson et ai. , 1988). However, the biological complexity underlying these effects can not be defmed so easily. 'Virtually all phenotypic effects are not related to the gene in any simple way. Rather they result from a chain of physico-chemical reactions and interactions initiated by genes but leading through complex chains of events, controlled or modified by other genes and the external environment, to the fmal phenotype' (Allard, 1960). The identification and investigation of cis- and trans-acting loci begins to offer a complex mechanistic basis for these events (reviewed by Suzuki et aI., 1989). However, it is apparent that there are an ever-increasing array of promoters, enhancers and inhibitors of transcription initiation, splicing and termination, plus regulation of translation (Kass et aI., 1997; Maniatis et al. , 1987; Russo et al., 1997). In addition , there are many reports of post-translational regulation (Depicker and Van Montagu, 1997) and protein product activation affecting gene ex.pression (Gallie, 1993). These control elements interact not only with each other but may also interact with their biochemical environment (Kingsman and Kingsman, 1988; lang and Sheen, 1997). Of course, subsequently the biochemical environment interacts with the physiological environment, which in tum interacts with the plant 's external environment (Crosby and Vayda, 1991; Thompson and White, 1991).

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

For these reasons QTL analysis, with its restricted ability to detect genes of apparent minor effect (in that particular environment) may be highlighting only those factors with a pivotal or rate limiting effect upon that chain of reactions. This is not a practical problem where the ranking of loci (in order of relative effect) is consistent across environments. However, differential gene expression may be observed in response to changing environmental pressures, which in tum may result in an altered ranking of loci importance. For example, during an investigation of a quantitative trait in tomato, 29 different QTI.. were mapped using phenotypic characterization in three different environments. However, only 4 were expressed in all three environmerits while 10 were expressed in two environments and 15 expressed in a single em'ironment (paterson et a1.. 1991). Clearly, a marker-assisted selection procedure based upon phenotypic data from a single location would only target a fraction of the genes affecting that character. This will be most devastating when genes having a pivotal effect in one environment have only a minor effect in another envirorunent. In response to this most important issue, researchers in molecular breeding are beginning their study ofQrr x environment effects (Jansen et a!.. 1995; Zhuang et at., 1997). Our perception of the genetic basis of a trait is directly related to the environment(s) in which we have observed the phenotype. From this standpoint, marker assisted selection techniques can only be unambiguously applied to populations growing in similar conditions to that of the original mapping population. For the wide spread use of a marker-assisted selection procedure to be appropriate it must have been developed using phenotypic data collected from replicated experiments in a representative set of locations. In the same way, that it is fashionable to advocate crop breeding for specific ecoregions and ecozones (Ceccarelliet ai. , 1994), we must now be willing to apply a similar logic to the development of marker assisted selection procedures and accept that the modern techniques of molecular genetics are not poised to present an all encompassing short cut to the prolonged methods of traditional plant breeding. 1his is particularly crucial when breeding cultivars for farmers in sub-Saharan Africa. The resource poor nature of fanners in these areas seriously limits their control over the crop environment through maintaining pest and weed control, together with adequate soil fertility and moisture content. There has been a six-fold increase in maize yield over the past 60 years, yet in general, we understand little about the actual genetic basis for these improvements (Helentjaris, 1991). In a sense, the technology underlying modern molecular genetic analysis has surpassed our understanding of mechanisms of plant growth and development and this is a major impediment to future progress. This may be a legacy of the tendency for the power of breeding selection to outstrip knowledge of how and why enhancement in a character has been achieved. However, the modem techniques of biotechnology which promise to make breeding programs more rapid and efficient, rely heavily upon the knowledge in the very disciplines that has been neglected during the past two decades. Metric traits are known to be sensitive to environmental effects and one must anticipate that the same genes cannot be acting in all genetic backgrounds (genetic environments). Otherwise plant breeding would have already reached its zenith in obtaining the maximal yield in many species (Helentjaris. 1991). Here then we approach a further factor; an intra路 genomic 'genetic by genetic environment' interaction. This is of great significance to the use of marker assisted selection procedures for introgressing characters from diverse gerrnpJasm into breeding populations. It is still not clear how gene expression is controlled in eukaryotic cells and even less clear how those control mechanisms are influenced by changes in the environment. Thus, when we transfer what is perceived as the important factors detennining

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

a character we may be ignoring interactions with the genetic background of the donor genotype which are crucial to the successful expression of the trait. Until we understand how that process is modulated by environmental influences, it will be difficult to utilize the full potential of molecular markers. These questions are important to not only recombinant DNA technology but also equally to marker-assisted selection and present one of the major challenges in modem biology (Kingsman and Kingsman, 1988). Nevertheless, it is clear that marker-assisted selection procedures have a considerable potential role to play in aiding the selection of characters that are expressed late in the season or are particularly difficult to score. Already, the enhancement of maize yield has been achieved through marker-facilitated introgression of QTL (Stuber, 1994). In addition, molecular markers provide mechanisms for applying linkage genetic techniques to complex inheritance problems that almost reduces them to the level of studying single gene traits. However, in both cases precision of phenotypic measurements and appropriate application of experimental design become even more critical than ever (Helen~aris, 1991). Furthermore, these techniques highlight the extremely complex genetic nature of many important agronomic characters. Finally, it should be noted that plant breeders are routinely selecting for a large number of complex characters. It is therefore, likely that for the foreseeable future, molecular marker aided selection will be limited to characters for which a large proportion of the variation is under the control of a few loci which are stably expressed across appropriate environments. There remains the Deed for considerable refinement of marker assisted selection procedures before their routine adoption by plant breeders. Furthermore, the integration of modem biotechnology into breeding programs must not overshadow the lessons learnt though traditional plant genetics and breeding.

References Allard R.W. 1966. Principles of Plant Breeding. John Wiley and Sons, New York Amheim N. and H. Erlich 1992. Polymerase chain reaction strategy. Ann. Rev. Biochem . 61:131-156. Arus P. and J. Moreno-Gonzalez 1993. Marker-assisted selection. pp. 314-331. In: Hatward M.D. , Bosema;rk N.O. and Romagosa 1. (cds.), Plant breeding, Principles and Prospects. Chapman Hall. London. Bematsky R. and S.D. Tanskley 1986. Toward a saturated linkage map in tomato based on isozyme and random cDNA sequences . Genetics 112:887-898. Botstein D., R.L.White, M. Skolnick, and R.W. Davies 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Gent. 32:314-331 . Breese E.L. and K. Mather 1957. The organization of polyploid activity within a chromosome in Drosophila: I. Hair characters. Heredity II :373-395.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

Cho Y.G., M.W. Blair, O. Panaud, and S.R. McCouch 1996 Cloning and mapping of varietyspecific rice genomic DNA sequences: amplified fragment length polymorphism (AFLP) from silver-stained polyacrylamide gels. Genome 9:373-378 . Clarke 1. 1993. Molecular a,nalysis of flowering characters in Arab idops is. Ph.D. Thesis, Cambridge Laboratory, John Innes Center, Norwich, England. Crosby J.S and M.E. Vayda 1991. Stress-induced translational control in potato tubers may be mediated by polysome-association proteins, Plant Cell 3: 1013-1023 . Darvasi A. and 1.I. Weller 1992. On the use of the moments method of estimation to obtain approximate maximum b1celihood estimates of linkage between a genetic marker and a quantitative locus. Heredjty 68:43-46. Depicker A. and M. Van Montagu 1997. Post-transcriptional gene silencing in plants. Curr Opinion Cell Biol. 9:373-382 . DeTagon J.-M . and B.S. Landry 1992. RAPD and other PCR-based analyses of plant genomes using DNA extraction from small leaf disks. PCR Meth . Appl. I : 175-180. Falconer D.S. 1989. lntroduction to quantitative genetics. Longman, London. 34Opp. Fregeau C.l. and R.M. Fourney 1993. DNA typing with fluorescently tagged short tandem repeats: a sensitive and accurate approach to human identification. BioTechniques 15:100-119. Gallie D .R . 1993. Post transcriptional regulation of gene expression in plants. Ann. Rev . Plant Physiol. Plant Mol. Bio/. ~4:77-lOS. Haley C.S and SA Knott 1992. A simple regression methods for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315~324. Hallden C., M. Hansen, N.-O. Nilsson, and A . Hjerdin 1996 Competition as a source of errors in RAPD analysis. Theor. Appl. Genet. 93:1185-1192. Harcourt R.L. and M.D. Gale 1991. A chromosome-specific DNA sequence which reveals a high level of RFLP in wheat. Theor. App/. Genet. 81 :397-400. Helentjaris T.O. 1992. RFLP analyses for manipulating agronomic traits in plants. Pp. 357372.1n Stalker H.T. and Murphy J.P. (eds.). Plant Breeding in the 1990's. CAB International, Wal1ingford, England. Higuchi R., G. Dollinger, P.S. Walsh, and R.Griffith 1992. Simultaneous amplification and detection of specific ON A ~equences. BiofI'ech 10:413-417. Hutton M., J. Evans, P. Pipena L. Sexton, and M.S. Malandro 1998 Conversion of AFLP bands to dominant and co路dominant allele路specific PCR based tests. Abstracts of Plant and Animal Genome VI [on. line). http ://probe.nalusda.gov:8300/pag/6review/. Innis M.A. , D.H. Gelfand, 1.1. Sninsky and TJ. White 1990. PCR Protocols: A Guide to Methods and Applications. Academic press, San Diego. 482pp.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

Jang J.c. and 1. Sheen 1997. Sugar sensing in higher plants. Trends Plant Science 2:208-214. Jansen R.C., J.W. Van Ooijen P Starn, C Lister, and C. Dean 1995 . Genotype-by-environment interaction in genetic mapping of multiple quantitative trait loci. Theor. Appl. Genet. 91 :33-37. Jeffreys A.J., V. Wilson and S.L. Thein 1985. Hypervariable ' minisatellite' regions in human DNA. Nature 314:67-73 . Jones CJ., K.J. Edwards, S. Castaglione, M.O. Winfield, F. Sala, C. Van de Wiel, G. Bredemeijer, B. Vosman, M. Matthes, A. Daly, R. Brettschneider, P. Bettini, M. Buiatti, E. Maestri, A. Malcevschi, N. Marmiroh, R. Aert, G. Volckaert, 1. Rueda, R. Linacero, A. Vazquez and A. Karp 1997. Reproducibility testing of RAPD, AFLP and SSR markers in plants by a network of European laboratories. Molecular Breeding 3:381-390. Kass S .U ., D. Pruss, and A.P . Wolffe 1997. How does DNA methylation repress transcription? Trends Genetics 13:444-449. Kearsey MJ . and H.S. Pooni 1996. The Genetical Analysis o/Quantitative Traits . Chapman and Hall, London. 381 pp. Keirn P., 1M. Schupp, S.E. Travis, K . Clayton, T . Zhu, L. Shi, A . Ferreira, and D.M. Webb 1997 A high-density soybean genetic map based on AFLP markers. Crop Science 37:537-543. Kingman S.M. and A.I. Kingman 1988. Genetic Engineering: An Introduction to Gene Analysis and Exploitation in Eukaryotes. Blackwell Scientific PubI., Oxford. 522 pp. Lander E.S., P. Green, J. Abrahamson, A . Barlow, MJ. Daly, S.E. Lincoln, and L. Newburg 1987. MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174-181. Lander E.S. and D . Botstein 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121 :185-199. Law C.N. 1967. The location factors controlling a number of quantitative characters in wheat. Genetics 56:445461 . Luo Z.W. and MJ. Kearsey 1992. Interval mapping of quantitative trait loci in an F2 population. Heredity 69 :236-242. Margrath R. , F. Bano, M. Morgner, I. Parkin, A. Sharpe, C. Lister, C.Dean, J. Turner, D . Lydiate, and R. Mithen 1994. Genetics of aliphatic glucosinolates. 1. Side chain elongation in Brassica napus and Arabidopsis thaliana . Heredity 72 :290-299 . Maniatis T. , S. Goodboum and JA Fischer 1987. Regulation of inducible and tissue-specific gene expression. Science 236:1237-1245. Martinez O . and R.N. CUrnow 1992. Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Th eor. Appr. Genet. 85 :480-488.

Crouch, Crouch, Fatolcun and Mignouna. 1999. MAS & G x E unpJications

McPherson MJ., P. Quirke, and O路.R. Taylor 1993. peR, A Practical Approach. Oxford University press. 253 pp. Micheli M.R., R Bova, E.Pascale, and E. D'Ambrosio 1994. Reproducible . DNA fmgerprinting with the random amplified polymorphic ON (RAPD) method. Nucleic Acid Research 22 : 1921-1922. Morgan T.H. ] 911. Random segregation versus coupling in Mendelian inheritance. Science 34:384. Morton N. 1955 . Sequential tests for the detection oflinkage. Am. J. Hum . Genet. 7 : 277~3l8.

O'Brien S.1. 1993. Genetic Maps: Locus Maps of Complete Genomes, 6th edition. Cold Spring Harbor Laboratory Press. pp272 . Paran 1. and R.W. Michelmore 1993. Development of reliable PCR-based markers linked to downy mildew resistance genes in lettuce. Theor. App/. Genet. 85:985-993 . Paterson A.H., E.S. Lander, J.D. Hewitt, S. Peterson, S.E. Lincoln, and S.D. Tanksley 1988. Resolution of quantitative traits into Mendelian factors by using a complete linkage map of restriction fragment length polymorphism. Nature 335:721-726. Paterson A.H ., S. Damon, I.D. Hewitt, D. Zarnir, H.D. Rabinowitch, S.E. Lincoln, E .S. Lander, and S.D. Tanksley] 991. Mendelian factors underlying quantitative traits in tomato: comparison across species, generations and environments. Genetics 127 :181-197 . Penner G.A ., A. Bush, R. Wise, W. Kim, L. Domier, K. Kasha, A. Laroche, G. Scoles, S.l. Molnar, and G. Fedak 1993 Reproducibility ofrandom amplified polymorphic DNA (RAPD) analysis among laboratories. PCR Methods Appl. 2:341-345 . Powell W., G.c. Mackray, and J. Provan 1996 Polyniorphism revea:led by simple sequence repeats. Trends Plant Science 1:215-222. Romagosa 1. And Fox P.N. 1993. Genotype x envirorunent interaction and adaptation . pp. 373~39 . 0 In Hatward M.D., Bosemark N.O. and Romagosa I. (eds.), Plant Breeding, Principles Qnd Prospects. Chapman and Hall, London.. Russo V.E.A., R.A . Martienssen and A.D . Riggs 1997. Epigenetic Mechanisms of Gen e Regulation. Cold Spring Harbor Press, New York. 692 pp. Saiiki R.K.S., S. Scharf, F. Faloona, K.B . Mullis, G.T. Horn, H.A. Erlich, and N. Amheim I985 . Enzymatic amplifICation of beta路 globin genomic sequences and re:.rriction analysis for diagnosis of sickle cellaoemia. Science 230:1350-1354. Sambrook J. , E.F Fritsch., and T. Maniatis 19B9. Molecular Cloning: A Laboratory Manual, 2掳0 edition, Vols. 1,2 and 3. Cold Spring Harbor Laboratory Press, New York. 1626 pp. Shroch P. and 1. Nienhuis ]995 Impact of scoring error and reproducibility ofRAPD data on RAPD based estimates of genetic distance. Theor. Appl. Genet. 91: 1086-1091.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

Shan X., T.K. Blake and L.E. Talbert 1998 Conversion of AFLPs to sequence-tagged-site PCR markers. Abstracts of Plant and Animal Genome VI [on-line]. http ://probe.nalusda.gov:8300/pag/6review/. Sharkey D.1., E.R. Sealice, K.G. Christy, S.M. Atwood, and J.L. Daiss 1994. Antibodies as thermolabile switches: High temperature triggering for the polymerase chain reaction. BiofTech L2:506-509. Stuber C.W. 1994. Enhancement of grain yield in maize hybrids using marker-facilitated introgression of QTLs. pp. 44-46. In Proceedings of the Symposium on Analysis of Molecular Marker Data. Joint Plant Breeding Symposia series, American Society for Horticultural Science/Crop Science Society of America. Stuber C.W., S.E. Lincoln, D.W. Wolff, T. He1en~aris, and E.S. Lander 1992. Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genetics 132 :823-839. Suzuki D.T., AJ.F. Griffiths J.H. Miller and R.C. Lewontin 1989. An Introduction to Genetic Analysis. 4th edition. Freeman & Co., New York. 768 pp. Thompson W.F. and M.1. White 1991. Physiological and molecular studies of light-regulated nuclear genes in higher plants. Ann. Rev. Plant Physiol. Plant Mol. Bioi. 42 :423-466. Vallejos C.E. 1983. Enzyme activity straining. Pp 469-516. In Isozymes in Plant Genetics and Breeding. Part A. Tanskley S.D. and Orton T.J. (cds.). Elsevier, Amsterdam. Van Eck H.J. , J.R van der Voort., 1. Draaistra, P. van Zandvoort, E. van Enckevort, B. Segers, J. Peleman, E. Jacobsen, J . Helder, and 1. Bakker 1995. The inheritance and chromosomal localization of AFLP markers in a non-inbred potato offspring. Molecular Breeding 1:397-410. Watson J.D., N .H Hopkins, lW Roberts, lA. Steitz, and A.M. Weiner 1987. Molecular Biology of the Gene. Fourth edition, Benjamin Cummings Publishing Co., California. 1163 pp. Wolff R .K., Nakamura Y. and White R. 1988. Molecular characterization of a spontaneously generated new allele at a VNTR locus: no exchange of flanking DNA sequence. Genomics 3:347-351. Weber J.L. and P.E. May 1989. Abundant class of human DNA polymorphisms which can be types using the polymerase chain reaction . Am. J. Hum . Genet. 44 :388-396 . WolffR.K., Y. Nakamura, and R. White 1988. Molecular characterization of a spontaneously generated new allele at a ~1R locus: no exchange of flanking DNA sequence. Genomics 3:347-351. Zabeau M . and P. Vos 1993. Selective restriction fragment amplification: a general method for DNA fmgerprinting. European Patent Application 92402629.7; Publication number EP 0534858 AI.

Crouch, Crouch, Fatokun and Mignouna. 1999. MAS & G x E implications

Zhuang J.-y' , H.-X. Lin, J. Lu, H.-R. Qian, .S. Hittalmani, N. Huang, and K.-L. Zheng 1997. Analysis of QTL x environment inte'faction for yield components and plant height in rice. Theor. Appl. Genet. 95 :799-808.

Carskyand Versteeg. 1999. On-farm testing and G x E

Chapter 6 Farmer-managed on-farm testing: Approach and G x E considerations Robert J. Carsky and Mark N Versteeg 6.1 . Purposes of On-Fann Testing 6.2. Stages of technology testing 6.3 . Farmer participatory methodology 6.4. Choice of sites for OFT 6.5 . G x E in OFT 6.6. Environment effect in OFT References

6.1. Purposes of On-Farm Testing Farmer-managed on-farm testing (OFT) of technology traditionally has three main purposes (Fig. 6.1). The first is to expose the technology to farmers for their reaction as an indicator of adoption possibilities. The second is feedback to the technology developer, if the technology is not acceptable, with suggested modifications. The third is to gather additional diagnostic information on fanners ' practices, resources, production constraints, and perceptions. This additional information should aid in modifying the technology being tested and allow more appropriate technology development in the future. Increasingly, researchers are trying to find ways to incorporate farmers' perceptions early in the development of technologies. The most common way for this to be done for cultivar development is a process known as participatory breeding, in which researchers encourage visits by farmers to fairly large collections of genotypes.

6.2. Stages of technology testing Fig. 6.2 depicts the stages of cultivar testing and their important characteristics, number of cultivars and exposure to farmers. This simplmed scheme may be much more complex in the case of natural resource or crop management systems. Further steps after on-farm testing are handled by extension services. In researcher-managed screening on-station, the ecological adaptability of the candidate technologies is compared. Because there are many genotypes. few other components of the cropping system are tested. In researcher-managed testing on fanners' fields, the ecological adaptability of a limited number of genotypes is tested under variable biophysical conditions. Additional components of the cropping system may be tested simultaneously (technology package). In farmer-managed testing on farmers' fields , ecological adaptability is evaluated under variable biophysical conditions and variable management. Farmer acceptability is also assessed. One could argue that farmer acceptability is more important than measurable yield improvement because it is an indicator of potential adoption and therefore evaluation of fanner acceptability should be emphasized in farmer-managed on-farm trials.

Carsky and Versteeg. 1999. On-fann testing and G x E

1Diagnosis

Technology Dissemination

On-farm Testing

Fig. 6.1. A simplified farming systems research framework showing the purpose of on-fann tests of technology, 1) to indicate adoption potential for dissemination, 2) to suggest modifications to technology generators, and 3) to add to diagnostic information.

6.3. Farmer participatory methodology Very often in the past, and sometimes in the present, researchers ask farmers to test a technology without a thorough discussion of their problems and possible solutions. Our approach is to 1) discuss production problems and their causes with farmers in group interviews and concentrate on the most stressing ories (Tripp and Woolley, 1989; Versteeg and Koudokpon, 1993 ; Werner, 1993), 2) discuss possible solutions to be tested, 3) propose a way to test each solution (objectives, treatments, observations to be taken, responsibilities of partners), and 4) ask for volunteers . Very important for good participation is that the experimental layout is completely clear so that the trial can be monitored independently by the farmers themselves (Versteeg and Koudokpon, 1992). The unifying principle in all of the approaches proposed (e.g . Lightfoot et at, 1988; Kean, 1988; Werner, 1993; Ouedraogo et aI., 1994) is that interaction between researchers and farmers must be increased to help orient the research program.

Carsky and Versteeg. 1999. On-farm testing and G x E

Res earc her - Managed Varietal Screening On-S tation

!.1

1 "~edb~~'--

!....

Res earc her - Manaoed Screening on Farmers' Frelds

I ~

Farmer-Managed Testing on Farmers' Fre Ids

â&#x20AC;˘ IncreaSing Exposure to Farmers

Fig. 6.2. Stages of cuJtivar testing and their principal characteristics. number of cultivars and exposure to farmers

We find that this makes the farmer feel part of the research team. Consequently this assures appropriate field selection, lessens the probability of trial abandonment, and increases the value of farmer observation/feedback. An important bonus of this approach is that researchers learn about farmers' criteria for judging the performance of the technology. Fanners may even propose other treatments or trial designs. Farmers who are conducting the same trial should interact with each other and with researchers during the season and at harvest (Norman et aI., 1988; Versteeg and Koudokpon, 1993). This helps to elucidate the causes of the problem that the technology is meant to address and it helps researchers to learn about farmers' perceptions. Another approach is to give farmers a small quantity of seed and have them grow it in their system. Th1s was used to spread new IITA maize varieties throughout the Far North province of Cameroon by the Maroua Testing and Llaison Unit. This is also the method being used by the Kano team to obtain feedback on cowpea varieties. Even better is to invite farmers to the research station to observe and criticize technology in the pipeline. At the

Carsky and Versteeg . 1999. On-farm testing and G x E

i7lstitul des Sciences Agronom iques du Burundi fanners visited to rate multipurpose trees in the station nursery and take seed of those that they rated highest (Hitimana et aI., 1994).

6.4. Choice of sites for OFf The fU'St criterion for selection of OFT sites is similar to that for on-station screening, i.e. the agroecotogicaL zone (LGP/vegetation and soils). Other important criteria are socioeconomic in nature. We look at factors, which influence adoption of technology such as population density and market access (Smith, 1992). Population density determines the scarcity of very important production factors, land and labor. Market access determines accessibility and price of purchased inputs and outputs. Land use intensity and crop diversity are other important characteristics of farming systems and therefore they must be known for OFf sites. For example, cropping intensification is stimulated by increasing population, by improved marketing opportunities, or by a combination of the two factors. Different technologies will be appropriate for these different situations. Market access has been shown to influence crop diversification (D. Baker, unpublished data, 1994) and will therefore influence technology adoption. Thus, within an AEZ, multiple OFf sites should be chosen to represent different socioeconomic characteristics. Examples would be poor versus good market access in the northern Guinea savanna and low versus medium or high population density in the humid forest. Fanners ' availability to resources (land, labor, animals, equipment, cash, inputs) should be recorded in each OFT site as this will help in the interpretation of the results and fonnulation of recommendations.

In the testing site, the technologies or systems being tested should address the most important constraint facing farmers. Our experience is that motivation rises dramatically when fanners are confronted with a problem for which they have no endogenous solution. This will ensure a large number of farmer participants. They will be more likely choose an appropriate testing site and less likely to abandon the trial. This results in enough data for meaningful statistical analyses. We feel that the results are then statistically reliable and more meaningful than results from on-station trials in a limited number of sites. Relevance to the farmers' major problem should increase the probability of close observation of the trials by farmers and result in useful farmer opinion data.

5. G X E in OFT Farmer-managed OFT are generally unrepHcated. Farmers' fields provide the replicates because it generally more useful to sample as many environments as possible (Hidebrand and Russell, 1996). Thus, G x E is the residual source of variation in an ANOV A (Table 6.1). Table 6.1. Sample ANaYA Source of variation Rep (i) Variety CD Replication x Variety Total

Degrees of freedom

i-1 j - 1 (i - 1)( j - 1) ij - 1

Carsky and Versteeg. 1999. On路farm testing and G x E

Therefore the statistical significance of G x E cannot be assessed in the ANOVA . However, the fraction of the variation (SS) due to G x E can be calculated and compared to that of the environment and the genotype factors.

In OFT, replicate (i.e. environment) SS is usually very high. This is of major interest for our diagnostic purpose and for better targeting of technology. The analytical technique often used is stability analysis, as for multi-locational researcher-managed trials . However, in onfarm trials, there are generally more environments than in multi-Ioeational cultivar screening trials. Therefore, this use of regression analysis has become very popular with on-farm researchers (Hildebrand and Russell, 1996; Mutsaers et a1. , 1997). While in an A...~OVA, high G x E may mask an important genotype effect, stability analysis will make evident the nature of the G x E . An example is shown in Fig. 3a and 3b. In some cases there may be no genotype effect but G x E shows a cross-over indicating that one variety does best in one set of environments and the other variety does best in another set of environments (Fig. 3c). For on-farm experimentation, this technique is now called Adaptability Analysis (Hildebrand and Russell, 1996; Mutsaers et a1., 1997) and is used to identify specific conditions under which a technology can perform relatively better than others. Hence, it can be used to identify specific adaptability .

It is important to remember that in a farmer-managed trial, an environment is not simply the biophysical envirorunent in which the crop is growing (climate, soil, pests). The environment includes the management imposed by the farmer. Thus collateral data collection must include important aspects of trial management by the collaborating farmers. In one example of maize varieties in southern Benin (Fig. 6.4), the genotype effect was statistically significant in the ANOVA. Nevertheless, stability analysis shows that the yield advantage was present only with the 50% best farmers. Hence, the clear advantage is only present in fields with more optimum growing conditions. The variety may. however, be recommended, if other characteristics (such as resistance to storage pests and product quality) are acceptable, as the yield is never worse than the traditional variety. Stability analysis for a cassava variety trial in northern Benin (Fig. 6.5) shows a clearer yield advantage for the improved varieties observable even on fields with low yield potential and when G x E was minimal.

Car sky and Versteeg. 1999. On-farm testing and G x E

Fig. 6.3. Stability analysis with important G x E

6.6. Environment effect in OFT In OFT there are many contributions to variation due to environment. One of the challenges of the on-farm researcher is to elucidate the main factors, which detennine this variation. This is the essence of the diagnostic Tole of OFT mentioned in section 1. One of the easiest ways to elucidate important relationships in an on-farm trial is to stratify the farmers' fields (reps) based on important differences in management.

An example of the use of stratification to analyze data from farmer-managed trials was provided by Palada et a1. (1989) who gave rice varieties to farmers to grow at two fertilizer rates. Since conditions are highly variable on farmers rice fields, a small number of parameters believed to be critical to rice production were monitored. These included duration of floodwater, age of seedlings at transplanting, and frequency and quality of weeding. Rice

Carsky and Versteeg. 1999. On-farm testing and G x E

yields in fields flooded for more than 50% of the crop cycle were twice those in fields flooded for less than 50% of the crop cycle . Yields from seedlings less than 30 days old were 64% higher than when seedlings were more than 30 days old. Yields on fields where weed 2 management was good, fair, and poor were 3, 1.8 and 1.2 thaÂˇ , respectively. These estimated effects of non-treatment factors were much greater than the treatment effects, variety and fertilizer (Fig. 6). This suggests that research might be oriented toward technology development to address these factors ,

MEAN PER TREATMENT tlha ,.VERAGES:

â&#x20AC;˘

LOCAL

6- It.APROVED

TOTAL 50i! BEST 50"; WORST 1.090 1 .530 11 .630 1 .<450

2.060

0.8 1 5

MEDIAN 1.010 t/ha

1.310t/ha

3 2 1

1 2 3

MEAN OF ALL TREATMENTS (E.I.) tlh8

Fig . 6.4. Stability analysis of maize grain yields as influenced by variety on 202 farmers' fields in southern Benin (1986-1991).

Carsky and Versteeg. 1999. On-fann testing and G x E

MEAN PER TREATMENT tlhl1

AVE RAGES;

lOTA L 50~ 8 EST So. WORST

MEDIAN

TMS 30572

11.2

11.7

7, 6

10 .611ha

8EN 86052

9.1

12.2

6.1

9.3 II ha

â&#x20AC;˘

LOCA L

15 10

MEAN OF ALL TREATMENTS (E .I.) t/ha

Figure 6.5. Stability analysis of cassava fresh root yields as influenced by variety on S6 fanners' fields in northern Benin (1993).

Another example of the use of stratification to elucidate useful relationships is provided by de Steenhuijsen Piters (1993). He found that a great part of the variability in sorghum yield in a village in northern Cameroon was explained by distance of the field from the house (Table 6.2) because distance to the field was related to a few determinants of sorghum yield, major ones being manure application and weeding. This suggests that technology developed for faraway and nearby fields might be different. An example of treatment of observatl'onaI data by regression is reported by Mutsaers and Walker (1990). In fanner-managed tests of maize varieties and fertilizer, additional observations included soil properties, plant density, distribution of trees and shade in the plots, frequency and quality of weeding, and incidence: of pests and diseases . Shade and plant density were significant covariates explaining not only differences in yield between fanns but differences between plots within fanns.

Carsky and Versteeg. 1999. On-farm testing and G x E

Regression was also used to elucidate relationships between millet yield and selected management and environmental factors in the Sahelian zone of Niger by ICRISAT (1983). The relationships shown in Table 6.3 suggest areas of future research for the millet-based system.

Variety

Fe rtlliler

Seed li ng age

Flood duration

Weed management

0\0

100

120

140 160 180

Yield InCfelSse (l!l)

Fig. 6.6. Relative contribution to yield improvement in rice by some agronomic factors in inland valleys (from Palada et aI. 1989). Table 6.2. Relationships between distance from house to field and selected determinants of sorghum yield (from de Steenhuijsen Piters 1993) Distance to field Number of field s measured Sorghum grain yield (kg ha- I) Years of continuos cropping Manure application (kg ha-1) Urea application (kg ha-I ) Emerged striga (ha-I) Number of weedings Weeding labor (h ha- l )

0-0.8 km 95 2890 35

1.5-7 kin

1200 7 40,200

150 28 19,100

1590 4

1.8

1.5

570

310

Carsky and Versteeg. 1999 . On-farm testing and G x E

Table 6.3 . Regression coefficients (b) and significant levels (P) of millet yield on management and environment factors in fanner-managed on-fanntrials in Niger, 1983 (from ICRISAT Sahelian Report, 1983)

Southern site P

Northern site Number of farmers Days from planting to fIrst weeding

b 26 -8 .1

26 0.05

-7 .2 0 .001 Shibras millets Ear head caterpillar -• Striga .. Stem borers Soil organic matter • -20 0 .22 , Soil silt(%l Soil clay (%) 19 0.16 SoilP • mdicates regression coefficient With an unexpected Sign.

.. ..

•

---

b 28 -3.0

28 0.03

-5.0 -0.7 -1.6

0.24 0 .92 0.64

13 .9 107

0.04

0 .001

0.01

•

A somewhat ideal case is when the environmental index can be tied to only one measurable parameter or field characteristic. Singb (1990, cited by Stroup et al. 1993) superimposed the easily known fIeld history (categorized as first, second, or third year out of the primary forest; flISt, second, or third year out of secondary forest; and wasteland) on the environmental index in the stability analysis of different soil amendments for cowpea. This allows recommendations to be made simply based on the field history.

The above techniques are not a substitute for discussion with fanners. They serve to strengthen the knowledge base on which technology development can be oriented and should complement the knowledge 'Of the cropping system and production constraints normally learned by talking to farmers and observing their practices in the field.

References Hildebrand P.E. and IT. Russell 1996. Adaptability Analysis: A method for the Design, Analysis and Interpretation of On-Farm Research-Extension. Iowa State University Press, Ames, Iowa, USA. Hitimana L., S. Franzel, and E. Akyeampong 1994. On and off station with farmers. Agroforestry Today 6(3): 11-12. ICRlSAT. 1983 . feRfSAT SaheJian Center Annual Report for 1983. ICRIAT, Niamey, Republic of Niger. Kean S.A. 1988. Developing a partnership between farmers and scientists: The example of Zambia's adaptive research planning team. Experimental Agriculture 24:289;299.

Carsky and Versteeg. 1999. On-farm testing and G x E

Lightfoot C., O. De Guia Jr., and F. Ocado 1988. A participatory method for systems-problem research: rehabilitating marginal uplands in the Phi1ippines. Experimental Agriculture 24 :301-309. Mutsaers H.1.W. and P. Walker 1990. Fanners' maize yields in S.W . Nigeria and the effect of variety and fertilizer: An analysis of variability in on-farm trials. Field Crops Research 23:265-278. Mutsaers H.1.W., G.K. Weber, P. Walker and N.M. Fischer. 1997. A Field Guide for OnFarm Experimentation. IITAJCTNISNAR, Ibadan. Nigeria. Norman D., D. Baker, G. Heinrich, and F. Worman 1988. Technology development and farmer groups : Experiences from Botswana. Experimental Agriculture 24:321-331. Ouedrogo S., P. Kleene, G. Faure, and A. Djiguemde 1994. Le conseil de gestion comme methode de recherche participative pour une agriculture durable: Experince du Burkina Faso. Paper presented at a symposium on Indigenous knowledge systems and participatory research for natural resource management and sustainable agriculture in West Africa, Cotonou, Benin, 20-22 June, 1994. West African Fanning Systems research Network. Palada M .e., T. Wakatsuki, and 0 .0 . Fashola 1989. Farmer-managed on-fann trials (OFT) in Bida, Nigeria. 1987 Annual Report of Resource and Crop Management Program . UTA , Ibadan, Nigeria. Smith J. 1992. Socioeconomic characterization of environments and technologies in humid and sub-humid regions of West and Central Africa. Resource and Crop Management Research Monograph /0. fiT A, Ibadan, Nigeria. Steenhuijsen Piters B. de. 1993. Variation et variabilite de la culture du sorgho pluvial. Unpublished report to Agronomy Department, Agricultural University of Wageningen, Departement des Systemes Alimentaires et Ruraux, ClRAD, and Institut de la Recherche Agronornique, Maroua, Cameroon. Stroup W .W., P.E. Hilderbrand, and C.A. Francis 1993. Farmer partICIpation for more effective research in sustainable agriculture. In. 1. Ragland and R. Lal (cds.). Techn ologies for Sustainable Agriculture in the Tropics . American Society of Agronomy Special Publication No. 56. ASA, Madison, Wisconsin, USA. Tripp R. and 1. Woolley 1989. The Planning Stage of OrI-fann Research: Identifying Factors for Experimentation. CIMMYT and CIA, Mexico, D.F. and Cali, Colombia. Versteeg M.N. arid V. Koudokpon 1992. Methodologie de I' experimentation en milieu reel. . pp. 54-67. In V. Koudokpon, ed. Pour une recherche participative. Starategie et development d 'une approacbe avec les paysans au Benin. Direction de la Recherche Agronornique, Cotonou and Royal tropical Institute, Amsterdam Versteeg M.N. and V. Koudokpon 1993 . Participative farmer testing of four low external input technologies to address soil fertility decline in Mono Province (Benin) . Agricultural Systems 42:265-276.

Carsky and Venteeg. 1999. On-farm testing and G x E

Werner J. ]993. Participatory Development of Agricultural Innovations: Procedures and Methods of On-Farm Research. GTZ, Eschbom, Germany.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Chapter 7 The Value of Crop Quality Evaluation and End-user Response in Genotype x Environment Analysis R. ShaunB. Ferris, Mpoko Bokanga, Rodomiro Ortiz, and Dirk Vuylsteke 7. 1. Introduction 7.2 . Background infonnation to the yield drive 7.3. The challenge for postharvest research 7.4. Future quality screening techniques 7.5. Conclusion References

7.1 Introduction Traditional genotype x environment (G x E) places emphasis on yield and yield stability with little attention being given to environmental effects on crop quality and regional perceptions of food quality. The priority for yield relates to the need for increased production and this approach has been maintained by an agricultural system, which is dominated by agronomy. However, commercial improvement programs are changing their strategy from yield driven selection to a more comprehensive approach in which the selection is dictated by the broader quality aspects of a crop. This strategy .targets crop breeding populations towards specific markets and tailors the end product, j.e., the food to the consumer. To integrate this quality based approach within the selection process, improvement programs need an in depth knowledge of the marketable products of a crop, the relative demands for the product range and a clear unde~tanding of the measurable quality traits which underlie each product. Having established qhality thresholds, these values can be used to select for quality based ideotype when evaluation techniques have been developed. These tests are required at the physical, chemical, sensory and market levels to facilitate selection. Moreover for practical adoption, quality tests need to be rapid, consistent, and if possible economic. In situations where the accuracy of a data.set is compromised by time delays, a range of tests are required which offer different levels of data resolution at rates, which are compatible with the breeding process. This paper considers some of the tests used in quality selection and how these methods could be incorporated into a G x E selection process, to integrate the demands of the market, thereby providing fanners with more profitable cultivars and consumers with better quality food.

