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Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman Woredas of Horoguduru- Wollega Zone, Oromia Region, Ethiopia

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Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman Woredas of Horoguduru- Wollega Zone, Oromia Region, Ethiopia Journal of Agricultural Economics and Rural Development Vol. 5(3), pp. 648-655, December, 2019. © www.premierpublishers.org, ISSN: 2167-0477 Research Article Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of Horoguduru- Wollega Zone, Oromia Region, Ethiopia *1Amsalu File, 2Oliyad Sori 1Wollega University, The Campus’s Finance Head, P.O. Box 38, Ethiopia 2Wollega University, Department of Agricultural Economics, P.O. Box 38, Ethiopia Credit repayment is one of the dominant importance for viable financial institutions. This study was aimed to identify determinants of loan repayment capacity of smallholder farmers in Horro and Abay-Chomen Woredas. The study used primary data from a sample of formal credit borrower farmers in the two woredas through structured questionnaire. A total of 120 farm households were interviewed during data collection and secondary data were collected from different organizations. The logit model results indicated that a total of fourteen explanatory variables were included in the model of which six variables were found to be significant.; among these variables, family size and expenditure in social ceremonies negatively while, credit experience, livestock, extension contact and income from off-farm activities positively influenced the loan repayment performance of smallholder farmers in the study areas. Based on the result, the study recommended that the lending institution should give attention on loan supervision and management while the borrowers should give attention on generating alternative source of income to pay the loans which is vital as it provides information that would enable to undertake effective measures with the aim of improving loan repayment in the study area. Key words: Loan repayment performance, Smallholder farmers, logit model, Horro and Abbay Chomen Woredas INTRODUCTION The economic growth of developing countries depends to productivity among the resource poor farmers. However, a great extent on the growth of the agricultural sector. lack of financial resource is one of the major problems Ethiopia is one example of a developing country, facing poor households. Formal financial institutions are characterized by a predominantly subsistence agrarian inefficient and inaccessible in providing credit facilities to economy. The nature of farming in Ethiopia is dominated the poor. Delivering productive credit, low cost, efficient by traditional micro holdings of the subsistence type, with credit services and recovering a high percentage of loans less than two hectares of land being the average holding granted are the ideal aims in rural finance (Wenner, 2015). (CSA, 2015). Over the last four decades the international donor agencies and governments of less developing countries The use of credit has been envisaged as one way of have spent billions of dollars on projects, rapidly promoting technology transfer, while the use of expanding the volume of agricultural loan and the number recommended farm inputs is regarded as key to of rural institutions (Adams and Graham, 2011). agricultural development (Tomoya M. and Takashi, 2010). (Medhin, 2015 and Million, 2014) have indicated that credit is the largest source of farm capital in Ethiopia. Agricultural credit has a key role for the development of different sectors (Sileshi 2014, Tomoya and Takashi, 2010). *Corresponding Author: Amsalu File, Wollega University, The Campus’s Finance Head, P.O. Box 38, The provision of sustainable formal credit for agricultural Ethiopia. E-mail: [email protected] inputs is one of the most effective strategies for improving Co-Author Email: [email protected] Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia File and Sori 649 The Loans taken from credit institutions vary from country Guduru woredas, from East by Hababo Guduru woreda, to country, region to region, sector to sector. But farmers from the West by Abe Dongoro woreda. Shambu and in the developing countries have been identified as the Fincha town. Horro and Abay Chomen woredas are most defaulting group of credit beneficiaries. While credit comprised of the three main agro-ecological zones remains the largest source of farm capital, prospective namely, Woina Dega (moderate), Dega (cool) and Kola. borrowers are denied access to credit by financial Woina Dega Zone lies almost at the middle of the Woredas institutions as a result of high loan delinquency among itself and having the average elevation between 1500- farmers. This phenomenon does not only reduce farmer 2400 meters above the sea levels. There are different productivity but contributes also to dwindling household crops produced in the study area’s agro-ecological zone income and food security. In order to improve agricultural like maize, Teff, bean, wheat, sorghum, pea, barley (Zonal credit within financial institutions, it is very important to Agricultural office report, 2015). examine the loan