The main objective of the study was to investigate the causes underlining small-farmers default on bank loan repayments in the North West Province. One hundred and sixty farmers were randomly selected to be part of the sample. Questionnaires were issued to both farmers and bank officials. Descriptive statistics, correlation and regression models were used to analyse the data. The overall results indicate that most of the small farmers are in the old age category (58 years on average) with very low educational level. This scenario poses a challenge to the stakeholders in agriculture specifically the succession plan to these elderly people when they leave agriculture due to retirement. It was revealed by the study that the farmers do not keep either financial or production records. The analysis shows that the small farmers lack skills in financial management therefore, they are unable to execute the prerequisite to modern farming which are literacy and numeracy as indicated by Woohall et. al.,( 1985). Most of the respondents have outstanding debt from Agribank yet they received loans from Landbank. Because of their low production and other many responsibilities, they are unable to repay loan instalments thus leading to loan default to their current financial supplier, which is Landbank. Lack of monitoring of loan funds was identified as one of the causes of the farmers Joan default. The analysis also indicates that the small farmers have access to finance but the major problem is lack of financial management skills, more involvement in household responsibilities, and lack of technical assistance from relevant stakeholders like extension officers and project managers from the bank or from the Department of Agriculture. Since the Land bank have no field officers to assist the farmers, it is recommended that the bank should have field officers to assist farmers in their business, especially with production, marketing, financial management and farm management Skills. The inability of the farmers to access good value markets for their products was identified as one of the problems, which led to loan default because the farmers are unable to market their products at the right time for good value in excess of their cost. It is recommended that financial institutions should assist their clients to access better markets for their products for better price which will in turn give them better income in order to repay their loans. / M.Sc. (Agric. Economics) North-West University, Mafikeng Campus, 2005

The aim of the study was to investigate the impact of agricultural infrastructure on agricultural productivity and agricultural income of smallholder farmers in the North West Province, South Africa. Factors that contribute to the availability, accessibility and satisfaction of smallholder farmers with regards to agricultural infrastructure were also assessed in the study. Using cross sectional data from the North West Province of South Africa, one hundred and fifty smallholder farmers were selected using stratified sampling to group farmers into those who had agricultural infrastructure and those who did not have. Data were collected using a structured questionnaire, divided into six sections as follows: personal socio-economic characteristics of farmers; characteristics of the land; agricultural infrastructure of smallholder farmers; agricultural production and markets; and production activities and financial support rendered to farmers. The data were coded, captured and analysed using STATA 14.0. Data were analysed through descriptive analyses, Principal Components Analysis (PCA), Stochastic Frontier Analysis, Heckman selection procedure and Tobit Regression Models. This result revealed that most of the farmers were male, aged between 41 and 60 years of age, had contact with extension services only occasionally and did not engage in non-farming activities. Most of the smallholder farmers had less than 10 years of farming experience, had household sizes of less than or equal to five members, had about one household member assisting in the day-to-day farming activities. Most of the farmers did not belong to any farmer organisation. Generally, the farmers were involved in dry land farming. Farmers who irrigated their farms, did so on approximately 15 and 45 hectares of land. Farmers also received agricultural support from CASP and used commercial seeds, fertilizers and animal vaccines as their production inputs. Furthermore, smallholder farmers in the study area received support for inputs and majority did not have to repay for the inputs. Majority of farmers indicated that infrastructure impacted on their farming enterprises through increases in both productivity and sizes of their farming enterprises. The study found that the factors influencing agricultural income for smallholder farmers with agricultural infrastructure were: Physical infrastructure index (Coef=0.78: P<0.01); Social infrastructure availability index (Coef=0.61: P<0.01); Institutional infrastructure availability index (Coef=1.05: P<0.01); Level of education of farmers (Coef=0.96: P<0.01); Access to extension services (Coef=1.05: P<0.01); Membership of farmers’ organisations (Coef=0.59: P<0.05); Age of smallholder famers in the study area (Coef=0.05: P<0.01); and Household members assisting in farming activities (Coef=0.24: P<0.05). In terms of farmers without agricultural infrastructure