Determinants of Access to Agricultural Credit and its Effect on Coffee Production among Smallholder Coffee Farmers in Gisagara District, Rwanda

Abstract

Agricultural credit plays a dominant role in the development of the agricultural sector especially in developing countries. Credit remains important in financing production especially in the acquisition of inputs such as improved seed, fertilizers and pesticides, payment for labour services, purchasing of agricultural equipment and value addition among others. Despite the importance of agricultural credit, smallholder farmers in Rwanda face the challenge of access to agricultural credit. Consequently, the agricultural productivity has not only fluctuated but also declined over the last 5 years (since 2015 up to 2019). Thus, the purpose of this study was to investigate the determinants of agricultural credit access among smallholder coffee farmers in Rwanda. The objectives of the study were two-fold. First, the study assessed the determinants of credit access by smallholder coffee farmers in Gisagara District, Rwanda. Second, the study analyzed the determinants of technical efficiency in coffee production among smallholder coffee farmers who access and those who do not access agricultural credit in Gisagara District, Rwanda. A multi stage sampling technique was employed to select the respondents. The study collected primary data (for a period from 2015 up to 2019) from 222 smallholder coffee farmers using structured questionnaire. The binary logistic regression and stochastic production frontier models were employed to estimate the data. The results from the logit model indicated that the determinants of access to credit for the coffee farmers were gender, age, farm size, interest rate and cooperative membership. The results from the stochastic production frontier model revealed that all the production inputs (land under coffee, organic fertilizer, inorganic fertilizer and pesticide) except labour were significant (p<0.01) and had a positive influence on technical efficiency in coffee production for the farmers with access to agricultural credit. Amongst others, only the land under coffee was significant (p<0.01) and had a positive influence on technical efficiency in coffee production for the coffee farmers without access to agricultural credit. For socio- demographic and institutional characteristics in the stochastic model, the findings showed that there is a relationship between gender and technical efficiency in coffee production at 5%. Another relationship was found between farm size and technical efficiency in coffee production at 1% for coffee farmers with access to credit and at 5% for the coffee farmers without access to credit. The last relationship was found between cooperative membership, training and the technical efficiency in coffee production at 5% for coffee farmers without access to credit. The results of gamma show that the variations in coffee production were attributed to technical inefficiency at 89 % for farmers’ users of credit and at 99 % for farmers’ non- users of credit. The mean efficiency scores of the farmers were 0.94 and 0.84 respectively for coffee farmers who had accessed credit and those who had not accessed agricultural credit. This implies that on average the farmers who had accessed credit operated on a higher technical efficiency with a potential of increasing coffee production by a further 6 % only given the same level of inputs keeping all the other factors constant while those who had not accessed credit had lower technical efficiency than their counterparts who accessed credit. Thus, the farmers who did not access credit had a potential to increase coffee output by a further 16 % given the same level of inputs if they accessed credit keeping all the other factors constant. The minimum efficiency scores of the farmers were 0.64 and 0.25 respectively for coffee farmers who had accessed credit and those who had not accessed agricultural credit. The maximum efficiency scores of farmers were 0.99 and 0.98 respectively for coffee farmers who had accessed credit and those who had not accessed agricultural credit. Further, the results of one-way ANOVA showed that the coffee farmers who had received credit were more productive than those who did not receive credit. Based on the findings of the study, the study concluded that 3 socio- demographic factors (gender, age, farm size) and 2 institutional factors (interest rate and cooperative membership) influenced the access to agricultural credit, and that agricultural credit had a significant effect on coffee production since in most cases it facilitated acquisition of farm inputs such as purchases of fertilizers and pesticides as well as payment of labour services. The study recommends that programs that encourage and put gender at an equal footing, policies aimed at empowering farmers’ groups, and improving agricultural credit access will help boost coffee production in the study area. The study also recommended the introduction of more financial institutions such as agricultural and microfinance banks in rural areas so that the farmers can have more options and procedures for securing agricultural credits. There should also be streamlined in order to make it simple for the farmers.