Ferris, Bokanga, Ortiz and Vuylsteke . 1999. Crop quality and G x E analyses

7.2. Background information to the yield drive The convenient use of G x E analysis has become a routine method by which breeders explain variation in crop performance across locations, years, and genotypes. This analysis is typically used to assess yield stability and select genotypes, which are less likely to Wldergo unexpected failures or low yields due to poor adaptation in farmers fields. Advocates claim that G x E analysis is a particularly important part of crop testing in developing countries where fanners need higher yielding varieties but have little insurance against hardship when crops fail. In terms of attaining cultivars for food security, G x E analysis is a powerful aid to the selection process, but interpretation of results however should be viewed critically as insufficient site data often causes problems with explaining variations in yield. Hence, low yield attributed to genotype may in reality be caused by fertility or water gradients, adverse or atypical climatic conditions, poor plot management or incidence of random levels of attack from pests and disease. In addition to the often over-simplified environmental link, many G x E experiments do not consider the regional effect (E) on crop quality. Moreover little effort is maintained to document the local understanding of crop and food quality. Consequently the market demand side of the breeding or product development is neglected and quality assessment is left to the marketplace, which is a hazardous approach. Disregard of market demand may be well suited to the crop evaluation process during the initial stages of a breeding program or at times of frre brigade against a virulent new diseases. This approach is however less appropriate for improvement programs, which propose to release a continual flow of materials aimed at increasingly targeted markets. Hence as breeding programs mature, more attention should be given to the locational qualities desired by conswners and how to tailor the quality of breeding populations to the market demand. Although investing in a quality based breeding strategy may prove expensive at the time of adoption, over the long teon, this approach will enhance the rate of adoption of new cultivars by fanners . This may avoid the prospect of released material being rejected at the market place after many years of yield evaluation. Any cultivar could undergo extensive G x E analyses and a high profile release campaign may be given, but it can fail at the adoption phase due to poor cooking qualities. In order to avoid similar problems an alternative but compatible approach to the standard G x E analysis is to incorporate the most important quality screening techniques into the multi-IDeational selection procedure, and to relate this infonnation to locality specific utilization. The aim of the additional infonnation being given is to gain a more comprehensive idea of market demand, to acCOWlt for the end used perspective, and thus generate more profitable genotypes . Integrating a food quality screen to the G x E procedure may at fust meet with some resistance, as more tests will increase the time of evaluation, add cost, and also pose the problem of when and to what detail should quality traits be evaluated in the flow of genetic materials . Unlike physical gradients, quality perspectives may differ according to location, ethnic group, fmancial status, and whether an individual is a farmer, a processor or a consumer. Hence, the improvement program should clearly identify which quality traits may be difficult to quantify and evaluations methods may not be readily available to capitalize on traits which are known.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Despite these obstacles, evidence shows that product quality-based breeding is successful and as the market is the major consideration, farmers can serve their clients better. A good example of breeding for the market or the end user perspective is given by the improvement in wheat bread quality in the United Kingdom (UK). In the 1940s and 19505, high quality "strong" bread making wheat was imported from Canada (environment 1) and blended with the local wheat (environment 2). This approach was followed because the lower temperatures in Canada produced grain with higher gluten content, than could be produced in the UK. Since that time the content of UK grown wheat in bread making grist has risen from 25% in the mid 19705 to over 86% in 1992. This was achieved by breeding for spec1fic bread making quality traits, i.e. related to gluten content.

In order to improve the quality of the UK wheat varieties, techniques were devised to test for glutenin sub-units known to produce good quality bread, from part of a single grain. This method was refmed so that a quality assessment could be made from a part of a seed that was subsequently grown in a single seed-des cent-breeding system. The success of this breeding system had a major impact on the bread making industry and for the farmers who were able to grow and sell high value wheat at a premium price (Simmonds, 1993).

7.3. The challenge for postharvest research To emulate this success a major challenge for postharvest research is to a) identify market requirements, b) develop screening techniques which are practical for specific stages in the flow of materials and c) particularly to enable improvement programs to take full advantage of GxE experimentation. The first step in a quality-based breeding approach to develop a comprehensive database on quality standards of a crop as perceived by the consumers. This information needs to be collected and assessed as the basis for developing an ideotype from which quality-based screening methods can be developed. GxE trials may provide an extremely useful tool in collating this information. A database such as these will become increasingly useful as multilocational trials encompass more countries and therefore more agro-ecological and cultural zones. The advantage for improvement programs is that by linking agronomic performance with quality based data and integrating this with market studies, the materials are developed within a commercially driven framework and therefore run less risk of poor rates of adoption.

7.3.1 Market data To develop a quality-based screen, the starting point is to determine the market demand or end user perspective. For a given crop there may be certain products which dominate the marketing or utilization profile, and within a specific product there are usually many variations ona theme. Taking the example of plantains and bananas, the information in Tables 7. 1 and 7.2 shows that the end uses of these crop products in the fresh and processed markets are numerous . Although this list is not exhaustive, it provides an idea of the variation in utilization according to ripeness, cultivar, region, and sophistication of processing and marketing. The benefit of such a chart will be improved with additional infonnation on sales volumes and profit. Such data would indicate current and potential markets. However gathering this information would require a detailed study.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

When markets are considered plantain breeders have a complex target because the fruit are consumed when unripe, ripe and over-ripe (Tables 7 .1 and 7.2). The fruit can be eaten when raw, boiled., steamed, pounded, fried., dried or when made into a porridge, pancakes or pressed to produce a juice, from which beer, wine or gin can be processed. The difficulty for the breeder is whether to produce a hybrid which has average quality across full range of potential products or target a specific genotype to a particular product. Success of this latter option was discussed in an earlier paragraph (refer to section 2). A major obstacle for the breeder in this type of decision making may be access to information and G x E studies could be used to identify, which genotypes are suited to location specific markets. The location specific data may also prove a useful means to identify the important quality traits, which require improvement and this enabling the breeder to tailor the product to the market.

7.3.2 Locational perception of food quality After establishing market utilization studies there is a need to determine locational perceptions of quality . This is well described for products in the United States and a comparison of eating habits of people in the United States compared with Africa, highlights the increased challenge for African crop breeders. In the United States, staple foods have become homogenous across large blocks of the country and this influence has spread across the globe thereafter. The demand for specific quality attributes is typified by the global fast food chain, which mostly considers one type of potato cultivar to produce their French fries . To accommodate the preferred potato cultivar, processors and restaurants have developed machinery and techniques specifically for a specific cultivar in order to reach a global standard. Chips provide one example, but the Irish potato bas many end uses and for each potato product, there is a well-established market, providing the breeder with a defined regional or global goal. Hence, new varieties of potato are classified according to their size, color and suitability i.e. chips, crisps, French fries, boiling, baking, roasting , new potatoes, and also for processing potential to provide starch and dextrose. For the breeder, this common appreciation of a product is highly desirable as crop selection can be made in the knowledge that there is a large potential demand.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Table 7,1 , Traditional methods of cooking and

e~(j!J.g

plantain and banana as a fresh product.

Method (fresh market) Raw fruit

Process

Source

Peel and eat

Bananas (AAA) and cooking bananas (ABB)

Boiled

Unripe fruit cut into pieces and boiled Parboiled and then pounded into a dough Unripe East African highland banana Ripe sliced plantain, fried in cooking oil Ripe fruit fried in oil and soiled with pepper or ginger Unripe fruit, sliced at 2 mrn and fried in oil, then flavored with salt or honey Fruit roasted over a fire

Boiled and pounded Steamed

Fried

Fried and spiced Fried and flavored

Roasted

Mixed with maize flour and fried

Pancakes made from over-ripe fruit mixed with flour to make a pancake

Product made from plantain and banana

East and West Africa , Boiled plantain

l Unripe plantain

I Unripe

Ripe plantain

Location

West Africa II

Fufu

Ghana

Matooke

East Africa

Dodo

Nigeria

Dodo ikire, red-red plantain

West Africa

Roast pLantain

West Africa

Chips

Unripe - semi ripe plantain roasted Over-ripe fruit

Pancakes

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Table 7.2. Methods and storage life of processed food and other plantain and banana products. Blanching

Headng tbe fruit (S-lOminsln water at600q

Dehydration

Reduction of water content can be achieved by a number of methods: Sun dryjng

Unripe fruit

Fruit is easter to peel and Rour produÂŤd from blanched fruit is whiter

Storage period

Unripe fruit

Banana stIcks which are ground to flour when needed Fiour-bread cookies, SoyMusa baby food Powderconfectionery filler. Chips

For 2 to 3 .days

Fan assisted oven Unripe fruit Spray .dryer Ripe fru it Unripe fruit

Fermentation

Change of sugars to alcohol

Addilionof acetobacor to wine Removal of alcohol from water Canning Placing fruit in a syrup solution which is sterili zed and then sealed in an aluminum can Jamming Fruit boiled in sugar and solid KetchupJPuree ' Blending of excess ri pened fruit Blending of unripe Glue fruit Anaerobic Si laRe Saponification Soap Roofing material Acetic fermentation Distillation

Bio gas Fiber

Anaerobic digestion Rening of pseudostem and leaf

I year in sealed container

Fritters, Akara

Frying Frying Freeze drying

I year in sealed container

I Desiccated chips,

6 months in sealed bags I day I year in sealed bags 4 to 5 days. 10 days; 5 years jf pasteurized. !fsealed indefinite Ifsealed indefmite If chilled 2 to 3 years; non chilled 6 months

breakfast cereals RIpe fruit Ripe fruit Ripe fruit

Banana wine

Beer 2-5 '/0 alcohol, Wine 10-15% alcobol Low grade vinegar

Banana beer

Gin

Ripe fruit

Fruit salads

Ripe fruit

Dessert fruit spreads

Over ripe fruit

Sweet glue

Unripe fruit Pulp and peel Peel Cut and dried leaves or pseudo-stems Pul p and peel Pseudo-stem and leaves

Starch glue Animal feed Toilet soap

, Fuel Fiber for cloth

I t02 years if seated I to 2 years if sealed I to 2 years jf sealed 6 months Unknown

Immediate use Unknown

Ferris, Bokanga, Ortiz and Vuylsteke. ] 999. Crop quality and G x E analyses

In Africa, cultures have remained more defmed and traditional African recipes retain their cultural roots and heterogeneity. Therefore, when developing a staple crop such as cassava or plantain for Africa, there may not be a continent-wide concept of best quality. The end use and quality criteria fOJ the crop are far more likely to be influenced by how it is prepared, where it is tested and by whom. Incorporating the end user perspective of product quality into the G x E analysis will enable breeding programs to have a broader understanding and perhaps take advantage of this heterogeneity. In order to test the hypothesis that end user perspective significantly changes according to location, a range of Musa genotypes from a multilocational trial were compared across sites and countries. Sensory evaluations were made using taste panes, to quantify the acceptability of plantains (AAB), cooking bananas (ABB), plantain hybrids (AAAB) and desert bananas (AAA). In Nigeria, plantain and banana fruits were assessed as boiled unripe fruit and fried ripe, whereas in Ghana the same clones were tested when cooked as boiled unripe fruit and 'fufu' , ' Fufu' is the traditional Ghanaian dish, which is made from parboiled plantain, which is subsequently mixed with cassava and pounded into dough. The results from this simple analysis clearly showed that the rank order for clonal preference depend on method of preparation, as determined by location. The general order of preference for the panelists group in southeast Nigeria for both boiled and fried fruit was for plantain landraces, plantain hybrids, cooking bananas, and dessert bananas (Table 7,3). When the same taste panel tests were made in Ghana, panelists preferred the plantain landraces as boiled fruit but surprisingly preferred plantain hybrids to the landraces when tests were made for fufu , This was a valuable fmding for lIT A considering the potential for release of plantain hybrids in Ghana. This fInding indicates that when sensory data is incorporated as part of the G x E approach, the breeder is provided with the added dimension of food quality and acceptability. This data set also reveals the ability of the breeder to seek yield stability while capitalizing on locational quality based advantages of clones, even if these are not the highest yielders , The level of analysis from the combination of agronomic and quality based data can also provide useful infonnation of the parental influence on quality and the regional bias for such effects. The data in Table 7.3 shows effect of the parental influence on quality and the regional bias for these effects. In the breeding scheme of I1TA, the majority of hybrids are derived from crosses between the female landrace plantains, Obino l'Ewai (OL) and Bobby Tannap BT), crossed with pollen from two diploid bananas, cultivated dessert banana Pisang lilin and a wild diploid banana Calcutta 4 (C4). The information in Table 3 shows that for the Nigerian taste panelists, the OL x C4 crosses were preferred while hybrids from crosses with Pisan lilin were regarded of low quality. In contrast, in Ghana Pisang lilin-derived hybrids were either preferred or were given a high score.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Table 7.3. Descriptive acceptance scores (AS) of fried ripe Musa fruit in Nigeria and Ghana. Cultinr

Musil

Parents

Agbagba Obino l' Ewli TMPx S48-4 TMPx 548-9 TMPx 582-4 TMPx 1112路1

TMPx 1658-4

TMPx 2796路5 Bl~ggoe

C.ardaba Pelipita FHIA路3#

Plantain landracc Plantain landrace Plantain hybrid Plantain hybrid Plantain hybrid Plantain hybrid Plantain hybrid Plantain hybrid Cooking banana Cooking banana Cooking banana FHIA hybrid

Nh~eria

set

Fried ripe frull1 AS

unripe Crult: AS Boiled

Boiled unrlpe

Good

Crult: AS Good

Good

Acceptable

best

C4 xOL

Acceptable

C4xOL

Acceptable

C4xBT

C4x FR

Improvement required Acceptable

PI xOL

! !

PI xBT

Improvement required Improvement required Improvement requ ired Acceptable

Acceptable

Improvement required

pounded fruit: AS

Acceptable

Good

Acceptable

Good

Accep:able I

Very good

I 0 I

Ghaoa R Boiled

j 7

Il11lroVemeDl

required Improvement required Improvement required Improvement required Acceptable

Acceptable

Good

Improvemc nl require<!

Good

Unacceptable

C4 = Calcutta 4, OL = Obino l'Ewai. BT = Bobby Tannap, FR = French Reversion, PI = Pisang lilin. R - Rank.

The reasons for this result are not clear. But for the breeder, this would mean that hybrids derived from crosses between plantain landraces and Pisang Iilin are more suited to Ghana than in Nigeria., hence these hybrids should be targeted accordingly. Building a regionally based idea of quality can be initiated with a limited number of quality tests, but to expand this to a broader quality profile, as indicated by the data in Table 7.4, would be a more desirable goal. Thereafter, as breeder could use such inforrnatiOl:l to target clones to specific eco-zones or regions with greater confidence that they would be readily accepted. This general quality testing approach can be applied across crops to provide a broad-based profile for national programs to select and test the breeding genetic materials.

7.3.3 Specific commodity traits Although a general quality profile can be applied within a GxE database, certain crops also have highly specific quality trait, which require special studies. For cassava, the cyanogenic potential (CNp) of a hybrid is of particular interest to breeders as the CNp can change dramatically due to location (Bokanga et al., 1994). An understanding of the G x E effect on this characteristic is critical in the development of cultivars aimed at large ecoregional spread and particularly for the release stage.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

In crude tenns, cassava gennp1asm can be divided into two categories, viz., 'sweet' and 'bitter' cassava. Although there are exceptions, most bitter cassava cultivars as compared with sweet cassava cultivars have the potential to contain significantly higher levels of the cyanogenic glycosides (linarnarin and lotaustralin) within their cells. When cells of bitter cassava are crushed, the li.namarin and lotaustralin are hydrolyzed by enzymes, such as linamarese, held within the cellular vacuoles, to yield the toxic compound hydrogen cyanide. TIlls pathway is common to more than 2000 spec ies in the plant kingdom and it is believed that these mechanisms provides the plant with a fonn of biological defense against attack from pests, foraging mammals, and more recently from thieves. There is now a wealth of information, which has associated insufficiently processed bitter cassava with acute toxic effects (Mlingi et aI., 1992) and neurological disorders (Rosling et al., 1994). The most commonly described ailments include headaches, dizziness and general apathy. However, in more severe cases cassava related exposures has been linked with iodine deficiency disorders (Ermans et al., 1980), tropical ataxic neuropathy (Osuntokun, 1981), the paralytic disease 'Konzo' (Tylleskar et al., 1992) and even death (Essers, 1995). The toxicity of cyanogenic glycosides is not well understood but the implications of this public health issue require that strategic research be done to elucidate the problem. Through CNp evaluations across ecozones, it is possible to target specific genotypes to specific ecozones or regions and thus avoid UIUlecessary public suffering. The research at TITA has shown that although cassava is generally divided according to low CNp sweet and higher CNp bitter cu\tivars, in reality, the CNp distribution is a continuwn in cassava gennplasm from very low to high levels (Bokanga, 1994). In relation to GxE studies, research has shown that for a given genotype CNp is affected by location, planting season, rainfall pattern, water stress and nutrient status . According to the effect, cyanogenic potential for the same genotype can rise up to five fold, when planted in different locations, hence a cultivar may be safe in one location and potentially toxic in another (Bokanga, 1994). In field trials conducted in 1990/91 season, 10 genotypes were assessed at six locations. The ecozones included the humid forest zone (Onne and Uyo), humid forest and moist savanna transitional zone (Ibadan and Ubiaja), and southern Guinea savanna zone (Horin and Mokwa) of Nigeria (Table 7.5). The infonnation from these studies found that CNp was significantly effected by genotype and location. G x E interaction however was nonsignificant, i.e. , CNp amounts were relatively conserved for genotypes across locations (Bokanga et aI. , 1994). More recent but contrary reports are being further evaluated (Githunguri et aJ., 1998). These studies also revealed that one genotype, TME 1, which had the lowest CNp in certain locations was also the highest yielder, dispelling the commonly held belief that only high CNp genotypes can produce high yields. Recent data confmned this observation 9Githunguri et al., 1998). Nevertheless, TMEI was also the cultivar that produced a CNp up to 5 times higher in the drier sites compared with the high rainfall sites (Table 7.6). For breeders working in areas where fanners grow a combination of low and high CNp cultivars according to cultural preferences, types of cultivars for general release is a problem. One of the problems with regard to CNp is how to make a recommendation on a safe level of cyanide exposure. This idea is further complicated as G x E trials clearly show that a cultivar which has a low CNp in one area may have a high CNp in another area. Agronomic practices which influence CNp in a number of ways may further aggravate this problem (Ekanayake and Bokanga, 1995).

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Table 4. Physic-al and sensory profile data for quality evaluation of Musa fruit quality . Culti~ar

MUlQ

Bun

Fruit

wt.

(10

Ohino

l'Ewai TMPx 548-4 TMPx 548-9 TMPx 582-4 TMPx 1112-1 TMPx 1658-4 TMPx 2796-5 Bluggoe Cardaba Pelipita FHIA-J Valery

leDgth (cm)

DMC

Pulp

(%)

(0/.)

DAH

re (kc

801

led

cm-~ 6.7

12.2

1152

12 2

14.2

IS4

11.9

15.9

178

18.2

12 .4

10.4

12.8

10 .1

15.5

143

18.2

19.4

148

14.1

3.8

Dessert

Preference

girth

185

Plantain land race Plantain landracc Plantain hybrid Plantain hybrid Plantain hybrid Plantain Ilybrid Plantain l1ybrid Plantain hybrid Cooking banana Cooking banana Cooking banana FHIA hybrid

Pulp

tat.

(em) 15.4

(kiI)

Agbagba

!inalt

Fried

, 4.6

2.6

2.4

3.1

11.2

1.9

17.0

11.2

169

16.5

11 .6

14.0

188

18.3

15.9

2.1

14.2

182

15.3

14.8

1.S

12.5

119

15.0

132

12.1

N.A

1.4

19.2

10.5

2.4

banana

C4 = Calcutta 4, Pl = Pisang LHin, OL = Obino l'Ewai, BT "" Bobby Tannap , FHIA-3 "" hybrid derived from a cooking banana and many diploid bananas. DMC := dry matter content at stages 1 to 3, DAH '" days after harvest to ripening. N.A. - not available.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Table 7.5. Mean cyanogenic potential (CNp, in mg HCN equivalent kg- I) of tuberous roots of 10 improved cassava genotypes at five locations in Nigeria (1990/91 season). Location

Agroecological zone

Humid forest Humid forest Forest'savanna transition Southern Guinea Mokwa , savanna ' Southern Guinea Ilorin savanna Source: Bokanga et al., 1994

Onne Uyo Ubiaja

Rainfall (mm) 2501

CNp

1867

75.7 103.9 112.1

1235

124.1

1283

148.9

2782

Table 5.6. Mean cyanogenic potential (mg HCN equivalent kg-I) of the cassava genotype TMEl in eight different locations (1990191 to 1991192 seasons). Location

Cyanogenic potential (mg HeN equivalent kg-I) 1990/91 1991192 season

season 15.7 Onne 20.2 Uyo Mokwa 53.3 Ilorin 85.1 8004 Ubiaja Ibadan Umudike Zaria Location mean 50.9 Source: Bokanga et a1., 1994

55,2

175.7 55 .6

98.4 50.2 128.0 88.1 93.0

In Uganda, where there has been a devastating loss in cassava production due to the African Cassava Mosaic Disease (ACMD), the National Root and Tuber Prognm has released three varieties, viz., TMS 60142, TMS 30337, and TMS 30572. The cultivar TMS 30572, renamed Migyera in Uganda, is the b itterest of the cultivars released with a low to medium CNp status. This cultivar has been well received by fanners. However, problems may arise in the utilization phase when a consumer group, which is culturally adapted to eating low CNp sweet cultivars, is sold a glut of cheap but potentially high CNp, bitter roots. lITA has responded to this situation by supporting the East African regional breeding activities with additional postharvest support, so that valid data is produced on CNp levels across the country. Also in those areas where high CNp cultivars are being newly adopted, postharvest assistance is aiming to provide training and information on the safe use of bitter cassava, and also to disseminate this information at the major retaining centers.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

7.3.4 Rate of data retrieval For best results, the enzymatic assay described by O'Brien (1991) is the preferred methods to assess cassava CNp across locations. However this method is slow, requires highly qualified staff and a considerable capital investment in terms of equipment and training to set up and maintain. The need to conduct cassava CNp studies as part of the pan African GxE studies therefore, raises the issue of how to incoIporate this elaborate quality test as a standard activity of breeding programs in the region. Although CNp research has become a high priority, cassava programs need to reconcile the accuracy and rate of data they desire with the cost. An alternative is to use the less accuplte, but faster and cheaper, picric tests. The resolution of the data required is the most important point for national programs and in cases where GxE trials use low numbers of genotypes, the enzymatic process will provide the most reliable and accurate, results. However, if the number of genotypes to be tested increases beyond 20 to 30 clones, the picric test may be more suitable. The. picric test can give the improvement team a relatively good idea of the cyanogenic class, Le., whether the genotype is high, medium or low. This method is also considerably faster and cheaper than the enzymatic method.

Similar problems relating to cost, time and accuracy of data and analytical procedures used are common to many quality traits. Hence, postharvest researchers need to work closely within breeding programs to develop appropriate tests. In this way, breeding programs can decide which tests are best suited to their specific needs .

4. Future quality screening techniques For most crops in Europe and the United States, quality screening has become an integral part if not the priority basis for selection. Yet this emphasis is not apparent in the staple crop breeding programs in sub-Saharan Africa. This lack of market or end-user perspective may have several causes including the over riding priority to exploit higher yields, lack of information relating to quality traits, and lack of methods to access these specific quality characteristics. To overcome these information gaps, African improvement programs should adopt a more comprehensive approach to their breeding strategy. This would involve documenting market quality demand, analyzing the economic attributes of quality, and developing novel means of quality evaluation. Collating and assessing this information would be highly compatible with a GxE strategy, as quality assessment needs to consider both ecoregional and culturally based demands. QuaJity screening techniques are available to some extent. Most rapid tests are mainly nutritionally based, i.e., measuring protein content, or the trypsin inhibitor in soybean. Some of these tests have been derived from breeding programs in the industrial world. Quality measurements based on specific attributes of African foods are generally slower, less sophisticated or remain in the development stage. This is a key bottleneck in quality based crop improvement research. The need for more research in this area is highlighted by the current difficulties in measuring characteristics such as dry matter content of bulky roots, storage life, cooking characteristics, and food quality. Texture is a typical example of important quality traits for African food, which is common to recipes such as 'eba', 'chikwangue', pounded yam, 'ugali',

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

'fufu', or 'kenkey'. Developing a method for objective testing of texture quality would be of great value to the breeding programs. However, texture is a difficult characteristic to measure and evaluation is currently conducted on elite clones by the laborious method of taste panel analysis. Postharvest research needs to investigate the use of more recent methodologies to fmd modem tests for the rheology of pastes and dough like food products. Techniques such as neal' infrared transmittance may then offer a routine means of testing for protein, fat, moisture, fiber, starch, and sugal' in feeds, foodstuffs and grains. This type of technology may be particularly useful for testing African foods as it is also designed to handle pastes and food compounds. Field laboratories running a 24 hour-operation of this type may then provide researchers with the ability to test large numbers of samples cooked according to regional recipes. The utilization of this technique can also provide the standard, from which other more simple tests can be derived.

7.5. Conclusion Although postharvet quality evaluation is becoming a routine actlVlty at the elite evaluation stage, quality screening is yet to become an integral part of the early selections and the advanced GxE studies. To persuade breeding programs that quality screening should be conducted throughout the flow of materials and particularly at the GxE phase, postharvest research needs to provide clear evidence that tests can be done accurately and rapidly. Market studies also need to show that consumers within the African retaining system recognize and buy products based on standard quality traits and that GxE studies can take advantage of the regional dimension to quality recognition. Having established the links between quality and profit, screening techniques need to be fme-tuned to breeders' needs. This will eventually enable breeders to develop populations targeting specific markets while regional studies will defme the germplasm best suited to a specific region.

In a lecture on breeding in the tropics, N.W. Simmonds concluded by stating that international competitiveness means not only cost competition but an increasing focus on the quality of crops and the food derived from our agricultural systems. Given this statement pertaining to market demand, crop improvement programs in Africa should take full advantage of a quality oriented breeding approach to provide farmers with more profitable cultivars and consumers with better quality food .

RefereDces Bokanga M. 1994. Distribution of cyanogenic potential in the cassava gennplasm. Acta Horticulturae 375:117-125 Bokanga M., I.1. Ekanayake, A.G.O. Dixon, and M.C.M. Porto 1994 . Genotype-environment interactions for cyanogenic potential of cassava . Acta Horticulturea 375: 131-141. Ekanayake, I.1. and M. Bokanga. 1995. Cassava: a review on agronomy and cyanogenesis. pp. 548-563. In The Cassava Biotechnology Network Working Document No. 150. CIA T, Cali, Colombia. Errnans A.M., N.M. Mbulamoko F. Delange, and R. Ahluwalia 1980. Role of Cassava in the Etiology of Endemic Goiter and Cretinism . International Development Research Center, Ottawa, Canada. 182 pp.

Ferris, Bokanga, Ortiz and Vuylsteke. 1999. Crop quality and G x E analyses

Essers A.I.A. 1995. Removal of cyanogens from cassava roots: studies on domestic sundrying and solid substrate fennentation in rural Africa. Ph.D. Thesis, Wageningen Agricultural University. 131 pp. Githunguri, C.M., I.J. Ekanayake, I.A. Chweya, A.G.O. Dixon. and J. !mungi. 1998. The effect of different agroecological zones and plant age on the cyanogenic potential of six. cassava clones. pp. 71-76. R.S.B. Ferris (ed.). Postharvet Technology and Commodity Marketing. IITA, Ibadan, Nigeria. Mlingi N., N .H. Poulter, and H. Rosling 1992. An outbreak of acute intoxications from consumption of insufficiently processed cassava in Tanzania. Nutrition Research 44:677687. O 'Brien G., A.1. Taylor, and N. Poulter 1991. An improved enzymatic assay for cyanogens in fresh and processed cassava. Journal of the Sdence of Food and Agriculture 56:277-2&9. Ortiz R ., D., Vuylsteke, J. OkofO, C. Pasberg-Gauhl, and F. Gauhl 1994. MET-I Multisite evaluation of hybrid Musa gennplasm at IITA stations. MusaAfrica 4:6-7.

Osuntokun B.O. 1991 . Cassava diet, chronic cyanide intoxication and neuropathy in Nigerian Africans . World Review a/Nutrition and Dietetics 36:141-173. PBIP (Plantain and Banana Improvement Program). 1995 . Annual Report 1994 of Plantain and Banana Improvement Program. [ITA, Nigeria. Rosling H. J994. Measuring effects in humans of dietary cyanide exposure from cyanide.

Acta Horticulturae 375 :271-285 . Simmonds N.W. 1993. Tropical plant breeding: success, failure or a bit of each? TropicaJ Agn'cufture Association Newsletter 13 (4):3-9. Ssemwanga J. 1995. Quality attributes ofmatooke banana cultivars according to farmers and traders in Uganda. MusaAfrica 7:6-8. Tylleskar T., Banea T., Bikangi M., Cooke N., Poulter N. and Rosling H. 1992. Cassava cyanogens and Konzo, an upper motor neuron disease found in Africa. Lancet 339:208211. Vuylsteke D.R., R. Ortiz, R.S.B Ferris, and R.L. Swennen 1995. PITA-9: A black Sigatokaresistant hybrid from the "False Horn' plantain gene pool. Hortscience 30:395-397.

Section II: lITA's Manadqte Crops in SSA: Current Status of G x E Approach

Ortiz, Crouch, Vuylsteke, Ferris and OkOTO. 1999. G x E analysis of Musa

Chapter 8 Cultivar Development, Genotype x environment Interaction and Multi-site Testing of Improved Plantain and Banana Germplasm in sub..Saharan Africa Rodomiro Ortiz~ Jonathan H. Crouch, Dirk R. Vuylsteke, R.Shaun B. Ferris and Josephin U. Oko,.o 8. t . Introduction 8.2. Flow of IlTA Musa breeding material in sub-Saharan Africa 8.3 . G x E interaction and proper testing and assessment for selection of stable genotypes with potential durable resistance 8.4. Stability ofbiack sigatoka resistance and the search for durable resistance 8.5. Genotype-by-cropping system interaction 8.6. Local preferences and fruit quality evaluation 8.7. Final Remarks References

8.1. Introduction Black sigatoka (Mycosphaerelfa fljiensis) disease has become a major constraint to banana and plantain production worldwide (Mobambo et a1., 1993). UTA has given high priority to reducing the impact of this disease (Vuylsteke et at, 1992, 1993a). Present strategies mainly target the identification and production of cultivars that possess enhanced resistance to the black sigatoka disease (Vuylsteke et al., 1993c). UTA , with its breeding stations at Onne (southeastern Nigeria) and Sendusu (near Kampala in Uganda), has been developing black sigatoka-resistant tetraploid hybrids of plantains (PITA) and bananas (BITA) since 1987 (Vuylsteke et al., 1993e). Through a combination of conventional and novel approaches, including interspecific hybridization, ploidy manipulation. embryo culture. in-vitro rapid multiplication, host-plant pathogen interaction studies, and field testing and selection, IITA managed to breed resistant gerrnplasm in just 5 years (Vuylsteke et al., 1993t). The breeding material consists of genotypes at different levels of evaluation. The tetraploid material that is already available has been derived from interploidy-interspecific 3x (Musa AAB. ABB) x 2x (Musa AA) crosses (Vuylsteke et aI., 1993g). Currently, secondary triploids and tetraploids are also being developed (Vuylsteke et al. , ]994). However, the ultimate impact of this work will depend on how acceptable the new breeding material is to national researchers and farmers. and on their success in making these an integral part of their plantain and banana production systems.

Ortiz. Crouch, Vuylsteke, Ferris and OkOTO. 1999. G x E analysis of Musa

Hence, there is a genuine need within a breeding program to thoroughly evaluate experimental material through a sequence of trials (Ortiz and Vuylsteke, 1995), which will result in the selection of promising genotypes as potential new cultivars for further release and distribution by the National Agricultural Research Systems (NARS). Due to the existence of several distinct plantain and banana breeding programs (Ortiz et aI., 1994), which produce material for release to African NARS, there is a need for agreement on, and coordination of, the efforts aimed at evaluating these products.

8.2. Flow of liT A Musa Breeding Material in sub-Saharan Africa The sequence of trials already in place for the evaluation of UTA bred genotypes and its interface with other trials and products is discussed below. The experimental design, i.e., field layout, plot size and number of replications at each step is based on the probability of a false rejection of the null hypothesis ("all the treatmen~ are equal") and the availability of the breeding material (Ortiz and V).lylsteke, 1994a, 1995).

8.2.1. Early Evaluation Trial (EET) Newly bred genotypes are initially evaluated in small plots (1-5 plants in an unreplicated trial) for black sigatoka resistance (BSR) , large bunch and fruit size, parthenocarpic fruit development, improved ratooning, and dwarfism. The early evaluation of experimental material takes place at the breeding station and involves observations on more than 100 new genotypes each year.

8.2.2.Preliminary Yield Trial (PYT) The hybrids which combine increased BSR with good bunch qualities are selected for further evaluation in replicated trials at the breeding station (2 reps of 4 plants each). At this stage the traits mentioned above are evaluated along with earliness, resistance to other pests and diseases, fruit quality and palatability, as well as postharvest storage ability_ Each PYT consists of 15 to 30 genotypes.

8.2.3. Multilocational Evaluation Trial (MET) The black sigatoka resistant tetraploid hybrids CBSRTH) which express most of the desirable traits in PIT are included in multilocational evaluation trials. The MET is carried out in a minimum of 3 different locations using 2 replications of 5 plants each of 25 to 30 genotypes (hybrids and landraces) in lattices or randomized complete block designs (ReBD). Hybrids from other breeding programs, which have been successful in the International Musa Testing Program (IMTP) of the International Network for the Improvement of Banana and PLantain (INIBAP), may also be evaluated in METs for direct comparisons with lITA hybrids (PYT selections). IITA introduces these materials in collaboration with the relevant breeding programs, For example FHIA-3, the improved tetraploid cooking banana developed by the Fundacion Hondurena de Investigacion Agricola (FillA), was sent by UTA to African NARS for testing at 11 sites. However, the main objective of MET is to test the performance of the BSRTII under a wide range of agroecological conditions in sub-Saharan Africa. In this way it is possible to assess the genotype by environment interaction for specific traits and the stability of both yield and BSR of the BSRTH.

Ortiz, Crouch, Vuylsteke, Ferns and Okoro. 1999. G x E analysis of Musa

A first set of 8 BSRTH was included in the MET-I, which was planted in 1991 in four locations: three lITA stations in West and Central Africa (Ibadan and Onne in Nigeria, and M'Balrnayo in Cameroon), and one at the National Horticultural Research Institute in Ibadan, Nigeria. The MET-2, planted in 1992, tested 12 promising hybrids and involved the participation of 4 countries in sub-Saharan Africa (Table 8.1). A total of 12 NARS in Ghana, Nigeria and Cameroon were involved in this multisite trial, which detennined the stability and adaptation of these hybrids across the different environments of these plantain and banana producing countries. Table 8.1. Musa entries sent by UTA to NARS in sub-Saharan Africa (1987-1994)z. IITA Code

Cross ID

Parentage/G enome

BSRY

Multisitetrial

Advanced testingV

Onfarm

fS:

WCA

ESA

testingU

PR PR PR PR PR

X X

X X X X

X X X

Hybrids PITA-l PITA-2 PITA-3 PITA-4 PITA-S PITA路6 PITA-7 PITA-S PITA-9

548-4 548-9 5511-2 582-4 2796-5 4698-1 1658-4 7002路1 1112-1

PITA- I 0 PITA-II PITA-I 2

1621-\ 2637-49 6930-1 597-4 2481

BITA- I BITA-2r

FHIA-2xr FHlA-3

612-74 1378

OLxC4 OLxC4 OLxC4

BTxC4 BTx PI OLxC4 OLxPL OLxC4

X X X

X X

OL x AAs BlxC4

X X

PR LS PR PR

Ag-FRt x C4 OLxC4 OLxC4 OLxC4 OLxC4

X X X

Fox BSq

Aas

SH-3362 SH-3365

[{CaxBB}xA ASlx AAs

X X X

X X

Agbagba (Ag) Bobby Tannap (BIl Obino l' Ewai (OL) Bluggoe (HI) Cardaba (Cal Fouym.ou (Fo) Nzizi Pelipita NaIcitengwa Valery Yangambi Km 5 Calcutta 4 (C4) Pisang lilin (PI)

AAB

AAB AAB ABB ABB ABB ABB ABB

AAA AAA AAA AA wild AA cultivar

Landraces S X S X S X LS X

LS PR

LS LS S S HR HR R

X X X X

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

All hybrids have been sent to INIBAP Transit Center at KUL, Belgium. Some of these hybrids and landraces are freely available upon request to INIBAP or lIT A. Z Hybrids have been officially requested by NARS outside Africa (Brazil, China, Colombia, Costa Rica, Cuba, Haiti, india, Mexico, Peru, Singapore, Virgin [slands, and Western Samoa). UTA will re-start shipments of in vitro virus tested explants towards the end of 1994 or early 1995. y Host response to black sigatoka disease acrosS' locations: HR: no necrotic spots, highly resistant; R: streaks but without necrotic spots; resistant; PR: partially resistant; LS: less susceptible; S: susceptible. x MET-) locations (planted in 1991): UTA stations at M'Balmayo (Cameroon), Onne and lbadan (Nigeria) and NIHORT at Ibadan (trial was lost due to drought). w MEI-2 countries (sent to NARS in 199211993): Cameroon (2 sites, 1 trial lost due to potential in situ virus problem), Ghana, Nigeria (12 sites, 3 trials lost due to poor managementllack of resources/ethnical clashes), and Uganda. Crops Research Institute (Ghana) set-up its own MET-2 in a second location and a demostration plot with all MET entries in iti headquarters at Kumasi in 1993/1994. MET set was also sent to Australia, Cuba and Dominican Republic. v AMYT countries (sent to NARS in 1993): Cote d'lvoire, Ghana and Nigeria (3 sites, I trial lost due to poor managementllack of trained manpower) in West Amea (WCA) and Burundi, Kenya, Malawi (2 sites), Uganda and Zanzibar in East and Southern Africa (ESA). AMYT路 WCA was requested by Gabon, Gambia and Guinea-Conakry, while AMYT-ESA was requested by Rwanda, Tanzania and Zanzibar. Sets will be sent to respective NARS or regional centers at the end of 1994 or early 1995. IRA.z (Burundi) plans to extend trials to other sites in 1995. U Trials, in several fanners' fields and backyards, were set up in southeastern Nigeria by the respective Min. of Agriculture, or Agric. Development Projects in each state, and extension services of Nigerian AGIP Oil Company or Shell PLC. t French Reversion somaclonal variant of the cultivar A,gbagba. s Improved diploids from intraspecific crosses and selection in M. acuminDta. r Sent directly to lRAZ. BITA-2 was also sent to NAROIUNBRP (Uganda). q M. balbisiana (donor of the B genome).

