(PDF) Value Generation through Mobility and Transshipment along the Value Chains: The Farmer Producer Organisation (FPO) Reality

Journal of Community Mobilization and Sustainable Development Vol. 18(1), January-March 2023, 213-218 Value Generation through Mobility and Transshipment along the Value Chains: The Farmer Producer Organisation (FPO) Reality and Ecosystem Saumyesh Acharya1*, S.K. Acharya2, T.K. Mandal3 and B.K. Mohanty4 1 Ph.D. Research Scholar, Department of Agricultural Extension, Institute of Agriculture, Visva-Bharati University, Sriniketan731235, West Bengal 2 Professor, 3Associate Professor, Department of Agricultural Extension, BCKV, Mohanpur, Nadia-741252, West Bengal 4 Associate Professor, Department of Agricultural Extension and Communication, Faculty of Agricultural Sciences (IAS), S‘O’A Deemed to be University, Bhubaneswar-751030, Odisha ABSTRACT Value chain in enterprise ecology plays the pivotal role and it provides an arterial services to the enterprise ecosystem dynamics. Now almost 35-40 per cent of agricultural produce are damaged due to lack of value chain. If you want to enhance the marketability and consumer’s preferences, this is the high time to focus on the value chain management with extreme importance. The success of Farmer producer organisations (FPOs) depends on how best they can keep on doing value chain operations, else there won’t be any discernible success in placing the agricultural produce to the target market. The present study was carried out to elicit the facts and information in regard to value chain operation and marketability of certain selected FPO enterprises, which has been studied in state of Odisha. One hundred (100) respondents in total were selected from two FPOs, fifty (50) from each FPO of Ranpur block of Nayagarh district of Odisha to conduct the study following snowball sampling method. The data were collected through pilot survey and structured interview schedule. The statistical tools used for data analysis are; correlation coefficient, multiple regression analysis, stepwise regression analysis and path analysis. The correlation coefficients found that marketed surplus is showcasing significant relationship with expenditure for surplus movement. Regression results implied that 24 causal variables together have contributed 71.10 per cent of variance in the consequent variable. Four out of twenty-four independent variables were retained in the last step of step down regression analysis. The results of path analysis reveals that the variable marketed surplus have got highest indirect effect on expenditure for surplus movement (y). This empirical study has got tremendous policy implications for Odisha and anywhere in India as well. Keywords: Farmer producer organization (FPO), Institutional innovation, Marketed surplus, Training exposure, Transport cost, Value chain INTRODUCTION The ecology of value generation imbibes both structural and functional inputs, when one is a framework enterprise, the other is taking care of the entrepreneurial kinetics. Value generation and value addition are both ways to make enterprise acceptable, labile and tenable. Value addition helps faster movement and wider adaptation across the economical class and layers. Transport costs are vital to explain cropping decisions *Corresponding author email id:

in a deterministic setting (Omamo,1998). Higher transport costs drive up the size of the agricultural workforce and the fraction of subsistence (Gollin and Rogerson, 2014). Changes in relative transit costs are demonstrated to alter relative regional wage rates, therefore also affecting the location of “productioncost-oriented” enterprises (Kilkenny, 1995). Farmer producer organisations (FPOs) provide a desirable platform for farmers to make institutionalised 214 Saumyesh Acharya et al. collective decisions about their farm enterprises. It also sets the door for the acquisition of numerous company prospects on a local and large scale. FPOs are reinventing and modernising agriculture’s commercial dent through public-private partnerships, entrepreneurial concepts, marketing methods, branding, and socialisation in order to navigate numerous government conventions and formalities. The basic concept of farmer producer organisations is bulk buying of inputs used in farming like fertilizers, pesticides, seeds, etc. and then distributing it amongst the member farmers. FPOs try to bring small and medium farmers together to reduce the cost of their supply chain in order to increase the benefit