The Impact of Contract Farming on Income of Smallholder Vegetables Farmers in the Central Rift Valley of Ethiopia

Recently, due attention was given to commercialization of smallholder agriculture through contact farming hoping that it would bring positive impact on income of smallholder produces in Ethiopia. This article presents the results of data analysis conducted to generate empirical data required to inform smallholder agricultural commercialization policy using data collected from 424 (194 participants and 230 non-participants) randomly selected vegetables producer farmers from two districts of the East shewa zone of Oromia Regional State, Ethiopia. The propensity score matching was used to assess the impact of contract farming on smallholder farmers’ income in the study area. The probit model result shows that the proportion of total crop area allocated to vegetables, access to credit, frequency of extension contacts, market information, and distance of the market center have a positive and signi�cance effects on decision of vegetable producers to participate on contract farming. The impact assessment result using PSM reveals that gross annual income of contract participants is lower by 3.8% (Birr 2,942) compared to the non-participants. This may due to the nature of contract itself, being prone to manipulation, absence of effective contract enforcement mechanism, and lack of government support. However, some of the contract farming participants would like to continue to access inputs, credit, and technical advice and to reduce marketing risks and uncertainty. In conclusion, contract farming is a useful institutional arrangement which increases smallholders’ income and bene�t from remunerative value chains but it is not a panacea that works in different contexts. Hence, policy makers should base their choices on empirical evidence and consider alternative and complementary intervention options to enhance smallholder commercialization and ensure sustainable livelihood in the research area.


Introduction
Commercialization of smallholder agriculture is considered as means of poverty reduction in less developed countries like Ethiopia (FAO, 2019).It represents an attempted to alter the subsistence production to the highly market-oriented agricultural production (Addisu et al., 2020).However, for smallholder commercialization to be successful and to bring structural transformation, it requires capital (Rehber, 2019), access to market information (Bellemare & Lim, 2017), technical knowledge (Otsuka et al., 2016), and modern agricultural technologies (Belay and Bewket, 2013).These constraints smallholder producers face to access market, nance, technology and other required services, affect input demand, yield, sales (quality, quantity and price of sales) and incomes, which, in turn, reinforce and perpetuate subsistence-orientation (Wainaina et al., 2014).
Contract farming is one of the contested issues in the literature on the commercialization of smallholder agriculture.Some scholars argue in favor of contract farming as it increases farmers' income (Gemechu et al., 2017), improve access to technology and inputs (Usman and Zeleke, 2017), provides secured outlet and price for products (Holtland, 2017), improve productivity (Girma and Gardebroek, 2015), increase the quality and quantity demand for the market (Repar et al., 2017) and it also offers more pro table business activity to farmers (Barret et al., 2012).Other scholars refute contract farming for several reasons.Contract farming can increase smallholder farmers' indebtedness, loss of control over their lands, causes gender inequality, increases labour dependence, lack of fairness and low participation of smallholder farmers in contract enforcements.Moreover, one side favourism in contract agreement, unclear, incomplete, and misguiding contract clues leads to diminish the possibility to improve smallholder farmers' income and livelihood through such institutional arrangement (vath et al., 2019; Soulliner and Moustier, 2018; Ray et al., 2021;Dube andGuveya, 2016 andAzumah et al., 2016).There are mixed results regarding the role of contract farming in the context of smallholder farmers.The evidence base remains inconclusive and debatable.This study was initiated to contribute to this debate by producing empirical evidence at household level in the context of smallholder farmers in Ethiopia.
The research area is known for irrigated vegetable production mainly tomato, onion, cabbage, and pepper.However, there is widely held view that the smallholder producers were not able to get fair share of the bene t from the high-value vegetable value chain.Perishability of the product, lack of local processing facilities, long market chain dominated by brokers, market information asymmetry, and season price uctuation are among the factors negatively in uencing the bene t accrue to the vegetable producers (DDAB, 2020).Hence, contract farming was considered as appropriate institutional option to help the vegetable producers overcome some of the challenges and increase their market linkage and income.
Recent empirical studies in Ethiopia found out that appropriate contract farming arrangement could enhance smallholder producers' access to inputs, credit, technical advice, and remunerative market leading to increased income (Addisu et al, 2020;Gemechu et al, 2017).It has also been found that Local context matters for the success of contract farming as institutional arrangement to facilitate smallholder commercialization and enhance producers' income and related bene ts.To that end, the study aimed to identify the determinants of smallholder participation in vegetables' contract farming and their impacts on households' income based on household data in East Shewa Zone of Oromia Regional State, Ethiopia.