repayment capacity of farmers (Million, 2014). The main economic activity of the Woredas is agriculture, which is based on land resource. However, due to rapid Hunte (1996) argued that default problems destroy lending population growth, per capita land holding is declining and capacity as the flow of repayment declines, transforming this result in a very intensive agriculture that degraded the lenders into welfare agencies and loan default is a disaster quality of the soil (Zonal agricultural office report, 2015). because failing to implement appropriate lending The decline on the quality of the soil adversely affected the strategies and credible credit policies often result in land productivity. Rapid population growth also results in termination of credit institutions. Farmers incapable to high exploitation of the scarce water and forest resources. repay loans timely or they face a serious problem to repay The excessive deforestation and soil erosion caused by which is a problem for both agricultural credit institutions very intensive agricultural system are some of the densely and smallholder farmers (Million, 2014 and Amare, 2006). populated part of the area has reached the stage where According to Horro Guduru Wollega Rural Development the land resource can no longer support animal and human Office second Quarter Report (2015/2016), about 24.3 lives (CSA, 2010). million birr loan which was given from 2010 to 2014, has not been repaid in general and according to data obtained b. Data Sources and Type from the institutions in Horro and Abay chomen districts in (2016/2017), about 5.75 million birr loan, which was not In order to under-take this study both primary and repaid in particular. Similarly, since farmers use loan for secondary data were used. The primary data were non-productive purposes, they become unable to repay it collected through personal interview and focused group and even they borrow it for agricultural product which is discussion through semi-structured questionnaires, which climate dependent, they fail to generate more profit. was prepared for the study. The secondary data were Although there are such like problems that affect loan collected from available books, magazines, articles, repayment performance of small holders, there is no detail relevant research papers, annual reports and internet study conducted which is related with detrminants of loan sources. repayment performances of smallholder farmers in the study area. Therefore, this study was aimed at examining c. Sample Size and Sampling procedure the loan repayment performance of farm households in Horro and Abay Choman woredas of Horro Guduru In this study, two -stage random sampling procedure was Wollega administrative zone. employed for the selection of the respondents. In the first step of the sampling, In the first stage, forty-two kebeles in Research Methodology the Woredas are listed and six kebeles (three from each district) were selected using simple random sampling a. Description of the Study Area technique. The study was conducted in the oromia region, Horro In the second stage, from 2720 the total household in the Guduru Wollega zone specifically Horro and Abay six kebeles were stratified in to two groups. These are 582 Chomen woredas. Shambu is the capital town of Horro credit participants and 2138 non-participants of formal Guduru Wollega zone which is located at 315km away source of financial institutions based on the household lists from the capital city of Ethiopia Addis Ababa in Western which are obtained from the office of the kebeles and part of the country. Horro and Abay Chomen woredas are formal financial institutions. among 9 Woreda’s of Horro Guduru Wollega zone. According to CSA population projection, Horro and Abay Finally, the list of farmers who have obtained loans from Chomen woredas have 97296 and 59371 total population, formal credit sources were recorded from each kebeles respectively (CSA, 2015). and a total of 120 farm households were selected randomly using probability proportional to size sampling The woredas are bounded from the North by Jardaga Jarte technique. and Hababo Guduru woreda, in South by Jima Geneti and Determinants of Loan Repayment Performance of Smallholder Farmers in Horro and Abay Choman woredas of HoroguduruWollega Zone, Oromia Region, Ethiopia J. Agric. Econ. Rural Devel. 650 푁 approximately equal to 2.718; Xi is the ith explanatory The study used a simplified equation: 푛 = , 1+푁푒2 variables; and α and βi are parameters to be estimated. where n is sample size, N is population size and e is level Hosmer and Lemeshew (2013) pointed out that the logistic of precision provided by Yamane (1967) to determine the model could be written in terms of the odds and log of required sample size at 95% confident level. odds, which enables one to understand the interpretation of the coefficients. Table 1: Sampled Households 1 (1 − 푃 ) = (2) No. Name of Kebele No. of borrowers of No. of 푖 1+푒푍푖 formal financial sampled Therefore, 푍푖 institutions in the borrowers 푃푖 1+푒 푍푖 study area (in the ( ) = ( −푍푖) = 푒 (3) 1−푃푖 1+푒 year 2017) 푍푖 푃푖 1+푒 ∑ 훽푖푥푖 1. Didibe Kistana 247 51 ( ) = ( −푍푖) = 푒(훼+ ) (4) 2.
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