Farm and non-farm activities are playing an important role in improving the lives of rural families. However, non-farming activities are determined by various factors. The aim of this study was to identify and analyze the variables that affect non-farm participation. Both a descriptive and an econometric model were used to analyze this issue. Mean and percentage were used as descriptive methods, while the binary probit model was used to try to explain the econometric model. Variables such as age, distance to market, access to credit, training, land size, membership in iqub, and marital status significantly determine non-farm participation. The recommendation of the results of this study was that the government should focus on gender equality, land tenure, providing various pieces of training, and establishing infrastructure connecting rural and urban areas such as roads, electricity, and telephone services to improve the livelihood of rural families.
This study is concerned with analysis of factors affecting loan repayment performance of smallholders in East Wollega zone Nekemte town. As credit is one of the most important factors required for smallholders input utilization, its repayment to the lender is also of paramount importance to have sustainable agricultural development and financial institutions. Different agricultural development programs and strategies were implemented in the country in the past three decades. Secondary data from relevant institutions were used just to highlight on different approaches used in different decades regarding agriculture and agricultural credit development strategies. In the course of this study primary data were collected from 120 randomly selected farm household borrowers of fertilizer credit using structured questionnaire. These respondents were drawn from six Peasant association and two Saving and Credit service in the study area. For the analysis of farm data, descriptive statistics such as mean, standard deviation and percentages were used to describe socioeconomic characteristics of the respondents. Partial correlation coefficient and variance inflation factor (VIF) were calculated to detect multi-co linearity and association among the continuous and discrete variables, respectively. Then, a multivariate tool, linear discriminate analysis, was used to identify the most important variables attributed to discriminate between non-defaulters and defaulters of fertilizer credit for 2018 production season. The result of the analysis showed that the frequency of contact with development agents, livestock ownership in livestock unit and celebration of occasional ceremonies were statistically significant factors responsible for the optimum discrimination rule between the two groups. Another multivariate tool, multiple linear regression analysis, was used to identify critical variables that contributed to timely repayment of loan for non-defaulter respondents. Experience in own farm, experience in credit use, proportion of area under pea, beans and wheat production, annual farm revenue, number of draught oxen owned, ownership of livestock in livestock unit, number of contacts with development agents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.