Data and description of study area
The study was carried out in East Shewa Zone which is located in central rift valley of Ethiopia.Of the total ten districts in the zone, Dugda and Bora districts were selected as the predominantly vegetable growers (CSA, 2017).Lake Danbal and Meki River are located/ cross the selected districts in addition to availability of subsurface water at depth ranging from 15-43 meters.Small scale irrigation has been practiced around the coast of Lake Danbal and Meki River.Farmers produce vegetables like tomato, onion, peppers and cabbage 2-3 times per year using irrigation.
The study used both primary and secondary data sources.Quantitative and qualitative data were gathered through sample household survey using semi-structured interview schedule, focused group discussion, and key informant interview.Multistage sampling procedure was used to select districts, kebeles, and nally vegetable producing sample households.Bora and Dugda districts were selected as research area for the prominent irrigated vegetables production.Secondly, 8 kebeles known for their vegetable production and participation in vegetable contract farming were identi ed in consultation with local experts.Third, vegetable producing households were strati ed into two strata based on their status of participation in contract farming over the last two years to draw representative sample households from both vegetable contract farming participants and non-participant.The total sample size was determined using a formula which provides the maximum size to ensure the desired precision following Cochran (1963).
Where n is desired sample size; Z is con dence level; e is the desired level of precision; p is estimated proportion (values of 0.5) as suggested by Israel (1992) to get the minimum sample size of the households at 95% con dences level and 5% precision.Additionally, 40 respondents (10.42%) were kept as a reserve considering possible errors, omissions, and non-response rates and thus, 424 household heads (194 participants and 230 non-participants) were selected from eight kebeles and interviewed through semi-structured interview schedule.In addition, 16 focused group discussion were conducted with kebele administrative, elders, experts and others who have depth knowledge and experiences of the topic at village.key informant interviews were also conducted from bureau of agricultural experts through check list.

Methods of Data Analysis: propensity score matching
Propensity Score Matching (PSM) method was preferred for impact assessment.PSM is commonly used for impact assessment where baseline data is not available and where randomized design is not feasible (Rosenbaum and Rubin, 1985;Rubin and Thomas, 1992).The model can reduce bias in observational studies by identifying the non-participants and participants who are similar in all characteristics except for status of participation in vegetable contract farming (Addisu et al., 2020 andGemechu et al., 2017).
Recommended steps were followed in applying PSM method to analyze impact of participation in contract farming on smallholder vegetable producers' income.First a probability model of participation in vegetables' CF was estimated to calculate the propensity score of each sample household head simply by running Psmatch2.Second, the selection of the best matching algorithm was made from their alternatives (the nearest neighbor, calliper or radius matching and kernel and matching methods) (Caliendo and Kopeinig, 2008), based on their performance criteria like number of insigni cance variables after matching, low pseudo R 2 , large sample size and lower standard bias.Third, check for common support region.It helps to ensure the comparable observations of treated and untreated distribution overlaps and then the predicted observation of propensity scores is discard, if it falls outside the range of common support.Fourth, test of the matching quality was conducted through standard bias, t-test, joint signi cant and pseudo R 2 are suggested.After score matching quality was checked, the impact of participation was made using matched sample.The parameter value is called ATT (The average treatment effect on the treated).Finally, check result for sensitivity confound.Sensitivity analysis gives answers to whether the inferences about outcomes can be altered by unobservable or confounders (Rosenbaum, 1987).
The explanatory and matching variables were identi ed based on the review of relevant empirical literature on contract farming and impact assessment.were participating in vegetable contract farming, while 54.25% were not participating in the scheme.In terms of the sex of households, 80.42% were male-headed households.From the total female-headed sample households (83), 39.76% were participating in vegetable contract farming in the study area.There is no signi cant difference between contract farmers and non-contract farmers in terms of sex of the household heads (Table 1).This shows that, there is no sex restriction for participation in vegetables contract farming in the study area.
Actual or perceived price is another factor that may in uence the decision to join contract farming.In the 2021/22 production year, of total sample households who perceived uncertain price (193), 88.7% of the household heads participated in vegetable contract farming in order to reduce risks.A chi-square value of 243.4 test indicated that there is statistically signi cance difference at 1% probability level between contract participants and non-participants in terms of perception on price uncertainty.This implies that majority of smallholder farmers participate in contract farming due to fear of price uncertainty.Access to credit and other institutional services were also expected to improve smallholder farmer's production and their welfare.The average of households' access to credit for participants and non-participants in vegetable contract farming were 34.93% and 39.56%, respectively.The chi-square value of 8.3 tests for independence indicated that there is a signi cance difference between participants and non-participants at 1% probability level to the credit institutions.
The frequency of extension contacts expected to give farm households opportunities to get advisory service for their vegetable production.The average frequencies of extension contracts for the total sampled household heads were 33.96% once for every fortnight.A chi-square value of 10.4 for independence indicates that there is a statistically signi cant difference in the percentage of contract and non-contract farmers in terms of their frequency of extension contacts at 5% probability level.Of the total sample households about 53.3% were participated in training related to vegetable production.A chi-test result indicated that there is a statistically signi cant difference at 1% probability level between contract participants and non-participants in terms of their training attendance.
Access to market information enables smallholder farmers to search for and associate information available for different market channels to manage the cost bene t analysis and related factors in vegetable contract farming in the study area.Of the total sampled household heads, 84.2% had access to market information and of these 75.2%% participated in vegetable contract farming.A chi-squared test shows there is statistically signi cance difference and 5% probability level in the percentage of participant and non-participants vegetable contract farming in terms of access to market information.In the 2021/22 production year, the average experiences of household heads in vegetable farming were 10. 8 years.The mean of smallholder's farm experience for participants and non-participants in vegetable farming were 11.35 and 10.36 years, respectively.The t-test result show that, there is a statistically signi cance mean difference between the two groups in terms of their farm experience at 10% signi cance level.
The total average value of asset owned by sampled household heads from off and non-farm income sources in the study area were 55,128.95ETB for the whole sample; this was estimated at 52,258.6 and 57,550 ETB for participants and non-participants respectively.The t-test for equality of means for household heads income from off-farm and non-farm income sources among two groups are statistically signi cant at 5% probability level.
The area average of total farm land allocated to vegetable crops in 2021/22 of the entire sampled households was 1.24 hectares with 1.25ha and 1.23ha for participants and non-participants.The t-test result shows that, there is a statistically signi cance mean differences between the two groups in terms of the proportion of total farm land to vegetable farming at 1% probability level.