The involvement of key NARS in this relatively early stage of hybrid evaluation is an路 important source of feedback which greatly enhances the selection procedures of Musa breeding at UTA. The MET also allows IITA's breeders to decide what material can be further evaluated by NARS in the target area.

8.2.4. Advanced Musa Yield Trial (AMYT) Selected materials from IITA and FIllA, along with local cultivars used as checks, have been included in the flIst Advanced Musa Yield Trial (AMYl), which was undertaken simultaneously in 11 local evaluation sites of 8 countries in sub-Saharan Africa. In all cases the NARS were the executing agencies. Eight of the most promising BSRTH, along . with FHIA-3, and local cultivars were tested by NARS together with regional and international centers in Burundi. Kenya, Malawi, Uganda. Zanzibar, Cote d'Ivoire, Ghana and Nigeria. The results of these wide-ranging trials are of regional relevance. The objective of the AMYT is to identify elite genotypes for potential release as new cultivars by the NARS according to the specific needs and regulations in each country. AMYT eviUuates 5 to 7 previously selected promising hybrids over a period of at least two production cycles in a RCBD . Criteria for selection include disease resistance and productivity, but also local preferences which affect consumer acceptability. The mandate of TITA to strengthen the NARS is also accomplished through this collaborative effort.

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

8.2.5. On-farm trials The development of a cultivar profile (rate of N-P-K fertilization, planting density, type of alley cropping, etc.) should be fme-tuned through on-farm testing (OFf) of the improved germplasm compared with. a local cultivar. OFT requires plots comprising at least six competing plants. Four replications are required when comparing 5 to 7 treatments in order to accurately identify proper agronomic practices which yield a bunch weight 25% more than that of the control (Ortiz, 1993a). In OFT maximizing the nwnber of trials is generally more important than replicatioru: within sites. Therefore, replications could be decreased within sites and replaced by additional sites. The most common experimental field layouts are completely randomized (CRD) and ReBD. Blocking should be used in sites where a sensible basis for this exists (e.g ., fertilizer gradient) or where there are a very large Dlunber of treatments per genotype. CRD are recommended for OFT especially in backyards. On-fann trials should have a susceptible local cultivar (e.g. 'Valery' or 'Agbagba' for black sigatoka disease) at certain intervals (i.e., every two testing lines) and at the edge of a yield trial (METs, AMYfs or OFTs) in order to provide a uniform infection pressure (Ortiz, 1993a).

8.2.6. Safe Movement of Musa Germplasm and International Testing Aseptic shoot-tip culture is used as a vehicle for the safe exchange of banana and plantain germplasm. Shoot-tip cultures confer considerable advantages for the international transfer of germplasm because (1) the mass of plant material involved in the movement is greatly reduced, (2) the plant material is contained, (3) the technique overcomes nearly all of the problems associated with non-obscure pests and pathogens, and (4) they are amenable to rapid multiplication (Vuylsteke, 1989). The Nigerian Plant Quarantine Service visits IITA's Tissue Culture Laboratory every year to check the health status of in vitro stocks. After this inspection, phytosanitary certificates are officially issued for export planting materials for the METs and AMYTs planted outside Nigeria. Each non-Nigerian cooperator of the MET and AMYT networks also sends in advance import permits, which are issued by the respective national plant quarantine authority. This process, therefore, follows all international, regional and local quarantine regulations for the implementation of yield trials outside of the breeding station.

8.3. G X E Interaction and proper Testing and Assessment for Selection of Stable Genotypes with Potential Durable resistance The clonal phenotype which corresponds to a specific genotype, can vary from year to year in the same location and or from location to location within an agroecozone in the same year. This phenomenon which affects genotype ranking in different environments, is known as genotype-by-environment interaction. Breeding programs aim to identify genotypes which have both a high and stable yield in a range of environments across a targeted a region.

8.3.1 Year effects and genotype-by-year interaction in the breeding station Year and genotype-by-year (G x Y) interaction of the phenotypic expression of quantitative traits in plantain and banana where studied during two years at Onne (Ortiz, 1994). Year variation significantly affected plant height, bunch weight, fruit circumference and youngest leaf spotted by black sigatoka. The genotype by year (G x Y) interaction at

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

Onne only significantly affected plant height, height of tallest sucker at harvest and fruit filling period, but more importantly had no effect on bunch weight and the components of black sigatoka resistance. PIT A-2 (a progeny of 'Obino l' Ewai' X 'Calcutta 4') was identified as a stable yielding clone over a 2-year period at Onne (Ortiz et aI., 1994b). Confirmation of this result required data from multilocational trials to assess yield stability and durability of BSR of the plantain-banana hybrids across environments.

8.3.2. Location effects and genotype-by-location interaction 8,3.2.1. Within the same agroecozone (Lowland Humid Forest). The location effect within the humid forest zone of West and Central Africa and the genotype-by-Iocation (GxL) interaction were investigated using UTA's stations at Onne and M'Balmayo (near Yaounde, Cameroon) (Ortiz and Vuylsteke, 1993a; Ortiz et al., 1994b). There were significant differences between locations for total number of leaves, bunch weight, fruit weight and its components. On average the plants at Done produced 11 more leaves than plants at M'Balmayo and the increased photosynthetic area may explain the higher yields and larger fruit size observed at Onne. GxL interaction significantly affected plant height, days to fruit filling, bunch weight, fmger number, fruit weight and circumference. These results support the need for multilocational testing before cultivar release, even in the same agroecozone. Preliminary yield analysis indicated that PITA-l (a hybrid of 'Obino l' Ewai' x 'Calcutta 4') and PITA-7 (,Obino l' Ewaj' x 'Pisang lilin') were high and stable yielding clones in the humid forest zone (Ortiz et a1. 1994c; Vuylsteke et a1. 1994). These clones significantly outyielded their susceptible plantain parents and the ABB cooking bananas (Table 8.2). Their high yields might result from their short production cycles due to lack of apical dominance in suckering behaviour (Ortiz and Vuylsteke, 1994b), or nonadditive gene interactions for bunch weights (Ortiz and Vuylsteke, 1993b; Vuylsteke et aI., 1993d) due to high order intraand inter- locus interactions, which are important for yield in polysomic polyploids with vegetative propagation (Peloquin and Ortiz, 1992).

8.3.2.2. Across agroecozones in the same region. Plantain hybrids, which nonnally out-yielded their parents and cooking banana landraces in the lowland humid forest stations of lITA, had lower yields than the drought tolerant cooking banana 'Cardaba' (Ortiz ct aI., 1994c) at IITA farm in the derived savanna - transition zone (Ibadan, Nigeria). This was not surprising because Ibadan has a longer dry season than Onne or M'Balmayo (Table 8.3), and all the hybrids were selected at a high rainfall breeding station. Nevertheless, PITA-5 ('Bobby Tannap' x 'Pisang lilin'), the highest yielding hybrid, had a yield potential (t ha- 1year- l ) not significantly different than that of 'Cardaba'.

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

Table 8.2. Yield potentialZ (t ha- l year- l ) of improved black sigatoka resistant tetraploid plantain germplasm (PITA) and Musa landraces tested at ITTA stations in the humid forest (Onne and M'Balmayo) and the derived savanna or transition zone (Ibadan) (MET-

1, 1991-1994)Y. Group

# Clones

PITA ABE

6 4

AAA

Onne 24.9 + 1.6 18.7±1.9

Location M'Balmayo 21.3 + 1.3 19.4.± 1.4

11.1 + 2.7 13.8

11.4+3.0 17.2

lbadan 12.9 -+- 0.5 17.2 x 10,7 + 1.5 13.0

Mean ± standard error.

Y For details about entries within groups see Table 1. x Bluggoe, Fougamou and Pelipita had most of their fruits rotten due to seasonal cigar end or crown rot.

Table 8.3 . Agro-ecological characterization of testing sites in MET-I.

Character Onne Latitude Longitude Altitude (masl) Agroecozone Soil Total rainfall (mm1year) Rainfall pattern

Resource and crop management practices

4 0 43'N 7QQl'E

5 Degraded rain forest - swamp Ultisol derived from coastal sediments 2400 unirnodal(± 1 month dry spell: mainly in January)

Location M'Balmayo

Ibadan

3 0 2S'N

7 0 30'N

11028'E 640 Humid forest

30 54'E

Ultisol derived from schist band 1500 bimoda1(± 2 month dry spell: (Dec-Jan.; July-August)

Multispecies alley croopping with natural regrowth of hedgerows in between testing geno!ypes

210 Forest- savanna , transition Ferric luvisols

1300 bimodal (+ 4 m'o nth dry spell: Dec- March; JulyAugust) Land clearing in hydromorphic area

These results showed the potential lack of adaptation to dry areas of the improved plantain germpJasm (PITA) and the importance of this selection site and its link with its respective agroecozone. Hence, selection of plantain hybrids suited to long dry seasons should be pursued, and the breeding activity should take place in a location in that target agroecozone .

It seems that plantain breeders must develop an ecoregional approach for their genetic improvement programs. This means that the objective (or ideotype) changes according to the targeted ecosystem. For example, plantain breeders might consider the selection of hybrids

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

which have low conductance of water vapor in the afternoon to increase their adaptation in environments where drought stress may occur.

8.3.3. AMMI analysis in Musa yield trials. In the presence of a significant genotype-by-environment interaction (G x E), both the stratification of environments according to their agro-climatological similarities and the detennination of stability parameters for genotypes' across environments are important tools for the management of that interaction. Several techniques have been developed to determine the most stable genotype in a set of replicated trials across years and locations or combinations of both environments. However, these postdictive models are not usefuJ in the identification of which genotypes and environments contribute to the GxE interaction. Moreover, a breeder may be interested to identify which genotypes are adapted to specific environments andlor to predict their performance in a specific location. The additive main effects and multiplicative interaction (AMMI) model was developed to provide answers for such questions (Gauch, 1992). AMMI uses the analysis of variance (ANOV A) to study the main effects of genotypes and environments and principal component analysis (PCA) for the residual multiplicative interaction. Five tetraploid hybrids, which are currently undergoing evaluation in the multilocation trials, were chosen to determine the validity of AMMI analysis of factorial (genotype-byenviromnent) designs for Musa yield trials (Ortiz, 1993b). Data from early evaluation trials (EET at Onne 198911990), preliminary yield trials at Onne in the plant crop (PYT-PC 19901199L) and ratoon (PYT-R 1991 /1992), and multilocational trials (MET) at Onne, M'BaJmayo and Ibadan (1992/ 1993) were used for this analysis. The AMMII model for bunch weight of five tetraploid hybrids evaluated in six trials, shows the genotype (G) and trial (T) main effects on the abscissa and the interaction peA 1 score (IPCAI) for G and T in the ordinate (Fig 1). G and T are indicated with open and fllied symbols, respectively. Imaginary lines parallel to the axes can be drawn to indicate the grand mean (perpendicular to the abscissa point 12.3 kg) and zero in the ordinate axis, respectively. Among points of the same kind (either between G's or between Ts) displacements along the bunch weight axis indicate differences between hybrids or trials (main effects) while displacements along the IPCAI axis indicate different interaction effects. The most stable, but average yielding, genotype was the PITA-4 (= TMPx 582-4). PYTs and METs at Onne and M'Balmayo differed only in interaction effects. To deal with points of different kinds the AMMI model equation provides the expected bunch weights. For example, the AMMI expected yield for PITA-2 (= TMPx 548-9) in EETOnne, [(PITA-2 mean) + (EET-Onne mean) - Grand mean + (IPCAI EET)路 (IPCAI PITA 2)], should be equal to 17.8, which was not significantly different from the observed bunch weight (17 .9) of PITA -2 in the EET-Onne. This means that the AMMII model left a residual of 100 g. This biplot (Fig. 8.1) also gives additional interesting information. The main effects for genotypes may indicate their susceptible or resistant host response to black sigatoka, with the smallest genotype mean for the black sigatoka susceptible tetraploid clone 597-4. Similarly the main effects for environment represented overall site quality, with EET-Onne having the best weather and soil conditions in 1989-1990, while the poor bunch weights of the MET-

Ortiz, Crouch, Vuylsteke, Ferris and OkofO. 1999. G x E analysis of Musa

Ibadan confmns the lack of adaptation of the tetraploid hybrids to the dry season of this Nigerian location in the savanna-humid forest transition rone.

7.0

, ,

3.5

•

« () 0.0 -

0..

•

-3.5 x

-7.0

TMPx 548-4 TMPx 548-9 )( 597-4 Ll TMPx 582-4 + TYPx 2796-6 EET-Onne $ - PYT-PC Onne PYT-R Onne

o 0

• ••

• • •

MET-Onne MET-M'balmayo MET-Ibad.n

10. 15 20 Bunch weight mean (kg)

Figure 8.1. AMMI 1 Model for bunch weight in plantain tetraploid hybrids tested at rITA stations in Cameroon and Nigeria.

The AMMI2 model (interaction analysis) for bunch weight is illustrated in Fig. 8.2. Points near the origin (0,0), e.g. PITA-4 (= TMPx 582-4) or MET-Onne, have little interaction and should be weU fitted by the additive submodel. Points near each other have similar interaction patterns [e.g. the full-sibs PITA-l (= TMPx 548-4) and PITA-2) while points distant to each other are indeed different (e.g., the unrelated PITA-5 (= TMPx 2796-5 ) and 597-4). The IPeA 2 also helps in the identification of those trials or genotypes with the worse fit, i.e., farther from zero (origin). For example, PITA-5 in EET-Onne was farthest from the AMMII model, i.e., its expected bunch weight was 21.3 kg instead of the 25 .9 kg

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

observed bunch weight in this trial. This also suggests that bunch weights from unreplicated EETs should be interpreted with caution.

6 ,~

<•.J

.:;{

C,; 0..

•

o 0

-x

0 -2

-7.0

TMPx 582-4

lMPx 2796-5

•

EEl-Onne

.•.

~ PYT-PC OM.

•

-4 .

-6

597-4

~ ;''I'

•• •

+ I

-3.5

0.0

IPeA 1

TMPx 548-9

0 (

TMPx 548-4

3.5

7. ~

PYT-R Onne MET-Onn. MET-U'balm.yo MET-Ibadan

Figure 8.2. AMMI2 Model for bunch weight in plantain tetraploid hybrids tested at UTA stations in Cameroon and Nigeria.

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

Hence, AMMl seems to be a powerful tool to analyze yield trials with a factorial structure, e.g. Locations x Genotypes. AMMI can analyze trials with witrun-site replications or trials with only one replication per location (IMTP-l).

8.4. Stability of black sigatoka resistance and the search for durable resistance In order to minimize variability in data recording caused by different surveyors at each location, it was decided that the youngest leaf spotted (YLS), during an advanced vegetative growth stage or at flowering, should be used as the cornman parameter to measure the response to black sigatoka for each clone across genotypes and environments (Vuylsteke et al., 1993b). This method of assessment was used because YLS is highly correlated with the timing of disease development. Recording YLS also bas the advantage of being a simple trait to score, requiring the surveyor to merely record the number of the youngest leaf, counting down from the ftrst (top) lUlfurled leaf, to the ftrst leaf that shows mature spots caused by black sigatoka. The inclusion of resistant and susceptible reference clones in the MET trials enables comparisons to be made of results collected in different locations. The data can also be used to assess potential patbotype differentiation. To make a more accurate assessment of black sigatoka response, all clones were compared with known susceptible cultivars, 'Agbagba' and 'Valery', and the hybrids together with their triploid parents CBluggoe', Bobby Tannap' and 'Obino r Ewai'). TMPx with significantly more leaves without necrotic spots than the susceptible parent were considered as being less susceptible or partially resistant to black sigatoka disease, respectively. It is accepted that this susceptibility-resistance scale is arbitrary, but as stated several times by Prof. N .W. Simmonds: "resistance simply means 'less disease' on whatever scale is adopted" . Genotype-by-environment interaction influences black sigatoka reaction in plantain and banana (Vuylsteke et aI., 1993b). Genotype-by-Iocation effects are more important that the non-significant genotype-by-year interaction (Ortiz et al., 1994b).

8.4.1. Genotype-by-environment interaction and black sigatoka resistance inMETs Preliminary analyses of MET-l results collected at Onne, Ibadan and M'Balmayo (Vuylsteke et aI., 1993b), indicated there were no differences (p > 0.05) between locations for response to black sigatoka. The genotype-by-Iocation interaction was highly significant (p < 0.001) for black sigatoka response as measured by YLS, reflecting the different response to black sigatoka in each environment. Differences between locations may be attnbuted to the bias of different surveyors. However, the significant genotype-by-environrnent interaction due to changes in ranking (cross order interaction) might be explained by the presence of different strains of Mycosphaerella fl}iensis in the three locations. These results reinforce the need for multilocation testing, before cultivar release, even within the same agro-ecological region, e.g. Onne and M'Balmayo.

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

8.4.2. MET-2 at lowland humid forest stations in Cameroon and Nigeria Host response to black sigatoka of the BSRTH at Onne during the mid-rainy season 1993, was similar to the crop response at Nyombe (station of Centre Regional Bananiers et Plantains in Cameroon) during the end-rainy season 1992 (Vuylsteke et a1., 1993b). Spearman's rank correlation coefficient for youngest leaf spotted scores over both locations was estimated as RS =0.62 Âą 0.29. Ibis correlation would arise by chance less than 2 in 100 times (P < 0.02). This may indicate that the genotype-by-environment interaction did not significantly affect black sigatoka resistance in the improved germplasm. At both locations most of the PITA showed high partial resistance, while BITA- I ('Bluggoe' l{ 'Ca1cutta 4') was rated as highly resistant (no necrosis at Nyombe). The highest Levels of stable partial resistance (YLS ~ 9) were shown by PITA-5, PITA-ll and PITA-12 (all 'Obino ]' Ewai' x 'Calcutta 4' hybrids). PlTA-7 showed lower partial resistance (YLS ~ 6) at both locations while the susceptible reference banana cv. 'Valery' had a mean YLS of 6 and 4 at Nyombe and Onne, respectively (Vuylsteke et aI., 1993b).

8.4.3. Stability analysis of host response to black sigatoka based on YLS Youngest leaf spotted was the parameter used to assess the host response to black sigatoka leaf spot disease of 20 clones (hybrids, parents and reference cultivars) in 9 environments. Regression analyses, following the linear model BSRij= I.l + ~ilj + Oij, were used to estimate the genotypic basis of the response to black sigatoka (BSR ij) of the ithclone in the respective jth environment (Ortiz et aI., 1993a). In this equation, I.l is the clonal mean across all environments, ~i is the regression coefficient which measures the response of the i th clone to varying environments, &ij is the deviation from the regression of the ith clone at the jh environment and I is the environmental index. 'Calcutta 4' and BITA-I were not included in this analysis because no leaves with black sigatoka-induced necrotic spots were observed in these accessions in any of the nine environments. Similarly, 'Pisang lilin' was excluded because it has the same response in specific environments, e.g. Nyombe. A practical way to select stable black sigatoka resistant genotypes is illustrated in Fig. 8.3 . The vertical lines are one LSDOO5 above and below YLS grand mean and at the grand mean, whereas the horizontal line is drawn parallel to the X axis from the slope 13=1. Thus, eight regions were defmed. Cultivars and experimental hybrids in the right lower part of the graph should be considered as having stable black sigatoka resistance. Indeed. PITA-3 (= TMPx 5511-2, a hybrid from 'Obino l' Ewai' x 'Calcutta 4') had a ~ = 0.40, which was not significantly different from zero. This suggests that the environment and the genotype-byenvironment interaction did not affect the expression of black sigatoka resistance in this clone. The PITAs had different p values which indicates that selection for stable black sigatoka resistance may be possible in this population. On average the 'Obino l'Ewai' derived hybrids had a higher stability level of black sigatoka resistance than those derived from 'Bobby Tannap'. This was not surprising because the susceptible black sigatoka response of 'Obino l'Ewai' is more stable than that of 'Bobby Tannap'. Independent analyses of variance across all environments for each group of genotypes were carried out to compare the improved PITA versus their susceptible parents ('Obino l' Ewai' and 'Bobby Tannap') and other reference Musa cultivars. Thus, estimates of variance

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

components were obtained, and the coefficient of variability (CV) and broad-sense heritability (ratio of genetic variance to total phenotypic variance) were calculated for the hybrids and the susceptible cultivars (Ortiz and Vuylsteke, 1994d). Brpad-sense heritability estimates (0.45 for PITAs and 0.29 for landraces) show clearly that the improved germplasm has. on average a more stable (o2cE was 1.2 and 1.9 for PITA and landraces, respectively) as well as a higher level of heritable variation for black sigatoka response than the susceptible Musa germplasm (o2ciwas 1.0 for PITA and 0.8 for landraces). Thus, more efficient through selection for black sigatoka- resistance may be expected in the PITA population than in the landraces. The CVs (11% and 22 % for PITA and landraces, respectively) indicated that on average the recorded YLS scores were smaller (6.3 ± 0.5) and more variable in the landraces than in the improved TMPx germplasm (9.8 ± 0.3). This result suggests that the improved germplasm has a more stable resistance to black sigatoka than Musa landraces (Fig.SA) . A more &:tailed analysis of host-plant/pathogen interactions could be carried out with the aid of the additive multiplicative model interaction (AMMI) .

1.4

2796-5

1.2 -

Bobby i amap

1.0

Clugl1-

ObinOrEwaI

1621-'

...

.,ElJ8.4

•26'37-49 •• 2481

TMPx

AAB plantains

ABB baNina.

o 'Valery' (AAA)

4008-1 ~

•

548-4

5511-2

...

, 6

•

Cardaba

Valery

0.4

•

0.8

0.6

~-4.

~lPio

0000.1

VlS

-----65S----- BSLS -BSPR- SSR

---- -_._-- _.-

- --~--~---

FigureS. 3. The r~lation between black sigatoka response, as measured by youngest leaf spotted (YLS), and stability (5). [BSS == black sigatoka susceptible, BSLS = black sigatoka less susceptible, BSPR = black sigatoka partially resistance and BSR = black sigatoka resistant].

Ortiz. Crouch, Vuylsteke. Ferris and Okoro. 1999. G x E analysis of Musa

~: L S 13 - . - - - - - - - - - - - - - - - - - - - ,

o TMPx 5511-2

.. 'Oblno I' Ewel'

4~~~~~--~~-T----._--~----;

10 YLS mean site

Figure 8.4. Black sigatoka response, as measured by youngest leaf spotted (YLS). of the susceptible plantain cultivar 'Obino l'Ewai' and its black sigatoka resistant hybrid PITA3 or (TMPx 5511-2).

8.4.4. Index of spotted leaves for black sigatoka evaluation in multilocational testing. Black sigatoka response in the host-plant has been assessed befoIe by determining youngest leaf spotted (YLS). A more precise evaluation across environments and genotypes could be achieved by an index of black sigatoka spotted leaves (ILS). This index considers both YLS and number of standing leaves (NSL), i.e. ILS(%) = 100 X [NSL-(YLS- l)]/NSL. Thus, ILS measures the percentage of standing leaves showing spots with a necrotic center.

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

This is the fmal stage of the sigatoka disease. ILS, therefore, is a relative value indicating 1he severity of black sigatoka disease in each host. The lower the ILS the more resistant the genotype to black sigatoka. ILS provides an accurate and rapid means to establish disease severity and it was highly correlated with other measurements of host plant response to black sigatoka (Craenen, 1994). The ILS of 20 genotypes across 9 environments of West and Central Africa, based on YLS and ~SL location means for each host, were statistically analyzed (Ortiz et a1., 1993b). There were significant differences between environments (P < 0.001) and genotypes (P < 0.001). The interaction genotype-by-environment was not significant (P = 0.25) . Hence, ILS seems to be more reliable for black sigatoka scoring than YLS because the latter was found to be significantly affected by the genotype-by-environment interaction when scored across agroecozones (Vuylsteke et al, 1993b). With respect to host-plant response to black sigatoka, as measured by ILS, across environments the following conclusions were drawn (Ortiz et aI., 1993b): AAB plantains and the 'Cavendish' export AAA banana 'Valery' are equally susceptible to black sigatoka. They had about 50% of their standing leaves with fmal stages of disease development; ABB cooking bananas such as 'Bluggoe' and 'Cardaba', initially considered as potentia1 substitutes for plantains (Hahn et al., 1990), are less susceptible to black sigatoka. The cooking bananas have more functional leaf area for photosynthesis than plantains due to their high NSL as well as their host response to black sigatoka. This explains the high yielding bunches of cooking bananas in environments where black sigatoka affects the crop. Although there were significant differences in host response to black sigatoka among the hybrids (either PITA or BITA), they show partial resistance or non-innnunity to the disease; i.e., slow disease development in their leaves. None of the PITA have the high level of resistance (considered as hypersensitivity) of 'Calcutta 4'. However, this type of resistance has been observed to break down in other environments (Fullerton and Olsen 1991). The resistance in TMPx gerrnplasm might be more durable because the host-plant response to black sigatoka slows the progress of an epidemic without inhibiting its initiation. Moreover, clones such as TMPx 2481 could have durable resistance due to the combination of resistance alleles from two different banana sources, 'Pisang Jilin' and a wild diploid accession, with the recessive - additive resistance alleles of plantains (Ortiz and Vuylsteke, ] 994c).

8.4.5. Epidemiology of black sigatoka and the search for durable resistance. The aim of any resistance breeding program is to develop superior high yielding genotypes with durable resistance. An important component in successfully achieving this goal is the understanding of pathogen-host plant interactions. The release of an improved and resistant {:ultivar has often be followed by the emergence of a novel pest or disease strain, which occurs due to mutation or recombination events in the pathogen. Therefore, the search for new sources of resistant alleles as well as their incorporation into the breeding program is a continuous process. Moreover, epidemiological

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Mus a

studies, aiming at the identification of different pathotypes with variable degrees of virulence on well known standard cultivars (or differentials), help in monitoring the appearance of more virulent strains and to establish a resistance breeding strategy able to maximize the probability of generating durable resistance. Fullerton and Olsen (1991) determined the pathogenic diversity within populations of Mycosphaerellafljiensis collected in the Pacific Islands, Papua New Guinea, Southeast Asia, Central America and Nigeria. They used as differential hosts most of the standard cultivars currently being used by INIBAP in the IMTP. The authors used a scale from 1 (avirulent strain with host plant showing no or very slight symptoms) to 5 (very high virulent strain which causes the drying out and subsequent death of host plant tissue within blotches). They observed a different host response to the whole range of strains, being the black sigatoka response of susceptible cultivars such as 'Grande Naine' and SF 215 consistent across cell isolates. 'Calcutta 4', the most widely used source of black sigatoka resistant alleles in breeding programs elsewhere, was susceptible to some strains, especially those collected in the Pacific Islands and Papua New Guinea. Only 'Tuu Gia' was consistently resistant but showing partial resistance or low level of susceptibility to some isolates.

8.4.5.1. Assessment of pathogen diversity and stabUity for resistance in bananas. The information published by Fullerton and Olsen (1991) was statistically re-analyzed to determine the stability of black sigatoka resistance in M acuminata using simple linear regression [ll0dels and to study the pattern of strain and genotype variation in the black sigatoka-host plant interaction using principal component analysis (pCA). Ten M acuminata genotypes evaluated across 33 different M jijiensis isolates were included for this investigation. A stable source of black sigatoka resistance was defmed as that having an overall genotypic mean smaller than the grand experimental mean, the coefficient of regression of the sigatoka reaction of a specific cultivar on the isolate mean (over genotypes),

Il, smaller than

1, the deviation from the linearity (s;' equal to zero, and the coefficient of

determination of the regression model (R~ higher than 0.50 . The stability parameter b was considered consistent if its standard error (sb) was not significantly different from zero. The identification of alleles conferring potentially high and durable sources of black sigatoka resistance was achieved by plotting the mean of the accessions (X axis) versus the stability coefficient b (Y axis). Nine regions were defined drawing imaginary parallel lines to each axis; two parallel to Y axis; either from black sigatoka responses significantly lower or higher than the general mean in the x axis, i.e., 2.5 (resistant) and 4.3 (highly susceptible), respectively, and the other two parallel to the X axis from b values statistically different from lÂą 0.25 (Fig. 8.5). Musa accessions located in the area where the overall genotypic response across strains was smaller than 2.5 (low virulence) and b < 1 were regarded as hav:ing genes providing stable and adequate black sigatoka resistance. 'Tuu Gia' was in this area, therefore, this clone may have a non-specific (horizontal) resistance to the different strains used in this experiment. Moreover, it seems that its resistance does not break down even under the pressure of highly virulent strains. Conversely, 'Calcutta 4' had a very unstable black sigatoka resistance, which may easily break down in environments, e.g. Papua New Guinea, where highly virulent strains have evolved. The graph of PCA axis 2 against peA axis 1 for black sigatoka response of 10M. acuminata genotypes evaluated across 33 isolates of M. fljiensis is illustrated in Fig. 6.

Ortiz, Crouch. Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

PRINt was equally loaded on genotypic means. Therefore, the PRIN I axis should be regarded as the overall genotype and strain performance. Moreover, a significant positive correlation between the M. acuminata accession mean and PRINl coefficients (r = 0.99) supports this statement. PRIN2 was also significantly but negatively correlated with the stability parameter b (r = 0.74). This means that genotypes with PRlN2 scores close to zero response to strain virulence; i.e., the more virulent the strain the lower the have a resistance to black sigatoka disease in the plant host. PiUN2 also indicates that C4, the most unstable genotype (Fig. 8.5), wu the accession contributing the most to the genotype x strain interaction. PRIN2 for strains was not as clear in its loading but the isolates from Papua New Guinea bad negative scores, which might be indicative of the high virulence of the isolates coUected in this area.

rmear

2.5

,.....

o SF215

2.0

1.5

o o

•

1.0 -

(l)

0.5

H,'ne

Salm ••• a Pl.ang ma.

.f! (.I)

Grande

KokodJa + PI••ng Bertin

.q..,

Pahang

(1.0 1

Calcutta 4 TU 8 TUIJ

Gla

Bleck alptob reaction In hoeI cult. v... o (.vlrulenl) to I(hlghly _Irulenl)

Figure 8.5. Black sigatoka resistance stability in M. (Jcuminata accessions.

100

Ortiz, Crouch, Vuylsteke. Ferris and Okoro. 1999. G x E analysis of Musa

Interpretation of PCA graphs also takes into account the relationships among points of the same kind and relationships between points of different kinds. The distance between points on these graphs is proportional to their level of similarity. For example, the susceptible cultivars are clustered together while the most resistant and stable accessions, 'Tuu Gia' and ru8, are closer to each other than to the resistant but unstable accessions 'Calcutta 4' and 'Pahang'. Furthermore, strains collected in the satne country can be closely related (e.g. Nigeria or most of the Pacific Islands' isolates). have a continous viruJence distribution (e.g. in Papua New Guinea) or be completely different (e.g. Central America). The latter may indicate a different geographical origin of the introduced M. fijiensis in Honduras (upper part) and Costa Rica (lower part). Both strains are equa1l~ virulent but the Honduras isolate had a more variable interaction with different M acumlnata accessions. This would indicate a change of virulence genes in the strains found acros~ different sites in Central America, which may be due to 1he substantially high fungicide pressure applied in the region as compared to Africa or Asia. The second interpretation of peA graphs conCerns the relationship between points of different kind, ie. genotype-strains interactions. The PCA graph (Fig. 8.6) suggests that black sigato1ca resistant genotypes such as 'Tuu Gia' and rug do not have a cross order interaction with the M. fijiensis isolates. This means that their rankings are similar across isolates with 'Tuu Gia' being almost always more resistant than ruS. Furthermore, this graph shows that susceptible cultivars (upper right) did Dot co-evolve with M. fijiensis , probably because they have different geographical origins. Therefore, as was suggested by Fullenon and Olsen (1991), M. acuminata accesions from Papua New Guinea should be screened to identify potential black sigatoka resistance alleles.

o SF 215 l~

3 f..7-J

-< 0

,,,......

.. -x .. 0 ...

•

...

,;.

••

)(

·00"1

0 a

Grande Nelne

Pllang mal

Salmaea

• KokodJa

+ PI..ng Berlin ,' .

Pahang

0 C.lcuttll 4

-3

o Tuu GJa •

-6

0 •

• •

•

10 12 14 16 18 20 22 24 26

peA 1

~~,

)(

PacUlc Island. Papua-N.Guln •• Philippine. Centr.1 Amer. Nigeria

Filun 1.6. PCA model for black siptob respoa.se in banana across different isolates. Oose and distant points of the same kid, filled symbols for strains and open symbols for cultivars, are similar and dissimilar, respectively. In other words, a PCA graph represents

101

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

the data structure by presenting the closeness of similar points and distance of dissimilar points.

8.4.5.2. Breeding strategy for durable host plant resistance to black sigatoka. The above study shows that epidemiology research of this natuIe provides baseline infonnation which can be used by breeders to define strategies for durable disease resistance. For example, most banana breeding programs have used 'Calcutta 4' as the primary source of resistance to black stgatoka. Therefore, it is likely that a narrow genetic base for black sigatoka resistance presently exists in the Musa breeding material. The search for other sources of resistance is mandatory to address this problem. lITA Musa breeding population combines recessive - additive resistance alleles from plantain with 'Calcutta 4' and 'Pisang lilin' resistance alleles. However, ITTA has decided to search for more different alleles for black sigatoka resistance and by pyramiding them in a single hybrid it is hoped that a durable bost plant resistance will be generated. Another way to achieve durable host plant resistance may be to incorporate different types of resistance (based on their response to different isolates of black sigatoka) in the same genotype. To this end, several secondary triploids, derived from crosses between non-related black sigatoka resistant primary tetraploid x plantain derived diploids (Vuylsteke and Ortiz, 1993), have already been produced and field established in early evaluation trials at the breeding station (PBIP 1993). Moreover, plantain-derived diploid hybri,ds have been crossed with different black sigatoka resistant diploid bananas. In this way IITA has been developing populations with a broad genetic base for sigatoka resistance which may result in durable resistance. This analysis clearly demonstrate that appropriate mycological techniques (utilization of differentials to assess host response to different isolates), as well as powerful statistical tools (peA) provide means to establish genetic differences for virulence, and to distinguish between strains or races of M. fijiensis. However, the application of molecular techniques for the differentiation of black sigatoka strains should be combined with similar studies of the host-plant in order to defme the host-plant interaction. The similar response across environments of susceptIble cultivars (mean> 3 in 1-5 scale, and clustered together in the upper right peA graph, Fig. 8.6) indicate that only one M. acuminata susceptible accession should be included as reference cultivar for the assessment of black sigatoka variability (with respect to both severity and incidence) in multilocational trials. Currently, IITA uses 'Valery' and 'Agbagba' as their standard reference susceptible banana and plantain cultivars. respectively.

8.5. Genotype-by-cropping system interaction Careful management of organic matter is essential to achieve sustained perennial productivity of plantain under large scale field production conditions (Vuylsteke et al., 1993a). Agroforestry systems such as alley cropping and the management of regrowth of natural bush fallow species in plantain fields h~ve been investigated (PBIP 1992) for their capacity to maintain productivity over long periods of cultivation without degradation of the resource base. Also, the performance of improved genotypes relative to cropping systems should be assessed because on-site cultural practices used in breeding trials may differ considerably from typical farmer practice and could influence genotype selection.

