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ACHARYULU et al. / (IJAEBM) INTERNATIONAL JOURNAL OF ADVANCED ECONOMICS AND BUSINESS MANAGEMENT Vol No. 1, Issue No. 1, 001 - 005

Factors Contributing to Perishability in Traditional Fresh Produce Distribution System: A Study on Tomato and Banana Chains in Andhra Pradesh, India

Abstractâ&#x20AC;&#x201D; The present study is aimed to identify the various factors that contribute to the perishability of fresh produce which are caused during logistic operations in fresh produce distribution. These activities are mostly human and facility oriented and gives a scope for damage to fresh produce. The activities taken for the study are purely concern with the logistic and supply chain related. The study examined the banana and tomato chains which are more susceptible to the spoilage in distribution operations The study reveals most contributing factors to perishability using regression model so that the spoilage of fresh produce can be minimized using appropriate mechanism and hence the overall supply chain is strengthened. Keywords- Perishability, Supply chain, Fresh Produce,

Regression I.

INTRODUCTION

India is continued to be the worldâ&#x20AC;&#x2DC;s second largest producer of fresh produce in last five year. It produced 68.4 million tonnes of fruits and 129 million tonnes of vegetables in the year 2008-09 [1] National horticulture mission statistics forecasts annual production growth is at 8.8 percent for fruits and 10.9 percent for vegetables by 2015. A total consumption of 90 million tonnes of fresh produce is supplied through 7300 wholesale assembly markets and 27294 rural weekly markets[2]. The consumption of fresh produce is expected to reach 140 million tonnes by 2015 [3]. The research revealed that the fresh produce accounts for 50 per cent of the food and grocery bill [4]. It indicates there is an enormous growth opportunity in fresh produce sector. India can become market leader in world horticultural produce through the vertical and horizontal integration of different components of the supply chain.

The major issue in the supply chain is the inefficient postharvest management. Cultivable waste of about 24 to 40 per cent is reported due to the inefficiency and the extent of losses of fruits and vegetables in India is estimated at about Rs.

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SUDHAKAR MADHAVEDI Doctoral Research Scholar School of Management Studies, University of Hyderabad, Hyderabad, India reachfirst@gmail.com

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ACHARYULU G.V.R.K Assistant Professor School of Management Studies, University of Hyderabad, Hyderabad, India acharyu_gvrk@yahoo.com

10,000 crore to 12,000 crore per annum [5]. Approximately 20 per cent of India's fruit and vegetable produce goes waste on account of the lack of cold chains. India has around 1,300 cold storage facilities, of which 50 per cent are being used for potatoes and the rest remain underutilized (CII, 2007). II.

SPOILAGE OF FRESH PRODUCE

The fruits, vegetables and root crops are still alive after the harvesting process. They contain 65 to 95 percent water, depending on the type of produce. They also contain food materials which enable living processes to continue. As soon as produce is harvested the processes leading to breakdown begin, and cannot then be stopped. The rate at which breakdown occurs can, however, be slowed up and losses minimized by employing the correct handling methods after harvest. Most fresh produce is highly perishable and if it is to reach the consumer in the right condition it must be marketed properly bearing in mind the most suitable temperature and humidity for each commodity as well as appropriate packaging and handling methods. Failure to address these issues leads to stress to the produce rapidly followed by spoilage and losses. The enormous losses of fruits and vegetables produced in the country are mainly because of the lack of proper infrastructure for storage and transportation under controlled conditions. Effect of Injuries: Injuries take many forms, including cuts, punctures, scraping of outer surfaces, internal and surface bruising, sunburn, heat damage and cold damage. Their effect on harvested produce is to speed up the rate at which water is lost by as much as five times, to provide sites for attack by decay agents such as moulds and bacteria, to increase the rate of heat production at injury sites, to cause dis-colouration due to internal damage and to cause off-flavours to develop. Effect of Pests and Disease: Increase in post-harvest decay occurs where produce is washed before packing. Most moulds and bacteria causing decay require free water to establish infection, particularly where injuries, even though small, are present on washed produce and the washing water is stagnant

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ACHARYULU et al. / (IJAEBM) INTERNATIONAL JOURNAL OF ADVANCED ECONOMICS AND BUSINESS MANAGEMENT Vol No. 1, Issue No. 1, 001 - 005

or recycled. It may also be a problem where condensation occurs on the surface of produce when it is moved from cold stores to high ambient temperatures, or when produce is exposed to rain after harvest. Produce saturated with water, from rain or other causes may become 'soft' and more easily damaged than when dry. This damage not only provides opportunity for infection by decay agents but may in itself leave unsightly surface damage, leading to down-grading and lower prices. This is often seen in citrus fruits, where fruit harvested when wet develop the skin blemish. It may not always be possible to keep produce dry but field-men should avoid harvesting freshly wet produce. III.