Propensity score matching results
The probit regression model was used to estimate the propensity score for participant and nonparticipant households in contract farming.The pre-intervention variables were taken as explanatory variables and assumed to affect the participation in vegetables contract farming.Before proceeding to the impact estimation, the variance in ation factor (VIF) was applied to test for the presence of strong multicollinearity problem among explanatory variables.There was no serious problem of multicollinearity and hence no explanatory variable was dropped from the estimated model.Similarly, Breusch-pagan test for heteroscedensity was used to check the existence of heteroscedasticity of the variance and there was no heteroscedasticity problem in the model.
The estimated probit regression model (Table 3) appears to perform well for the intended matching exercise.The pseudo-R 2 value is 0.0962, low R 2 value shows that participants households do not have much distinct characteristics overall and implies there is a good match between contract participants and non-participants.The interest of the matching producer is to get households from vegetable producer in non-participants contract farming with similar probability of participants in a contract farming given explanatory variables.Probit model was used to calculate propensity scores by running psmatch2 command.If the numbers of explanatory variables affecting the participation decision are limited, it created a good opportunity for matching and it makes the matching producer less di cult since matching algorism is implemented to estimate signi cant differences of explanatory variables between participants and nonparticipant groups.The maximum likelihood estimates of the probit regression model result shows that, ve variables out of thirteen variables were signi cant and affect the participation of smallholder farmers in vegetables contract farming.
Before proceeded to the next steps, it is better to see the common support region that was imposed (the second steps of PSM).The common support helps to check to identify the region of common support between the treatment (participants) and comparison groups (non-participants).In the literature review, different ways were suggested to analysis the common support region.The most used one is visual analysis of the density distribution of the propensity scores for both groups.
To choose the best matching algorithm, the most commonly used methods are nearest neighbor, kernel and radius caliper matching methods.To select the best matching algorithms, different criteria are recommended and used by different scholars.As Dehejia and Wahba (2002) states, the equal means test referred as balancing test, pseudo-R 2 , Mean Standard Bias and matched sample size are recommended as best criteria to selected best common support region in propensity matching scores.Table 5 shows all matching algorithms were undertaken and offer the same results.The caliper radius matching with radius (0.1) has relatively low pseudo with best balancing test (all explanatory variables insigni cant) and large sample size as compared to the other alternatives in both outcome variables.The third stage is to conduct the balancing test to know whether there is statistically signi cant difference in the mean value of the two groups of the sampled households.It is better to compare the in uence of the background characteristics of both treated and comparison groups before matching for variable selection.Table 6 shows that, the t-test of covariate balance test resulted in statistically insigni cant difference between treated and comparison groups in selected variables.Once the difference between the outcomes of participants in contract farming and non-participants was computed, the next stage is to provide evidences of the impact of participation in vegetable contract farming on the household's incomes (Table 7).After controlling the pre-participation differences, we found out that participation in contract farming has decreased average income of the participant by 2,941.95ETB in 2021/22 production year.This result shows that, participating in contract farming decreases the household's gross annual income by 3.8%.This implies that, contract farming is not a panacea that bene ts smallholder farmers in different contexts.Information from key informant interview and focus group discussion con rms that vegetable producers were willing to enter into contract farming in order to access production inputs, advice on market speci cation and production management and to reduce risks and uncertainty.This nding is consistent with the nding of Finally, sensitivity test was computed to check the robustness of the estimation covariates to show whether the hidden bias affects the estimated ATT or not.Therefore, a sensitivity test was used to investigate whether the causal effect estimated from the PSM is susceptible to the in uence of the unobservable covariates.Table 8 shows the sensitivity analysis of hidden bias for the impact of vegetable contact farming on household's income.To check for unobservable biases Rosenbaum bounding approaches were used.As reported in Table 8, the inference for the effect of vegetable contract farming is not changing, though the participant and non-participant households have been allowed to differ their odds of being treated up to gamma = 5 in terms of unobservable covariates.This shows all outcome variables estimated at various levels of critical values of gamma and p-values are signi cant.