102

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

One of the most popular Nigerian plantain landraces (cv. 'Agbagba') and the improved PITA-l were established in a field at the IITA breeding station. The two clones were evaluated under four treatments: control (fanner's system, no fertilizer), and multi-species alley cropping without fertilizer, with 400 kg NPK, and with 800 kg NPK. Preliminary results (plant height 4 months after planting) showed that PITA-l always grew faster than the susceptible landrace even under 0 NPK. This early vigorous growth of the improved BSRTH may result in higher yields than in the landrace.

8.6. Local preferences and fruit quality evaluation The BSRTIi were obtained from crosses with diploid banana relatives, which have very different fruit characteristics compared with traditional plantain. Hence, hybrids are likely to have altered fruit quality that may affect consumer acceptability. Consequently the BSRTH gennplasm is being assessed for postharvest characteristics to determine fruit palatability and durability . Taste panels are used for sensory evaluation of unripe and fried ripe fruits. Criteria for testing include taste, color, sweetness and texture. An initial consumer acceptability study per fanned by a panel of 17 trained assessors at the breeding station in Nigeria, revealed little difference in taste between PITA-2 and its susceptible plantain parent (Vuylsteke et 81., 1993g). However, fruit color of the plantain hybrid was less preferred. Therefore, the plantain landrace had a significantly better overall consumer acceptability than the resistant hybrid, which was nevertheless still rated as good. Preliminary analyses of a more complete study (Ferris, 1993; PBIP, 1993) showed that PITA-I, PITA-2, PITA-S and PITA-9 should be considered for more advanced testing. PITA-9 has the best fruit quality among the improved

BSRTIf.

8.7. Final Remarks The aim of this breeding work is not only to develop hybrids with disease- and pestresistant but to produce improved breeding populations and develop cultivars appropriate for the African farming community. The breeding goal is driven by an ideotype, which requires the integration of several genes into a selected genotype. The improved plantain or banana cultivar should combine alleles for high stable yield, resistance, adaptation, and fruit quality. Success of this complex breeding goal can be enhanced with an indeptb genetic knowledge of the breeding population (Ortiz, 1993c). Genetic records were taken from the beginning of the program, in the early evaluation trials. Basic analysis of this fundamental information is providing a new understanding of Musa breeding and is being complemented with strengthened data from preliminary yield trials that have a higher level of replications. Future strategic decisions in the breeding program will be based on this genetic information to solve specific problems, such as how to incorporate a gene for disease or pest resistance into the breeding population without losing value from other traits. Genotypes selected using this approach will undergo multilocational testing to identify clones suitable for cultivar release by NARS. Hence the basic information is being rapidly transferred to applied research. Using the knowledge and materials obtained in early and preliminary trials to generate new technology, the improved gennplasm. NARS are conducting advanced trials, with support from the breeding program, to screen for stable, high yielding genotypes as identified in the multilocational testing. The most promising genotypes are being used in a cultivar release initiative for their specific

103

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

agroecozone. With fruit available from METs and AMYTs, taste panels were set up with NARS in Ghana, Nigeria and Uganda for further evaluation of the iInproved germplasm and landraces according to local preferences. NARS will also conduct on-farm testing to develop a cultivar profile that will help farmers to maximize yields. This adaptive research is concerned with adjusting ('fme-tuning') the germplasm to each specific environment. In conclusion, IITA and NARS are equal partners in applied research and adaptive testing of improved germplasm. Both contribute equally by dedicating part of their resources towards the success of joint activities. This demonstrates commitment of both partners in the implementation of the flow of materials and technology transfer, and ensures local adaptation and acceptance of new germplasm. IITA assist NARS to get donor support, as well as in technical backstopping. Networking is required to bring all concerned scientists together during workshops. Research linkages between NARS and IITA are thus fostered through this regional system for multilocational testing and highly productive cultivars with a high market acceptance are delivered to fanners .

REFERENCES Craenen, K., 1994. Assessment of black sigatoka resistance in segregating progenies. MusAfrica 4:4-5. Ferris, S., 1994. Developing screening techniques for postharvest quality of plantain. MusAfrica 3:6-8. Fullerton, R.A. and T.L. Olsen, 1991. Pathogenic variability in Mycosphaerella fijiensis Morelet. pp. 105-114. In R.V. VaImayor, B.E. Umaldi and C. P. Bejosano (eds.). Banana diseases in Asia and the Pacific, Proceedings of a regional technical meeting on diseases affecting banana and plantain in Asia and the Pacific. Brisbane, Australia, April 1991. INIBAP, Philippines Gauch, H.G., Jr., 1992. Statistical analysis of regional yield trials: AMMJ analysis of factorial designs. Elsevier, Amsterdam-London-New York-Tokyo. Hahn, S. K., D. Vuylsteke, and R. Swennen, 1990. First reactions to ABB cooking bananas distributed in southeastern Nigeria. pp. 306-315 . In R. A. Fullerton and R. H. Stover (eds.). Sigatoka leal spot diseases 01 bananas, Proceedings of an international workshop. San Jose, Costa Rica, March 1989. INIBAP, Montpellier, France. Mobambo, K.N., F. Gauhl, D . Vuylsteke, R. Ortiz, C. Pasberg-Gauhl, and R. Swennen, 1993. Yield loss in plantain from black sigatoka leaf spot and field performance of resistant hybrids. Field Crops Research 35 :35-42 . Ortiz, R.., 1993a. Field plot techniques for Musa yield trials. MusAfrica 2:4 . Ortiz, R., 1993b. Additive main effects and multiplicative interaction (AMMI) model for analysis of Musa yield trials. MusAfrica 2:4-5 . Ortiz, R., 1993c. Do plant breeders still have a place in the CG centers? IITA Research 7:2425.

104

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

Ortiz, R., 1994. Musa genetics. pp. 84-109. In S. Gowen (ed.) Bananas and Plantains. Chapman & Hall. Ortiz, R. , RS.B. Ferris, and D. Vuylsteke, 1994a. Banana and plantain breeding. pp. 110- 146. In S. Gowen (ed.) Bananas and Plantains . Chapman & Hall. Ortiz, R. and D. Vuylsteke, 1993a. Preliminary results of fIrst multilocational evaluation trials (MET-I) in the Humid Forest Zone (HFZ) of Cameroon and Nigeria. Musa Circular

1:2 Ortiz, R and D. Vuylsteke, 1993b. The genetics of black sigatoka resistance, growth and yield parameters in 4x and 2x plantain-banana hybrids. In 1. Ganry (ed.) Breeding Banana and Plantain for Resistance to Diseases and Pests. CIRAD, in collaboration with INIBAP. Montpellier, France. p. 379. Ortiz, R. and D. Vuylsteke, 1994a. Plot technique studies on yield trials of plantain propagated by in-vitro methods. InfoMusa 3(1):20-21.

Ortiz, R. and D. Vuylsteke, 1994b. Genetic analysis of apical dominance in plantain and improvement of suckering behavior. Journal of the American Society for Horticultural Science 119, in press. Ortiz, R. and D. Vuylsteke, 1994c. Inheritance of black sigatoka resistance in plantainbanana (Musa spp.) hybrids . Theoretical and Applied Genetics, in press. Ortiz, R. and D. VuyIsteke, 1994d. Plantain breeding at IITA. pp. 130-156. In D. Jones (ed.) The Improvement and Testing of Musa: a Global Partnership. Proceedings of Global Conference of the International Musa Testing Program, San Pedro Sula, Honduras. 27-30 April 1994. INffiAP, Montpellier, France.

Ortiz, R. and D. Vuylsteke, 1995. Reconunended experimental designs for selection of plantain hybrids. InfoMusa 4(1) :11-12. Ortiz, R., D. Vuy]steke, and S. Ferris, I 994b. Development of improved plantainlbanana germplasm with black sigatoka resistance. pp. 233-236. In Proceedings. of the First Crop Science Conferencefor Eastern and Southeastern Africa. Kampala, Uganda, JWle 1993. Ortiz, R., D. Vuylsteke, E. Foure, S. Akele, and A. Lawrence, 1993a. Stability of black sigatoka resistance in TMPx germplasm. MusAfrica 3:10-11. Ortiz, R., D. Vuylsteke, 1. Okoro, R.S .B. Ferris, O.B. Hemeng, D .K. Yeboah, c.c. Anojulu, B.A. Adelaja, O.B. Arene, A.N. Agbor, A.N. Nwogu, Kayode, I.K. Ipirunoye, S. Akele, and A . Lawrence, 1993b. Host response to black sigatoka across West and Central Africa. MusAfrica3 :8-1 O.

Ortiz, R., D. Vuylsteke, 1. Okoro, C. Pasberg-Gauhl, and F. Gauhl, 1994c. MET-I : Multi-site evaluation of Musa germplasm in IITA stations. MusAfrica 4:6-7. PBIP, 1992. 199/ Annual Report. International Institute of Tropical Agriculture, Ibadan, Nigeria. 32 pp.

105

Ortiz, Crouch, Vuylsteke, Ferris and Okoro. 1999. G x E analysis of Musa

PBIP, 1993. Annual Report 1992. Crop Improvement Division. International Institute of Tropical Agriculture, Ibadan., Nigeria. 208 pp. Vuylsteke, D. R., 1989. Shoot-tip culture for the propagation, conservation and exchange of Musa germplasm. Practical manuals for handling crop germplasm in vitro 2. IDPGR, Rome, Italy. 56 pp. Vuylsteke, D., E. Foure, and R. Ortiz, 1993b. Genotype-by-environment interaction and black sigatoka resistance in the Humid Forest Zone of West and Central Africa. MusAfrica 2:6-7 . Vuylsteke, D. , and R. Ortiz, 1993. MusAfrica 2: 1-2.

Diploid plantains with black sigatoka resistance.

Vuylsteke, D., R. Ortiz, and S. Ferris, 1993a. Genetic and agronomic improvement for sustainable production of plantain and banana in sub-Saharan Africa. African Crop Science Journal1:1-S . Vuylsteke, D., R. Ortiz, R.S.B. Ferris and I.H. Crouch, 1997. Plantain improvement. Flant Breeding Reviews 14:267-320. Vuylsteke, D., R. Ortiz, C. Pasberg-Gauhl, F. Gauhl, C. Gold, S. Ferris, and P. Speijer, 1993c. Plantain and banana research at the International Institute of Tropical Agriculture. HortScience 28:873-874, 970-971. Vuylsteke, D., R. Ortiz, and R. Swennen, 1992. Plantains and bananas. pp. 44; 86-91. In Sustainable food production in sub-Saharan Africa. Chapter 3, Crop Improvement. International Institute of Tropical Agriculture, Ibadan Nigeria. Vuyisteke, D., R. Ortiz, and R. Swennen, 1993d. Genetic improvement of plantains at IITA. pp. 267 - 282. In 1. Gamy (ed.) Breeding banana and plantain for resistance to diseases and pests. CIRAD, in collaboration with INIBAP. Montpellier, France. Vuylsteke, D., R. Ortiz and R. Swennen, 1993e. Genetic improvement of plantains and bananas at UTA. InfoMusa 2(1):10-12 . Vuylsteke, D., R. Swennen, and R. Ortiz, 1993f. Development and perfonnance of black sigatoka-tesistant resistant tetraploid hybrids of plantain (Musa spp., AAB group). Euphy lica 65:33-42. Vuylsteke, D., R. Swennen, and R. Ortiz, 1993g. Registration of 14 improved tropical Musa plantain hybrids with black sigatoka resistance . Hortscience 28:957-959.

106

Ekanayake. 1999. Cassava environments and G x E analysis

Chapter 9 Environmental Classifications and G x E Considerations for Better Adaptation of Cassava Ekanayake. IJ 9.1. Introduction 9.2. Agro-ecological Classification Systems for Africa 9.3 . Cassava Environmental Adaptation and Classifications 9.4. Yield physiology, G x E Interactions, and Breeding 9.5, Ideotype breeding and Crop growth models References

9.1. Introduction Cassava (Manihot escu/enta Crantz) is an important food crop in the African fanning system. Root yields up to 80 tJha can be produced under optimal conditions during a 12 month growing period (CIAT, 1980). The current low productivity of cassava in these fanning systems can be improved further using various advances in technical approaches. These include biophysical, biological, and sociological considerations that are either traditional or novel methodologies. Genetic improvement of cassava is however the backbone of achieving sustained improvements in root and leaf production for human consumption.

This paper attempts to analyze some of the issues involved in developing a sound classification system of agroecologies for cassava improvement based on climatic sensitivity and genetic adaptation. The ultimate goal is to strengthen the existing research efforts on cassava improvement at rITA and its' partner National Agricultural Research and Extension Systems (NARES). Delineating agroclimatalogical regions for research provides infonnation regarding climatological factors, e.g. rainfall patterns and duration of drought stress, with respect to crop productivity (Weber et aI., 1979). Agroecological based classification is useful to breeders and physiologists who are involved in improving the genetic base of cassava adaptation to various ecological niches in sub-Saharan Africa. More importantly corresponding agro ecological based groupings help in setting research priorities and in the monitoring of the allocation and efficient use of resources (CGIAR, 1991). Despite its importance in classical breeding, agroecologicaJ zoning and G x E issues have not received its' due attention. Agroecological characterization involves the description and analysis of characteristics of a site or a region, which are relevant to potential agricultural output and nature of agricultural systems in the region(s) (Goldman, 1988). The values and goals of environmental characterization are multi-fold, and at experimentation level, are useful for a). site selection (determination of the appropriate number of cassava breeding and testing sites), b). interpretation of results (relative to optimum and marginal environments and G x E interactions), and c). technology recommendations (i.e., improved and adapted genotypes of cassava) and for fme-tuning and recommendations of domains (Ortiz and Ekanayake, this publication). End product of site-based cassava breeding research efforts has to be of wider applicability over the dermed broad agroecozones. A lower number of experimental test sites is important to keep costs low.

107

Ekanayake. 1999. Cassava environments and G x E analysis

9.2. Agro-ecological classification systems for Africa Before evaluating the existing cassava environmental classification systems, it is desirable to look at those systems developed for Africa in terms of climate and agricultural land use patterns. Some of these cOlmnonly used environmental classifications and groupings are listed in Table 9.1. In addition to the climate, which is the most common basis of classification, soilrelated constraints must be integrated into a classification system for optimum agricultural production. These continental environmental classification schemes have provided the backbone for crop-specific schemes as discussed in the next section.

Table 9.1 . Agro-ecological classifications and zoning systems for Africa. Reference FAO 1986 CIAT, 1987

APproach Climate-based Climatic and edaphic classification Okigbo 1991 Vezetation-based Okigbo, 1991 using FAO, Land-use-based and edaphic 1986 data constraints-based llTA 1992a Climate-based

No. of major groups/zones 9 17 climate groups and 7 soil groups . 6 6 groups land-use-based and 9 soil constraint related grou~s i 6 main with 2-3 subgroups.

9.3. Cassava Environmeotal Adaptation and Classifications 9.3.1. Ecological limits of cassava adaptation

In Africa cassava is grown in aU agroecologies except the Sahel. is essentially a tropical humid and subhumid lowland crop, with limited cultivation in highlands with a very high potential for cultivation in semi-arid agroecology due to drought adaptation and long season or perennial nature of cassava. The area under cassava is lower in the semi-arid region despite its well mo'Nll drought tolerance. The optimal ecological adaptation of cassava is similar to a number of other tropical crops (Cock. 1985; Lawson, 1988; Ekanayake, 1998). Yet it tolerates marginal conditions for water budget and soil nutrients and has a comparative advantage in other suitable areas (De Bruijin and Fresco, 1989). Figure 9.1. shows a monocrop cassava cultivated in the forest-savanna transition zone (left) as compared to moist savanna zone of Nigeria (right) where the seasonal water availability is lower. In optimal environments cassava grows more vigorously producing higher yields. In constraint environments, vigor and yields are reduced. Cassava improvement activities are targeted to alleviate major abiotic stress factors as listed in Table 9.2.

108

\jPtll' 1111'

,,".:J'J 1Il !hl: 1~'rt:st-sa~a ; n.J Ira ~Hh.m z n

J\-JnO:l l tll1,

(nght) ot

ileft) and 111 the mOl t

Ig"f1<J

109

Ekanayake. 1999. Cassava environments and G x E analysis Table 9.2. Abiotic stress facto~ for cassava in different agroecologies of West and Central Africa by IITA classification (Ekanayake, 1998) and agro-ecological zone approach used by the cassava improvement program at liTA (rITA 1992b; Dixon et aI., 1994). Agroec:ozone

Cassava breeding

Zone

LGP (days)

Code

Major Abiotic Stress Factorls

Salinity, alkalinity, seasonal drought Acidity, low solar radiation, excess soil moisture. Seasonal mild drought

Coastal savanna

None

Humid forest

Humid.

>270

lIF

Derive(havanna

Hwnid

211··

270 Southern savanna

GUinea

Northern Guinea savanna Sudan savanna Mid-altitude savanna Inland valleys

Humid

18"1-

SGS

210 Semiarid

ISO-

NGS

Seasonal mild to moderate drought Seasonal moderate drought

180 Semiarid

<150

Mid-altitude

Humid

IVs

Severe drought, high tentperature salinity, alkalinity Low temperature, seasonal mild to moderate drought Excess humidity. mild to moderate drought, mineral toxicity.

LGP - Length of growmg penod.

9.3.2. Classification systems for cassava 9.3.2.1. Purposes of classification A well described, synthesized and inventoried classification of cassava environments has

a broad range of uses and users foremost of which are the breeders. For example, the cassava breeding zone approach used at nTA is shown in the second column of Table 9.2. The main uses of a classification system can be listed as follows. • • • •

assist in the defmition of research priorities, for example, an ecoregional perspective for project planning and execution help in the identification of homologues areas (across political and geographic boundaries) for technology transfer aids in selection of field test sites, interpretation, and analysis of regional or multilocational data sets or results of networking activities Knowledge of distinct environments can help develop research guidelines to generate appropriate technologies, i.e., help in the development of improved and eeo-zone adapted cassava clones, new agronomic technologies, and their adoption by the African cassava grower.

110

Ekanayake. 1999. Cassava environments and G x E analysis

9.3.2.2. Cassava Environments The physical environment (climate and soil) regulates directly or indirectly water, energy, and natural resources required by the crop. Cassava environment characterization and classification systems deal with the physical aspects needed for successful development and growth of a cassava crop. Some of these can be listed as follows . • soil physical characteristics and constraints (slope, erodibility, infiltration rate, water release, and soil temperature). • soil nutritional factors and constraints (deficiencies and toxicities) • irradiance during the growing season • water availability and air temperature suitability

In addition, the physical environment has an influence on biological stresses as well as weeds, insects and diseases) of cassava. As presented in the previous section, cassava-growing environments are complex and large in number. Al the same time there are also various classification systems used to categorize and describe each of these environments by different disciplinary scientists. The major aim of an environmental classification system is to subdivide culture range into units with minimal genotype by environment interactions within a given class, at each hierarchical level that provides a practical tool. Each system is drawn from actual crop response infonnation or bio~physiological data. At the first level, it is subdivided into broad but useful and recognizable categories. Each category is then subdivided into useful clusters for increased accuracy and practical utility. A workable classification is simple rather than more comprehensive with a minimum number of levels. A preliminary or a working model could then be refined as data become available on G x E interactions. Individual environments could be reclassified as the responses of improved clones are known. This procedure helps to improve the efficiency of selection of test sites as well as descriptions of crop ideotypes for different environments.

9.3.2.3. Cassava Environmental Classification Systems Several environmental classification systems exist describing cassava production areas and germplasm adaptation (Tables 9.3 and 9.4; IITA, 1992a; ClAT, 1987). Most crop-specific environmental classifications use minimum of three criteria, i.e., length of growing period (LGP), moisture sufficiency or probability of water stress, and inherent soil fertility limitations. There are two broad types of crop-environment classifications: factor matrixbased and inventory-based approaches. For example, the CIAT cassava environment scheme embodies the inventory-based approach based on LGP, altitude and latitude, and temperature (Carter et al, 1992).

111

Ekanayake. 1999. Cassava environments and G x E analysis Table 93. The climatic and edapbic classification for cassava producing areas in South America (After CIAT, 1987)

Climatic charllcteristics Class Lowland humid tropical Lowland humid subtropical Lowland semi hot isothennic Lowland semi hot non-isothennic Lowland hot isothermic Lowland hot non-isothermic Lowland semi-arid isothermic Lowland semi-arid non-isothermic Lowland arid isothermic Hi~hland humid tropical Highland humid subtropical 'Andean' semi hot isothermic 'Brazilian' hot isothennic 'Brazilian' hot non-isothermic Highland semi-arid isothermic Highland semi-arid non-isothennic Highland arid isothemtic

Soil characteristics

Class Fine textured soils Soils with permanent depth restrictions Soils with potential depth restrictions Soils with seasonal moisture restrictions Soils with permanent moisture andlor saliniJy restrictions Soils with acidity restrictions Soils without restrictions

N(1~: Lowlands are up to 800 m and hlghland above 800 m. The LGP for and or seml-arid is 30 to 150 days, sub-humid 151 to 270 days and humid 271 to 356 days. Temperature th~5holds are in excess of 18 DC for wann tropics, and !I to 18 DC for cool tropics . The LGP combined with temperature IIIso accounu for the altitude.

Another good example of a similar methodological approach to group environments and study its characteristics for productivity improvement purposes is the Collaborative Study of Cassava in Africa (COSCA) survey approach (Carter and Jones, 1989; Nweke et aI. , 1994). This approach (Table 9 .4; Stoorvogel and Fresco, 1991). uses Papadakis climatic map classification (Papadakis, 1970).

112

Ekanayake. 1999. Cassava environments and G x E analysis Table 9.4. Classification system used for agroecozoncs of cassava in Collaborative Study of Cassava in Africa (COSCA) study (After.,Stoorvogel and Fresco, 1991).

Climatic subdivision Semi-arid,tropical climate Semi -arid'subtropical climate Savannatropicalclinlate Savanna subtropical climate Humid tropical climate

lAnd suitability Highly suitable Moderately suitable Marginally .suitable Non suitable (no phases added) Saline and Calcic soils (non suitable and no phases added)

Soil Phll!Ses Textural restrictions T~logica1 restrictions Lithic andlor stininess phases Petric andlor petroferric phases

Humid subtropical climate Mediterranean climates Tropical hisdilimdsf1200m) Desertic climates .. Note: Annual temperatures of 20QC 15 used as the cntJcallimit while altJtudinal zonallumt USed was llOOm. Lower limit of rainfall used was 9OOmm.

The variations in the above two systems are based on the disCiplines of the system endusers. Systems developed by breeders are with fewer categories whereas agronomists and soil scientists may consider more environment specific and detailed classifications; cropping systems specialists also need greater information on the spatial micro-variability (Garrity, 1984). FromJi plant breeding point 'of view thus a broader agroecological zoning is desired. A more practical approach would be to use the breeders' agro ecological classification system as the basis and then to expand to the needs of other users (i.e., physiologists, agronomists, cropping system specialists etc.).

9.3.2.4. UTA's Agroecozone Approach for Cassava Breeding Related Research The classification used by cassava resear.c hersat lITA now is based on the TAC (Technical Advisory Committee) definition of the LGP and temperatures rather than vegetation alone. The LGP with a positive water balance (the difference between rainfall and evapo-transpiration) is defined as the period when rainfall is equal to or greater than potential evapO-ttanspiration (PET) (Lawson, 1988). It is dermed as begintililg when rainfall exceeds 0.5 PET and ending when rainfall falls below 0.5 PET plus stored soil moisture (as assumed to be 100mm; FAO, 1978). Lawson (1988) also noted that LGP is adequate to serve the purpose of broadly delineating representative domains for research aimed at determining productive root-crap-based cropping systems and analogue areas of application. Use of LGP concept for cassava needs adjustments, due to its drought tolerance, perenniality and root growth patterns, where 0.5 PET is not the true limit for describing the end of a growing season for cassava. Further improvements are needed in the second order classification of agroecozones, taking into account major tri-tropic pest-climatic-edaphic interactions, and 10 appropriate site selections that are representative to the extent possible, the micro-variations in the target zones for cassava breeding. It is a simple and global defInition for crops in general but adaptable to cassava as well, with additional parameters, for example decreasing rainfaU, increasing drought intensity, and low K soils, Length of growing period also relates to pest IVld diseases distribution, cropping patterns, and rain fed crop production. Although cassava is tolerant of

113

Ekanayake. 1999. Cassava environments and G x E analysis low fertility levels. low P content, high Al content and low pH. cassava agroecozoning for breeding purposes, have not put heavy emphasis on the soil restrictions. Those soil related aspects are important in the light of a variety of soils in which cassava is cultivated in Africa (Asadu et at, 1998). Socio-economic factors (e.g. market accessibility or consumer preferences) can be added in this approach as needed , i.e., COSCA survey approach (as discussed before; Nweke et al., 1994). Broad classification grouping of environments based on biophysical data for cassava improvement activities (Table 9.2) is simpler and generally adequate. As noted by Goldman (1988), a vegetation-based classification is appropriate for West and Central Africa within the inter-tropical zone. However in Eastern Africa and other areas, most suitable classification would be one based on water balance plus altitude (Goldman., 1988). Tropical and subtropical latitudinal categories are often sufficient to take ihto account those additional cassava target areas in the Southern Africa. A matrix of altitude (lowland and highland) in one dimension and water balance (humid, subhumid and arid) in the other dimension is therefore more suitable.

9.3.2.5.

Cassava in the wetland ecology

An additional category of wetland areas across these agroecozone belts is the inland valleys swamp ecology (IVs). In West Africa, Ns is a minor cassava ecology. Cassava is grown as a short season crop ÂŤ 6 months) in these seasonally wet inland valleys (Ekanayake et aJ., 1994; Lahai et al., 1999). The constraints to cassava production such as soil physical properties associated with water logging and drainage conditions in these agroecozones vary widely (Table 9.2). A more comprehensive classification system proposed for upland (Ekanayake et at, 1994a and b) and IVs (Ekanayalce et al., 1994c) cassava culture based on latitude, altitude, seasonality, moisture/flooding regime, temperature and soil conditions is described in Table 9.5. The utility of such system for cassava breeding pmposes is yet to be tested except for screening for adaptation to transient and seasonal water table depths are been done (Ekanayake et at, 1994c; Lahai et a1., 1999). It is a modified categorization from rice enviromnent classifications (Khush, 1984; Barker and Herdt, 1979; and IRRI, 1986).

Table 9.5. Proposed classification system for cassava after rice in traditional rice ecosystems in the upland and inland valleys (Adapted from IRRI, 1986). Zone level 1 Rainfed with favorable temperature, tropical zone Rainfed with low temperature, tropical zone, mid elevation Rainfed with low temperature, tropical zone, high elevation

Rainfall reUability Reliable

Drainage l:ia!ls l.Upland!

Other factors IA.Free draining

dryJand lB. Hydromorphic shallow ground water table Variable

2. Irrigated

Inadequate

3. Inland swamp

3A. Non-toxic 3B. Toxic

114

Ekanayake. 1999. Cassava environments and G x E analysis Rainfed with low temperature, temperate zone and long days Coastalseasonal or perennial saline water

4. Flooded

4A.Riverine shallow 4B.Riverine deep 4C.Poorly drained depressions flooded; 3-6 months to depths of 1.5m mangrove

5. Flood prone/ water logged

depth

6. Favorabl e water levels

7. Drought j)_rone

5AI.Shallow < 30 em SA2.Moderately deep 30 - 60 cm SB. Flooding 5B 1. P01m.ded clear water type 5B2. Siljy river water 5B3. Tidal water Sc. Flood SCI. 0.5 to 3 duration 5C2. 3 to 6 (months) 5C3. 6 to 9 5C4. 9 to 12 5D. 501. Flowing stagnation 502. Sta~t 503. Flowing followed by stagnant seepa~e I up welling 6A. Long growing 6A 1: With acidity season 6A2. Without acidity 6B. Medium growing season 6C. Short growing seasen 7A. Short growing season 7B. Very short growing season 5A. Flood

9.4. Yield physiology, G x E Interactions, and Breeding Interaction ofphysiologists and breeders is an environment x genotype interaction (Harrison, 1975 as cited by Treharne, 1975) In order to achieve efficiency in cassava improvement, analysis of morphological, phenological, and eco-physiological reasons for the presence of G x E interactions and the effects of these individual factors on within and between seasonal and locational variations in temperature, length of growing season, and soil fertility is needed. lnfonnation such as above is valuable to breeders in indicating the plant types (ideotypes) required for different agronomic and culinary purposes at different locations (Bunting, 1989). Physiologists tend to observe yield potential of an individual genotype as the yield obtained under ideal (optimum) conditions. For cassava, this definition is often inappropriate since cassava is very rarely grown under optimum conditions (or systems) and since very few farmers use external inputs (i.e. fertilizer , liming, and irrigation). A working definition of yield potential would be to consider it as the yield obtained under representative edapho-climatic conditions with minimum pest and disease pressures, and under improved and economical management conditions, and representative of farmers target

115

Ekanayake. 1999. Cassava environments and G x E analysis area. Physiological analysis of G x E also involve an analysis of the causes of potential versus actual perfonnance of a given genotype in a given environment. Physiological basis of G x E interactions for cassava has been previously reported (irikura et al., 1979) for a few traits and agroecological zones (Ekanayake et aI., 1994a,b,c). Further to the discussion on yield physiology and breeding of crops in general (Ortiz and Ekanayake, this publication), the physiological considerations of G x E interactions are illustrated below using two traits as examples associated with developmental adaptation of cassava to a particular environment.

9.4.1. Branching habit Canopy s~cture and light interception are important factors for adaptation of cassava to various environments, both in terms of the biophysical environments and cropping systems for cassava (Ekanayake et at, this publication). Canopy architecture plays a major role in agronomic decisions such as selecting desirable crop and genotype mixtures for intercropping of cassava or various other traditional or improved cropping systems, i.e. cassava in alley farming systems (Osonubi et aI., 1998). According to COSCA data (NweIce et al., 1994), branching habit preferred by fanners is low branching. Particularly where cassava leaves are consumed low branching is preferred as it allows more growing points with new leaves and is not always based on suitability for intercropping. Low ranching habit is also desired for early season weed control and reduced crop lodging, as compared to late or non-branching habit. Canopy architectUre refers to a set of morphological characteristics associated with the yieldirig ability of a given genotype. As shown in Fig. 9.2, G x E differences exist for canopy morphological traits of 3 clones in 5 locations or zones . Lower branching and a higher nwnber of branching events are observed as cassava is grown in relatively wetter ecologies with differences among genotypes as compared to drier ecologies. In drier zones, the same genotype may branch later while the number of branching events is also lowered. In addition to genotypic differences, time to first brancrung is a trait sensitive to ambient temperatures. Temperatures above or below a base level of24 OC, in the subtropics are said to delay branching (Irikura et aI., 1979). Differences in branching habit between mid altitude and lowland sites may be an effect of temperature.

9.4.2. Rooting habit. In drought prone ecologies, a genotype with weU-distn'buted fibrous root system (vertical and horizontal) is more efficient in water harvesting . An analysis of root length density in two drought prone sites showed both cross order and non-cross order interactions on genotype x soil depth (Fig. 9.3.). In selecting cassava genotypes for better rooting ability, across broad agroecologies, within location, and between soil proflle (depth) differences must be considered (Ekanayake et a1.. 1994a). Yields of cassava are reflection of temporal changes in weather on various physiological traits. Trends in changes of selected water relation parameters, crop ontogeny and weather are illustrated in Figure 9.4, which are pertinent in yield determmation.

116

Ekanayake. 1999. Cassava environments and G x E analysis

140

(a) TMS 86100106

5°

120 100

80 1·

40 20

1 ..!

Onne

.&:.

140

120

c f

100

= -• cu

=c 0

-80

-....

1·

Ibadan

Vom

Minjibir

(b) TMS4492

2·

1·

0 Ibadan

140

Vom

Samaru

Minjibir

(e) TMS 83/002 14

120 100

Onne: 7°1 0'E , 4"46'N, 30mAsi Ibacian : 3"S4'E, 7"26'N, 150mAsl

Yom : S· SO'E , 9 a 40'N, 940mAsI

Samaru: 7a 44'E, 11°7'N, 730mAsi

Minjibir: 8°3"E, 12°12'N, 490mAsl

40 20

1·

Yom

1·

Samaru

Minjibir

Fig. 9.2. Genotype x Envirorunent interaction effects on cassava canopy architecture (Onne: 1'IO'E 4°46'N , 30masl; Ibadan 3°S4' E, l' 26'N, 150masl; Vo~: 8° 50'E, 9° 40'N; 1140masl; Samaru: 7° 44 'E, 110 70'N, 730 masl,; Minjibir: g O31 'E, ]20 12 'N, 490 masI).

11 7

Ekanayake. 1999. Cassava environments and G x E analysis

0 .25

Root length density (em/em:!) .---.-:::.--......:...:...........---=---------------, 0--20 an IO~ profile ....._ â&#x20AC;˘â&#x20AC;˘"...-..._u.. ......"..._--".........,..........-....... .......... ....-......." ..-

--______-.J ~

O.2r~;;;,;;;,;,;;.----

o~---------------------------~

Minjibir

Samaru

Location (Nigeria)

Fig. 9.3. Cross.order and non-cross-order genotype x soil depth interactions for root length density of cassava.

9.5. Ideotype breeding and Crop growth models Donald (1968) fust suggested a breeding approach on the basis of model plant types or the commonly known ideotype bTeeding. An ideotype is a hypothetical plant descnbed in terms of traits that are thought to enhance the yielding ability of that plant in a particular environment. Ideotype breeding is defmed as a breeding method which enhance yield potential by modifying the individual traits for which the breeding goals are set (the phenotype) (Rasmussan, 1987). Ideal plant types have been described for several crops (for example common bean, wheat. com, navy beans, cassava, rice and barley (Fageria, 1992), and agroecological zones. According to Wilson (1977), an ideotype presupposes detailed and accurate knowledge of range of anatomical, morphological, and physiological, and biochemical traits existing in varieties within specie grown to a particular ecology. The previous discussion sections indicated the complex features of agroecologies where cassava is targeted, indicating strongly the need for developing plant types (different ideotypes) suitable for distinct envirorunentslagroecology

118

Ekanayake. 1999. Cassava environments and G x E analysis ideotype to fit a lower water budget in drier environments is a classic example. Such an ideotype has specific architectural features (morphological characteristics such as reduced branching and stem production, smaller leaf area, longer leaf life etc. are) and drought tolerance, which better use available solar energy and moisture as a function of its ideotype to produce higher yields. Our cassava program has identified clones for example, which has the above features plus pest resistance, i.e., cassava green mite (CGM) resistance (Ekanayake, 1998; Akparobi et al., 1998) for target ecologies. Several ideotypes for cassava has been proposed in the past and are sununarized below (Table 9.6).

Table 9.6. Proposed ideotypes of cassava Referente Alcinyeroiju and Adegoroye, 198B)

CIA T (1976) for Latin America and Asia

lITA (70's ideotype) for Africa (refer to Ekanayake (1998) lITA (90's ideotype)

Description to-month growth. cycle; planting with onset of rains and harvested at following dry season; single short erect stem, rapid leaf area development, maximwn LI of 3-4, slow decline of leaf area during senescence and early initiation and cellular tuber development Greater than normal leaf longevity; HI of 05 to 0.6 (but not higher); and large root storage capacity Significant plant architecture differences mainly in terms of branching habit, nwnber of stems and leaf color, root yield Resembles CIAT type but different in 1iliYsiolo~cal traits of adaptation

Additional to monocrop ideotype, mixed crop ideot)'Pes are also needed for various cropping systems (refer to Ekanayake et a1. , this publication). A tool that can be effectively used to describe, test and implement the use of ideotype breeding is cassava growth models and decision support systems. Where the physical environment is not conducive for high levels of production of root yield, fanners may have the following options: • adj ust the choice of cultivar in favor of drought adapted and improved clone • adjust the choice of cropping system to fit the prevalent physical environmental elements • adjust unfavorable elements of physical environment to the specific needs of cassava during dry season Fanners can be assisted in this decision making process by the use of functional decision support systems based on crop, climate and their interactions (Ekanayake and Lyasse, 2000). Refmements can also be made in crop environmental classification systems, to be favorably responsive to management (for example, fertilizer, cover crops, green manure, soil conservation efforts etc.) than inherent soil fertility alone. Modulation of physical environment by biological factors such as dry season pests (cassava mealybugs and cassava green mites) could be assessed using various models (Ekanayake, 1998b).

119

Ekanayake. 1999. Cassava envirorunents and G x E analysis

Moisture status (mm)

2S00.....-----~....;....--------------.,

~.w••ww Pan

Evaporation -

Moisture status (mm) ~r-----------------------,

Cum Pan Evaporation

- - - Total Rainfall -

Cum Rainfan

2000

1500

1000

· 1000 500,--_~

500

o Ibadan

.soo

·500

52 70 101 114160 210 226 2n 310365374399417 442497531547

Resistance (s em') Lower Surface

- 4(2)1425 , - 30572 -

--"_J--.J

L.-..........- . I . . _ L . -..........- - ' - - - ' _. . .~--'L.-...........--'-_"'--.......

52 70 101 114160 210 226 277 310 365 374399 417 442497531547 Water Potentail (MPa) Or-------------------------------------~

- - ~ 4(2)1425 ~~, 30572 -

91934

-02

91934

. ";-

..~..

,,,., .~ ..~ .~.