from their produce (Chaudhary et al., 2023). Some constraints identified by FPO members and/or office bearers that play a significant part in creating impediments to FPO growth, development, and management based on their priorities are low produce prices, high transportation costs, the nature of products (perishability), and delayed payment (Chauhan et al., 2021). Farm households’ participation in FPOs is significantly influenced by the distance to the closest market, the extension contact, the transportation facility, and the intention to extend the scale of activity in the future (Gurung and Choubey, 2022). Few variables such as Age, no. of enterprise, year of enterprise, size of holding, size of homestead land, size of cultivable land, crop yield, livestock yield, income (on-farm and off-farm), family expenditure, marketable surplus and marketed surplus have a critical contribution to transportation cost (Roy and Acharya, 2021). MATERIALS AND METHODS The study was conducted in two farmer producer organizations (FPOs) from Ranpur block of Nayagarh district of Odisha. Hundred (100) respondents in total were selected from two FPOs, fifty (50) from each FPO to conduct the study following snowball sampling method. Appropriate operationalization and measurement of the variables have helped the researcher land upon the accurate conclusions. Therefore, the selected variables for this study had been operationalized and measured in the following manner: I) Independent variables II) Dependent variables. Independent variables selected for the study were age (x1), education (x2), no. of enterprise (x3), year of enterprise (x4), training exposure (x5), family size (x6), mean family education (x7), material possessed (x8), size of holding (x9), size of homestead land (x10), size of cultivated land (x11), size of land under irrigation (x12), no. of fragments (x 13), crop yield (x 14), livestock yield(x15), cropping intensity (x16), income (x17), family expenditure (x18), marketable surplus (x19), marketed surplus (x20), family labour (x21), no. of male workers (x22) no. of female workers (x23) and dependency ratio (x24). Dependent variable selected for the study was Expenditure for surplus movement (y). Appropriate statistical tools have been used to carry out the study viz, Correlation coefficient, Multiple regression analysis, Step wise regression analysis and Path analysis with the help of IBM SPSS v26.0. RESULTS AND DISCUSSION The subjective information is measured utilizing explicit numerical methodology. Then data analysis i.e. Coefficient of correlation, multiple regression analysis, stepwise regression analysis and path analysis has been done to evaluate the information. Table 1 presents the coefficient of correlation between expenditure for surplus movement (y) and 24 dependent variables. It has been found that the following variables viz. education(x 2), number of enterprise (x3), year of enterprise (x4), training exposure (x5), family size (x6), materials possessed (x8) and family labour (x21) of FPO members are having negative but significant correlation with the dependent variable. The variables age (x 1), cropping intensity (x 16), family expenditure (x18), marketable surplus (x19) and marketed surplus (x 20 ) have recorded positive significant correlation with the dependent variable. The correlation coefficients revealed that the respondents those who are younger, they are pertaining to higher education category. So, both young and educated respondents have been able to rationalize the cost after transportation. It may so happen that the young age respondents are closely associated with transportation system like small motor vehicles which have become natural for them to access. they are capable of rationalizing cost reduction. incur less expenditure for surplus movement. Also, it has been found that the exogenous variable year of enterprises (x4) is negatively Value Generation through Mobility and Transshipment along the Value Chains 215 Table 1: Coefficient of Correlation (r): Expenditure for surplus movement (y) Vs. 24 Independent Variables (x1x 24) movement by the FPO members and expenditure associated with them respectively. Independent variables Table 2 presents the full model of regression analysis between exogenous variable expenditure for surplus movement (y) vs. 24 causal variables. It is found that 24 causal variables together have contributed 71.10 percent of variance in the consequent variable expenditure for surplus movement (y). It has been found that