Abstract:
The gap between demand for and supply of malt barley grain in Ethiopia is mainly attributed to the low productivity of the malt-barley sub-sector. The establishment and expansion of agricultural cooperatives, which are meant to provide improved inputs and marketing options and thereby increase the productivity of smallholders, is being encouraged for over two and half decades. However, participation of farmers in these cooperatives and the impact of these organizations on farmers’ livelihood had not been well documented. Thus, this study was done with the purpose of assessing the impact of participation in agricultural cooperatives on technology adoption, production efficiency, food security, and income of rural households in Arsi zone, Ethiopia. Field surveys were conducted during May-June 2021 on a total of 385 sample farm households that were selected using a multi-stage sampling technique from seven kebeles in two districts of Arsi zone. The data were analyzed using descriptive statistics and econometric models including endogenous switching regression, propensity score matching, Tobit model, stochastic frontier model, and the bivariate probit model. The result from Tobit regression pointed out that age, livestock size, dependency ratio and distance from road negatively and significantly influenced intensity of technology package adoption, whereas cooperative membership, frequency of extension contacts and leadership position of the household head positively and significantly influenced it. The overall farm households’ average TE, AE, and EE scores obtained from stochastic frontier model were 87.1, 80.6 and 70.6%, respectively. The Tobit model results for factors of efficiency confirm that education, credit access, livestock size, distance from market, landholding, proximity to homestead, soil fertility status and distance from main road were significant factors affecting all efficiency estimates. Family size had a significant association with allocative and economic efficiencies, whereas sex of the household head, cooperative membership and off/non-farm income were the major determinants of technical efficiency. The separate food security status analysis of the sample households revealed that about 62 and 69% of them were food secure based on the threshold values of per capita calorie intake and food consumption score, respectively. xviii However, by combining the two indicators of food security status, we found that nearly 52% of the households were food secure in both calorie intake and FCS measures while 20.78 were food insecure in both indicators. About 11% of the sample households were food secure in calorie intake but deficient in FCS whereas around 17% of the households were food secure in FCS but were poor in energy consumption. This shows that using different measures of food security status is better than relying on results from single indicator. Results from the bivariate probit model indicate that landholding, extension visits, cooperative membership, and income from off/non-farm activities positively and significantly determine food security status while age, sex, dependency ratio, and distances from nearest market and all-weather road negatively and significantly influence food security. The results also show that only 41% of the sample households were members of agricultural cooperatives (ACs), and the major factors affecting farmers’ participation in ACs were family size, education, distance from market, credit access, land size, distance from cooperative office and number of member neighbors. The ATT results from ESR reveal that participation in ACs significantly improved farm income, total income, adoption intensity index, food consumption score, energy intake, and economic efficiency of participants approximately by 20, 18, 14, 8, 7 and 4%, respectively. Except for slight differences, the results of the PSM coincide with the ESR results. The overall results imply that the major improvements related to smallholder farmers’ participation in ACs, adoption intensity, production efficiency and food security status would require attention on the identified significant factors. Generally, there is no single and best (universal) strategy that can be recommended to solve farm households’ problems and factors limiting their participation, technology adoption, production efficiency as well as food security situation. Hence, the findings of this study unveil the need for implementing differential policies that separately target and address the specific determinants of farm households’ participation in ACs, technology adoption, production efficiency and food security status.