40 -04 30 -0.6 20 -0 .8

O~~~_L_-L~_L_~~_~~r:~~_~~ · ~~·~·'·~~~~ ~·_ · A~~~~~ .,~ 52 70 101 114160 210 226 277 310 365374399 417 442 497531547

· 1 L . _..........~_"'--~~_. . .~__'L._~~_ _..........~_ _~_L__'~

52 70 101 114160 210 226 277 310 365 374399 417 442 497531547

Fig. 9.4. Temporal changes in physiological traits (leaf water potential and leaf diffusive resistance) of cassava and trends in rainfall and pan evaporation during the growing season.

120

Ekanayake. 1999. Cassava environments and G x E analysis A review of cassava modeling work was fIrst done in 1997 (Ekanayake, 1998b), and updated more recently (Ekanayake, 1999a; Ekanayake and Lyasse, 2000) illustrating the slow but definite progress made to collate existing datasets for model validation (Ekanayake and Hunt, 1998). Despite the well descnbed advantages of cassava growth modeling to modulate G x E effects in breeding and field agronomy, it's use is relatively small. Most recently a concerted effort is on-going (Ekanayake, 1999; GCTE, 1999) under the crop networks initiative to better utilize the state-of-art technologies (i.e. GIS, growth models, and statistical software) on cassava crop improvement activities with respect to global change impact on cropping systems, climate, land use and human expectations, with special emphasis on sub-Saharan Africa. In conclusion, the above discussion summarized the current platfonn associated with agroecological classification and G x E interaction considerations and highlighted the increasing awareness for use in cassava improvement research in sub-Saharan Africa. In broader terms, more attention must be given to the effective use of new and powerful tools in the following areas: use of GIS skills in classification, ideotype defmitions and better integration of physiology knowledge in breeding.

REFERENCES A1ctnyemiju, OA and A.S. Adegoroye. 1988. Physiological considerations for tuber yield improvement in cassava (Manihot esculenta Crantz). pp. 21-30. In Cassava Based Cropping Systems Research I. Contnoutions from the First annual meeting of the collaborative group in cassava-based cropping systems research. Ibadan 16-19 November 1981. UTA, Ibadan, Nigeria. Akparobi, S.O., A.O. Togun, and I.J. Ekanayake. 1998. Assessment of cassava genotypes for resistance to cassava mosaic disease, cassava bacterial blight and cassava green mite at a lowland and mid-altitude site in Nigeria. African Crop Science Journal 6(4): 1-12. Asadu, C.L.A., F.I. Nweke, and 1.1. Ekanayake. 1998. The fertility status of soils grown to cassava in sub-Saharan Africa. Communications in Soil Science and Plant Analysis 29(1):

141-159. Barker, R. and Herdt, R.W. 1979 Rainfed lowland rice as a research priority - an economist's view. pp. 3-50. In Rain/ed Lowland Rice. International Rice Research Institute .. Los Banos, Philippines

8ruijin, G.H. and L.O . Fresco. 1989. The importance of cassava in world food production. Netherlands J Agric. Sci. 31: 21-34. Bunting, A.H. 1989. Impressions of the Cameroon National Root crops improvement program. Evaluation seminar, Ngaoundere, Cameroon, 22 September, 1989. 11 pp. Carter, S.E. and P.G. Jones. 1989. COSCA site selection procedure. Collaborative study (CaSCA) Working Paper No. 2. UTA, Ibadan, Nigeria. 19 pp. Carter, S.E., L.O. Fresco, P.G. Jones, and IN. Fairbain. 1992. An Atlas of Cassava in Africa. Historical, Agroecological, and Demographic Aspects of Crop Distribution. Centro Internacional de Agricultura Tropical (CIA T), Cali, Colombia. 86 pp. illus, maps. CGIAR (The Consultative Group on International Agricultural Research Teclmical Advisory Committee). ]991. A Review ofCGJAR Pn路orities - Part II: Essence Paper. TAe secretariat, Food and Agriculture Organization of the United Nations. 29 pp. ClAT (Centro Intemacional de Agricultura Tropical). 1980. Annual Report of Cassava Program . CIAT. Cali, Colombia.

121

Ekanayake. 1999. Cassava environments and G x E analysis CIAT (Centro Intemacional de Agricultura Tropical). 1987. Distnbution of cassava amongst different climate and soil types in South America. pp. 12. In Annual Report of Cassava Program. CIAT, Cali, Colombia. Cock, J. 1985. Cassava - New Potential for a Neglected Crop. Westview Press, Boulder and London. Dixon, A.G.O., R. Asiedu, U . Ekanayake and M.C.M. Porto. 1994. Cassava improvement in Africa: Contributions of the Internationallnstitute of Tropical Agriculture. pp. 207-215. In E. AdipaJa, M.A. Bekunda, I.S. Tenywa, M.W. Ogenga-Latigo, and 1.0. Mugah (eds.) African Crop Science Society Proc. Volume 1, No. 1. African Crop Science Society, Kampala, Uganda. Donald, C.M. 1968. The breeding crop ideotypes. Euphytica 17: 385-403. Ekanayake, I.J. 1998a. Screening for Abiotic Stresses of Root and Tuber Crops with Emphasis on Cassava. IITA Research Guide No. 67. lITA. Ibadan, Nigeria. 46p. Ekanayake, I.I. 1998b. Conceptual issues on in the use of cassava modelling for improved production in drought environments. Tropical Agriculture (frinidad) 75(1): 102-105. Ekanayake, I.J. 1999. Activities in Cassava Modeling, Productivity, Food Security, and Global Change. START Report No. 1. 61p. Available on-line with updates at hÂŤp://www.start.org Ekanayake, 1.1. and L.A. Hunt. 1998. GCfE Focus 3 Cassava Network: Experimental Damsets. Crop Science/GcrE/START Report No. lJE-01-98. Crop Science Division, Department of Plant Agriculture, University of Guelph, Canada. 68p. Ekanayake, I.J. and O. Lyasse. 2000. Growth and productivity of long season cassava: efficacy of predictive models and a systems research approach. In . Root Crops in the 21" Century. Proc. Seventh ISTRC-AB, in press. Ekanayake, 1.1., A.G.O. Dixon, and M.C.M. Porto. 1994. Performance of various cassava clones in the dry savanna region of Nigeria. pp. 205-215. In. G.T. Kurup, M.s. Palaniswamy, V.R. potty, G. Padrnaja, S. Kabeertharruna, and S.v. Pillay (eds.). Tropical Tuber crops. Problems. Prospects and Future Strategies. Oxford & IBH Pub. Co., New Delhi, India. Ekanayake, !.J., I.N. Kasele, and R. Kapinga. This publication. Implications of G x E interactions of cassava under different cropping systems. In I.J. Ekanayake and R. Ortiz (eds.) Genotype x Environment Interaction Analysis of llTA Mandate Crops in Sub-Saharan Africa. IITA, Ibadan, Nigeria. Ekanayake, I.J., M.C.M. Porto and A.G.O. Dixon. 1994. Response of cassava to dry weather: potential and genetic variability. pp. 115-119. In Adipala, MA. Bekunda, J.S. Tenywa, M.W. Ogenga-Latigo, and J.O. Mugab (eds.). African Crop Science Society Proc. Volume 1, No.1. African Crop Science Society, Kampala, Uganda. Ekanayake, I.J., A.G.O. Dixon, R. Asiedu and A-M.N. Izac. 1994. Improved cassava for inland valley agro~ecosystems . pp. 204-208. In . M.O. Akoroda (ed.). Proc. 5111 Symp. ISTRe-AB. IITA, Ibadan. Nigeria. Fageria, N.K. 1992. Maximizing Crop Yields. Marcel Dekker Inc. 274p. FAO (Food and Agriculture Organization). 1978. Report on the Agroecological Zones Project. Vol. 1. Methodology and Resultsfor Africa. FAD, Rome. FAO (Food and Agriculture Organization). 1986. African Agn'culture: the Next 25 Years. Atlas of African agriculture. Rome: FAO. GCfE (1999). GCTE Crop netWorks Cassava Network (prepared by 1.1. Ekanayake). Available on-line with updates at htm://mwanta,nmw.ac.uklGCIEFocus3/networks/cassava.htmJ) (posted May 1999; verified October 28,1999). Goldman, A. 1988. The role of agroecological characterization in West and Central Africa. pp. 264-271. In . Cassava Based Cropping Systems Research Vol. 1. Contributions from the First Annual Meeting of the Collaborative Group in Cassava-based Cropping Systems Research. 16-19 November, 1987. UTA, Ibadan, Nigeria.

122

Ekanayake. 1999. Cassava environments and G x E analysis Garrity, D.P. (1984) Rice environmental classifications: a comparative review. pp.l - 35. In Terminology for Rice Growing Environments. IRRl, Los Banos , Philippines. International Institute of Tropical Agriculture (llTA). 1992a. Agroecologica/ zones in Africa. A map. Agroecological Studies Unit. IITA, Ibadan, Nigeria. International Institute of Tropical Agricu1ture (lITA). 1992b. Cassava Breeding at lfTA. TRIP, lITA Internal Review Document. lITA, Ibadan., Nigeria. International Rice Research Institute (IRRl) 1986. Terminology for Rice Growing Environments. IRRI, Los Banos, Philippines. 35 pp. lrikura, Y., J.H. Cock, and K. Kawano. 1979. The physiological basis of genotype-temperature interactions in cassava. Field Crops Res. 2:227-239. Khush, G.S. 1984. Terminology for rice growing environments. pp. 5 -10. In International Rice Research Institute. Terminology for Rice Growing Environments. IRRI, Los Banos, Laguna, Philippines. Lahai, M.T., 1.1. Ekanayake, and lB. George. 1999. Cassava (Manihot esculenta Crantz) growth indices, root yield, and its components in upland and inland valley agroecologies of Sierra Leone. Journal ofAgronomy & Crop Science (Berlin), 182: 239-247. Lawson, T.L. 1988. Characterizing the crop environment; an agroclimatic perspective. In Cassava based cropping systems research Vall. pp. 11-20. In Contributions from the First Annual Meeting o/the Collaborati1!e Group in Cassava-based Cropping Systems Research.

16-19 November, 1987. IITA, lbadan, Nigeria. Nweke, F.l., A.G.O. Dixon, R. Asiedu, and SA Folayan. 1994. Cassava varietal needs of farmers and the potential for production growth in Africa. COSCA Working Paper No. 10. lITA, Nigeria. 239 pp. Okigbo, B.N. 1991. Development of sustainable agricultural production systems in Africa. Roles of International Agricultural Research Centers and National Agricultural Research Systems. lITA, Ibadan, Nigeria. 66p. Ortiz, R. and I.J. Ekanayake. This publication. Genotype-by路environment interactions, multilocational testing, site selection and yield stability. In. l.J. Ekanayake and R. Ortiz (eds.) Genotype x Environment Interaction Analysis of llTA Manda te Crops in SubSaharan Africa. TITA, Ibadan, Nigeria.

Osonubi, 0 ., 11. Ekanayake, I.E. Okon, and O. Fagbola. 1998. Mycorrhizal inoculation and mulching applications for continous cassava production in alley cropping systems. pp. 190194. In M.D. Akoroda and I.J. Ekanayake (eds.). Root Crops and Poverty Alleviation. Proc. ISTRC-AB, 22-28 October 1995. Lilongwe, Malawi. Papadakis, 1. 1970. Climates of the World. Their Classification, Similitudes, Differences and Geographic Distribution. Buenos Aires, Argentina. Rasmussan, D.C. 1987. An evaluation of ideo type breeding. Crop Sci. 27: 114()"1146. StooIVogel, J.J. and L.O. Fresco. 1991. The identification ofagro路ecological zones for cassava in Africa with particular emphasis on soils. COSCA Working Paper No. 5. TITA, Nigeria. 5 pp. and map. Trehane, KJ. 1975. Summary of discussion sessions. pp. 107-1 t 1. In Proceedings of Crop Physiology Program Formulation Workshop. liTA, Ibadan, Nigeria. Weber, E., B. Nestel, and M. Campbell. (eds.). 1979. Discussion summary. In Intercropping with Cassava. Prce. of an International workshop held at Trivandrum, India, 27 November - 1 December 1978. CTRCIfIDRC, Ottawa. 144 pp. Wilson, L.A. 1977. Root Crops. pp. 234路261. In P.T. Alvim and T.T. Kozlowski (eds.). Ecophysiology of Tropical Crops. Academic Press, London.

123

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Chapter 10 Implications of G x E Interactions of Cassava under Different Cropping Systems Indira J. Ekanayake, Idumbo N. Kasele and Regina Kapinga 10.1. Introduction 10.2. G x Cropping system considerations 10.3. Stability in Intercropping 10.4. Analytical approaches to G x E in Intercropping 10.5. Conclusion References

10.1. Introduction The production of cassava for commercial and subsistence purposes is increasing rapidly in sub-Saharan Africa (Nweke, 1996). This increase is marked in some African countries. For example. during the 1974.1996 period, Nigeria achieved a substantial increase in cassava production, from a yearly mean of 10.3 t fresh roots (from 1974 to 1976) to 31.3 t (from 1994 to 1996), with a comparable mean rate of increase in production (10 .1% per year) (FAO, 1998). In contrast, rate of increase in world cassava production during this period was much lowl1r (2.5% per year). Irrespective of expansion in the area of cassava production, evidence is Jackbtg to support an increase in cassava sole cropping vis-a-vis intercropping. Simultaneous cultivation of two or more crops on the same piece of land or mixed cropping or intercropping is a widespread and time immemorial agricultural practice in the humid and the sub-humid tropics. In traditional cassava growing areas of Sub-Saharan Africa, mUltiple crops are produced in a single field by most farmer, and for cassava mixed cropping is the usual practice (Nweke, 1996). Results from the Collaborative Study of Cassava in Africa (COSCA) show that farmers grow an average of6 to 7 crops, with a range of 1 to 15 crops (Nweke et al., 1996; Spencer and Kaindaneh, 1997). Less than 25% of the surveyed fields were planted to a single crop such as cassava (Nweke et at, 1996). Rice, yam and cassava were the crops grown most often as sole crops in the COSCA-sampled countries. The diversity of intercropping systems for cassava is well described across many of the agroecological zones where it is a dominant crop (Dorosh, 1988; Elemo et al., 1990; Mutsaers et aI. , 1993; Okigbo, 1994; Nweke, 1996; Nweke et at, 1994 and 1996). Despite attempts by extension workers to bring about the adoption of the sole cropping, the practice of intercropping has persisted because of its adaptability to ecological, socio-economical, and socia-cultural conditions of the tropics (Steiner, 1982; Zandstra, 1979; Porto et aI., 1979). In recent years, IITA has focused on improving cropping systems as well as introducing improved cassava genotypes into lowland dry and moist savanna. These are non-traditional cassava growing areas with good potential owing to its ability to withstand stressful environments (i.e., drought and low soil fertility) and to its high yield potential. A large bulk of cassava production is in the forest zones. Savannas are dominated by cereals and legumes and are characterized by low rainfall, poor soil fertility, and a diversity of traditional cropping systems. Urgent priority in this approach is to relate growth patterns of cassava to measurable physiological and morphological traits, which can be incorporated in the selection

124

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

procedure in an effort to identify genotypes suitable to existing major cropping system (Ekanayake, 1995; Ekanayake et a1. , 1996) . This allows the selection of desirable genotypes, without expensive multi-site field screening under mixed cropping.

10.2. G

Cropping system considerations

G x E interactions are detected in multi-crop field trials because the cassava genotypes and companion crop species respond differently to environmental fluctuations . Cropping systems are considered as an essential component of the micro-environment of the plants. Cropping systems have a direct influence on the performances of genotypes and therefore, can cause significant changes in relative yields (Steiner, 1982). Attempts to increase the productivity of traditional cropping systems through the introduction of improved genotypes have not always been satisfactory, partly because the new genotypes did not produce the expected yields under crop competition. Most improved genotypes are usually developed under sale cropping conditions. These perform differently when associated with other crop species. Under intercropping conditions, a plant may influence its neighbors by changing the microenvironment or by competing for growth resources (i.e., light, nutrients, and water). Therefore, a genotype suitable for sole cropping may not necessarily be suitable for intercropping. Table 10.1 shows a significant clone x cropping systems .interaction for height at fIrst branching but with no significant effect final root yield.

Table 10.1. Mean squares of the analysis of variance for various cassava growth characteristics if grown as a sole crop, intercropped with maize or groundnuts (After Osiru and Hahn, IITA, unpublished data) Source of variation

Degrees of freedom

Sprouting percentage

Reps Treatments Clones (C) Cropping systems (CS)

0.101 0.695路'"

41 13 2

26.599"'路 4.833-

Height at first branchine

4.512 10.881"'''' 16.52B"'''' 95.081"

2.024路 Cx (CS) 1.617 26 C.V.(%) 6.4 17.1 "', -- indicate significant at 5% and 1% respecbvely.

Fresh root yield

0.546 9.39"'''' 22 .612"'路

29.66"''''

1.219 17.9

Performance of cassava as a sale crop differs from an intercrop (Mutsaers et a1., 1993; Kapinga et aI., 1995). The influence of plant morphotype and intercropping with sweet potato on fresh root yields of cassava are given in Table 10.2. Table 10.3 provides data to illustrate the effect of plant density on root weight under intercropping as compared to sole cropping. Situation with reference to various agroecological zones under a range of soil conditions (Stoorvogel and Fresco, 1991), is a challenge for cassava-based intercropping research.

125

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Table 10.2. Effects of morphotype, fertilizer application, and intercropping \ltith,sweet potato on the tuberous root yield of cassava (Ukiriguru in Tanzania, 1989-1990). Fertilizer rate was 6ON-30P-30K kg ha'i (After Ndibaza, 1994).

Morphotype of cassava Erect, tall, non branching variety (Mzimbitala) Semi-erect, tall, high-branching variety (Aipin Valenca) Spreading, medium-tall, intennediate branching variety (Msitu Zanzibar) Spreading, short, low-branching variety (Liongo control) Mea.'l LSDo.05 CS Fertilizer (F) CS at same F F at same CS

Fresh cassava root wei2ht (t ha'I) With Cropping Without system {CS} fertilizer fertilizer Intercropped 6.9 10.7

Difference

(%) 36

Sole

10.8

13.3

lntercropped

8.9

12.0

Sole

10.9

15.2

Intercropped

8.7

13.3

Sole

13.l

19.8

Intercropped

6.8

115

11.1

16.5

9.7

14.1

Sole

0.7

0.4 1.1 1.0

Therefore, selection of genotypes must be done under actual intercropping situations. The goal of selection is to minimize intercrop competition and maximize complementary effects. Selecting cassava genotypes for intercropping is more complicated and costly than selecting for sole cropping conditions. Early generation selection is advocated under monocrop conditions by screening for yield, plant type and resistance or tolerance to major biotic and abiotic stresses. Selected genotypes can then be evaluated under actual cropping systems at later breeding stages when smaller number of genotypes are tested. It is not always possible in a breeding scheme to compare the various crop combinations with species of different growth habits as well. Some of the common cassava-based cropping systems include cereals (Mutsaers et aI., 1993), grain legumes (Mutsaers et al., 1988) and root crops (Kapinga et aI., 1994; Kapinga et aI., 1995). An example of the performance of selected cassava-based cropping systems and their individual crop componenets in southern Guinea savanna site in Nigeria is given in Table 10.4. The G x E interactions of on-farm trials and associated technologies was discussed in detail in an earlier chapter (Carsky and Versteeg, this publication). Analysis of cassava-based systems involving fanner-managed and on-farm conditions is a topic reviewed in 1993 (Mutsaers et a!.) though important is outside the scope of our review.

126

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Table 10.3. Effects of plant density. fertilizer application, and intercropping with sweet potato on the tuberous root yield of Msitu Zanzibar cassava (Ukiriguru in Tanzania, 1989-1990). Fertilizer rate was 60N-30P-30K kg ha-I (After Ndibaza, 1994)

Plant density

6,666 plants cassava ha-I

Fresb cassava root weiEht (t ha' l ) Cropping Without system (CS) fertilizer Intercropped 5.8

Difference (%)

With fertilizer

8.0

I 28 j32

Sole

9.5

13.9

Intercropped

7.8

9_6

Sole

10.3

14.1

Intercropped

8.1

9.9

Sole

11.6

15.0

Intercropped

9.3

12.3

Sole

13.0

16. ]

10,000 cassava plants ha-I

13,333 cassava plants ha- I

20,000 cassava plants ha路 1

LSDo.o5 CS Fertilizer (F) CS at same F F at same CS

1.2 1.2 1.8 1.7

Studying G x CS interactions and identifying physiological and morphological traits of cassava genotypes for their response to intercropping and explaining observed. effects in physiological terms may be useful in developing cassava ideotypes suitable for a particular cropping system. Little is known about the specific influences of various physiological parameters on genotype x cropping system interaction in cassava and most work reported involve mainly the final yield (Weber et aL, 1979; Mutsears et at, 1993, 1997).

127

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Table 4. Storage root yields (t ha路 l ) of variou.s cassava clones intercropped with maize, soybean, and cowpea (Molcwa, 1994-1995 growing season) (After Kasele, 1996) Cassava clones

Intercropping with Sole

Early

Late

ate

Clone

cassava

maize-

maize

soybean

Mean

soybean TMS 30572

27.5

24.5

22.8

21.3

24.0

TMS 91934

32.7

27.1

31.5

32.8

31.0

TMS4(2) 1425

33.6

34.6

39.6

38.4

36.6

TMEI

29.8

32.9

25 .4

23.7

28.0

Cropping system (CS)

30.9

29.8

29.0

29.9

mean S.E. for clone means (C) S.E. for cropping system meanslCSl S.E. for CS x C

2.74 2 .59

2 .38

10.3. Stability in Intercropping The concept of stability is used most frequently to analyze the G x E interactions in agronomic and breeding trials. The G x E variance is partitioned into stability variance components for each genotype. A significant stability variance for a particular genotype denotes lack of stability (Yates and Cochran, 1938; Wricke, 1962; Eberhart and Russel, 1966; Freeman and Perkins, 1971 ; Shukla, 1972): Shukla (1972) partitioned G x E variance into stability variance components assigned to each genotype in order to interpret agronomic stability. This estimator is a minimum nonn quadratic unbiased estimator (MINQUE). Weber et al. (1979) observed that stability is an important issue in evaluating the merits of G x E interactions of intercropping. They further mentioned that the subsistence farmer is concerned that yields do not fall below a certain level, whereas the commercial faImer is concerned with maximizing net returns ; both of these aspects are affected by G x E interactions. Furthermore, Weber et al. (1979) mentioned that the relationships and causes of stability reduction as yields increase in multiple cropping systems are not clearly defmed or understood. The whole subject of stability in relation to multiple cropping is a topic that appears to require a great deal of more study. Twenty years since this recorrunendation was made on intercropping of cassava, we can recognize that we have progressed only a little, to improve our understanding of stability in multiple cropping systems as practiced by a majority of fanners , despite the generation of voluminous data. One could therefore ask, ' Is it a question of limitations in analytical tools?' Weber et al. ( 1979) also noted that because of the difficulty in obtaining good time-series data in multiple

128

Ekanayake, KaseJe and Kapinga. 1999. Cassava based cropping systems and G x E analyses

cropping systems, emphasis need to be given to developing analytical techniques for the use of cross-sectional data, embracing a wide range of variability and cropping patterns. Variance and co-efficient of variation have been used in evaluating stability, and perhaps only means. Since the time of the article by Weber et a1. (1979) many other statistical methods have been used to interpret stability and G x E relationships. These issues are discussed in more detail in a subsequent section.

10.4. Analytical approaches to G x E in Iotercropping 10.4.1. Treatments in G x CS Studies Conducting intercropping experiments involving various genotypes is quite difficult due to the complexity involved. Not only is there usually a range of crop species to choose from for association, but the proportions in which they are mixed can be varied indefinitely. Treatments to be used often depend on objectives of trials and may include one or more of the following (Mead and Riley, 1981): • assessment of yield advantages of intercropping compared to sole cropping, • identification of suitable genotypes for intercropping, • consideration of level of agronomic factors, such as fertilization, to maximize crop yield, • determination of optimum spatial arrangements and plant densities, and • assessment of yield stability imparted by intercropping. One problem with the treatment selection for intercropped cassava research as compared to research on sole cassava is that intercrops involve two or more crops grown in association with each other and cassava. Consideration must be given to the inclusion of sole-crop treatments, not only for comparison with the intercrops but also as basis for standardizing the intercrop performance and yields. Number and type of sole-crop treatments to be included in an experiment depends, to some extent, on the objectives of the experiment. In cassava genotype screening trials, the number and type of sole crop plots to be included depend on the growth phase of cassava in the trial. In the early phases, there are many possible selections of bom components from which to choose. It is best to use a single genotype of companion crop as one of the components and multiple selections of cassava. For a dominant crop, the criteria of selecting sole cropping will be adequate for intercropping also while a dominated crop should show a rapid recovery from competition in early stages by compensation in the later stages of growth. Cassava is dominated when intercropped with maize (Mutsaers et aI. , 1993) or sweet potato (Kapinga et aI., 1994). Therefore, a suitable cassava genotype for cassava/maize of cassava/sweet potato intercropping needs to be characterized by vigorous recovery growth after companion crop harvest. Physiological and morphological traits of cassava for successful intercropping with major crops traditionally grown by fanners may not be the same and need quantitative investigations through a better understanding of genotype x cropping system interactions. Some of the crop growth characteristics important for cassava intercropping were described by Zandstra (1979). Whereas agronomic factors, i.e., planting time and seasonal effects, planting time and relative planting date, spacing. and crop-soil interactions effecting intercropping and G x E effects were descnbed by Leibner (1979).

129

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

10.4.2. Assessment of intercropping advantages One of the problematic areas of intercropping research has been the quantitative assessment of intercropping advantages over sole cropping. The theories that provides the basis of identifying agronomic and economic productivity of intercropping has been presented in several fora (Heady, 1952; Heady and Dillon, 1961; Makhiyani, 1975; Hilderbrand 1976; Zandstra et al., 1981 ; Mutsaers et al., 1997). Some of the quantitative parameters used to evaluate the efficiency of intercropping systems were summarized earlier (Ekanayake, 1995). The most straight forward method used has been to compare costs and benefits of intercropping and sale cropping, as expressed in monetary (income equivalent ratio, lER) or nutritional (plant constituents) terms (Flinn, 1979; Willey. 1985). Problem with this approach remains as how to express any ecological costs or benefits in terms of money (Willey, 1985). Another problem is the competition among associated crops leading to the proportion of total yield from components crops in intercropping system being different from that of equivalent sown proportions of sole crops. Several other measures have been suggested for assessing the output of intercropping. The most widely adopted is the land equivalent ratio (LER) proposed by Willey (1979) and the effective land equivalent ratio (ELER) proposed by Mead and Willey (1980). However, when there is a considerable difference in length of growing cycle of the component crops (i.e., cassava with maize or grain legumes), LER is flawed (Mead and Willey, 1979; Mutsaers et a1., 1993) and overestimates intercropping advantage. To remove this bias due to different growth cycles which is inherent in LER Mutsaers et aI. (1993) suggested the use of area x time equivalency ratio CATER) as proposed by Hiebsch and McCollum (1987), as more appropriate for cassava/grain legwne or cassava/cereal cropping systems. Evaluation of advantages and disadvantages of cassava based intercropping systems requires an analysis of resource use (light, nutrient, and water), growth analytical measurements as well as yield and ecological stability in the system, which are being currently studied at ITTA and its collaborators for cassava-based intercropping systems.

In general cassava-cropping system affects the root yields and yields advantages. Table 5 shows a few key parameters of comparison for yield advantages under various systems with cassava and late cowpea intercropping.

10.4.3. Design and statistical considerations in intercropping studies Statistical research is needed for facilitating efficient experimental design and statistical analysis of data from intercropping experiments. Apart from systematic designs suggested (Willey, 1979) for comparing varying plant population and planting patterns of component crops, split-plot and randomized block designs are commonly used for intercropping studies. There is no single, universally accepted procedure for analyzing intercropping trial data. The basic problem has to do with jointly evaluating two or mOTe crops grown together at the same time on the same plot There are a number of statistical alternatives for evaluating the magnitude and nature of genotype by system interactions, such as analysis of variance or a regression analysis (England, 1974; Finney, 1990). Using this approach, the actual yield of genotypes under sole cropping can be regressed with the corresponding yield or any index of evaluation used of the genotype in intercropping. The lack of relationship implies the necessity of selecting under intercropping conditions because intercrop performance is dependent on crop characteristics not directly related to sole crop performance. If different genotypes of both components are tested, index used or yield of one component, and both components (total) can be generated and compared. Partial values of assessment index for genotypes of one component can be plotted against the partial values of assessment index of genotypes of the other

130

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

components and combinations giving greater yield advantages can be identified (Willey and Rao,1981).

Table 10.5. Yield and yield advantages of cassava - late cowpea intercropping (After Kasele, 1996).

1995-1996 season

Cropping systems

Cassava root OI yield (t ha )

Cowpea grain yield (kg ha'l

LER

ATER

V1 +C2

20.6

173.8

1.47

0.97

V2+C2

29.2

340.4

2.45

1.46

V3 + C2

21.6

226.3

1.88

1.22

V4+ C2

10.5

179.6

1.66

1.14

V5 +C2

20.5

182.9

1.30

0.77

V6 + C2

21.7

206.7

2.13

1.53

Sole VI

28.9

Sole V2

30.1

Sole V3

24.1

Sole V4

22.9

Sole V5

20,8

Sole V6

17.7

229 ,6

Sole C2 Mean

22.4

219.9

CV(%)

24.2

56.7

S.E.

0.8

71.9

TMS 30572, V2

TMS 91934, V3

4(2} 1425, V4 TMS 56395, VS =TMS 30001, V6 = TMEI, AND C2 =

late cowpea.

Hilderbrand (1984) suggested the use of modified stability analysis on complicated on-farm trials where often the treatments consist of several crops. The treatment x site mean. interaction term, measures whether treatment effects differ with a field's level of production reflected in its mean yield (Mutsaers et a1., 1997). An excellent review on calculation techniques of adaptability tests including the merits and demerits of various statistical packages (e.g., MSTAT, SAS-GLM, and SAS regression packages) is presented by Mutsears et aI. (1997) which is very relevant to cassava intercropping studies, A computer program (STABLE) for calculating yield-stability

131

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

statistic (YS j ) which integrates yield and stability based on Shukla's (1972) stability-variance statistic is available (Kang and Magari, 1995). Moreover, a recent addition is the SAS-STABLE program that computes stability variances via the Restricted Maximum Likelihood (REML) approach while also capturing the contributions of environmental factors used as co-variates to G x E (Margari and Kang, 1997). Since multiple crops are involved, Pearce and Gilliver (1979), suggested the use of multivariate analyses but this approach has not been widely used in cassava based intercropping research (Mutsears et al., 1993). Perhaps a very useful introduction to multivariate analysis in the context of on-fann research given by Mutsaers et at (1997) can assist us in this regard. Principal component analysis (PCA) has been used for analyzing the relationships among several quantitative variables, which are measured on a number of parameters, through calculated linear principal components. Eigenvectors or the coefficients of the principal components and their contribution in explaiIring the overall variance are then compared. A specific feature of peA is its ability to reduce a ,large set of variables to few calculated and unities! values, which helps to simplify many irlteractive and complex relationships among biological measurements. Some of the more recent and important technologies are computerized database management systems, which can be useful tools with which crops and cropping systems are to be selected for a target area can be made. These expert systems or decision support systems (DSS) (COOMBS, 1993; Mutsaers et aI., 1997) and crop growth models (Matthews and Hunt, 1994; Ekanayake et aI., 1998;1999), which are increasingly used particularly used on sole crops are yet to be fully exploited for cassava intercropping systems. The ecological suitability of various crop combinations can be spatially exploited with reference to climatic databases using presently available asy to use software paclcages on Geograpbic Infonnation Systems (GIS). Various GIS and crop based models at the scale oftesearch or on-farm and farmers' fields have been developed (Hoogenboom et aI., 1993; La1 et al., 1993; Wei et aI., 1994). These expert systems and growth models are also expected to facilitate the description of ideotypes which suit intercropping systems. Ideotype description for companian crops have been reported (Donald, 1967; Rasmussen, 1987).

10.5. Conclusion It is evident that there is a vast potential and scope for increasing the productivity of traditional cassava-based cropping systems, particularly in the savanna zones, through understanding of G x E in terms of adaptation to specific stress factors, genotype x location interactions and genotype x cropping systems interactions. For the success of any cropping system, efficient use of available resources by the component crops is a prerequisite. A cassava genotype suitable for any cropping system should be able to minimize competition or maximize complementarity. Several analytical tools and statistical methods are available enabling the selection of suitable genotype for a particular cropping system Identification of cassava plant traits to explain in physiological terms, the observed genotypic responses to a cropping system, as a monocrop or intercrop, is needed, in addition to the influence of the environmental factors and G x E interaction effects. Detailed growth analysis studies and descriptions of dry matter distribution in component crops are required in this regard and the defxnition of ideotypes and a simulation growth modeling approach is expected to be beneficial.

132

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Acknowledgement This work was supported in partially by lITA and COSCA funded by the Rockefeller FOlUldation. Authors are grateful to the able field assistance provided by staff of the physiology unit for roots and tubers at IITA in Nigeria.

References Akinyemiju., O.A. and A.S. Adegoroye. 1988. Physiological considerations for tuber yield improvement in cassava (Manillol esculenta Crmtz). In Cassava Based Cropping Systems Research 1 Contributions from the First annual meeting of the coUaborative group in cassava-based cropping systems research. Ibadan 16-19 November 1987. rITA, Ibadan, Nigeria. pp. 21-30. Bruijin, G.H. and L.O. Fresco. 1989. The importance of cassava in world food. production. Netherlands J. Agric. Sci. 37: 21-34. Bunting, A.H. 1989. Impressions of the Cameroon National Root Crops Improvement Programme. Evaluation seminar, Ngaoundere, Cameroon, 22 September, 1989. 11 pp. Carsky, R.I. and M.N. Versteeg. This publication. Fanner-managed on-farm testing; Approach and G x E Consideration. In. I.1. Ekanayake and R. Ortiz. G x E Interaction . . . Carter, S.E., L.O. Fresco, and P.G. lones, with J.N. Fairbairn. 1992. An A.tlas of Cassava in Africa: Historical, AgroecologicaI and Demographic Aspects of Crop Distributions. CIAT, Cali, Colombia. 86 pp. and maps. CIAT (Centro Intemacional de Agricultura Tropical). 1987. Distribution of cassava amongst different climate and soil types in South America. In A.nnual Report of the Cassava Program. CIAT, Cali, Colombia pp. 12. Cock, 1. 1985. Cassava. New Potential for a Neglected Crop. Westview Press. Boulder and London. COOMBS. 1993. LEXYS-Legume Expert System . Decision Support for the Integration of Legumes into Farming Systems. I1TA, Ibadan and WAFSRN, Samaro, Nigeria. 22 pp. Donald, CM. 1968. The breeding of crop ideotypcs. Euphytica 17:385-403 . Dorosh, P. 1988. The Economics of Root and Tuber Crops in Africa. Resource and Crop Management Research Monograph (RCMD) No.1 . RCMD. IITA, Ibadan, Nigeria. Eberhart, SA and W.A. Russel. 1966. Stability parameters for comparing varieties. Crop Sci. 6:36-40. Ekanayake, I.I. 1995. Cassava in multiple cropping systems: temrinology and parameters for evaluation. Tropical Root and Tuber Crops Bulletin. 8(2): 3-6. Ekanayake, 1.1. 1998. Conceptual issues in the use of cassava modelling for improved production in drought environments. Tropical Agriculture (Trinidad) 75(1): 102-105. Ekanayake, I.1. 1999. Activities in Cassava Modelling, Productivity, Food Security and Global Change. START Report No.1. online at http://www.startorg (posted November 30, 1999; verified December 10, 1999). International START Secretariat, Washington DC, USA. Ekanayake, 1.1., A.G.O. Dixon and M.C.M. Porto. 1996. Performance of various cassava clones in the dry savanna region of Nigeria. In. G.T. Kurup, M.S. PaIaniswamy, V.R. Potty. G. Padmaja, S. Kabeerat:bamma & S.V. Pillai (cds.). Tropical Tuber Crops. Problems, Prospects and Future Strategies. Oxford & IBH Pub. Co .â&#x20AC;˘ New Delhi, India. pp. 207-215. Ekanayake, U ., A.G.O. Dixon, R. Asiedu and A-M.N. Izac. 1994. 1mproved cassava for inland valley agr~ecosystems. In M.O. Akoroda (ed). Proc. 5th Symp . ISTRe-AB. Kampala, Uganda. pp. 204-208. Elemo, K.A.. V. Kumar, J.O. Olukosi, and A.O. Ogunbile. 1990. Review of Research Work on Mixed Cropping in the Nigerian Savanna. Institute of Agricultural Research (IAR), Samaru, Ahamadu-Bello University, Zaria, Kaduna State, Nigeria.

133

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

England, F. 1974. Genotype x environment interactions in mixtures of herbage grasses. J Agric. Sc. (Camb.) 82:371-376. Evans, L.T. 1993. Crop Evolution, Adaptation and Yield. Cambridge University Press, UK. 500 pp. Fageria, NK 1992. Maximizing Crop Yields . Marcel Dekker Inc. 274 pp. FAD (Food and Agriculture Organization). 1978. Report on the Agroecological Zones Project. Vol. I. Methodology and Results/or Africa. FAD, Rome.