RATIONALE OF THE STUDY

IV.

METHODOLOGY

This study is causal in nature and relies on survey method. Andhra Pradesh is chosen for this purpose based on the contribution its share in terms of production and growing area to the national agriculture sector in last ten years and it is also one among the top five produces of banana and tomato states in the country. The study examined the banana and tomato chains which are more susceptible to the spoilage in distribution operations. The study is conducted in three districts for each produce namely, Krishna, Chittoor and Ranga Reddy for tomato and East Godavari, West Godavari and Chittoor for banana. These districts are selected on the basis of Area and production of tomato and banana during 1999- 2009. A total sample size of 305 farmers, 62 traders and 120 retailers are drawn from the population. Different questionnaires are designed for data collection from the target respondents such as farmers, traders and retailers to obtain views and level of agreement on the factors pertaining to fresh produce spoilage during the supply chain operations on Likert‘s five-point scale. The data is collected from the AMCs that are located in the selected districts where the respondents participate in the fresh produce distribution activity. The table 4 provide a full of information about the model. In which we find the slope (or slopes, in multiple regressions). The values of Constant and Predictors are listed as

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DESCRIPTION OF VARIABLES

Perishability (y): Perishable products are those that worsen in quality over time, and become lesser in value. Perish able goods decay rapidly, if preservation and pre-cautionary technique is not employed. Common perishable goods include foods, plants and agricultural products. Fruits and vegetables are examples of time and temperature-sensitive perishable products that can rot or spoil easily. Perishable products must be handled and transported by highly efficient distribution channels that can retain the integrity of the produce.

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The present study is aimed to identify the various factors that contribute to the perishability of fresh produce which are caused during logistic operations in fresh produce distribution. These activities are mostly human and material oriented and gives a scope for damage to fresh produce. However many other factors contributes to the perishability of fresh produce, the existing activities taken for the study are purely concern with the logistic and supply chain related. The study reveals most contributing factors to perishability using regression model so that the spoilage of fresh produce can be minimized using appropriate mechanism and hence the overall supply chain is strengthened.

‗unstandardized‘ values, and their standard errors (Std. Error) and SE (Beta) are in the second column. The standardized coefficient for the predictor in a multiple regression is simple the correlation. Since neither of the predictor variables has a variance inflation factor (VIF) greater than 10 (All VIFs are less than 2), there are no apparent multi-collinearity problems; in other words, there is no variable in the model that is measuring the same relationship/quantity as is measured by another variable or group of variables. From the table 4, the multiple-regression model is depicted as:

Perishability (Y) = f{X1,X2,X3…X9}

Independent variables (x1 to x9): Perishability of the fresh produce will influence with the operational variables such as care during transportation, use of plastic crates, removal of foreign bodies, condition of road, safety packaging, cleaning and washing, and foreign bodies. 1. X1- Damaged during the harvest by the farm men (PR_farm_men). 2. X2 - Road conditions causes the physical damage to the fresh produce (PR_physical_damage), 3. X3 - Lack of effective packaging (PR_lack_packaging), 4. X4- Non saperation of living organism like Bacteria, Fungus, insects etc. (PR_living_organism), 5. X5- Non-removal of foreign bodies causes perishability (PR_foreign_bodies), 6. X6 - Lack of plastic crates during the transportation (PR_plastic_crates), 7. X7- Lack of cleaning and Washing (PR_washing), 8. X8 - Lack of enough care while the loading and loading (PR_care_load_unload), 9. X9 - Lack of effective care during road transport (PR_packing_perishability), Prior to the carrying out of regression model, the Pearson‘s correlation statistic has been run to determine the variability among the independent variables. The following correlation matrix table (Table 1) has been generated. The Pearson correlation co-efficient indicates that there is no incidence of strong correlation exists positively with the coefficient value ranging from 0.6 to 1.0 among independent variables. It indicates that there is no multi-collinearity among the variables. Consequently, a multivariate statistical technique, multiple regression method is applied to analyze

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The model summary table indicates what predictors are relevant for the R and R2. The word ―co nstant‖ in parentheses refers to the intercept. The coefficient of multiple determinations is 0.883; therefore, about 78 percent of the variation in the perishability is explained by its quadratic relationship with the predictors which cause perishability. The regression equation appears to be very useful for making predictions since the value of R2 is close to 1. From the table 2, Durbin–Watson value of 1.841 indicates there is no autocorrelation among the independent variables.