Conclusion and policy implication
Contract farming expected to promote agricultural commercialization in small-scale vegetable farming by reducing transaction costs in supplying agricultural production and solves market imperfections through linking smallholder farmers to the markets.The nding of the study shows that, vegetable contract farming reduces the gross annual income of smallholder farmers.Nonetheless, smallholder vegetable producers who engaged in contract farming bene ted in terms of access to agricultural inputs, technical advice and management of risk and uncertainty.The smallholders who participated in the contract farming complained about cost of inputs, unequal power relation, unpaid of family labours and unfair market price.If the smallholder farmers continue like this, the bene t that added to their current resources will be washed away and the performance of the vegetable contract farming will become worse in the future.In general, vegetables contract farming is not a panacea that bene ts smallholder farmers in different contexts.Therefore, concerned bodies should look into the details regarding the way the contract was designed and implementation.In addition, the concerned public o ces should consider other options to increase participation and ensure that the smallholder vegetable producers get fair share of the bene t from the value chain.
Distribution of propensity scores for untreated and treated group (HH income).
contract farming might fail to deliver the expected bene ts to smallholder farmers due to both internal factors mainly related to contract and enforcement mechanism (Melesse et al., 2018; Gemechu et al., 2017; Usman and Zeleke, 2017) and external (Addisu et al, 2020; Joseph et al., 2019) factors including climate change, soil fertility degradation and pests and diseases.These may exclude poor farmers from parts of contract farming and increase social inequality in the community (Wainaina et al., 2014).

3 .
Results and Discussions3.1.Socio-economic and demographic characteristics of respondentsDemographic, socio-economic and institutional variables are hypothesized to determine smallholder producer's participation in contract farming in the study area.Of the total of sample households, 46% (Vath et al., 2019; Soullier and Moustier, 2018; Seba, 2016; Ray et al., 2021; Olounlade et al., 2020; Miyata et al., 2009; Bidzakin et al., 2019; Addisu et al., 2020; Gemechu et al., 2017 ) and matching.The variables are age, sex, education level, dependency ratio, farm experience, off-farm and non-farm income, proportion of vegetable to total farm land, access credit, extension contact, membership in saving group 'equib', market information, market distances, and disease and pest infestation.

Table 1
Descriptive statistics of dummy variables

Table 3
Probit regression estimates of vegetable CF participation on HH income (Psmatch2)

Table 5
Performance of the different matching algorithms on Household income

Table 6
Covariate balance test for the impact of participation CF on HH income.
Olounlade et al. (2020)Who found out that, smallholder participation in contract farming lower the income of farmers in rural Benin.Contract farming is still important institutional arrangement for linking smallholder high value crop producers to both output and input market.However, whether smallholder commodity producer's income would change as a result of participation in contract farming depends on context and the nature of contract farming and the way it is implemented.
Similarly, Abdulai and Al-hassan (2016) Reported that participation in soyabean contract farming in Ghana decreased the average annual income of farmers and Seerp, (2018) reported similar nding in his study comparing participation in contract farming and out-grower scheme in Wageningen, the Netherland.On the other hand, Gemechu et al (2017) and Addisu et al (2020) found positive income effect of participation in contract farming in Ethiopian context.In general, the nding is inconclusive and mixed.

Table 8
Sensitivity analysis of hidden bias for the impact of CF on the household's income