FAD (Food and Agriculture Organisation). 1998. Statistics Database - Agriculture, Crops Primary. FAD, Rome. htt;p:llwww.fao.org Finned, D.J., 1990. lntercropping experiments, statistical analysis, and agricultural practice. apl. Agrlc. 26:83-81 . Flinn, J.C. 1979. Agroeconomic considerations in cassava intercroppingresearch. Intercropping with cassava. In E. Weber, B. Nestel and M. Campbell.(eds.). Proc. of an International workshop held at Trivandnun, India, 27 November - 1 December, 1978. CfRCVIDRC. Ottawa. pp. 87-102. Freeman, and Perkins 1971 . Environmental and genotype-environmental componenets of variability: VII. Relations between genotypes grown in different environments and measure of these environments. Heredity 27:15-23 . Heady, E.O. 1952. Economics of A.gricultural Production and Resource Use. Prentice-Hall lnternational Inc., Hertfordshire. Heady, E.O. and I.L. Dil]on. 1961. Agricultural Production Functions. Iowa State University Press, Ames. Hiebsch, C.K. and R.F. McCollum. 1987. Area x time equivalency ratio: a method for evaluating the productivity ofintercrops. Agronomy Journal 79 :15-22. Hilderbrand, P.E. 1976. Multiple cropping systems in dollars and 'sense' agronomy. pp. 347372/71 Multiple Cropping. ASA Special Publication 27, ASA, Madison, Wisconsin, USA. Hilderbrand, P.E. 1984. Modified stability analysis of fanner managed, on-fann trials. Agronomy Journal 76:2 71-274. Hoogenboom, G., H. Lal, and D.D. Gresham. 1993. Spatial yield prediction. ASAE Paper 933550. Am. Soc. Agric. Eng., 81. Joseph, Mi, USA. Goldman, A. 1988, The role of agroeco]ogical characterization in West and Central Africa. pp. 264~271.1n Cassava Based Cropping Systems Research Vol. 1. Contributions from the first ann~ meeting of the collaborative group in cassava-based cropping systems research. 1619 November, 1987. UTA, Ibadan, Nigeria. Gotoh, K., T.T. Chang, lC. O'Toole, R. Riley and B.P. Murty. 1979. Crop adaptation. pp. 234261 In. Plant Breeding in Perspective. J. 8neep and A.J.T. Hendriksen (cds.). Pudoc, Wageningen.. Irikura, Y, J.H. Cock and K. Kawano. 1979. The physiological basis of genotype-temperature interactions in cassava. Field Crop Res. 2:227-239. Kang, M.S. and R. Magan. 1995. STABLE: A basic program for calculating yield-stability statistic. Agron. J. 87:276-277. Kapinga., R.E., I.A. Omueti, and LJ. Ekanayake. 1994. Cassava/sweet potato intercropping studies in Tanzania-summary. Roots 1(1):14-15. Kapinga, R.E., lA. Omueti, and U . Ekanayake. 1995. Soil N, P, and K and land use efficiency under cassava/sweet potato intercropping system in Tanzania. African J Root and Tuber Crops 1(1): 14-19. Kasele, I.N. 1996. Evaluation of cassava genotypes for intercropping with grain legume and cereal crops in lowland savanna of Nigeria. Final Project Report. Crop Improvement Division, IITA,lbadan, Nigeria. 63 pp. Lal, H., G. Hoogenboom, J.P. Calixte, J.W. lones, and F.H. Beinroth. 1993. Using crop simulation models and GIS for regional productivity analysis. Trans ASAE, 36(1): 175-184.

134

Ekanayake, Kasele and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Landsberg, J.]. 1972. Microclimate and the potential productivity of sites. Scientia Hortie. 24:126-14l. Lawson, T.L. 1988. Characterizing the crop environment; an agroclimatic perspective. pp. 11-20 [n Cassava Based Cropping Systems Research Vol. l..Contributions from the first annual meeting of the collaborative group in cassava-based cropping systems research. 16-19 November, 1987. UTA, Ibadan, Nigeria. Leihner, D.E. 1979. Agronomic implications of cassava-legume intercropping. pp. 103-112. In E. Weber. B. Nestel, and M. Campbell (cds.). Intercropping with Cassava. Proc. of an International workshop held at Trivandrum. India, 27 November - 1 December 1978. CfRCIIIDRC. Ottawa. Makhiyani, A. 1975. Energy and agriculture in the Third World. Ballinger Publishing Co., Cambridge. USA. Matthews. R.B. and L.A. Hunt. 1994. GUMCAS. A model describing cassava growth. Field

Crop Res. 36:69-83 . Margari, R. and M.S. Kang. 1997. SAS-STABL: Stability analyses of balanced and unbalanced data. Agronomy 1. 89:929-932. Mead, R and 1. Riley. 1981. A review of statistical ideas relevant to intercropping research. J. Roy. Stat. Soc. A 144:462 - 509. Mead, R. and R.W. Willey. 1980. The concept of land equivalent ratio and advantages in yield from intercropping. Expl. Agric. 16:217-228. Mutsaers, H.J.W., H.C. Ezwnah, and D.S.O. Osiru. 1993. Cassava-based intercropping: a review. Field Crop Res. 34:431-457. Mutsaers, H.J.W.â&#x20AC;˘ G .K. Weber, P. Walker, and N.M. Fisher. 1997. A Field Guidefor On-form Experimentation. IITNCfAlISNAR, The Hague. 235 pp. Ndibaza, RE. 1994. Intercropping of cassava (Manihot esculenta Crantz) and sweet potato (Ipomoea batatas (L.) Lam.) in the semiarid zone of Tanzania. Ph.D. Thesis, University of Ibadan, Ibadan. Nigeria. Nweke, F.I. 1996. Cassava: A cash crop in Africa. Collaborative Study of Cassava in Africa (CaSCA) Working Paper No . 14. IITA, Ibadan, Nigeria. 79 pp. Nweke, F.I., A.G.O. Dixon. R Asiedu and S.A. Folayan. 1994. Cassava varietal needs of farmers and the potential for production growth in Africa. COSCA Working Paper No . 10. IITA - Intec Press, Ibadan. 239 pp. Nweke, F.I., B.O. Ugwu and A.G.O. Dixon. 1996. Spread and performance of improved cassava varieties in Nigeria. Collaborative Study of Cassava in Africa (COSCA) Working Paper No. 15. IITA. !hadan, Nigeria. 34 pp. Okigbo, B .N . 1994. Major fanning systems of the lowland savanna of sub-Saharan Africa: Potentials and constraints for crop production. [71 B.T. Kang, 1.0. Akobundu, V.M. Manyong, R.1. Carsky, N . Sanginga, and E.A. Kueneman (eds.). IITA - FAD. Ibadan. Nigeria. Papadakis, 1. 1970. Oimates of the World. Their classification, similitudes, differences and geographic distribution. Buenos Aires, Argentina. Pearce, S.C and B. Gilliver. 1979. Graphical assessment of intercropping methods. J Agr. Sci.

(Camb.) 93 :51-58. Porto, M.C.M., P.A. de Almeida, P. Luiz, P. de Mattos. and R.F. Souza. 1979. Cassava intercropping in Brazil. pp. 25-30. In E. Weber, B. Nestel, and M. Campbell (eds .).!ntercropping with Cassava. Proc. of an International workshop held at Trivandrum, India, 27 November-1 December 1978. CTRCVIDRC. Ottawa. Rasmussen, D.C. 1987. An evaluation of ideo type breeding. Crop Sci. 27: 1140-1146. Shukla, G.K. 1972. Some statistical aspects of partitioning genotype - environmental components of variability. Heredity 29:237-245.

135

Ekanayake, Kase1e and Kapinga. 1999. Cassava based cropping systems and G x E analyses

Spencer, D. and P. Kaindaneh. 1997. Cassava in Africa: Past, Present and Future. An IITA/ lFAD Report. Dunstan Spencer and Associates, Guy Sueet, Freetown, Sierra Leone. 64 pp. Steiner; K.G. 1982. Intercropping in Tropical Smallholder Agriculture with Special Reference to West Africa. Schrifterihe der 01Z. 137. Stoorvogel, 11 and L.O. Fresco. 1991. The identification of agro-ecological zones for cassava in Africa with particular emphasis on soils. COSCA Working Paper No. 5. UTA, Ibadan, Nigeria. 5p and map. Trehane, K.J. 1975. Surrunary of discussion sessions. Field Crop Res. 34:431-457. Tsuji, G.Y., G. Uehara, and S. Balas (eds.). 1994. Decision Support Sytemfor Agrotechnology Transfer (DSSA1) version 3. International Benchmark Sites Network for Agrotechnology Tranlfer (IBSNA1j, University of Hawaii, Honolulu, USA. Weber, E., B. Nestel, and M. Campbell (eds.) 1979. Discussion summary. In.lntercropping with Cassava . Proc. of an International workshop held at Trivandrum, India, 27 November - 1 December, 1978. CI'RCI/IDRC, Ottawa. 144 pp. Wei, Y., G. Hoogenboom. R.. W. McClendon, and D.D. Greshem. 1994. hnpact of climate change on crop production at a fann level. ASAE Pap. 94-3523. Am. Soc. Agric. Eng., St. Joseph, MI. USA. Willey, R.W. 1979. Intercropping. Its importance and research needs. Part 1. Competition and yield advantages. Field Crop Abstr. 32(1):1-10. Willey, R.W. 1985. Evaluation and presentation of intercropping advantages. apl. Agric. 21: 119-133. Willey, R.W. and M.R. Rao. 1981. A systematic design to examine effects of plant population and spatial arrangement in intercropping, illustrated by an experiment on chickpea/safflower. Expl. Agric. 17:63-73. Wilson, L.A. 1977. Root crops. In P.T. Alvim and T.T. Kozlowski (eds.). Ecophysiology of Tropical Crops. Academic Press, London. Wrickle, G. 1962. Ober eine Methode zur ErfassWlg der okologischen Streubreite in Feldversuchen Z. Pflanzenztichtg, Heredity 47:92-96. Yates, F. and W.G. Cochran. 1938. The analysis of groups of experiments. J. Agric. Sci. 28:556580. Zandstra, H.G. 1979. Cassava intercropping research: agroclimatic and biological interactions. pp. 67-76. In Intercropping with .Cassava . E. Weber, B. Nestel, and M. Campbell (eds.). Proe. of an International workshop held at Trivandrum, India, 27 November - 1 December 1978. CfRC1'IDRC. Ottawa. Zandstra, H.G., E.C. Price, lA. Litsinger and R.A. Morris . 1981. A Methodology for Onjarm Cropping Systems Research . International Rice Research Institute, Los Banos, Laguna, Philippines. 147 pp.

136

Chapter 11 First Approximation of Mapping Agricultural Environments in Nigeria for Cassava Field Trials Malachy O. Akoroda 11 .1. Introduction 11 .2. Methodology 11.3. Clustering Technique Analysis 11.4. Mapping Agricultural Environments for Cassava 11 .5. Conclusions References

11.1. Introduction The presence of genotype x environment (G x E) interaction poses great practical and conceptual difficulties to breeders in detennining the true performance of genotypes. We may derme an agricultural environment to be the totality ofvariabJe factors that influence the growth and developmental requirements of crops such as, light, water, nutrient elements, and temperature regimes. The environment in which we test genotypes are not in steady states, but continually undergo changes; although they may exhibit temporary stationary states (Kay, 1993). Weather changes often occur but after about 35 years, the predictable generalized weather pattern at a place can be regarded as the climate (Ayoade, 1974). One approach used to classify environments is to use climate. According to Brinkman (1987) environmental variables are a) relatively stable factors such as altitude, latitude etc., b) aspects that vary with time such as rainfall, temperature, and actual incidences, severity, and timing which fluctuate from time period to another (e.g. pest attacks, total rainfall etc.). As time and resources are scarce, a minimum set of trial sites is needed. Number and locations of trials depend on a) budget, b) site representation of spatial spread and adoption domain, and c) representation of a range of biophysical factors, and d) accessibility of sites. Cost considerations mainly determine the number. A cut-off point in site-site similarity in breeding may vary (from 50, 60 .. .. 90 % similarity). In this analysis 85% similarity was used to declare similarity of sites with no two trials conducted in similar zones. In this 1proach inclusion of villages was not a consideration (for example in Nigeria 923,768 km contains 90,000 to 100,000 villages (Ene, 1992). A scale larger than above was conceptualized as an agroecological environmental unit) . For multi-site testing for breeding purposes, ecological adaptability of genotypes is tested. In contrast, in on-farm trials farmer acceptability is tested. A key to progress is to test clones in ecozones where they will be released. Cassava, which is used in semiarid areas, breeding programs seek to test elite clones across a range of environments. Introduction of clones between climatic homologues across continents is an approach used for cassava (Carter, 1987; Porto et a1., 1994; Dixon et al., 1994; Ekanayake et aI., 1996). This study presents a) a framework of matching ecozones in one country in order to optimize the conduct of multi-site testing and b) identification of recommendation domains in which early selection can be made.

137

11.2. Methodology According to Agoola (1979). cassava is grown in Nigeria from the Atlantic coast to border with Niger republic, and across every longitude. Therefore each of these ecozone is a potential cassava growing area despite the varied suitability of their soils. Data for 100 variables from published sources were used in this study. Map infonnation on a 30 x 30 feet grid or half-a-degree latitude and longitude (for Nigeria equals 55 .5 sq. kIn) resulting in 343 cells were used, excluding six which were dropped since land area was <10%. Each map was fitted into the grid and values were coded. Each ecozone was given the value of isolines or quantities encircling with a prorating, as needed. Data on 100 x 337 matrix were statistically tested using correlations, cluster and principal component analysis using SAS. A factor analysis was used detennine minimum set of variables. Two cluster techniques (group avaegare method of unweighted pair-group and average linkage method (Sneath and Sokal, 1973) were used to fonn groups of ecozones at 85% similarity.

11.3. Clustering Technique Analysis The two clustering methods produced slightly different number of groups for each level of similarity (Table 11 .1.). The group average clustering method fanned 10 groups at 85% similarity level, similarly placing 269 of the 337 ecozones (79.82%). Twelve groups were fonned by the average linkage method (Table 11 .2). In both techniques, four groups remained unchanged (5, 7, II, and 12) and three groups merged with adjacent groups (2 with 1; 8 with 9; but 10 was shared into two parts, which joined 7 and 9). Group average clustering formed one new group that was not formed by second method; a small group of four ecozones around Lagos that was excised from group 9 of the average linkage clustering. The resultant map (Fig. 11.1) is a combination of both groupings showing the intermediate nature of some of the ecozones.

Table 11 .1. Clustering techniques for level of similarity between ecozones Method Average linkage Group average

Level of similarity between ecozones (%) 95 90 8S 80 I 75 70 65 60 2 1 91 12 4 3 2 36 87

138

Table 11 .2. Twelve groups of ecozones defmed at the 85% level of similarity by the average linkage clustering of337 ecozones on 100 variables' Group

No. of

of Ecozones

cc~

zonet 10 the

% of total land area

Ef"Oup

28.0

Ann. ralnr.1

Monthly rainfall (mm) Apr. May June

Scp .

Oct

I (J931

- 1990 av.)

Very

% of calm winds

Max.

Hours

PET per

of sun shine

in a yr.

year

per yr.

(mm)

150

1-14

>2400

>3000

0 0 0

80 125 125

125

ISO

150 150

50 100 150 150

1-7 6-14 6-13 10-28

2300 22DO 2150 21DO

2875 2750

200 230 250 28()

100

200

230

250

200

7-14

1850

2125

100

230

250

300

250

1800

2125

IDO 100

250 250

330

250

250 230

10-33 10路33

1650 1650

2125 1750

100

250

300

360

250

1700

1875

200

275

360

435

330

<1700

<1500

200

330

435

330

33-47

<1700

<1500

lowe

2 3

21 74 14 30

6.5 23 .3 4.6 9.9

Low Low Low Moder

ISO

2850

2500

ate 6

11 .8

Moo.high

3.2

Moo.h igh

8 9

1.6

High

4.2

Very

2.9

high

Mod.high

Very high

2.7

Very biRii

Two other variables: 1991 population density and the longitude of the centre of the ecozone are not presented in this table being highly variable for each ecozone. b Rainfall (mm): very low = <900; low = 900-1100; moderate == lloo-1300; high = 13001500; and very high = >1500. c potential evapo-transpiration.

Of the 28 principal component axes that explain all of the variation among the 337 ecozones, fIrst three accounted for 91.3% (57.8, 27.1, and 6.4% for PRIN 1,2, and 3, respectively). Of the 14 major variables, 10 were related to water supply to plants in terms of amount, distribution, magnitude, rate of water loss due to solar radiation and wind speed etc. Population density was the important variable in PRIN I and 2 with 84.9% overall variation. Longitude was important in PRIN 3. fn a preliminary trial, for variables that are highly correlated (r2 = 0.90; n = 337), after careful consideration only one was considered.

139

•

L 13

~ . - .r

"...

a 7

1'.

,.. v.

• •

.. . · . •

(1)

0"-

., .. .. ~ ~,

5 4

l.J'

"" ...\ '"......

· · · · · · · · · · · · ·, · · · , · • · · ·, · · · · · · • • • • • • • • • • • 0

•• •• •• •• •• •• •• •• •• •• •• ••

+ ••

+ 0

... " ... ~ ... ... ... ...

"\.\

s R

)

., ·-. + + •• •• + ... ·. .. ..++ "

.. .. .. ·. ..- - - ..(1)

...... ~ 'f'

....

· · · · · ·, · • · • • • • 0 •

(1)

· ·

J · · · ~. • · f ·• • . ,

10 J

"...

.....

"...

""""" r--.."",

'"" "" '" '" .... "" ""

,~ ~

/ . ~

)

·· r--;-· .

·, · ·

·• ·•

• • •

•

• • • •

· ·

·•

, ,

•

0 0

'/.

IT"'

0 ~

(!)

(1) '(0 ~

2 L '3 K

•

4 j

•

5 I

•• 7

-· 0

icJ

G 8 F , 9A E 9B 0 + 10 C 0 11 B .... 12 A R S T U V wx y

l+t

A B C 0 E F IG H I J K L'M NO P

Group

· r-;- • • • • • · • • • .. ·, · .:I • • • • ~. ,.f · • • • 0 0 (!).

f' . · . .. · '- ..... · · ·1 ,I'

p 0

Figure 11.1. The 13 agricultural environments of Nigeria produced from a combination of the outputs of the average linkage and group average clustering techniques at 85% similarity between ecozones of the same group based on 100 variables on selected aspects of: relief, rainfall, geology, meteorology, vegetation, soils, population density, groundwater potentials, and other related statistics. Each square of 30' longitude x 30' latitude is an ecozone (55 .5 km 2) . Each group is a distinct and separate agricultural environment comprising a number of similar ecozones. Any intermediate ecozone belongs to two groups.

140

11.4. Mapping Agricultural Environments for Cassava The groups so formed are fairly compact and easy to identify. Stratification of land area enables to execute probe trials across representative locations. Issues related to basis of stratification and efficient and statistically valid representation is discussed below. Climatic zones proposed by Carter et al (1992) and FAO (1978) are not fully compatible with areas where cassava is cultivated. Detailed comparison of cassava environmental classifications are described in Chapter 9 (Ekanayake, this volume). Papadakis (1965) grouping of 8 for Nigeria and the zones described here agree partially. Group 10 agrees with his zone 7, groups land 2 are combined into zone 4 (ut lake Chad is in Zone 6). Papadakis zone 3 combines all groups of 5, 6, 7, 8, and parts of group 10 of this classification. Fagberni (1985) vegetation and mean annual rainfall map-based grouping (for smaller cells of 1296 hal does not also match with this analysis. Above grouping was grossly inadequate for envirorunent-specific targeting of multi-site trials in cassava breeding activities and pest and disease testing. The essential role of root or soil water status and its relation to drought stressphysiology of crops is illustrated by the findings of Gbadegesin and Areola (1987). Maize yield was highly correlated with soil organic matter (78% of variability) which in tum was correlated with available soil water (r=O.84路路) and water holding capacity (r=O.81") than other soil variables. Northern part of study area (group 3) is in low rainfall area with 1190 mm while southern part in group 6 receives 2078 mm; or have similar soil type but different rainfall. Some Temporal variation is another consideration (Lin and Binns, 1988). researchers believe many trials spread across ecozones in one year will obviate the need for testing over years and has been tested (Shorter et al., 1991; Jensen, 1988). Jensen's concept of subdivision of large geographical areas to achieve homogeneity of testing sites is similar to our approach. The need to defme ecozones for cultivar development and then select for local adaptation was emphasized by Simmonds (1991 ).

11.5. Conclusion In conclusion, mapping approximation descried here provides the cassava breeders with a first approximation for multi-site testing in Nigeria (and perhaps else where in West Africa).

References Agboola, S.A. 1979. An Agricultural Atlas of Nigeria. Oxford University Press, UK. Ayoade, 1.0.1974. A statistical analysis of rainfall over Nigeria. The Journal of Tropical Geography 39: 11-23. Brinkman, R. 1987. Agroecological characterization, classification, and mapping. Different approaches by the International Agricultural Research Centers. pp. 31-42. In A.H. Bunting (ed.). Agricultural Environments: Characterization , Classification, and Mapping. CABI, Wallingford. Carter, S.E. 1987. Collecting and organising data on agro-socio-economic envirorunent of cassava crop: case study of a method. pp. 11-29. In. A.H. Bunting (ed.). Agricultural Environments: Characterization, Classification, and Mapping. CABI, Wal1ingford.

141

Carter, S.E., L.O. Fresco, P .G. Jones and IN. Fairbairn. An Atlas of Cassava in Africa: Historical, Agroecological and Demographic Aspects of Crop Distribution . CIAT, Cali, Colombia. Dixon, A.G.O., R. Asiedu, U . Ekanayake, and M.C.M. Porto, 1994. Cassava improvement in Africa: Contribution of the International Institute of Tropical Agriculture. pp. 466-469. In. E. Adipala, M.A. Bekunda, I.S. Tenywa, M.W. Ogenga-Latigo, and J. ~. Mugah (eds.). African Crop Science Conference Proc. Vol. 1, Kampala, Uganda. Ene, L.S.O. 1992. Prospects for processing and utilization of root and tuber crops in Africa. pp. 7-16. In M.O. Akoroda and O.B. Arene (eds.). Tropical Root Crops : Promotion of Root Crop-based Industries, ISTRC~AB, I1TA, Nigeria. Fagbami, A. 1985. The application of the Gebgraphic Information Systems (GIS) to esource development and land management:路 the Nigerian example. pp. 319-331. In R.A. obulo and E.1. Udo (eds .). Soil Fertility, Soil Tilth and Post Clearing Land Degradation n the Humid Tropics . Proceedings of the International Society of Soil Science (Commission IV and VI).

FAO. 1978. Report on the agro-ecological zones project. Vol. 1. Methodology and Results for Africa. World Soil Resources Report 48. FAO, R.ome, Italy. Ekanayake, 1.1.. This pufication. Agro-climatological aspects that are important to classify cassava environments for better adaptation and maximized production. in press. Ekanayake. 1.1., A.G.O. Dixon and M.C.M. Porto. 1996. Performance ofvanous cassava clones in the dry savanna region of Nigeria. pp. 207-215. In Tropical Tuber Crops, Problems, Prospects and Future Strategies. G.T. Kurup, M.S. Palaniswamy, V.R. Potty, G. Padmaja, S. Kabeerathatnrna and S.V. pillai (eds.). Oxford & IBH Pub. Co., New Delhi, India. Gbadegesin, S. and o. Areola. 1987. Soil factors affecting maize yields路 in the southwestern Nigerian savarma and their relation to soil suitability assessment. Soil survey and land evaluation 7(3): 167-175. Jensen, N.F. 1988. Plant Breeding Methodology. pp. 583-631. John Wiley & Sons, New York, USA. Jones, H.G. and J.E. Corlett. 1992. Current topics in drought physiology (review). Journal of Agricultural Science, Cambridge 119: 291-296. Kay, J.J. 1993. On the nature of ecological integrity: some closing comments. In S. Woodley, J.1. Kay and G. Francis (eds.). Ecological Integrity and the Management of Ecosystems. St. Lucie Press, USA. Lin, C.S. and M.R. Binns. 1988. A method of analyzing cultivar x location x year experiments: a new stability parameter. Theoretical and Applied Genetics 76: 425-430. Papadakis, 1. 1965. Crop ecology survey in West Africa (Liberia, Ivory Coast, Ghana, Togo, Dahomey, Nigeria). FAO, Rome, Italy. Vol. II - Atlas (map 23). Porto, M ., R. Asiedu, A. Dixon, and S.K. Hahn. 1994. An agroecologically oriented introduction of cassava gennplasm from Latin America into Africa. pp. 118-129. In. F. Ofori and S.K. Hahn. (cds.). Tropical Root Crops in a Developing Economy, Proceedings of the Ninth ISTRe. 20-26 October 1991, Accra, Ghana.

142

Sneath S. and Sokal, R.R. 1973. Numrical Taxonomy: the Principles and Practice of Numerical Classification . . W.H. Freeman Co. San Francisco. USA. Shorter R., Lawn R.I . and Hammer G.L. 1991. Improving genotypic adaptation in crops a role for breeders, physiologists, and modellers. Experimental Agriculture 27: 155-175. SinunondsN.W. 1991. Selection for local adptation in plant breeding programmes. Theoretical and Applied Genetics 82: 363-367. Shorter R., Lawn R.J. and Hammer G.L. 1991 . Improving genotypic adaptation in crops a role for breeders, physiologists, and modellers. Experimen tal Agricu Iture 27: 155 -17 5.

143

Fatolrun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

Chapter 12 Evaluation of Genotype by Environment Interactions in Some Soybean Lines Christian A. Fatokun, Ken E. Dashiell, P.O. Oyekan and D.K. Ojo 12.1. Introduction 12.2. Methodology 12.3. G x E Interaction Studies 12.4. Discussion and Conclusion References

12.1. Introduction Soybean production is being encouraged in many sub-Saharan African countries because of the high nutritive value of its grains. Additionally, being a legume, soybean has the potential of ftxing atmospheric nitrogen thus enhancing the fertility of soils in which it is grown. A way of encouraging and sustaining the interests of the fanners who grow this relatively new crop in this part of the world, would be to ensure that improved cultivars with potentially high and stable yield, are made available to them for planting. In order to accomplish this objective stability of performance should be considered as part of the selection criteria in a soybean breeding program intended for the development of cultivaIS meant for growing in variable environments as exist in tropical Africa. Knowledge of the existence and extent of genotype x environment (G x E) interaction is helpful to breeders since this can guide in decision making as to which genotypes to select and eventually release as improved cultivars. Studies on G x E interaction should assist in determining whether a genotype will be stable in performance over a range of environments, In less developed countries, as abound in sub-Saharan Africa, where facilities for strategic grain storage are almost non existent, it becomes necessary that crop cultivars should be able to produce some yield even during particularly poor growing seasons , Also since farmers in this region are resource poor their ability to control and maintain good soil fertility is very limited. Variable climatic conditions and cropping patterns would make conditions in any two locations different. Ideally, cultivars to be recommended to farmers for planting, therefore, should have good general adaptability and capable of consistently producing reasonable yields across locations that are characterized by vatymg weather and other environmental conditions. Genotype by environment interactions had constituted a problem for breeders in the past but now there are several methods available for measuring these interactions which have implications for stability of performance of crop cultivars. The most commonly used of the methods is that suggested by Eberhart and Russel (1966) in which cultivar yield is regressed on an environmental index, Environmental index is estimated from the mean yield of all genotypes considered in that site (environment). According to Finlay and Wilkinson (1963), regression coefftcients of 1.0 show average stability and when this is associated with high mean yield such cultivars have general adaptability; when associated with low mean yield, cultivars are poorly adapted to all the environments. Francis and Kannenberg (1978)

144

Fatokun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

suggested a genotype grouping method following plotting a genotype's mean yield against its coefficient of variation. On the graph, grand mean yield fonns the base line on the X-axis while mean coefficient of variation is on the Y-axis. The genotypes are thereafter classified into four groups as high mean yield, low coefficient of variation; high mean yield, high coefficient of variation; low mean yield, low coefficient of variation; and low mean yield, high coefficient of variation. Based on this classification a stable cultivar is that which is characterized by high mean yield and low coefficient of variation. The study being reported here was carried out in order to estimate G x E interactions among elite soybean lines and to identify among the lines those with acceptable levels of adaptedness to different environments.

12.2. Methodology 12.2.1. Genotypes The elite soybean lines from among those constituting the uniform cultivar trials that were used in these studies belong to two maturity classes: the early maturing and medium maturing. These were planted in different locations in Nigeria during the 1990, 1991, and 1992 cropping seasons. During the same period seeds were sent to collaborating scientists in some other countries .

12.2.2. Cultural practices At each location similar agronomic practices were adopted. Fields were disc ploughed followed by broadcasting of 50kg NPK and 150kg single super phosphate (SSP) and harrowing to ensure incorporation of the fertilizer. Seeds were planted by drilling at Scm along the rows spaced 75cm apart. Randomized complete block design with three replications was used in all the trials. Galex at 4.0 1 and Gtamoxone at 3.0 1/ha, both herbicides, were sprayed immediately after sowing the seeds. Manual weeding was carried out three to four weeks after planting and subsequently as the need arose. Harvesting was carried out by uprooting mature plants and threshing was by beating the dried plants on a platform with a stick or a threshing machine was used where available. Seeds were dried to 10 percent moisture content before weighing.

12.2.3. Data collection aDd analyses Data collected at each location included days to maturity, scores for disease reactions, grain yield, and pod shattering. Analysis of variance was carried out for each measured trait and grain yield was used to estimate G x E interactions and stability of performance. In the analysis random genotypes and random environments were assumed . Regression coefficients and deviation from regression (Finlay and Wilkinson, 1963) were obtained for each line. The genotype grouping method suggested by Francis and Kannenberg (1978) was also used to evaluate stability of the soybean lines.

12.3. G x: E Interaction Studies 12.3.1. Early maturing lines Four lines were tested at 12 locations (Table 12.1) in the 1990 and 1991 seasons. There were no significant differences among the lines in their grain yields (Table 12.2a). In fact the difference in mean yield, across the 12 locations, between the highest and lowest grain

145

Fhtokun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

ptoducing lines was 180.59 kg ha· l . A combined analysis of variance in which year and l()cation constituted the environment also showed no significant differences in grain yield atnong lines (Table 2b). It should be noted however, that the different lines used in this study were selected from among breeding lines belonging to a uniform cultivar-trial population. Table 12.1 . Mean grain yields of four soybean lines at different locations in Nigeria (National Early Varietal Trials, 1990 and 1991).

Loution

Zallaki Samaru Bauchi

Abua Yandev

Mokwa Borin Badeg~i

Hora Ikenne

Abeokuta He-Ife LSD 5%

Mean yield (kg ba l ) 2064.2 1714.9 1589.1 1585.2 1557.2 1479.2 1458.3 1154.2 1118.7 1101.6 998.0 496.9 212.5

Table 12.2a. Analysis of variance for grain yield in four soybean lines (National Early Varietal Trials, 1990 and 1991). Source of variation Year (Y)

Location (L) YxL Rep/YxL Line (V) VxY VxL VxYxL Error

De2rees of freedom 1 11 11 48

3 3 3 3 142

Mean~uare

828294.11

0.22 0.84 9.29

~78831.65

4581592.81· 279003 .48 465288 .16 289680.99·

4~5198 . 62·

0.93 1.26 1.99

229015.24· 138252.19

., ** = Significant at 5% and 1% respectively.

1.65

Table 12.2b. Combined analysis of variance across environments (E = year x location) for grain yield in four soybean lines (National Early Varietal Trials, 1990 and 1991).

Source of variation D~ees of freedom Mean sguare Environments (E) 23 4130128.83· ReplE 48 279003.48 Line (V) 3 465288.16 ExV 69 339827.54· Pooled Error 142 138252.20 *, •• indicate slgmficant at 5% and 1% respectively.

F 6.90

1.37 2.46

146

Fatokun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

Since ANOV A detected G x E interaction for grain yield a comparison of relative perfonnance of the lines over all locations cannot be valid (Breese 1969). However, performance in separate locations can be compared. Further investigations were therefore carried out by estimating regression coefficients as suggested by Finlay and Wilkinson (1963). None of the four lines tested showed significantregression coefficient i.e., their b values did not differ significantly from 1.0. Line TGx 1497-10 had a regression coefficient of 1.1. Line TGx 1566-2E had the highest above average stability (b = 0.93). The trends in response among three of the four lines to changes in the environment were similar as depicted by their regression lines (Fig. 12.1). When deviation from the regression was taken into consideration line TGx 1566-2E can be said to be most stable since it showed the lowest s2d. Each genotype was ranked according to yield performance, regression coefficient, and deviation from regression (s 2d). Line TGx 1485-10 was ranked highest as it had the lowest rank sum value of five white Hne TGx 10 19-2EB with the lowest yield also ranked lowest by having the highest rank sum (Table 12.3). The four lines were further evaluated for stability by plotting their mean yield against the coefficient of variation (CV) associated with each of them. A genotype was placed in each of the four possible categories. Line TGll 1485-1D was placed in group I because of its high yield and low CV (Fig. 12. 2). This line, therefore, is the most consistent in performance. On the basis of the two stability assessment procedures adopted in this study line TGx 1485-1D is the most stable of the four genotypes tested. When three of these lines (TGx 1485-1D, TGx 1566-2E, and TGx lOI9-2EB) were further evaluated in 1992 along with another line TGx 1019-2EN line TGx 1485-10 still maintained its superiority (data not shown) by being the most stable. 4000

y :: 250.09 + 0 .83717x • 1566-2E Y = 200.82 + a .B05S8x

•

3000

1019-2EB

A"2

= 0.832

R-"2 = 0.632

•

y ;::; 349.67 + O.81340x W'2 = 0 .662 m 1485-10

y ::: 704.31 + 0.42161x RA2 • 1497-10

..J W

2000

-< w

...w <

1000

O~--~--T-----~~---·--~----~-------r------~

1000

2000

3000

ENVIRONMENTAL YIELD (Kg/ha) Fig. 12.1. The response of four early maturing soybean lines to varying environments in Nigeria during 1990 and 1991.

147

= 0. 126

Fatolrun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

Table 12.3. Mean yield and estimates of stability in foW' early maturing soybean lines (National Early Varietal Trials, 1990 and 1991).

Line

TGx 1485-10 TGx 1497-1D TGx 1566-2E TGx 1019-2EB

Mean yield

Regression Coelfcient

k拢ha l

1414.08 1435.98 1345.59 路 1255.39

Rank (2) (11 (3)

0.99 1.10 0.93

(4)

0.98

Deviations

Rank

Sum Rank (1) . (4) (3) (2)

sld 266.41 393.47 208.87 293.80

Rank (2)

(3J

(1)

7 10

(4)

Mean

1500 -

-C'

.. 2

.. ,

or:(

:::E: ~

GROUP II

GROUP I

1400 -

Mean

..J

w > z C( w

-3 1300 -

GROUP III

GROUP IV

- 4

1200

COEFFICIENT OF VARIATION

Fig. 12.2. Mean yield four of soybean lines plotted against their coefficients ofvariation

12.3.2. Medium maturing lines Four breeding lines and two test lines (SAMSOY 2 and M351) were evaluated at ten locations in Nigeria druing the 1990, 1991, and 1992 cropping seasons. ANOVA revealed significant differences in grain yield among the genotypes tested and G x E interaction was also significant (Tables 12.4a and 12.4b).

148

F atokun. Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

Regression analysis was carried out to further test for G x E interaction. The results showed that two of the six lines (TGx 1448-1E with b ;; 1.21 and M351 with b = 0.76) had significant regression coefficients (Table 12.5). This observation suggests that genotype M351 is the least responsive to changes in the environment (Fig. 12.3). This genotype (M351) is more adapted to poor environments than others and it produced the lowest grain yield of all. The genotype with b closest to 1 was TGx 1455-2E (0.96). When deviation from regression was considered line TGx ]455-2E showed the lowest value. This genotype is therefore, the most stable according to the regression method. When the genotypes were ranked according to grain yield, regression coefficient, and deviation from regression lines TGx 1440--1E and TGx 1455-2E ranked best because both had the lowest rank sum value of seven each closely followed by TGx 1448-2E with a rank sum of eight. Line M351 showed the highest rank sum value of 18.

Table 12.4a. Analysis of variance for grain yield in four soybean lines (National Medium Varietal Trials, 1990 and 1992). Degrees of Freedom Mean square Source of variation 2 4527359.4 YearlY) Location (L) 4426130.0 9 18 8462274.1 YxL RepNxL 60 265698.4 Lines (V) 5 238678.5" VxY 10 188942.9 45 460105.0· VxL VxYxL 89 289620.8" 293 Error 164883.3 "', •• mdlcate slgmficant at 5% and 1% respectIvely.

F 0.56 0.53 0.99

4.12 0.65 1.59 1.76

Table 12.4b. Combined analysis of variance across environments (E = year x location) for grain yield in six soybean lines (National Medium Varietal Trials, 1990 and 1992). Source of variation Degrees of Freedom Mean square Environment (E) 29 f?938304.2Q ReplE 60 265698.41 Line (V) 2386878.49 5 ExV 144 335905.58 Pooled Error 293 164883.30 '" ,"'. mdlcate sigruflcant at 5% and 1% respectively.