Mode l

.773a

with the data that need to compute R2. If we compute SSregression divided by SS-Total, we should get R2. SS (Sum of the squares)-regression / SS (Sum of the squares)Total = 88.745/ 113.751 = .780 Table 3: ANOVAb Sum of Mean Model Squares df Square F Sig. 1 Regression 6.337 9 .704 8.595 .000a Residual 4.260 52 .082 Total 10.597 61 a. Predictors: (Constant), PR_packing_perishability, PR_lack_packaging, PR_farm_men, PR_plastic_crates, PR_care_load_unload, PR_physical_damage, PR_living_organism, PR_foreign_bodies, PR_washing b. Dependent Variable: Perishability

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the data. The enter method is adopted for the running of Regression model. From multiple regression analysis it is observed that Eigen values in the multi-colinearity diagnostic matrix found to be more than 1.0 for all the variables hence no independent variable is dropped from the multiple regression model.

Table 2: Model Summeryb Std. Error R Adjusted of the Square R Square Estimate .659 .635 .286

DurbinWatson

The table 4 provide a full of information about the model. In which we find the slope (or slopes, in multiple regressions). The values of Constant and Predictors are listed as ‗unstandardized‘ values, and their standard errors (Std. Error) and SE (Beta) are in the second column. The standardized coefficient for the predictor in a multiple regression is simple the correlation. Since neither of the predictor variables has a variance inflation factor (VIF) greater than 10 (All VIFs are less than 2), there are no apparent multi-collinearity problems; in other words, there is no variable in the model that is measuring the same relationship/quantity as is measured by another variable or group of variables. From the table 4, the multiple-regression model is depicted as: y = .473 + 0.136 x1 + 0.054 x2 + 0.100 x3 + 0.122 x4 + 0.055 x5 + 0.141 x6 + 0.118 x7 + 0.046 x8+ 0.073 x9

2.058

a. Predictors: (Constant), PR_packing_perishability, PR_lack_packaging, PR_farm_men, PR_plastic_crates, PR_care_load_unload, PR_physical_damage, PR_living_organism, PR_foreign_bodies, PR_washing b. Dependent Variable: Perishability

The table 3 below shows the ―A NOVA table‖ for the regression. ANOVA stands for Analysis Of Variance specifically the analysis of variation in the Y scores. At this point we observe the two sums of squares introduced in class the regression and residual (or error) sums of squares. The variance of the residuals (or errors) is the value of the mean square error —here it is .085. Also in this table we find the F test. This tests the hypothesis that the predictor (here our only predictor) shows no relationship to Y. The F test has two numbers for its degrees of freedom. These are called the numerator and denominator degrees of freedom of df1 and df2. Here the numerator df (df1) tells us how many predictors we have (it is 9) and the denominator degrees of freedom are n 1- df1 or n-2 for bivariate regression. The table provides us

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It is clear that Road conditions causes the physical damage to the fresh produce (PR_physical_damage), Non-removal of foreign bodies causes perishability (PR_foreign_bodies), Lack of enough care is not taken while the loading and loading (PR_care_load_unload) and Lack of effective care during road transport (PR_packing_perishability) are showing insignificant effect on the perishability of the fresh produce as

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ACHARYULU et al. / (IJAEBM) INTERNATIONAL JOURNAL OF ADVANCED ECONOMICS AND BUSINESS MANAGEMENT Vol No. 1, Issue No. 1, 001 - 005

their probability value are higher than the acceptable limit of 0.05. It is concluded that from the table 4, which indicates the significant values of predictors that affect the dependent variable. It is found that Damaged during the harvest by the farm men (PR_farm_men), Lack of effective packaging (PR_lack_packaging), Non saperation of living organism like Bacteria, Fungus, insects etc. (PR_living_organism), Lack of plastic crates during the transportation (PR_plastic_crates), and Lack of cleaning and Washing (PR_washing) variables falls under the impacting as their probability values .001, .008, .001, .000 and .010 respectively OTHER OBSERVATIONS & RECOMMENDATIONS

Handling and Storage during Transportation: Dropping of packages during loading and unloading is a frequent cause of damage to the produce and to the package, but can be minimized by:  

  

Using pack weights and designs which are compatible with the handling method. Correct supervision and management of loading and unloading to prevent careless handling and to ensure workers are strong enough and tall enough for the Job. Using ramped loading bays gives a tremendous advantage when loading trucks with produce. Providing shelter from sun and rain at loading and unloading areas. Using trolleys, conveyors and fork-lift trucks to reduce the amount of manual handling.

