According to FCA, the number of primary agricultural cooperatives increased from in to 15, in FCA This study was undertaken in the eastern part of Ethiopia particularly in East Hararge zone of Oromia region. The zone is classified into three major climatic categories. These are temperate tropical high lands, semi-temperate, and semi-arid, and they cover Central Statistic Agency CSA indicates the population of the zone to reach 3,, in from which 2,, are residents of rural areas.
There are 19 districts in this zone, and three districts, namely Babile, Fedis, and Gursum, are selected for this study. Babile district has a total area of CSA predicted the population of Babile district to reach , in whereas the population of Fedis and Gursum is expected to reach , and ,, respectively.
Agriculture is the source of income in the study area. The main cereal crops produced in those districts are sorghum, maize, and oat. Pulses and oilseeds such as horse bean, field peas, lentils, groundnut, and linseeds are also produced as cash crops. Besides, chat Footnote 5 and coffee are the two permanent cash crops in the district Fig.
If a population from which a sample is to be drawn constitute a heterogeneous group for our case, members and nonmembers of agricultural cooperatives , stratified sampling is applied. The main advantages of stratified sampling are i more reliable information can be obtained from the same sample size if the population is stratified than from the population as a whole and ii comparisons between the two groups are easy as a separate but similar survey is done in each group.
Hence, a multi-stage stratifying sampling technique was used to draw an appropriate sample for this study. In the first stage, three districts, namely Babile, Fedis, and Gursum, were selected. In the second stage, 15 rural kebeles Footnote 6 were selected from the selected district proportional to the size of farm households. Accordingly, seven kebeles from Babile, four from Fedis, and four from Gursum were selected randomly. Following this, a list of household heads was obtained from the district office and then households in each district were categorized into agricultural cooperative member and nonmember.
Finally, a total of household heads members and nonmembers were selected from the selected rural kebeles. We used both descriptive and econometric tools to analyze the empirical data collected for this study. Descriptive statistics such as mean, standard deviation, range, frequency, and percentage were applied to describe the characteristics of the respondents. In the econometric part, we used propensity score matching and endogenous switching regression models to quantify important empirical results.
Welfare can be measured either from income or expenditure perspectives. However, it is advised to measure welfare based on expenditure in less developed countries such as Ethiopia. Consumption data also have additional information because consumption decisions are related with other household decisions such as nutrition and health Atkinson ; Meyer and Sullivan Moreover, reports of household income are likely to be understated compared to consumption expenditures Getahun and Villanger Therefore, we measured welfare by using consumption expenditure per adult equivalent.
The aggregated figure was then re-estimated on a per-adult-per-annum base. Previous research that used consumption per adult equivalent to measure wellbeing in Ethiopia includes Hagos and Mamo , Abro et al. Since cooperative members and nonmembers may not be directly comparable as members may self-select or be selected into the program based on initial differences, the mean outcome of the two groups differ even in the absence of the treatment.
Therefore, before proceeding to future counterfactuals, initial comparability must be established to avoid initial selection bias Caliendo and Kopeinig To deal with this problem, we used the propensity score matching PSM technique. This technique helps to adjust for initial differences between member and nonmember groups by matching each member unit to a nonmember unit based on similar observable characteristics by conveniently summarizing the conditional probability of member given pre-treatment characteristics Rosenbaum and Rubin Therefore, the first step in PSM is to predict the propensity score using a logit model.
The mathematical formulation of the logit model is as follows:. After predicting the propensity scores, imposing the common support region is the next important step because average treatment effect on treated and on population should only be defined in this region Caliendo and Kopeinig The common support region is the area within the minimum and maximum propensity scores of treated members of the agricultural cooperatives and comparison groups nonmembers , respectively, and it is demarcated by cutting off those observations whose propensity scores are smaller than the minimum of the treated group and greater than the maximum of the comparison groups Caliendo and Kopeinig This stage is followed by identification of an appropriate matching estimator.
The fourth important step in PSM is checking for matching quality whether the matching procedure can balance the distribution of different variables or not. If differences exist, there is an indication of incomplete unsuccessful matching and remedial actions are suggested Caliendo and Kopeinig If good match is found with the predicted probabilities of participation of households, the next step is to check whether the treatment brought about a difference in the indicators of impact.
The average treatment effect on the treated ATT is given by the difference in mean outcome of matched members and nonmembers that have common support conditional on the propensity score.
The mean impacts of joining agricultural cooperatives will, therefore, be given by:. Most commonly used average treatment effect estimation is the ATT, and specified as:.