available, factors influencing agricultural income were: physical infrastructure availability index (Coef = 0.74; P<0.01); social infrastructure availability index (Coef = 0.77: P<0.01); institutional infrastructure availability index (Coef = 0.61: P<0.01); level of education (Coef = 0.89: P<0.01); access to extension services (Coef=1.24: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef=0.33: P<0.01). In terms of smallholder farmers with accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure access index (Coef=1.29: P<0.01); Social infrastructure access index (Coef=0.38: P<0.1); Equipment infrastructure access index (Coef=0.62: P<0.01); Level of education for smallholder farmers (Coef=1.21: P<0.01); Access to agricultural extension services (Coef=1.64: P<0.01); Membership of Farmers’ organisations (Coef=0.77: P<0.05); Age of smallholder farmer (Coef=0.01: P<0.01); and Household members assisting in the farming enterprises (Coef=0.39: P<0.01). With regards to smallholder farmers without accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure accessibility index (Coef=0.92, P<0.01); Equipment accessibility index (Coef=0.43, P<0.05); Level of education (Coef=1.25: P<0.01P); access to extension services (Coef = 1.63; P<0.01); membership of farming organisations (Coef = 0.86; p<0.01); age of farmers (Coef= 0.07; P<0.01) and assistance of household members in farming enterprises (Coef = 0.34; P<0.05). In terms of satisfaction of smallholder farmers with agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure satisfaction index (Coef=0.35: P<0.1); Social infrastructure satisfaction index (Coef=0.37: P<0.1); Institutional infrastructure satisfaction index (Coef=1.25: P<0.01); Equipment infrastructure satisfaction index (Coef=1.04: P<0.01); Level of education of respondents (Coef=1.24: P<0.01); Access to extension services (Coef=1.58: P<0.01); Age of smallholder farmers in the study area (Coef=0.05: P<0.01); Number of years farming (Coef = -0.57: P<0.1); and Number of household members assisting in farming (Coef=0.19: P<0.1). The results of the Heckman selection model revealed that the variables impacting on agricultural income were: agricultural infrastructure availability index (Coef=1.12: P<0.01); and access to extension services (Coef=0.62: P<0.05). With regards to farmers not satisfied with agricultural infrastructure, factors influencing agricultural income were: institutional infrastructure satisfaction index (Coef = 0.54: P< 0.05); level of education (Coef=1.25: P<0.01); access to extension services (Coef = 1.77: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef = 0.34: P<0.01). Furthermore, those impacting on agricultural production were: infrastructure satisfaction index (Coef=-1.31: P<0.01); infrastructure accessibility index (Coef=-0.59: P<0.05); Level of education of smallholder farmers (Coef=0.64: P<0.01); access to extension services (Coef=1.29: P<0.01); and membership of farmers’ organisations (Coef=0.66: P<0.01). The results of the Tobit Regression Model showed that among others factors influencing availability of agricultural infrastructure, the following variables played a critical role: assistance of household members in farming enterprise (Coef=0.702: P<0.01); farm ownership (Coef=0.962: P<0.01); farm acquisition (Coef=0.323: P<0.01) farmer occupation (Coef=0.785: P<0.01); member of farmers’ organisations (Coef=2.066: P<0.01); sources of labour (Coef=1.283: P<0.01); farming experience (Coef=0.100: P<0.01); and agricultural production inputs (Coef=-0.763: P<0.05). In terms of accessibility to agricultural infrastructure, the following variables played a critical role: engagement in non-farming activities Coef=1.275: P<0.01); contact with extension services (Coef=1.205: P<0.01); farm ownership (Coef=0.403: P<0.01); farmer occupation (Coef=0.456: P<0.01); membership of farmers’ organisations (Coef=1.111: P<0.01); sources of labour (Coef=0.653: P<0.01); farming experience (Coef=0.045: P<0.05) and land tenure (Coef=0.156: P<0.01). In terms of satisfaction with agricultural infrastructure, among other factors influencing satisfaction with agricultural infrastructure, the following variables played a critical role: organisation for extension services (Coef=1.779: P<0.01); assistance of household members in farming enterprise (Coef=0.411: P<0.01); government agricultural support to farmers (Coef=0.419: P<0.01); farm ownership (Coef=0.464: P<0.01); membership of farmers’ organisations (Coef=1.011: P<0.01); age of farmer (Coef= 0.030: P<0.01); level of education (Coef= 0.483: P<0.01); marital status (Coef=0.290: P<0.01); and gender (Coef= -0.576: P<0.01). The results of the analysis were used to close the knowledge gap with regards to the impact of agricultural infrastructure, availability, accessibility and satisfaction on the productivity and agricultural income of smallholder farmers in the North West Province. In terms of recommendations, the study highlighted that agricultural industries and government should commit in assisting smallholder farmers to be productive and to participate in economic activities. This could be achieved through collaboration with industries in implementing initiatives that assist and accelerate the development of smallholder farming and also through assisting smallholder farmers access agricultural infrastructure. / Agriculture and  Animal Health / D. Litt et Phil. (Agriculture)