the marketed surplus (x20) has exerted the highest direct effect on expenditure for surplus movement. It is discernible that higher the farm produce more will be their cost of transportation. ‘r’ value Remarks Age (x1) 0.350 ** Education (x2) -0.395 ** Number of enterprise (x3) -0.402 ** Year of enterprise (x4) -0.397 ** Training exposure (x5) -0.482 ** Family size (x6) -0.333 ** Mean family education (x7) 0.070 Materials possessed (x8) -0.207 Size of holding (x9) 0.044 Size of homestead land (x10) -0.115 Size of cultivated land (x11) 0.035 Size of land under irrigation (x12) 0.033 Number of fragments (x13) -0.121 Crop yield (x14) -0.145 Livestock yield (x15) -0.009 Cropping intensity (x16) 0.436 Income (x17) -0.137 Family expenditure (x18) 0.202 * Marketable surplus (x19) 0.449 ** Marketed surplus (x20) 0.760 ** Family labour (x21) -0.207 * No of male workers (x22) -0.137 No of female workers (x23) -0.056 Dependency ratio (x24) -0.099 * ** **Correlation is significant at the 0.01 level ; *Correlation is significant at the 0.05 level significant with the dependent variable. It implies that far mers who have good many years spent on entrepreneurship, they are also good negotiator. They may spend less in transit cost of their farm produce. It has also been found that the variable cropping intensity (x16) have also shown significant association with expenditure for surplus movement (y). The exogenous variables Marketable surplus (x19) and marketed surplus (x20) have been intrigued with the consequent variable. It implies that higher the surplus generated from the farm, then higher will be the cost incurred by the farmers for transporting the farm produce. These significant variables are found to be correlated with access and utilization of various sources of surplus Table 3 presents step-down regression analysis. In stepwise regression analysis, it is discernible that the variables marketable surplus (x19), marketed surplus (x20), cropping intensity (x16) and training exposure (x5) have been retained at the last step. It implies that these 4 variables have significant functional relationship with expenditure for movement of surplus generated by the farmers. In order to improvise the economies of scale of the FPO members, the prime concerns could be to improve cropping intensity. Focus on need-based training for farmer members on proper package of techniques as well as post-harvest handling will result in significant reduction of farm produce waste. The r2 value being 68%, these 4 variables have together contributed to 95.63 % of 71.10 % total variance of explicated variables to vindicate their distinctive contribution in characterising expenditure for surplus movement. Table 4 revealed that the variable marketed surplus (x20) has enrooted the highest indirect effect of as much as 13 exogenous variables to impact on the consequent variable. It has got cause and effect relationship. When a farmer produces higher bulk of marketed surplus, significant amount of cost is incurred for surplus movement. Training exposure (x 5) has exerted the highest total effect. It reveals that proper training exposure to farmers has got tremendous impact in accessing different sources for surplus movement. The residual effect been 0.290, it is to conclude that even with the combination of 24 exogenous variables, 29 per cent variance in dependent variable could not be explained. This suggests the inclusion of more numbers of relevant and consistent variables for this framework of study. 216 Saumyesh Acharya et al. Table 2: Multiple Regression Analysis: Expenditure for surplus movement (y) vs. 24 Causal Variables (x1-x24) Variables Reg. Coef. B S.E. B Beta t Value Age (x1) -0.040 0.124 -0.040 -0.322 Education (x2) 0.024 0.126 0.024 0.189 Number of enterprise (x3) -0.051 0.134 -0.051 -0.385 Year of enterprise (x4) -0.117 0.094 -0.117 -1.240 Training exposure (x5) -0.206 0.129 -0.206 -1.596 Family size (x6) -0.011 0.106 -0.011 -0.100 Mean family education (x7) -0.069 0.087 -0.069 -0.793 Materials possessed (x8) -0.035 0.099 -0.035 -0.353 Size of holding (x9) 0.228 0.546 0.228 0.418 Size of homestead land (x10) -0.047 0.070 -0.047 -0.669 Size of cultivated land (x11) -0.116 0.566 -0.116 -0.204 Size of land under irrigation (x12) -0.013 0.114 -0.013 -0.114 Number of fragments (x13) -0.022 0.097 -0.022 -0.231 Crop yield (x14) 0.005 0.087 0.005 0.062 Livestock yield (x15) -0.023 0.071 -0.023 -0.325 Cropping intensity (x16) 0.164 0.079 0.164 2.080 Income (x17) 0.034 0.077 0.034 0.448 Family expenditure (x18) 0.044 0.078 0.044 0.565 Marketable surplus (x19) 0.156 0.085 0.156 1.837 Marketed surplus (x20) 0.509 0.094 0.509 5.392 Family labour (x21) -0.049 0.091 -0.049 -0.539 No of male workers (x22) -0.033 0.097 -0.033 -0.343 No of female workers (x23) -0.057 0.085 -0.057 -0.672 Dependency ratio (x24) 0.017 0.072 0.017 0.240 R square: 71.10%; The standard error of the estimate: 0.618 Table 3: Stepwise Regression Analysis: Expenditure for surplus movement (y) Vs. 24 Causal Variables (x1-x24) Variables Reg. Coef. B S.E. B Beta t value Marketed surplus (x20) 0.574 0.068 0.574 8.480 Training exposure (x5) -0.211 0.063 -0.211 -3.360 Marketable surplus (x19) 0.169 0.063 0.169 2.690 Cropping intensity (x16) 0.153 0.063 0.153 2.431 R square: 68.00% ; The standard error of the estimate: 0.577 CONCLUSION Not more than three per cent of green produces of agri-horticultural production is either value added or processed. This evokes a huge wastage and close to Rs 30,0000 crores of argil produces are wasted due to lack of value addition, supply chain and transshipment to market destinations in India and abroad. The emergence of FPO ecosystems across India incubates new and robust enterprise opportunity and generation of millions of livelihoods. Value addition is the integral part of entrepreneurial growth and dynamics. Value addition starts from innovation and lands on the valley of customers’ satisfaction. In between there has been a journey through production, processing, branding supply chain, customer behaviour and commodity acculturation. While farmers are changing their Value Generation through Mobility and Transshipment along the Value Chains 217 Table 4: Path Analysis: Decomposition of Total Effect into Direct, Indirect and Residual Effect: Expenditure for surplus movement (y) Vs. 24 exogenous variables (x1-x24) Variables Total effect Direct effect Indirect effect Highest indirect effect Age (x1) 0.350 -0.040 0.390 0.154 (x20) Education (x2) -0.395 0.023 -0.418 -0.146 (x20) Number of enterprise (x3) -0.402 -0.052 -0.350 -0.16 (x20) Year of enterprise (x4) -0.397 -0.116 -0.281 -0.146 (x20) Training exposure (x5) -0.482 -0.205 -0.277 -0.170 (x20) Family size (x6) -0.333 -0.010 -0.323 -0.139 (x20) Mean family education (x7) 0.070 -0.069 0.139 0.095 (x5) Materials possessed (x8) -0.207 -0.035 -0.172 -0.089 (x20) Size of holding (x9) 0.044 0.234 -0.190 -0.122 (x11) Size of homestead land (x10) -0.115 -0.047 -0.068 -0.052 (x5) Size of cultivated land (x11) 0.035 -0.122 0.157 0.233 (x9) Size of land under irrigation (x12) 0.033 -0.014 0.047 0.181 (x9) Number of fragments (x13) -0.121 -0.022 -0.099 0.104 (x9) Crop yield (x14) -0.145 0.006 -0.151 -0.054 (x20) Livestock yield (x15) -0.009 -0.023 0.014 0.042 (x9) Cropping intensity (x16) 0.436 0.164 0.272 0.189 (x20) Income (x17) -0.137 0.035 -0.172 -0.068 (x20) Family expenditure (x18) 0.202 0.044 0.158 0.176 (x20) Marketable surplus (x19) 0.449 0.156 0.293 0.18 (x20) Marketed surplus (x20) 0.760 0.509 0.251 0.069 (x5) Family labour (x21) -0.207 -0.050 -0.157 -0.085 (x5) No of male workers (x22) -0.137 -0.034 -0.103 0.061 (x9) No of female workers (x23) -0.056 -0.057 0.001 0.084 (x9) Dependency ratio (x24) -0.099 0.018 -0.117 -0.042 (x20) Residual effect: 0.290; Highest Indirect Individual effect: x20 (13) approaches to agriculture and experiencing a radical change in agricultural pursuits, a change from subsistence farming to enterprising farming, they need more relevant appropriate market driven information to make the venture a successful one. The present study came up with a strong revelation in eliciting the fact that education, size of cultivated land, marketable surplus, marketed surplus, cropping intensity and training exposure are of immense application to make the FPOs a performing business organization to serve the yomen needs of the participating farmers and beyond. Success of FPOs would be more certain and thorough if transit costs were decreased. In order to identify the distinct functional factors influencing the functionality and success of FPOs, these types of studies must be replicated for each of the FPOs. This opens up opportunities for micro-sociological policy delineation which would be of massive utility for FPOs functioning to cover a state like Odisha at large. REFERENCES Achar ya, S.K. and S. Roy. 2021. Entrepreneurial communication in agriculture : The Probing and Perception. Astral International Pvt. Ltd., 2021. Chaudhary, S.; S. Srivastava and Neema. 2023. 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