If there is selection bias, matching estimators are not robust Rosenbaum To address this matter, we took several measures. First, following the work of Abebaw and Haile , we included several covariates in logit model specification to reduce bias, which could appear due to omitted variables. Secondly, we apply the bounding approach Rosenbaum to check the sensitivity of the estimated results to hidden bias.
Footnote 7 In addition, following the works of Cunguara and Darnhofer and Abebaw and Haile , we conducted what is referred to as a placebo regression Footnote 8 Imbens and Woolridge to assert the unconfoundedness assumption that all variables that need to be adjusted for are observed and included in the logit model.
In this analysis, an OLS regression was estimated with the same covariates used in the estimation of the propensity score, but with a different dependent variable, walking distance to the nearest hospital. This dependent variable is known a priori not to be caused by agricultural cooperative membership. If the coefficient of agricultural cooperative is significantly different from 0, then there are omitted variables that are correlated with agricultural cooperative membership. Otherwise, the unconfoundedness assumption can be maintained and a causal interpretation of the results is reasonable.
Most importantly, we also implemented the endogenous switching regression ESR model to check the consistency of the PSM result to control for selection bias. This model also helps to check whether the welfare impacts of cooperative membership are the same among member households.
If it is expected that agricultural cooperative membership has differential effects on household welfare outcome, different welfare outcome functions for members and nonmembers have to be specified, while at the same time accounting for endogeneity. Footnote 9 This justifies the use of the ESR model that accounts for both endogeneity and sample selection bias Alene and Manyong ; Di Falco et al. Defining the selection equation is the first step in the ESR specification.
The selection equation for agricultural cooperative membership can be specified as follows:. We adopted an endogenous switching regression model of welfare outcome measured as consumption expenditure per adult equivalent where farmers face two regimes 1 to join and 2 not to join agricultural cooperatives specified as follows:. The error terms are assumed to have a trivariate normal distribution, with 0 mean and non-singular covariance matrix expressed as:.
The expected values of e 1i and e 2i conditional on the sample selection are non-zero:. The selection of those variables is guided by previous empirical literature e. Among the personal characteristics of the household head, previous studies such as Bernard et al. Educational level of the household head is another personal variable that can determine cooperative membership positively Bernard and Spielman ; Verhofstadt and Maertens ; Mojo et al.
As confirmed by Abebaw and Haile , sex of the household head can also influence the membership decision. They indicated that male-headed households are more likely to participate in agricultural cooperatives than female-headed households.
Concerning the socioeconomic variables, Abebaw and Haile indicated that the position of the household head in the community has a direct relationship with cooperative membership. Previous studies also showed that family size is another variable that affects the membership decision positively Mojo et al.
For instance, size of owned land and livestock holdings are found to have a positive effect on cooperative membership Mojo et al. Contrary to this, Verhofstadt and Maertens indicated that owning more land decreases the likelihood of being a cooperative member whereas others such as Bernard and Spielman ; Ito et al.
Several institutional variables can also influence participation in cooperatives. Table 1 presents the summary statistics between agricultural cooperative members and nonmembers.
Age of the total sample respondents ranged from 17 to 80 years with a mean of about 37 years. The mean family size of the sample households measured in adult equivalent AE Footnote 10 was 5.
On average, sample respondents have 4. On average, respondents own 1. The size of land owned by the sample respondents ranges from 0. Thirty six percent of respondents have a social responsibility such as a security guard Militia , member of the local administration, and religious or traditional leadership.
The sample respondents are, on average, 5. This sub-section presents the result of the logit regression model, which was used to estimate the propensity score for matching the cooperative members with nonmembers.
The dependent variable in the logit model is coded as 1 if the household head is a member of agricultural cooperatives and 0 for nonmembers. As expected, the involvement of the household head in social responsibility determines the probability of agricultural cooperative membership significantly. The result is plausible as participation in social responsibility eases access to relevant information about the benefits of agricultural cooperatives.
This result is in line with the finding of Abebaw and Haile Being part of a cooperative can help a woman farmer take control of her own assets and destiny. If you subscribe to a local CSA community supported agriculture program, a meal delivery box or other local food network, your ability to access local, fresh foods is probably because your local small-scale farmers have formed a cooperative. If you want to support farmer cooperatives and better food systems, check out your local cooperatives, CSAs and farmers markets!
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The staff was accommodating, knowledgeable and patient. It was truly a breath of fresh air to have such a wealth of skill and resources available to a non-profit.
They were invaluable to our successful launch! They have helped us in multiple ways. Right from the first step in knowing how to incorporate, how to set our business and offering ideas for us to help steer us in the right direction plus allowing us to really make our own decisions. If our decisions go off track, they have gently guided us back to where we needed to be.