Care in harvesting, especially with tree fruit, which are severely damaged if they fall or are thrown to the ground. Being careful not to harvest wet produce, especially citrus fruit - because it is more easily damaged in this condition. The selection of suitable field and marketing containers, which should not be too large for careful handling. They should be strong enough to protect produce but should not cause damage to produce due to sharp edges, poor manufacture or assembly. The avoidance of overpacking or underpacking containers, which should be filled to an extent that will exert a slight pressure on the contents when

IJ b. c.

Avoid Post-Harvest Losses at Traders/Wholesalers: a.

Wholesalers handle produce which may or may not have been freshly harvested and may be packaged in various types of container. Depending on how well the farmer and transporter have done their job, the wholesaler must decide on the most appropriate sale and distribution system so that the produce can reach the consumer in the best possible condition.

Above all, the wholesaler must strive to keep postharvest losses at a minimum or he will have to raise the selling price to counter the losses or bear the financial loss directly himself. Wholesalers in the Andhra Pradesh rarely have specialized storage facilities other than an ordinary warehouse and it is recommended that they concentrate their activities on selling and moving the produce as quickly and carefully as possible in order to avoid losses from spoilage of all kinds.

The general principles of keeping produce cool by stacking it in well shaded and ventilated locations and avoiding exposure to sun and rain at all times are the most important things to remember. Whenever the produce is handled it should be done so carefully and cleanly so as not to inflict cuts and bruises. If the produce is purchased from the farmer in bulk, or 'off the tree', then suitable packaging should be invested in so that multiple handling steps can be avoided and the produce protected from compression and other injuries.

The best way to avoid post-harvest losses is to only buy good quality produce in the first place and to regularly inspect the produce for spoilage if sale is delayed. Re-grading and repacking of produce may be permissible but this will depend on the cost of the process and the value added as a result. Wholesalers are in the business to make money, not as charitable suppliers of fresh produce. All staff employed by the wholesaler should be trained in the basic causes of post-harvest losses and recieve demonstration type

Avoiding Injuries: The fragile nature of most fruit and vegetables products makes them very susceptible to injury, and the complete avoidance of such injury is not possible. However, damage may be reduced to a minimum by giving attention to: a.

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VI.

closed. This will prevent movement of produce within the container. The careful handling of produce at all stages, especially when in containers, which must not be rolled, dropped or thrown. Transport conditions. Loads should be stacked in a manner which will prevent either the movement of individual containers or the collapse of the stack during transport. Riders should not be permitted on top of the load, especially when it consists of produce in bulk or in sacks. Vehicles should have a canopy to protect the load from the direct heat of the sun, but it should not restrict ventilation.

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training on the importance of careful handling and stowing of fresh produce in preventing such losses. Avoid Post-Harvest Losses at Retailers‘ Level: a.

In the first place, the retailer should be aware of the importance of post-harvest losses and their effect on profits, as well as understanding the basic causes of post-harvest losses and what can and cannot be done to prevent them. The principal concern to the retailer will be the cost of any loss prevention programme and its effectiveness rather than any desire to prevent losses just for the sake of it.

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Reduction of post-harvest losses requires an investment in time, planning, management and possibly capital equipment. The level of investment will thus largely be governed by the nature of the retailer and the volumes of fresh produce sold VII. CONCLUSION

The proper integration of post-harvest technology into marketing supply-chain is critical. Post-harvest management not only means reducing waste but also maintaining the quality but also aim to address some of these issues by optimizing quality, safety and by reducing waste. Effective post-harvest management requires adequate and appropriate cooling and packing facilities, hygienic and speedy transportation, careful handling and adequate environment control. This includes aspects such as appropriate temperature, relative humidity, ventilation and sanitation. Difference in prices between the farmer and the retailer is highest in India when compared with other countries. REFERENCES

[2]

[3]

Indian Horticulture Database. National Horticultural Board. Ministry of Agriculture, Government of India, 2009 Acharya, S.S. Agriculture marketing and rural credit: Status, Issues and reform agenda, ‗Area, Production and Yield of Principal Crops in India‘, Directorate of Economics and Statistics, Ministry of Agriculture, 2005. Kumar, Praduman and Promod Kumar Demand, Supply and Trade Perspective of Vegetables and Fruits in India,‖ Indian Journal of Agricultural Marketing, Vol 17(3), pp: 121-130, 2003. Kumar, P. and Donato B. Antiporta. ― Expenditure and price elasticities of food and non-food consumption by income groups: region-wise analysis of India‖. Working Paper. FAO/RAPP. Bangkok, 2001. Surabhi Mittal. ― Can horticulture be a success story for India?‖ Indian Council for Research on International Economic Relations, 2007.

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