F J 1.81"

7.11 ... • 2.04'"

Based on the genotype grouping method two lines TGx 1440-10 and TGx 1448-2E were classified as belonging to group I, TGx 1448·1E was in group II, TGx 1455-2E and SAMSOY-2 in group III, while M351 was in group IV (Fig 12.4). The genotype grouping method a]so identified lines TGx 1440-1E and TGx t'448-2E as being most stable. Results obtained from the different methods used for evaluating stability of perfonnance among these six medium maturing soybean genotypes agree to a large extent. For example all of the

149

Fatokun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

methods identified lines TGx 1440-1E as the most stable and M351 as the least responsive to better growing conditions.

y 5000

225.66 + 0.83144x

R"2

• SAMSOY2 Y = 86.787 + 0.75710x R"2 • M 351

= - 0.81162

= 0.728

= 0.691

+ 1.0831x

= - 306.59

+ 1.1873x

•

3000

2000

~ ...J ooC(

1000

••

a: C

O~~~~--------~~·~~--------~----~--~

1000

2000

3000

4000

ENVIRONMENTAL YIELD (Kg/ha) Fig. 12.3. The response of four medium maturing soybean lines to varying environments in Nigeria during 1990 and 1992.

1700 -

Mean

• 1600

•

<3 ~

1500

-J W

> ~

•

1400

Mean

« w

GROUP II

GROUP I

5 1300 -

• 4

1200 -

GROUP til

GAOUPIV

• 6

1100 4.0

50 COEFFICIENT OF VARIATtON (%)

R"2

1448-1E

4000

RI\2

= 0 .856

1440-1 E

Fig. 12.4. Mean yield of six soybean lines plotted against coefficient of variation.

150

= 0.775

Fatokun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

Table U.S. Mean yield, growth response index, and estimates of stability parameters for grain yield of six medium maturing soybean lines (National Medium Varietal Trials, 1990 and 1992)

Line

Meim yield

TGx 1440-1E TGx 1448-1E TGx 1448-2E TGx 1455-2E SAMSOY·l

kgha- ' Rank 1537.54 (2) 1425.53 (3) 1635.65. (1) 1317.84 151 .1406.61 (4) 1162.13 (6)

M-351

Regression Coefficient bl 1.09 1.21$ 1.14 0.96 0.84 0.76$

Deviations

Rank

Sum sld 290.34 289.59 309.12 238.65 329.08 329.26

Rank (2) (5)

(3) (It (4) (6)

Rank

(3) (2) (4) (1)

(5) (6)

7 10 8 7 13 18

= Si~ificantly different from I at % level.

12.3.3. Performance of genotypes across vegetation zones The ten locations where the six medium maturing soybean genotypes were evaluated during 1990,1991. and 1992 could be divided into vegetation zones as follows: a) rainforest (He-I fe, Ikenne, and Abeokuta);b) Forest savanna (nora); c) Southern Guinea Savanna (Badeggi, Mokwa, and Yandev); d) Northern Guinea savanna (Samaru); e) mid-altitude (Zallaki); and f) Sudan savanna (Bauehl). When performance of the genotypes was compared across vegetation zones, the forest savanna seemed to support the highest grain yield (Fig. 12.5). It is worthy of note that lines TGx 1440-1E and. M351 consistently gave the highest and low~st grain yields respectively, except at Samaru in the northern Guinea savanna zone. There were only slight differences in grain yield among the genotypes within some vegetation zones. 3000

2500

i!:

• • •

2000

:!;

1500

..J

144o-1E

1448-1E

SAMSOY-2

~ '-1-351

1000

500

0 I-

(J)

<I: Z

< > <I:

&.:

r/)

<I: Z

l.U

<I:

< >

f--

Z Z

> <I:

< (J)

0 to

r/)

0 :l ~

> <I: r/)

~ (f)

Fig. 12.S. Grain yield of four soybean lines across vegetation zones in Nigeria during 1990, L991, and 1992 growing seasons.

151

Fatolrun, Dashiell, Oyekan and Ojo. 1999. G x E analysis of soybean

12.4. Discussion and Conclusion It is the usual practice for breeders to carry out evaluations of newly developed or imported crop cultivars in several locations before being recommended for release to fanners for planting in different agroecologies. Following such trials, individuals with good perfonnance in specific locations are recommended for release in such places. It would have been most desirable for a particular genotype to perform uniformly well at all of the trial locations. This is, however, not the case because of the phenomenon of G x E interactions.

In the present study, which involves the evaluttions of soybean genotypes for grain yield performance across a number of agroecological zones in Nigeria, G x E interactions were observed. However, when yield perfonnance is considered across vegetation zones it will seem that the envirorunents did not favor some genotypes to the disadvantage of others. Rather high yielding genotypes still produced relatively high yields at all locations. Whether an environment is poor or good it affected all genotypes almost alike. For example, line TGx 1440-1 E consistently produced the highest yield except in the northern Guinea savanna (Samaru) environment. The genotype therefore qualifies to be described as having stable grain yield in different environments as compared to the other genotypes tested. The different methods used in estimating stability ofpeIfonnance in this study gave results which suggest that any of them could be successfully adopted by the breeder if selection for stability of performance is to be carried out The relatively simple genotype grouping method suggested by Francis and Kennenberg (1978) would however be superior to the others especially where the number of genotypes being evaluated is large. With this method genQtypesare graphically displayed as to whether they are consistent or otherwise in producing low or high yield.

Acknowledgements The authors thank all scientists who participated in the Nationally Coordinated Soybean Research in Nigeria.

References Breese E.I. 1969. The measurement and significance of genotype x environment interaction in grasses. Heredity 24:27-44. Eberhart S.A. and W .A. Russel. 1966. Stability parameters for comparing varieties . Crop Sci. 6:36-40. Finlay K.W . and G.N. Wilkinson. 1963. The analysis ofadaptation in a plant breeding program. Aust. 1. Agric. 14:742-754. Francis T .R. and L.W. Kennenberg. 1978. Yield stability studies in short-season maize. 1. A descriptive method for grouping genotypes. Can. 1. Plant Sci. 58: 1029-1 034.

152

Chapter 13 Genotype x Environment Interactions Analysis of Maize at UTA Jennifer G. Kling 13.1 Introduction 13.2. Background and Rationale 13 .3 Magnitude ofG X E 13.4. Observation Trials 13.5 Characterization of genotypes 13 .6 Characterization of environments References

13.1. Introduction Maize is a major food crop in the [ITA mandate area. Evaluation of constraints in this mandate area has led to an emphasis on breeding maize for resistance to specific diseases, insects and abiotic stresses. Breeding objectives and germplasm have been targeted to fit the major ecologies in the region (Eberhart et al., 1991; MIP, 1996). Research to address consumer prefrances for processing characteristics, palatability, and storability has also been initiated. A number of breeding populations have been developed at UTA to target improved maize germplasm to various environments and various target traits (MIP, 1996).

13.2. Background and Rationale Interactions between genotype and environment (G x E) present conflicting goals for a breeding program. [f varietal performance in different environments is governed by different genes, selecting for broad adaptation is analogous to selecting for multiple traits. Attempting to select for too many traits at once will reduce the rate of progress that can . be achieved through breeding. Conversely, if agroecologies are too narrowly deflned, selection for specific adaptation may result in genotypes that are unstable across environments. A clear understanding of the maize growing ecologies in the region is therefore essential for effective management of a breeding program. Studies on G x Einteractions of maize have been carried out at lITA in order to: 1)

2) 3)

Quantify the magnitude of G x E and regions of relative homogeneity as a means for rationalizing testing sites and ensuring that breeding efforts adequately serve the mandate ecologies Detennine regions of adaptation for specific populations and relative stability of populations across environments Describe the distinguishing characteristics of varieties across a range of environments Understand the underlying causes of G x E.

We deflned a stable genotype as one that is relatively high yielding across a wide range of environments; additionally, it has the necessary resistance or tolerance to common diseases, insects, and abiotic stresses to minimize crop losses and the likelihood of crop failure.

153

In 1989, during a one-week training course was organized on analysis of G x E at IITA, and taught by Dr. I.H. Delacy from the University of Queensland MIP staff was trained in the use of a software packages for G x E analyses. These were later modified by MIP, and are routinely used to cluster environments based on G x E (using the criterion of squared Euclidian distance between means).

13.3 Magnitude of G x E 1J.3.1. G x E tl/Ialysis of I"ternatio"al Trials International Trials of maize from 1985- J988 conducted over a wide range of testing conditions, primarily by collaborators in West and Central Africa were analyzed to assess the relative contribution of G x E to the variation in yield of open-pollinated varieties across sites (Table 13.1). The genera) conclusion arising from these analyses is that among elite germplasm adapted to lowland environments, the magnitude of G x E is relatively sroal!; (about 10% on the average). This may reflect the fact that much of the early breeding work on maize in the region was carried out in the forest zone, so that most of the base populations possess a degree of resistance to the disease and insect pests that are prevalent there. Another explanation may be that emphasis at IITA has always been placed on resistance breeding and selection for broad adaptation. Table 13.1. G x E analysis ofIITA maize International Trials (1985-1988).

Trial

Year

No. of entries

No. of sites

%SS due to G xE

EVT-LSR-Y

EVT-LSR-W

EVT-ISR-W

EVT-ESR-W

EVT-E/ISR-Y

1985 1986 1987 1988 1986 1987 1988 1986 1987 1988 1985 1986 1987 1988 1987 1988

11 10 13 13 ]5 8 16

16.1 16.5 13 .0 8.3 13.3 11 .8

4.7

8 9

10 15

10.7

25 20

10.0 10.0 8.1 5.6

12 12

14.2 6.3

9 8 8

]0 14

8 8 5 10

13.4

10.8

13.3.2. Trials conducted to study G X E Field trials have been conducted for the specific purpose of studying G x E interactions among open-pollinated maize populations (13.2). The Probe Trials were intended primarily to characterize environments, whereas the main objective of the Observation Trials was to characterize early, intermediate, and late populations. The testing sites utilized are described in Table 13.3. When only adapted varieties were included in the trials, the magnitude of G x E ranged from 5.4 to 22.3% of the Total Sums of Squares. Inclusion of a temperate-adapted hybrid in the Probe Trials in 1990 increased the proportion of Sums of Squares due to G x E to 45.4%. This supports the idea that the adapted maize materials show considerable

154

similarity in their response to environments, although the environments are in fact quite heterogeneous.

Table 13.2. Trials conducted to investigate G x E. Trial

Years

Probe Trial Probe Trial Late Observation Trial Intennediate Obs. Trial Early Observation Trial

1988..1 1989 1990 1991

No. of entries 4 6 25

No. of sites 23 (l3)~ 6 (1) 8 (4)

% SS due toGxE 8.1 45.4 22.3

1992 1991

25 10

6 (3) 9 (4)

8.5 14.0

1992 1991

12 25

7(3) 9 (6)

7 (3) 25 1992 § Number In parentheses is the number of sites outside Nlgena

: 5.4 18.1 6.8

13.3.3. Probe Trials A series of trials were conducted over a three year period (1989·1990) in order to characterize the variation among environments in the major lowland ecologies of West and Central Africa (Table 13.3). Maize varieties known to have specific adaptation to either the forest zone or the savanna were included as indicators of the environmental characteristics of the testing sites.

13.3.4. Characterization of genotypes MearI yields of varieties included in the 1988-1989 trials indicated that EV 8329·SR arId EV 83 TZUTSR-W were found to be most similar in their G x E response. These varieties have relatively low yield potential in the forest zone and have high potential in the savarma. EV 8443·SR showed greatest adaptation in the forest zone, whereas DMR·LSRW had above average yields at most of the UTA sites. In 1990, eight genotypes were included in the Probe Trials, presumably to identify a better indicator variety for characterizing testing sites. Trials were carried out at six locations, Cameroon including four in Nigeria (Ikenne, Mokwa, Samaro, and Bagauda) and two (Sanguere and Maroua). Cluster analysis revealed that 90% of the G X E Sums of Squares could be explained by grouping genotypes into four groups. Average yield performance of the fust group across testing sites showed better adaptation to the forest zone, but not outstanding in the savanna. DMR·LSRW falls in this group, in contrast to results from 1988· 1989 where it showed broader adaptation. The perfonnance of EV 8443-SR was consistent its G x E with results from the previous trials. The next group is somewhat more diverse response, but could generally be characterized as fairly stable across environments (Figure 13.3). Hybrid 8321·18 had the highest yield among varieties at three sites and was above average at all sites. EV 86 TZUTSR-W showed its best relative perfonnance at sites classified as Sudan savanna, but nonetheless performed reasonably well in Ikenne and Mokwa. This suggests that some improvement in forest adaptation occurred as the result of breeding efforts on TZUTSR· W, since an earlier variety from the population showed more specific savanna adaptation (probe Trials, 1988-1989). Yield performance of two varieties

155

with unique G x E responses . Although they do not fall in the same group based on cluster analysis, both showed poor adaptation in the forest zone, and relatively high yields at most savanna sites. The response of US bybrid FR1l41xFR303 was most extreme, yielding only about one t ha- l in Ikenne while being among the top perfonning varieties at Bagauda and Maroua. It appeared to be a good variety for differentiating forest and savanna testing sites.

Table 13.3. Characteristics of maize testing sites used for G x E analyses. Location

Country

Latitude

Longitude

Ntui

Cameroon

3°50' N

11°51' E

Touboro

Cameroon

8°31' N

1So04' E

Njombe

Cameroon

4°20'N

90 20'E

Sanguere

Cameroon

9 0 20'N

13~3'E

Mayo Galke

Cameroon

lOoOO'N

9003 ' E

Maroua

Cameroon

10042'N

14°18' E

550

*lkenne A§

Nigeria

6 0 S4' N

3°42' E

* [kenne B§

Nigeria

6°54' N

3 0 42'E

·Ibadan

Nigeria

7 0 26'N

3 0 54' E

FST

·Mokwa

Nigeria

9 O l8'N

5°04' E

210

SGS

• Kaduna

Nigeria

lO036' N

-r'27'E

614

NGS

·Samaru

Nigeria

HOll'N

70:38'E

NGS

• Funtua

Nigeria

11033' N

70:35' E

687 685

·Bagauda

Nigeria

11°30' N

g015' E

640

·Cotonou

6°20' N

20 50'E

Fumesua

Republic of Benin Ghana

1°35' N

Kpeve

Ghana

6 0 06'N

Niangoloko

Burkina Faso

9°37' N

*Farako-Ba

Burkina Faso

11°06' N

60 41'W 0°07' W SOOS'W 4 0 20'W

405

·Kamboinse

Burkina Faso

12°28' N

lO33'W

300

·Saria ·Ferke

Cote d'lvoiIe

9~O' N

SOlO' W

370

SS NGS

·Sinemantiale Cote d'IvoiIe

9030'N

Altitude (masl) 760

Vegetation classification

HF HF

244

NGS ' SS

350

Burkina Faso

NGS

.S019'W . Hutrud Forest; FST - Forest/savanna traruntion; CS - Coastal savanna; NOS -

HF Guinea savanna; and SS - Sudan savanna; • Sites managed by lITAlSAFORADJClMMYT § A and B refer to ftrst and second rainy seasons, respectively

Northern

156

13.3.5. Characterization of environments Two approacbes were taken to characterize environments based on G x E response from the results of the Probe Trials in 1988-1990. A) The flrst was to express the average yield of the savanna路 adapted varieties, EV 83 TZlJTW and EV 8329-SR. as percent of the trial means, and rank the sites accordingly. Lower relative yields for these varieties indicate that the site belongs in the forest zone and higher relative yields show increasing savanna characteristics for a site. B) B) Cluster analysis of sites indicates groups with the most similar G X E response, but does not specify the order in which the groups should be arranged. Sites were therefore arranged to correspond as clo~ely with the ranking from the previous analysis as possible (i.e., from forest to savanna), within the limitations imposed by the clustering. Cluster analysis identified Ikenne A, Cotonou. and Mokwa as forest zone sites. Njombe was quite distinct, but more like the forest zone than the savanna. All other sites would broadly he characterized as Sllvanna sites. Use of the relative performance of indicator varieties gave similar results, although Ikenne B sites and Njombe might be included in the forest zone. It is interesting that Mokwa was classified as a forest site with both analyses, since according to its vegetative classification it is located in the Southern Guinea Savanna (SGS). Perhaps 1988 was a particularly wet year in Mokwa. Neither approach classified sites within the savanna zone satisfactorily. It is possible that the indicator varieties utilized do not possess sufficient drought tolerance to show II. near-linear increase in relative performance moving from the SGS into the Sudan savanna (SS). Hence, SS sites could be clustered with other sub-optimal sites such as those in the SGS. Duration of the rains in the SS in any particular year could also affect the relative performance of genotypes. Alternatively, it may be that site-specific variation in soil types, biotic constraints, and seasonal variation in the weather are so great that they mask the more consistent differences that exist among savanna ecologies. Results from long-term experiments of maize would be required to generalize about the effects of locations within the savanna on relative genotype performance. Ideally infonnation on weather and soil factors could be utilized in combination with a good indicator variety to characterize testing sites using multivariate clustering procedures. Unfortunately reliable soil and climatic data in West Africa are often difficult to obtain. Long-term averages may be available on a regional basis, but information about specific sites is often lacking.

13.4. Observation Trials In 1991 it was recognized that there was a need for better understanding and quantification of the adaptation and yield stability of existing OP populations. A collaborative study was initiated to characterize the available early, intermediate and late maturing maize populations for their performance across a range of lowland ecologies in Nigeria, Burkina Faso, and Cote d'Ivoire. Included in the trials were some new introductions from the ClMMYT Maize Physiology Program in Mexico which had been developed either for drought tolerance (designated "Sequia") or for prolificacy (designated "SP" for semi-prolific). These were however the only materials in the trials which were not resistant to Maize Streak Virus. Varieties were tested in standard 4-row plots, with 3 replications, and data were collected on the two central rows. Because there were some minor changes in the entries in the two years of testing, results are presented for each year separately as well as for the common entries combined across years.

157

The Late and Early Observation Trials were arranged as 5xS lattice designs, whereas the Intermediate Observation Trials were conducted as Randomized Complete Block Designs. Adjusted means are presented in the individual site summaries for the Late and Early Trials. Due to some changes in software utilized for across site analyses and difficulties in retrieving some of the original data sets from the mainframe computer, unadjusted means were used for G x E analyses across sites and cluster analyses, unless otherwise indicated. This permitted easier data verification and also eliminated the risk inherent in lattice designs of adjusting for actual genetic differences among neighboring plots, in addition to the micro-environmental variation.

13.4.1. Effect o/mllturity on yield per/ormlUlce and adaptation The differences in yield obtained among the three maturity groups are smaller than anticipated; the lower yield potential of the early materials was quite obvious in the field. Apparently the differences observed in the field were a small proportion of the total yield. Furthermore, the magnitude of the differences between the early and late groups are generally smaller than was reported by nTA in the 1970's. This suggests that the performance of the early materials has improved more rapidly than the late materials in the breeding program. Other explanations might be that the early and intermediate materials have increased in maturity or that the populations are not classified in the appropriate maturity groups. Data on days to 50% silking were compared for the sites at which all three maturity groups were tested in the same year. The data can be summarized as follows: Maturity Group Early Intermediate Late

Mean Across Sites 52 days 56 days 59 days

Range in Trial Means 45-58 days 48-63 days 50-66 days

Concerning possible misclasstficatioDS, EV 8749~SR was included in the Early Trial to permit comparison with the Intermediate Trial in 1991 . It was the second highest yielding variety that year, but being one out of twenty-five entries, probably did not bias the results very much . For the Intermediate Trial, EV 32-SR was consistently among the latest maturing entries in both years and EV 8762-SR was often too late, although the parent populations of the varieties are classified as intermediate by CIMMYT. The performance of these varieties was not outstanding, however, so they cannot explain the higber than expected yields for the Intermediate Trial as a whole. TZUTSR-SGY and EV 8363-SR, wbich were included in the trials in 1992 were also rather late; it would be better to consider them as late-maturing varieties. Suwan 2-SR and SP Mat C4 performed very well, considering that they were consistently among the earliest varieties in the trials. For the Late Observation Trials, DMRLSRY was generally too early. It would be better classified as intermediate in maturity. It is often assumed that varieties of early or intermediate maturity would be most appropriate for the second rainy season in the forest zone, wben rains are generally less reliable. In these trials the late maturing varieties performed the best in the second season in Ikenne in both years of testing. It may be that a larger plant type is favored to minimize losses from foliar diseases and stem boren, which are often more prevalent in the second season. It was only in Kamboinse, Burkina Faso, that there appeared to be a consistent advantage for early varieties, probably due to the shorter rainy season at that location in the SS.

13.4.2. Across site Analysis of Variance Results from the across site Analysis of Variance for the Observation Trials are presented in Tables 13.4. A portion of the standard stability regression analysis (Eberhart and Russel, 1966) is also included. This approach divides the SS due to locations plus the en1Iy by location interaction SS into a linear component due to regression of genotypes on site means (not shown), an interaction term (Entries x Loc. (linearÂť, and deviations from the regression

158

model. A significant interaction term indicates that the entries differed in their linear response to environments (i.e., they had significantly different b values).

Highly significant differences were observed among locations and among populations for all three maturity groups. For the Late Observation Trials, G x E was significant in the across year analysis, but not in the analyses for each year individually. G x E was highly significant for the Intermediate Observation Trials in 1991, but not in 1992. For the Early Observation Trials, G X E was significant in 1992 but not in 1991. In general it can be said that the effects of environments contributed the most to variation in yield performance; although there is evidence for specific adaptation of varieties to particular environments, differences 路 in mean yields of varieties across environmtnts were of greater magnitude than G X E interactions.

Table 13.4. Across site ANOVA for yield in the Late, Intermediate and Early Observation Trials.

Source Across Years

Locations (L) Reps. in Ls Entries (El ExL E x L (linear) Pooled deviations Residual

13 2B 21 273 (21) (264)

Fvalue

Pr>F

Late Observation Trials

588

53276910 2066794 3492197 802868 1007906 750064

25.78

0.000

4.35 1.23 1.34

0.000 0.020 0.147

651595

Intermediate Observation Trials L Reps. inLs E ExL E x L (linear) Pooled deviations Residual

15 32 8 120 (8) (126) 256

36950645 965194 2282382 603088 1093063 504962

38.28

0.000

3.78 1.43 2.16

0.000 0.009 0.034

421642

Early Observation Trials

(322)

68704130 2892737 3322859 710150 548346 690329

704

599920

L Reps. inLs

ExL Ex L. (linear) Pooled deviations Residual

330

(22)

23.75

0.000

4.68 1.18 0.79

0.000 0.035 0.733

13.5 Characterization of genotypes Mean yields and stability parameters for genotypes in the Late Observation Trials (1991 and 1992). Intermediate observation trials (1991 and 1992), and Early Observation trials (1991 and 1992) are presented in Tables 13.5, 13.6, and 13.7 respectively. For the Late and

159

Early Trials, means adjusted for the lattice design are given in addition to the unadjusted across site means. In most cases the values are similar, but cbanges in the ranking of varieties occurs when means are adjusted. Means are presented separately for the forest (lkenne, first and second season) and savanna ecologies (all other testing sites). An index of relative adaptation to the forest zone was calculated for each entry by dividing the mean yield of the entry in the forest by its mean yield in the savanna, expressing this ratio as a percentage, and then adding a constant such that the mean of the index across all entries in the trial equals 100. Values greater than 100 indicate better than average adaptation to the forest, whereas values less than 100 indicate better adaptation to the savanna. It is important to remember that the values are relative and do not indicate the absolute yield potential of the variety in either ecology. Correlations between results from 1991 and 1992 were significant for mean yield in each ecology, mean yield across ecologies, and for the Index. These correlations conium that there are consistent genetic differences in yield potential among varieties in each maturity group; and that although broad adaptation is the rule, there is still scope for selecting genotypes with specific adaptation to particular ecologies. Standard stability parameters were also estimated, including the CV across sites Francis and Kanenberg, (1978), regressiob coefficient (beta) and deviation from regression 1. The cultivar performance measure (Lin and Binns, 1988) indicates how close a variety tends to be to the best entry in each trial. Differences between the entry mean and the mean of the best variety are squared, summed across sites, and the total is divided by twice the number of locations. Thus, the smaller the , performance measure the greater the stability. Stability variance (Shukla, 1972) represents the contribution of each genotype to G x E variance. Again, smaller values indicate greater stability. Results for specific genotypes are discussed together with results from cluster analyses in subsequent sections for each maturity group.

13.S.1. Late Observation Trial The highest yielding geno~ across years were TZ 9043-DMRSR (white, dent) and Suwan I-SR (yellow, flint). Both varieties showed good yield stability. As much possible genotypes were arranged in descending order based on values for the forest/savanna index, within the limits imposed by the clustering. The clustering often reflects genetic relationships among the populations, but in other cases closely related genotypes fall in separate groups. For discussion purposes, genotypes are divided into Groups A, B, C, and TZL Compo 1 Cl (additional groups would be requited to adequately explain the G x E variation). Relative yields of the best materials in groups C-l and C-2 are shown in Figures 13.2-13.3. For Group A, TZL Compo 4 CO has outstan~g yield potential in the forest zone, but only average performance in the savanna. It is interesting that derivatives of all of the populations whi::b were used in forming TZL Compo4 (Pop. 43-DMRSR, TZPB-SR, and DMR-LSRW, Pop. 21SR) fall in a similar, but slightly different cluster. The perfonnance of these varieties is good in the forest zone and consistent across sites. The progress in TZL Compo 4 CO reflects the emphasis placed on resistance breeding in the forest zone during recent years, or perhaps the lack of good, reliable testing sites in the savanna. Population 22 has good, broad adaptation, but did not perform well in Kamboinse in 1991 .

13.S.2. Intermediate Observation Trial Across 88 TZUTSR-W and TZUTSR-W C5 (white, semidents) were the highest yielding varieties in the Intermediate Trial in 1991 and 1992, respectively. Because they did not seem to be similar enough to each other to pennit them to be included as a common entry in the across year analysis, cluster analysis of genotypes is presented for 1991 only (Figure 13 .4).

160

Varieties are divided into two major groups for discussion purposes. Suwan 2-SR was the most outstanding variety in Group A. It showed the greatest relative yields in the forest zone and in the Sudan Savanna. Across 88 TZUTSR-W was clearly the best variety in Group B, having the highest yields among all varieties at four out of nine sites in 1991. In 1992, TZUTSR-W CS was the highest yielding variety at four out of seven testing sites, including the first season in the forest zone. Considering the earlier data on the TZUT population from the Probe Trials in addition to results for the two versions of the population tested in 1991 and 1992, there has clearly been progress towards greater adaptation to the forest zone as well as improved yield stability and performance across ecologies.

13.6. Characterization of environments Although it was not a specific.objective of the Observation Trials, the data provided an opportunity to food out more about the characteristics of our testing sites, based on the yield response of a wide range of germplasm. An Index quantifying relative adaptation to the forest zone clearly demonstrates consistent differences between the Nigerian forest sites and all other sites in terms of varietal performance. Whether performance in Ikenne is representative of other forest sites in West Africa cannot be detennined from these trials . A similar index was calculated contrasting savanna sites in Nigeria and savanna sites farther west in Cote d'Ivoire and Burkina Faso, but values obtained in 1991 were not correlated with those from 1992. Thus, there was no evidence that some varieties perfotmed better in the Nigerian savanna than in the savanna of Cote d'Jvoire and Burkina Faso, or vice versa.

13.6.1. Cluster analysis Results from cluster analysis of environments for each of the maturity groups across years are presented in Figures 13.7. The Late and Early Trials are probably the most reliable, since they are based on a much larger number of genotypes. For the Late Trial, Ikenne 1991 (frrst and second seasons) and Ikenne A 1992 fell in the same cluster, but also showed close similarity in G x E to Sinemantiale in 1991 and 1992. Mokwa 1992 and Ferke 1991 were also associated with this group, and to a lesser extent to the group consisting of Ikenne B 1992 and Mokwa 1991. Sites in the northern guinea savanna of Nigeria and in Burkina Faso fell in the same broad group, but the sites within this group were not very similar to each other in terms of G x E. Results from the Intermediate and Early Trials also presented quite a few anomalies. Although the forest zone sites tend to cluster together, there are many exceptions. Thi.s may reflect the relatively small magnitude ofG x E among the varieties included in these experiments, which prevents a clear classification of testing sites. It is also evident that one cannot place too much emphasis on the results of cluster analysis from one set of trials. On the other hand, the results should not be completely discounted. In examining the graphs of relative yield across environments, it did seem that varieties which were poorly adapted to the forest zone in Nigeria tended to have below average perfonnance at testing sites in Cote d'Ivoire. One possible explanation could be that because the main cropping season in Cote d'Ivoire occurs in the second rainy season, when disease and insect pressures are greater. Leve1s of resistance could therefore be more important in determining varietal performance there than in the moist savanna of Nigeria.

13.6.2. Relationships between disease resistance and adaptation to the forest zone To substantiate the assumption that disease resistance plays a major role in detennining adaptation to the forest zone, correlations were calculated between genotype means in the two

161

major ecologies and average disease ratings from three seasons of data in Ikenne. For the late gerrnplasm, there were significant negative correlations between both yield in the forest zone and the forest savanna index and disease ratings for ear rot and P. polysora rust. There was a particularly strong relationship between the forest/savanna index and P. poJysora ratings (r = 0.75路路). Only CurvuJaria ratings were significantly correlated with yield in the savanna. For the early germplasm, highly significant negative correlations were obtained between mean yield in the forest zone and all four disease ratings; ear rot, P. polysora, B. maydis, and Curvularia. Similar correlations were obtained for the forest/savanna index. Correlations were smaller in magnitude, but significant for all diseases except B. maydis in the savanna as well. These correlations may be due either to a causal relationship between the disease and yield, or simply to breeding history. Varieties which have undergone more intensive selection may have "cleaner" leaves for aesthetic reasons, whether or not this contributed to increased yields. Nonetheless, results suggest that disease resistance is important in detennining adaptation to the forest zone, particularly for the early populations. Early varieties have less leaf area and may be more adversely affected by diseases and insects, which reduce potential for photosynthesis and carbohydrate supplies.

In conclusion, above analysis cleady indicate the progress made in the maiZe breeding program with varieties targetted to various agroecologies taking into consideration, the G x E interactions.

Acknowledgements Many collaborators from National Programs in West Africa are acknowledged for assistence in conducting the Probe Trials in their countries in 1988 and 1989. Other collaborators of the Observation Trials (Dr. A. O. Diallo, CIMMYT Liaison Scientist, Cote d'Ivoire; Dr. 1. M . Fajemisin, SAFGRAD Coordinator - 1991, Burkina Faso; and Dr. B. BaduApraku, - SAFGRAD Coordinator - 1992, Burkina Faso), and Dr. lH. Mareck and Dr. F.M. Quin are acknowledged for their contribution to UTA's maize breeding program.

References Eberhart, S.A., and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop

Sci. 6:36-40. Eberhart, S.A., S.K. Kim, , l Mareck, L.L. Darrah, and M.M. Goodman. 1991. A comprehensive reeding system for maiZe improvement in Africa. Pp. 175-193. In. Crop Genetic Resource.s of Africa, Volume 11. N.Q. Ng, P. Perrino, F. Attere, and H. Zeden (eds.), proceedings of an International Conference on Crop genetic Resources of Africa, Oct. 17-20, 1988, UTA, Ibadan, Nigeria. Francis, T.R., and L.W. Kannenberg. 1978. Yield stability studies in short- season maize. 1. A descriptive method for grouping genotypes. Can. J. Plant Sci. 58:1029-1034. Lin, C.S., and M.R. Binns. 1988. A superiority measure of cultivar perfonnance fOT cultivar x location data. Can. 1. Plant Sci. 68: 193-198. MIP (1996). Maize Improvement Program, Archival Report, 1988-1992. Part 1: Maize population improvement. Crop improvement Division, ITTA, Nigeria. Shukla, G.K. 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29: 237-245 .

162

4521 4303 4289 4062 3966

4550

4761 4728 4692 4642 4631 4571

4767

4999 4974 4935 4917 4902 4889 4873 4857 4822

Mean Yield Across Sites

ยง Calculated using adjusted means

TZ 9043-DMRSR Suwan I-SR EV 8722-SR TZL Compo 4 CO TZL Compo1 Cl SPLC4 TZL Compo 3 CO TZLComp.2 TZPB-SRC2 BV 8443-SR La Posta Sequia C 1 DMR-LSRW Tuxpeno Sequia C6 TZSR-W-l C4 EV 8721-SR BV 8725-SR Pop 22-DMRSR TZSR-Y-l C4 TZB-SRBC5 Pool 26 Sequia Cl TZB-SRSE DMR-LSRY

Variety

5108 5032 4956 4824 4829 4766 4848 4828 4976 4857 4727 4612 4714 4677 4656 4536 4564 4610 4337 4385 4005 4033

Mean Yield (adjusted)

3139 3329

3911

4645 4386 4499 5026 3772 4191 4384 4634 4229 4774 4261 4420 3602 4033 4141 3606 4080 4131 3869

Mean Yield Forestยง 5293 5290 5138 4743 5251 4996 5033 4905 5275 4890 4913 4689 5159 4934 4862 4908 4758 4801 4525 4574 4351 4314

Mean Yield Savannaยง

87 93

101 101 101

102 110 85 97 100 89 tol

113

ltO 96

102

121 87 99

103

16.7

103 98

23.6

32.7

21.3

19.4 18.0 20.0 18.0 19.2 20.9 22.1 24.1 17.9 21.0 26.4

19.4

22.4 19.7 30.4 24.1 22.8

22.3

Index For.lSav.ยง

Table 13.5. Stability measures for genotypes from the Late Observation Trials (1991 and 1992).

1086996 981461 976052 1056340 1153762 1611174 1571804 1772852 2049856

896600

650409 498039 536647 914710 522635 670191 625853 692631 769212 926042 736257 928103

Performance Measure

1021260 581800

770129

641601 568166 263956 1181244 389567 440969 356430 809603 617620 914246 480422 632032 903264

645357

521168 833865 806582 1688363 2595451

Stability Variance

0.87 1.39 0.93

1.12

0.98 0.86 0.93 0.92 1.02 1.07 0.82 0.93

0.72

1.23 1.13 0.94 0.98

1.38

0.75

1.10 1.10

0.83

Beta

163

-24447 -39150

11322

-80554 39680 31403 275937 502202 -60826 -30450 -42672 -139022 95853 -97920 -97763 -112809 34948 -23199 70284 -97721 -22298 57580

Deviation

SP Mat C4 EV 8762-SR EV 32-SR EV 8749-SR EV 8766-SR EV 8435-SR

TZUTSR-YC3

Suwan 2-SR EV 8744-SR

Variety

4994 4706 4669 4627 4625 4577 4545 4370 4210

Mean Yield Across Sites

3268

4269 3785 3368 3327 3845 3817 3902 3627

Mean Yield Forest

5102 5061 4885 4830 4759 4618 4524

5013

5236

Mean Yield Savanna

106 100 91 90 103 104 107 103

Index For.lSav. 25.1 27.2 30.0 27.8 22.1 29.4 24.3 22.8 25.6

Table 13.6. Stability measures for genotypes from the Intennediate Observation Trials (1991 and 1992).

66934 223981 270725 383645 272589 326921 455500 567664 831753

Perfonnance Measure 834642 348655 718178 646494 445169 553189 737881 365251 778333

Stability Variance

1.14 0.90 0.85 0.87

0.86

1.03 1.10 1.19 1.08

Beta

164

94190 路54033 12151 34404 -43320 -12957 53102 -70250 55490

Deviation

4820 4777 4735 4629 4592 4556 4532 4448 4407 4402 4397 4361 4313 4294 4293 4270 4227 4205 4067 4026 3999 3996 3842

Mean Yield Across Sites

§ Calculated using adjusted means

TZE Comp 3x4 C 1 TZE Comp 3 Cl TZE Comp 4 Cl EV 8731 -SR Acr 89 DMR-ESRW IK 88 BU-ESRW SPEC4 DMR-ESRY Acr 88 Pool 16-DT TZEComp7 C2 Pool 15 QPM-SR Pop. 31-DMRSR Pool 18 QPM-SR Pool 16 Sequia CO TZESR-YC3 Pop. 61-SR TZE Comp6 EV 8730-SR TZEComp 5 C2 Acr 86 TZESR-W Acr 87/88 Pool 16-SR TZESRW-SE Pool 18 Sequia CO

Variety

Mean Yield Forest§ 4203 3981 3871 3874 3872 3492 3349 3426 3255 2614 3297 3369 3495 3200 3120 3111 2971 3372 2952 2818 2803 2709 2194

Mean Yield (adjusted)

4856 4876 4763 4590 4450 4580 4526 4515 4459 4376 4403 4470 4310 4145 4261 4239 4333 4073 4136 4028 4085 3938 3763

5073 5174 5061 4828 4643 4942 4918 4878 4860 4964 4772 4837 4582 4461 4641 4615 4787 4306 4530 4431 4512 4347 4286

Mean Yield Savanna§ 114 108 107 111 114 101 99 101 98 83 100 100 107 102 98 98 93 109 96 94 93 93 82

Index For.lSav.§

Table 13.7. Stability measures for genotypes from the Early Observation Trials (1991 and 1992).