The Co-operatives first guys are always eager to answer questions, and give ideas to us that got us off the ground. June 28, Written by Aasa Marshall. A co-op was the solution. As always, though, the co-op model can be used to respond in new ways. Retiring producers want to see new people starting to farm And where does the land come from?
Stay in the know. Sign up for our monthly newsletter. In the United States, they are, in fact, exempt from antitrust laws. This is allowed because of the nature of cooperatives as organizations formed by the people for the people, with an open but limited membership. In other words, each co-op decides on who can or cannot become its member, never letting the outside influence, be it the government or a major corporation, to impact any decisions or goals.
A patronage is the share of the overall co-op profit paid to a member based on his or her activity and input for that year or any other specific period of time agreed upon by the members. In other words, if all farmers participate equally, they will get an equal share of profit.
However, if some have contributed more than others this year, they will get a larger patronage from the profit respectively. To put it more simply, every member receives what he or she justly deserves. Patronage can accumulate in the form of a retained profit and be paid out in full to a member of a farming cooperative as equity. In other words, an individual can be an active member for a certain period of time for example, 10 years , and, at the end of the period, get paid all the patronages accumulated in those 10 years.
Cost of business transactions can be enormous for one small business, and, without cooperation and proper investment, very little is possible to achieve. Moreover, it can be intimidating to face major business organizations and compete with them in the big market on your own.
Big corporations might not be interested in individual small businesses, but large agricultural co-ops will definitely attract their attention. One for all and all for one. The flip side of the independence and democratic control is the equally distributed economic responsibilities of the co-op members. Since no investment can enter the cooperative by outsourcing, loss of profit impacts every member. Every individual patronage has to be reduced to compensate for the loss. No one is left to deal with the loss of profit on their own.
Perhaps for someone the idea of sharing economic responsibilities may seem as a daunting prospect, but it is a kind of risk that is worth taking, considering the benefits of cooperation.
Your profit and well-being depend on how well the co-op is doing financially, which, in turn, depends on the level of your participation. It is a fair and transparent deal with predictable risks and countless benefits as a counterweight. There are many ways how producer cooperatives can benefit from using Crop Monitoring. Our digital agro-platform integrates satellite imagery, vegetation indices, and weather data, to provide precision-driven agricultural solutions to farmers, traders, insurers, and advisors.
Precision allows for a more rational use of resources, time, and budget, leading to both higher crop productivity and increased profitability.
Agricultural producers have to manage their fields on a regular basis, relying on traditional scouting, conventional weather forecasts, and historical harvest data records.
The reliability of these traditional methods has always been somewhat questionable. Therefore, Crop Monitoring offers farming coops updated ways of managing fields.
Our scouting feature is based on satellite data and vegetation indices, allowing for an automated detection of problem areas, and minimizing the reaction time. Thanks to a team account feature, field owners and assigned scouts stay connected while performing tasks. There is no need for using heavy equipment in the fields and plenty of time is saved in the process.
Similarly, satellite imagery and vegetation indices make it possible to identify zones with higher and lower productivity within every field owned by a producer cooperative.
This gives farmers an opportunity to precisely determine the amount of seeds and fertilizers required for each zone based on its productivity. As a result, there is a considerable reduction in waste of the supplies and budget along with an increase in the total crop yield. One more way a farming coop can be sure to increase productivity of their fields, is to know the history of crop rotation for every field.
Fumbling through the records is an unrewarding as well as time-consuming task, and sometimes records get lost. Instead of wasting time on fumbling through the historical weather records, you can look at the charts and see the weather data as curves and bars for a particular field for the past 5 years. Data relevant to farming, such as. Additionally, on the same chart you will see the history of vegetation development for the field, and growth stages for particular crop types.
This will also allow members of coops to make reliable predictions as to the future yields of their fields. What is more, EOS has partnered up with aWhere to deliver farmers a reliable day weather forecast specifically related to particular fields covering the area of 9 by 9 km.
At the same time, current weather conditions for every individual field, such as air temperature, humidity, and cloudiness to name a few , are always conveniently on display. With this feature, agricultural coops will be able to create a fuller picture of the climate influence on the development of crops, take appropriate measures, and thus ensure better yields.
We offer cooperatives not one but two versions of Crop Monitoring, one for PC, and one for a mobile device. Actually, there is no contest going on between the two; they are complementary with each other. While the classic software version is perfect for a desk at an office or home, the mobile app is ideal for the scouts in the fields.
The team account feature will make sure that agricultural coops can keep track of the assigned scouts performing all of the tasks.
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