19.1 2004 25.9 23.4 21.4 21.0 24.6 21.5 25.1 30.8 25.7 22.1 21.0 25.7 25.0 21.6 28.6 23.7 24.9 25.3 27.8 30.3 30.0

276413 380084 419269 433007 513913 440066 458126 600296 638083 754484 620322 685509 644981 729017 754484 752561 983080 850659 1058761 1093026 1179842 1180041 1352691

Performance Measure 542830 454490 1241751 787955 833412 284030 895205 533444 656119 1394716 514297 362259 486750 545943 522130 733042 1029065 565629 846969 575861 574228 571747 1381573

Stability Variance 0.87 0.93 1.11 1.00 0.88 0.94 1.02 0.91 1.05 1.27 1.10 0.94 0.86 1.06 1.03 0.84 1.12 0.94 0.91 0.96 l.07 1.19 l.00

Beta

165

-64362 -78911 170380 34763 35011 -133387 69130 -57846 -10658 156915 -65656 -108353 -84137 -48527 -53024 -11623 98342 -41500 45876 ·35946 -40162 -75791 228323

Deviation

130

->....

120

"Q 4.l

EV 8443-SR

110

(II

4.l

-.•

100

f-I

~ 0

La Posta Sequia C 1

•

TZLComp.2

•

TZSR-W-l C4

70 0-

C'l 0\

-< -< 2 2 Q)

C'l

III

-- ~

.:.0:

III

.:.0:

-0-

C'l

'" ~'" ~ '"

fa .~

CI)

::; ::E

~ -

-.£ 00-

N 0-

-; '" '~" '':; ~0 E 8 \.1.. I)

'';:

III

c (;ij

N 0~

0\ Q)

OIl

.S 0 .0

CI)

Figure 13.2. Relative yields of genotypes from the Late Observation Trials showing similar G x E response across years (Group 'e-t').

166

130 120

..->"C ~

110

= ~ ~

.•-

•

100

TZL Compo3 CO Suwan l-SR

"'" E-i

80 70

C'l 0-

-<u -<

ill

c:l

C'l 0I)

C'I til

§ ~0

N 0tIS

~0 ::E

N 0-

C'I

§ 5 § ~ Y':l

C'I

~u ~

N 0-

&.l

.!:l

"'iC

tIS

0'\ tIS

.0 0

r:v su ~

Y':l

N 0tIS .0 0

~ t;J

.....

.5'" ~

Y':l

Figure 13.3. Relative yields of genotypes from the Late Observation Trials showing similar GXE response across years (Group 'C-2').

167

% GXE SS Among Groups

100

lkenneA89 Cotonou 88 Mokwa88 lkenneA88 Cotonou89 Njombe89 lkenneB88 IkenneB 89 Ntui 89

Samaru88 Fumesua89 Mayo Galke 89

Niangoloko 89 8agauda88 Samaru89 Paralcoba 89 Touboro89 Funtua89 ~ve89

Ferke89 Sanguere89 Funtua 88 Soucounda 89

Figure 13.1 Clustering of environments based on G x E from the probe trials, 1988-1989.

168

% GXESS AmonsGrouEs

90 100 EV32-5R.

EV8762-SR Suwan2-5R

r---

Group A.

TZUTSR~YC3

EV87440-SR EV87.f90-SR

I I

Group B I I

EV8766-SR

EV843S-SR At:r 88 TZUTSR-W SPMatC4

Figure 13.4 Clustering of genotypes based on G x E from the intermediate observation trials in 1991

169

'" GXE-SS Among Groups

r-{

~Al991

Ikenne 8 1991

lk8tne A 1992 Si~tiale

1992 Si~ntia}Q 1991

Mokwa 1992 FÂŤke 1991

~ ~

I L

100

J 1

......

J~Bl91J2

Mokwa 1991 Kaduna 1992 Samaru 1991 FarakoBa t 992 Kamboinse 1991 Fanko Ba 1991

Figure 13.5 Clustering of environments based on G x E from late observation trials in 1991 and 1992

170

%GXE SS Among Groups

90 100

Theone A 1991 lkenne B 1991 IkeIIne B -1 m

I L

I 1

lkenne A 1992

Samaml991 Kamboinse 1991 Bagauda 1991 Kamboinse 1992.

Bagauda 1m Farako B.a 1992 ~

L[ I I

Mokwa 1991 Kaduna 1992

Sinemanlia1e 1992 Ferke 1991 Sineman.tiaJe 1991 Farako Sa 1991

Figure 13.6 Clustering of environments based on GxE from the intermediate observation trials in 1991 and 1992.

171

CJ(,

GXE SS Among GfOllp! 40

Jkert:ne A 1991

Ferke 1991

100

Ikenne A 1992 Ikenne B 1992

Sinemantiale 1994 Samarul991 Sln.emantiaLe 1991 lkenne B 1991 Farako Ba 1991 Bagauda 1991

Kamboinse 1991

I<arnbojnse 1992 Kaduna 1992

0.-

8agauda t 992 J I

Farako Ba 1992 Sana 1991

Figure 13.7 Clustering of environments based on GxE from the early observation trials in 1991 and 1992

172

Chapter 14 Breeding Cowpea Varieties for Wide Adaptation by Minimizing Genotype :x Environment Interactions Bir B. Singh 14.1. Introduction 14.2. G x E Interaction 14.3. Factors responsible for G x E interaction 14.4. General strategy for breeding cowpea varieties at lITA 14.5 Stability of performance of improved cowpea varieties References

14.1. Introduction International Institute of Tropical Agriculture (IITA) has a world mandate for cowpea (Vigna unguiculata [L.] Walp.) improvement. The main cowpea producing countries are located in Africa and Central and Southern America as well as in parts of Asia. Between 35 ~ and 300 S of the equator. Cowpea is a single crop species, but the varietal requirements in terms of plant type, seed type, maturity and end-user needs are extremely diverse from region to region (Singh et al., 1997). Most of the countrles do not have active breeding program on cowpea due to the lack of trained manpower and other constraints but they have well organised testing programs. Therefore, they look forward to receiving improved cowpea breeding lines from lITA for testing and selecting suitable lines for release and cultivation in the country. The chances of identifying superior lines by different national programs of over 60 countries worldwide, from the material provided by UTA, would be good if these materials had been exposed to major biotic and abiotic constraints while handling the segregating populations and during the initial testing for yield in diverse environments. In view ofthls, IITA developed a strategy right from its inception to breed cowpea varieties for wide adaptation by minimizing genotype x environment interactions. Being located in Nigeria, it has been possible to test cowpea breeding materials at diverse locations because the agroclimatic zones in Nigeria include transition zone, derived savanna, Guinea savanna, Sudan savanna as well as Sahelian ecologies with annual rainfall ranging from more than 2500 mm to less than 400 mrn (in broad belts from south to north) and diverse soil types and fertility regimes. Also, Ibadan, the headquarters of lITA has a bimodal rainfall with major rains from April to July and minor rains in September â&#x20AC;˘ October pemritting two crops each with natural rains under different photoperiod and temperature and a third crop with irrigation in the dry season (November -January) with low temperatures and short day lengths. This exposes breeding materials to diverse environments and ensures selection of widely adapted cowpea varieties. At the ITTA Kano station central cowpea breeding for semi-arid zone is done.

173

14.2. Genotype x Environment Interaction The genotype x environment interaction is dermed as "differential perfonnance of different varieties in different environments" as indicated in Fig. 14.1. The three cowpea varieties Vl, V2, and V3 respond differently when grown in different environments such that Vi gives high linear response to favorable environments and V2 gives low linear response to favorable environments whereas V3 does not respond at all. Therefore, these cowpea varieties may have site-specific performance and cannot be recommended for wide zones. In good environments. VI is better but in poor environments, V2 is better. Thus, significant genotype x environment interaction is a major problem for cowpea breeders because it narrows/limits the area of adaptation of a particular variety necessitating development of different varieties for different environments. In case of non significant genotype x environment interaction (Fig. 14.2) the same cowpea variety, VI is better than others in all the environments. Therefore, every plant breeder endeavors to identify the factors responsible for genotype x environment interactions and tries to eJiminate/reduce these factors so that the same cowpea varieties could be widely adapted.

V3 Variety Mean

Significant Interaction

Environmental mean

Fig. 14.1.

Significant genotype environment interaction

174

V3 Variety Mean

No G & E Interaction

Environmental mean

Fig. 14.2

Non-significant genotype environment interaction

14.3. Factors Responsible for Genotype x Environment Interactions Several workers have discussed the implications of genotype x environment interaction in breeding programs and how to estimate and minimize it (Finlay and Wilkinson, 1963; Allard and Bradshaw (1964); Eberhart and Russell, 1966; Lin et al,. (1986) and Singh and Dabas (1990). The differential performance of different varieties in different environments may be due to differential reactions of varieties to prevalent biotic factors such as diseases [anthracnose, cercospora leaf spot (XanthomoTUls vignicofa) and pustule causing Xanthomonas spp. etc1, virsus, insect pests [pod borer Maruca vitrata, and pod sucking bugs, thrips, and Bruchids etc.], parasitic weeds [Striga gesneriodes {wild} Vatke and Alectra vogelii {Benth}], nematodes (i.e. Meloidogylle incognita), and MycorhizallRhizobial popUlations etc, as well as due to differential reactions to abiotic factors such as day length, light intensity, temperature, rainfall, drought, water logging, soil fertility , soil texture and soil pH etc. The biotic and abiotic factors differ from location to location and even from year to year and therefore, they affect adaptability of a variety across locations as well as its stability at the same location over the years. Variations in cropping system (CS) requirements also influence G x CS interactions (Blade et at, 1997) and the breeding approach (Singh et a1., 1997).

175

14.4. General Strategy for Breeding Cowpea Varieties at lIT A The following strategy has been adopted at UTA to minimize genotype x environment interactions and the steps involved have been summarized in Fig. 14.3i) Breed for resistance to major diseases, insect pests, parasitic weeds and nematodes etc, prevalent in the target regions . ii) Breed for promiscuous and effective nodulation and Mycorhizal associations. iii) Breed for photoinsensitivity, tolerance to heat, shade, drought as well as water logging. iv) Breed for good growth in poor soils by incorporating genes enabling use of nutrients even at lower concentrations. v) Select for greater homeostasisilbetter perfonnance from varieties with similar reaction to biotic/abiotic factors (i.e. accumulation of minor genes affecting adaptability) by exposing segregating materials to different environments.

14.4.1. Selection of Parents Parents for hybridization are selected on the basis of their resistance to major diseases either singly or in combination with other parents as well as their stable performance across several locations in the previous years.

14.4.2. Handling of Segregating Populations The segregating generations beginning F2 onward are grown in diverse environments to sample major biotic and abiotic constraints so that only those plants/progenies are advanced for further testing which show resistance/tolerance to these factors . This is done by growing the Fl populations in the main crop season (April.July) and Fl , and F., in the short rainy season and dry season with irrigation, which provide a diverse set of biotic and abiotic stresses. Another cycle of selection is completed beginning with F, from the next main season. The progenies are bulked for yield testing at F, or F7 stage depending upon the homogeneity for plant type, maturity and seed type.

14.4.3. Variety Testing The advanced breeding lines derived from segregating materials exposed to different environments are then tested at several locations across north-south axis representing different agroc1imatic zones and varied biotic/abiotic constraints. The test sites are indicated in Fig 14.4 and their coordinates are given in Table 14.1. Selection criteria are indicated below:

14.4.3.1. Humid zone Selected progenies are tested at Ibadan for photosensitivity and for resistance to web blight, anthracnose, brown blotch etc. and adaptation to high rainfall areas.

14.4.3.2. Mid-altitude zone The crosses made for adaptation to mid altitude and related breeding lines are evaluated at (Jos) which is located at about 1300 m above sea level and receives about 1300 nun rainfall . It is also a hot spot for Ascochyta blight, Septoria and scab.

176

14A.3.3. Moist savanna ZODe The crosses designed for developing varieties for the Guinea savanna are evaluated at Samaru which receives about 1000 mm rainfall and is a hot spot for Septoria, scab, brown blotch, A lectra and bacterial blight. Also, the insect pressure is high.

UA..I

â&#x20AC;˘

~no

â&#x20AC;˘~I!WU NGERIA

Fig. 14.4.

Test sites for cowpea trials in different agro-ecological zones in West and Central Africa (note: CamerooD, Nigeria, and Niger Republic).

14.4.3.4. Sudan savanna zone The segregating populations and advanced breeding lines derived from the crosses designed for the Sudan savanna are evaluated at IlTA Kano Station research fann, Minjibir which receives about 600 nun rainfall and is a hot spot for aphid, viruses, leaf smut, PseudoCercospora, Striga gesnerioides and bacterial blight. The insect pressure is moderate.

14.4.3.5. The Sabel zone The segregating populations from the crosses designed for developing varieties for the Sahel are evaluated at Babura, Zinder and Olelewa which receive about 400 nun, 300 rom and 200 nun rainfall respectively and these are hot spots for drought, Striga, bacterial blight and aphid which are the key constraints in the region. The insect pressure is low.

14.4.3.6. Dry season testing All the breeding lines are further tested at IITA Kano Station in the dry season to check for photosensitivity and adaptation to dry season planting under irrigation or with residual moisture in receding river beds (fadamas).

177

Table 14.1 . Coordinates of cowpea testing sites

Location Ibadan Mokwa Samaru Minjibir 105 (Vom) Babura Malam Madori Maidu~

Zinder Olelewa Maradi Niamey Marona

Country Nigeria Nigeria Nigeria Nigeria Nigeria Nigeria Nigeria Nigeria Niger Republic Niger Republic Niger Republic Niger Republic Cameroon

Latitude 7°26' 9°18'N HOll'N l2°ll'N 09°38'N 12°47'N 12°27'N llo51'N 13°48'N 14°S0N 13°28'N 13°30'N lOO42'N

Longitude 3°54'E 5°04'E 07°38'E 08°37'E 08°90'E 09°02' lOoOO'E l3°0S'E 08°59'E 08°58'E 07°2S'E 02°08'E 14°18'E

The newly bulked breeding lines which are 200-500 in number with limited seeds are flfSt tested in initial evaluation trials (lET) grouped according to maturity and tested at several

locations with several checks (Fig. 14.1). The promising breeding lines are then tested at diverse locations in preliminary variety trials (PVT) with 3 replications. The best lines are then tested at the same locations in advanced variety trials (AVT) with 4 replications. Based on the overall performance, disease and insect resistance and stability over 2-3 years, the top performing lines are multiplied and distributed to various national programs in the form of cowpea international trials (CIT).

14.5. Stability of PerformaDce of Improved Cowpea Varieties The strategy for developing improved cowpea varieties discussed above has been very successful in identifying widely adopted cowpea varieties. As an example the yield perfonnance of selected advanced breeding lines at different locations in Nigeria in 1982 is presented in Table 14.2. These lines were then distributed to over 50 national programs in the form of cowpea international trial. The summary of their performance is presented in Table 14.3. The yield data and stability analysis indicated that the lines which perfonned well across different locations in Nigeria also perfonned well across different national programs (Table 14.2). The three best promising breeding lines, IT82E-18, IT82E-16 and IT82E-32 showed high yield and stable performance and consequently these have now been released in many countries.

178

Table 14.2 Performance of extra-early cowpea varieties at different locations in Nigeria (1982).

Yield kg/ha· IITAF IITAS Mokwa Varie!I 2444 737 2391 IT82E-18 IT82E-16 3023 986 2188 IT82E-32 1961 1143 2109 2003 2078 978 IT82E-9 1810 1713 1875 !T82E-56 IT82E-60 1374 1563 IT82E-77 1285 1594 IITAF = Ibadan, first rainy seasons (May-July) IlTAS;; Ibadan, second rainy season (Sept-Nov)

Samaru 1893 1620 1809 1619 1049 1127 1559

Mean Yield (kglhal 1866 1954 1755 1668 1612 1369 1479

Following this strategy, UTA develops and distributes improved cowpea varieties to over 65 countries and most of these countries have identified superior varieties and released them Many national programs for general cultivation. (Singh 1994; Singh et aI., 1997). worldwide have been so happy with the perfonnance of improved cowpea that they have given interesting names to these varieties. For example, 'Hope' and 'Pride' in Tanzania, 'Sky' and 'Light' in Nepal, 'Gold of the sand' in Sudan, 'Cubinata' in Cuba, 'Sangaraka' in Mali etc.

In conclusion, to fulfill the globalJregional mandates, it is imperative for International Agricultural Research Centers to adopt breeding strategies that minimize genotype x environment interactions. The results of cowpea breeding program at UTA have shown that this goal can be achieved if improved varieties have combined resistance to major biotic and abiotic stresses and are then tested in diverse sets of environments. Table 14.3. Mean perfonnance of extra-early cowpea varieties distributed to \iarious national programs worldwide in 1984. Variety

1st

2nd

3rd

4th

Mean yield· {kg/ha}

4 IT82E-18 12 8 8 1429 7 9 1354 5 7 IT82E-32 IT82E-16 7 1346 9 5 9 7 6 IT82D-81 6 1271 1 IT82D-885 7 1260 6 6 3 IT82D-789 5 1225 3 3 5 IT82E-60 1 2 1 1 985 3 2 2 4 1063 IT82D-889 2 1063 4 Local checks"" 3 Mean of 50 locations across the tropics. •• Check varieties differed from location to location. bVE = Regression of variety mean over environmental mean

bYE 1.28 1.15 1.16 1.11 1.03 0.88 0.89 0.69

179

REFERENCES Allard. R. W. and A.D. Bradshaw. 1964. Implications of genotype-environment interactions in applied plant breeding. Crop Sci. 4: 503-507. Blade, S.F., S.V.R. Shetty, T. Terao. apd B.B. Singh. 1997. Recent developments in cowpea cropping systems research. pp. 114-128. In : Singh B.B., D.R. Mohan Raj, K. Dashiell and L.E.N. lackai (eds). 1997. Advances in Cowpea Research. Copublication of UTA and JIRCAS, Ibadan, Nigeria. Eberhart. SA and W.A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6:36-40. Finlay K. W. and G.N. Wilkinson .1963 . The analysis ofadaptation in a plant breeding programme. Australian J. Agri. Res. 14: 742-754 Lin, e.S., M.R. Binus and L.P. Lekovitch. 1986. Stability Analysis: where do we stand? Crop Sci. 26:894-900. Singh, R. and B.S. Dabas. 1990. Genotype x environment interactions in cowpea. IntI. J. Trap. Agri. 8: 6-13. Singh, B.B. 1994. Breeding suitable cowpea varieties for West and Central African savanna. P. 77-85 In I.M. Menyonga T.B. Bezuneh, lY. Yayock and I. Soumana (eds.)

Progress in Food Grain Research and Production in Semi-Arid Africa. OUNSTRC-SAFGRAD, Ougadougou, Burkina Faso. Singh. B.B., 0.1. Chambliss and B. Sharma. 1997. Recent advances in cowpea breeding. P. 30-49 In: Singh B.B., D.R. Mohan Raj, K. Dashiell and L.E.N. Jackai (eds) . 1997. Advances in Cowpea Research. Copublication of liTA and JIRCAS , Ibadan, Nigeria.

180

Germplasm and Breeding Lines

Selected Parents

Development of Breeding Lines 3-4 generations/year no

Initial Evaluation Trials 4-6 Environments ~,

Preliminary Variety Trials 4-6 Environments Advanced Variety Trials Environments

Multiplication and Distribution To National Programs

Testing and Release by National Programs Fig. 14.3. General Strategy of Cowpea Breeding Program

181

Annexes

Jagtap. 1999. Agroecological zones in SSA

Annex I. The Agroecological Zones in sub-Saharan Africa

ss. Jagtap At .1. Background A 1.2. References

A1.1. Background Various agroecological, crop and agroclimatic suitability maps of Africa obtained from the resource Information System (RlS) database of the IITA-Agroecological Studies Unit are shown in the following figures. These maps include annual rainfall, major FO soil types, crop based systems, relative soil fertility, duration of growing season in months, vegetation (~"EPIFAO grid of Africa database- experimental), and agrociimatic suitability maps for IITA mandate crops. These suitability maps or spatially visualized data can be very useful for genetic improvement activities of UTA mandate crops.

Al.2. References Jagtap, S.S. and A.G . Ibiyemi. 1998. GIS databaseJor agricultural researchers and policy makers. Users' Manual. IITA, Ibadan, Nigeria. 38p. Rao, M.N., S.S. Jagtap, and A.G. Ibiyemi. 1998. Geographic inJonnations systems (GIS). Hands-on-manual. IITA, Ibadan, Nigeria. 42p.

182

00 W

"'"

路2500

876 C[llS

11.lJ CELL S

9968 C.[llS

I~OI-:.!OOO

2001-Z~OO

9 1 7 7 C[llS

1201 - I!lOO

~...,

1 7 7 2276

---- - - - - - - , - - -

Res o urce In formation System, Agroecologica l S tudi es . lilA. F AX:( INMAR S A l) 8 7 4

Annual Rainfa

IDJ

l1li

10907 CEl l S

1 134 2 Clll S

901- '120 0

~ &01-900

1, 1J81 CEL LS

t04'70 C拢 l lS

)01 - 600

00' a

( 10 0 nTn/No

100 - JOO

..-

-' 0

,---- - - -

880 I~-'

L::-:=1 Km c::=-_ 1 760

.... '" Er

>5t=

.....

1:1 0

"-1

"T1

0;;'

Jlt -

~~ ,

5 en :>

tlt:S

>00 ...,

\0 \0

;;;

..... II>

A, e .

C[l LS

526 0 CE LLS

l!iil] POd%OI U'I I,O I,

._x.roll\)

CE l lS

"I .l ell LS

Mr.rro, so ts 769 04

' 20. 2 C( LL S 1229~ CeLLS

~A r.n o.ol '

Re901o l ,

'0777 C[. LS

~ll l hoa o ll

l u ... i ao I.

.. a76 CElLS

Oth" .

~ Y . rmlsol '

"' 0

;::::>a

Resource Inforn"lation S ys t ern, Agroeco.ogi c ol S tu dies, IITA, FAX:(INMARSA1) 874

Ma jor F AO soi l types

I-

1 7 7 22 7 6

---.------

880

f"l

:r. '"

"l'1

....

~, 0

0 ...,

"'0

t-..l

"l'1

~' ..... (11

Kill

' 71'\ 0

E-------:J

_ ._ _ 1

~ E-----=J

----

-----

'-<

'" S

Ul Ul

'"5'

!:l 0

N 0

(JQ

0" r; '

(11

\C) \C)

(JQ

00 Vl

" 1' .... ..

Cec .f'I .. ,

' . I ••

\r'I~ ,

OI I.• ' I""O "~ S,. I .... .. S, . I""' . .....

,. " . . .. . ... S'I' .... .

Lt'

CHLI

to.

CH. l)

([US

70 (( LlS

tOOl CUll

'I':;'

Illl C(l l l

1 111 CeLl S

I I CU.LS

171 Cfllt

Clll'

ChlS

"'-

~ .,

~-"-\j

-~,.--../

\~-r

(

a se d S y stems

/" '

/'~

....

--. ~

177 2276

_/ r

r 880

L_E3

\;~.

/-\

/~-~---; I

1 760

3' n

El'

'"~ ~

'" '<

<1>

'0 0-

....n

0 ....,

r--j Kill

././/--'

<1> W

"T1 ~.

)

(

) ,(

'\.......,

"'\s'~'--' -- -', ~

Resource Inf orma ti on Sys tem, Agroecological Stud ies. IITA, FAX:(I NM ARSAT) 874

Map 1 2 : Crop

.c. .. .. .

_c. . . ...

., ........ S,. .t_ l.1li ''''' •• •. • S,I '''''' . .

, ... ,,, . . . ... S "t ...... ...

.C ... . ... . ""'. . .

., ~

10"

~ " "I .I'

•.• " S,.I_ .... _ . . Ct..".",... .... S"t.-n .

'21" Cl'llS

_ ~.' illu~ • • ••• s r'I~ .. . .

/' r-,1

~ W ••• • • • •• • S'.I .... .

CJ· .... ·

"/?

(

(''-----' - ,- ,---- ~.-'J~::'

)~"-

C/.l C/.l

C <1>

N 0

(JQ

(")

<1>

> (JQ

\0 \0 \0

......

'" OS

0'1

.....

high

Ier I

11I Y

fer I i i i t y

r9quirud

3 1 679

C (LL~

9033 CELL S

J5393 CELLS

32727 CELLS

. sr=t~'

1> ~

177 2276

~~. : . ~""i1!Il!9II ..

Resource Information S ys t em , Ag r o e cologicol S t udies, IITA, F AX:( INMA R SAT) 874

re lative soi l ferti li ty

~. mO derO le

imp

fer til i t Y

~moior

~ IOw

D n o dolo

1 76 0

o ~

'"~

S路

J'if(

(/) (/)

'"5路

I:l

o路

0 0 n

\C)

~_-_r-E--~~=1 I<m

880

.... ,.. Gel

j;;

0 ..., ..,

-0

...

'TJ

ciQ .

-.]

CELLS

12594 CElLS

678.l CELLS

640~

7507 ULL S

(

-,( ~, ...........

Resoc.r-ce Infonnatian S ystem . Agr-oecological Studies. IITA. FAX:(INMARSAT) 874 177 2276

Duratio-n-- ofgrowing season in months

9-12 lo4 onlh.

:>784 CELLS

nal CELLS

_d'

_6 _7 _8

6929 CELLS

Monlhs

~ I - .l

<To

'-.

- - ------

,-, -- J

:JJ

~--.

880

E-1

3' n

c:s

3 0

~ c:s

(l)

(JQ

0......,

::3

0 , 0

I>J

0......,

» S "0 '"

(/) (/)

(l)

0 ::l en

(=i ' ~

(JQ

n 0

(l)

(JQ

» ...0

'D 'D

""Kn. ,t"llI", ~

I I

(l)

'-0 (JQ

'Tl

dQ '

00 00

Marsh/Swamp

Ma ngroves

Grass lon d

Sa vanna

' orc.'

In undaled Area

No do lo

Tree !OOi!lll. l~lliI)

~ ~

. ~ ----- - - -

0 ....,

§.

t:l

(l)

(l) I (l)

'" S cr ..,

'"a.

("l

3':

::1.

O'Q

;, >

'"t:l

~tT1

g o· t:l

(l)

O'Q

< n

....,

Resou r c e I n f o r ma t io n Sys t em. Agro eco fogico t S lud ies. It TA. FA X:( INMARSA T) 874 1 77 2276

. , .-,....-

21 CE LL S

KlT)

. 760

()

'" I

S ." 0

1~ '

ryf.

»-

C/l C/l

'"S·

(l)

0 t:l

[

O'Q

()

(l)

\0 \0

q7~6

(l)

S "!='

...... O'Q

...,c:

..,., riQ '

0 CE LLS

('....,.,--...,-...)-

r. rl I S

35849 CELLS

12241 CELLS

1905 CEL LS

Vegetation ( UNEP / FAO .~ ~ .~

I ~~ ... \<."'

00 \0

(

\ -'.

. ""

~~94

':'"

eE l LS

0 "'''

--~J-

....

"<,l

_'f7

'--~

........

(

../'

C=E: I

880

--------

----- ,/

~J .

. ,""

L(" --- ~

'' \

'\. rJ

, P. \

r\,

Resource Inf o, motion Sys tem. Agroecologlcol S tudies, InA. FAX:(INMAR S AT) 874 1 / 7 2276

\!.,

-'f;~J\\

\_~~/

IU ~ y.J../\

(

.>--1-

)----~-

...

~......,.,--''''-

Banana Agr-ocllmatlc SUitability

~" .. , ,.,,"",.

, .".".

Sullob le

."""'"

NOI

i i i s u lIob I c

ifO/~

j,r--

/ - -'

.!'#'

1760

J I

I»

()

:r.

V> ~

I>l

§"

()

o;j

I» I»

~ ~

...... cr

"CI

8I»

--J

'Tl

'-< I»

fl\"

~:%

Ul Ul

N 0

0(JQ

> ~

\0 \0

::'

I»

...... o

Hat

Suitable

Resource Information Sys t em. Agl'oecological Studies. IITA, FAX:(INMARSAT) 874

Cassava Agr-oclirnatic

23884 CELLS

1i!!iIlhillVe ry Sui table

CELLS

14941

2569 CELLS

i.ii!jSu ; l obi.

~ Margina llY Suitable

177 2276

" -/

,/1

8 80

._-_ . - -

K 11\

1760

,-----------:J

II>

(')

,<'

S路

c::

('J

::t,

t\)

o;j

t\)

en en t\) <:

('J t\)

~ "0 0 ....,

(11

'Tl

00 ' ...,c::

V V

::1 u:

-'"

(JC

~ ~

(Jt

Jagtap. 1999. AgroecologicaJ zones in SSA

Figure 9. A map of cowpea agroclimatic suitability in Africa

I-~~- - ' ---

-~~~~-

~~~~~-

} .

to i"N

O~\

---------

i"i"-

'<I

i"IX)

,....., I-

'--"

<{ t....

I-

::J

.; Q)

VI 0 .~

-' -'

...,

'"..........

.....

....

'"'" "'"

.D 0

0 ~

:::0

0 Z

(/\

,.,

" >"

01 1

l!!!

2v:

(J)

0..

3 0

191

E« 0

0 0

E 0

~ Q)

....u

::J 0 III Q)

a::

Jagtap. 1999. Agroecological zones in SSA

Figure 10. A map of maize agrocJimatic suitability in Africa

- - - ---- - - - - -- o

OJ OJ

I I

to l' N N

l' l' '¢

J-

oel:

(f)

oel: ~

\ R

Z ~

\~-;'

X oel:

u.. /

/ ~ ..-

J-

-+-

..0 .2(f)

i ~:=~~>~J \

-+VI

..J ...J W

..... W u

""0

....

"-1\~.

CIO

:I.

VI ..J ...J W

U Q) L

..,....

.., -

.--

CJ)

-" 0

0 Z

" - . .--

:l VI

1«

Di l l 192

oel:

I/)

() ~

~ 0

0 .~

<Xl

.rl 0

vi .(1) \l

(j)

.N-

(f)

.~ 0

E ... 2 f Q)

u ... :J

a::

I/)

III

\C)

NO I

SU l l a bl e

S uitable

9 9 55 CEL L S

1 3805 CEL L S

1 585 1 Cf LLS

)

~~,--

=--

;>- -'( "'---"

R e s o u r ce In for m a t io n Sys t e m . AgroBco l ogical S tu die,;. IITA. FA X:( INMAR S A T) 8 7 4

Soybea n Ag ro cTi m a tic

mlli~~v ery

Suil nb l .

~ Mar9 1 nOIIY Sui t a ble

'-"'-,-"

}

\.-r - ' I,

\ "::,~-.,...

1 77 2276

--;

t==l

880

I»

()

:r.

;:;.

[

S·

()

I»

,., '"t:S

'< cr'

'"0

....,

l~" o

5' en en

;:J

e. N 0 ,.,

(",

()

,.,0

()Q

;J> "CI

'"0

::' 'D 'D 'D

1 760

'-<

'" S

()Q

;J>

'TJ

Na l

Sui labia

2368 6 CE LLS

!t~I!I~IVe ry

17 7

\'V

R e so urce In f o r ma t ion Sys t em, Ag r oecolog i c ol Stud i es. IITA, F AX : (IN MAR S A T) 87 4

Y arn Agroclimatic Suitability-

Su I lo b I .

~S u i l obi.

2550 CEL LS 1 3 0 55 c r L LS

~ Mor9 lno l l Y Sull o b l .

""0

c ./

~/~

2276

-'"

------

~.,/

C-

;

-- ~ /-~- ~f

CIl

I 760 i~ 88~~ K m Th f----------=j

I»

()

> S

[

iJ'

a, ()

I»

§'

(JO

...,

I»

......

"tj

3I»

;J>

('11

CJ CJ

C t::

(= ~

C (](

c(1

~ ~

:--

. 1]1

ciQ '

Annex 2. List of Abbreviations and Acronyms

AD.5 2

a. ~~ 2 a g. 7t.

<Pc

AAA AAAB

AAB ABB AEZ Al MIMI

AMYT ANOVA

ASA ASAE ATER AVT

B.C. BCP

BITA BSR BSRTIf

BSRTH BT C C4 CAB CER CGIAR

Grand mean 2 Joint regression constant estimated by L'~8al I L ga g I in AMMI models when all axes are nor used Convenient scaling for multiplicative parameters Variances due to error Variances due to genotypes Variances due to genotype x environment interactions Environmental mean Regression of each environment's interactions on the genotype deviations, i.e ., LII(8 Bca ll )l I:,a2g in AMMI models when all axes are nor used Environmental deviations Environment eigen vector Genotype slope Genotype deviations, i.e. mean minus grand means Interactions Residual (not explained by regression model) or the residual in the PCA Error term or random variation Genotype eigen vector for axis n Singular value for PCA axis n (which has unit of yield in AMMI) Phenotypic standard deviation of the trait under selection in alternative environment X Desert bananas Tetraploid plantains hybrids Plantains Cooking bananas Agroecological Zone Aluminum Additive multiplicative model interactions Advanced Musa Yield Trial Analysis of Variance American Society of Agronomy American Society of Agricultural Engineers Area time equivalency ratio Advanced Variety Trial Before Christ Biolgical Control program Black sigatoka resistant tetraploid hybrids of bananas Black sigatoka resistance Black sigatoka resistant tetraploid hybrids Black sigatoka, as measured by the youngest leaf spotted, of resistant tetraploid hybrids Bobby Tannap Clone Calcutta 4 Commonwealth Agricultural Bureau Carbon Exchange Rate Consultative Group for International Agricultural Research

195

CGM CGR ClAT

cm CIT ClRAD CM CNp COOMBS COSCA

CRy CRD CS CS

CTA CTCRJ CV CV DAH DMC DNA

DS DSS DSSAT ELER ESA FAO FHlA FHIA-3 FR GxE GxLxY

G GIS GTZ GUMCAS 2 h

HF HI H. andH y lARCs

rAT IBPGR

IBSNAT ICRISAT JDRC IER lET

Cassava green mite Crop Growth Rate Centro lntemacional Agricultura tropical Crop Improvement Division Cowpea International Trial Centre de cooperation intemationale en recherche agronomique pour Ie developpement CentiMorgans Cyanogenic Potential ? network Collaborative Study of Cassava in Africa Correlated response to selection Completely Randomized Design Coastal savanna Cropping system Technical Centre for Agricultural and Rural Cooperation (ACP-EU) Central Tuber Crops Research Institute Coefficient of variation Coefficient of variation Days after harvest Dry matter content Deoxy ribonucleic acid Derived savanna Decision support systems Decision Support Systems and Agrotechnology Transfer Effective land equivalent ratio East and Southern Africa Food and Agriculture Organization Fundacion Hondurena de Investigacion Agricola (Honduras) Hybrid derived from a cooking banana and many diploid bananas French Reversion Genotype by environment interaction Second order interactions (e.g . genotype x location x year) Genotype Geographic Information Systems Gesellschaft flir Technishe Zusamrnenarbeit (Germany) Cassava growth model broad sense heritability or the fraction of phenotypic variance caused by differences in heredity Humid forest zone Harvest Index Heritability of the trait under selection in each environment Intemational Agricultural Research Centers Initial Advanced Trial International Board for Plant Genetic Resources International Benchmark Sites Network for Agrotechnology Transfer Technology International Crops Research Institute for Semi Arid Tropics International Development Research Centre (Canada) Income equivalent ratio Initial Evaluation Trial

196

lITA ILS IMTP INIBAP IRRI ISNAR

lvs ix K KUL L LAl LER LGP LGP

LGS LOD MET MSTAT N

NARES NARO NARS NGS NrnORT OFT OL OST P P

PBIP

PCA PCR PET pH PHMD PITA PI PRINI and 2 PVT PYT QTL RAPD RCBD RCMD REML RFLD

rG RGR RIS SAS-GLM

International Institute of Tropical Agriculture Index of black sigatoka spotted leaves International Musa Testing Program International Network for Banana and Plantain International Rice Research Institute International Service for National Agricultural Research Inland valleys Intensity of selection in alternative environment X Potassium Katholic Universitat de Leuven Location Leaf Area Index Land equivalent ratio Length of Growing Period Length of growing period Length of growing season log-odds score or 10g l0 of the odds ratio Multilocational Evaluation Trial Michigen University Statistical Package Nitrogen National Agricultural Research and Extension Systems National Agricultural Research Organization National Agricultural Research Systems Northern Guinea savanna Nigerian Hoerticultural Institute On-fann testing Obino l'Ewai On-station testing Phosphorus Phenotype Banana and Plantain Improvement Program Principal Component Analysis Polymerase Chain Reaction Potential evapotranspiration Minus logarithm of Hydrogen ion concentration Plant Health Management Division Black sigatoka resistant tetraploid hybrids of plantain Pisang lilin Principal Component Coefficients Preliminary Variety Trial Preliminary Yield Trial Quantitative trait loci Random Amplified Polymorphic DNA Randomized Complete Block Design Resource and Crop Improvement Division Restricted maximum likelihood Restriction Fragment Length Polymorphism Genetic correlation between the two performances Relative Growth Rate Resource lnformation System Statistical Analysis System - General linear Models

197

SAS-STABLE Statistical Analysis System - Stability analysis SCAR Sequence Characterized Amplified Regions SGS Southern Guinea savanna SHMM Shifted multiplicative model SS Sudan savanna SSA Sub-Saharan Africa SSR Simple Sequence Repeats SSRLP Simple Sequence Repeat Length Polymorphism p Stability STR Short Tandem Repeats

Trial

TAC

Technical Advisory Committee Nigerian landrace of cassava (Tropical Manihot series) United Kingdom United Nations Environment Program United States of America Line Variable Number Tandem Repeats West Africa Year Yield of genotype g in replication r of environment e Youngest leaf spotted .

TMEI UK UKEP US, USA

V VNTR WCA Y

Y"er

YLS

198