Modelling Loan Defaults by Sugarcane Farmers

The agriculture sector employs substantial labour force and contributes substantially to the nation's Gross Domestic Product. Its success depends heavily on agricultural credit and financing. The significance of credit in agriculture is being marred by high rates of default. The high default rate on agricultural loans is cause for concern from both an academic and policy perspective. The loan default is a common issue that lessens the effectiveness of credit laws and lending practices. The study investigated the determinants of loan defaults by sugarcane farmers in the Lowveld region of Zimbabwe. The study is underpinned by the agency theory, social capital theory, and financial literacy theory. These theories help analyses the interaction between principals representing lenders, and agents in our case sugarcane farmers. The study used a binary logistic regression model to analyse the major determinants of loan default by sugarcane. The study established that farmer-related factors such as education level, experience, and off-farm income significantly influence loan default rates. Further the study found that lender-associated characteristics loan duration and interest rates drive loan defaults. The study recommends that borrowers should consider insurance schemes supported by lenders and government to mitigate default risks, while also embracing technology and resource-efficient land and credit use strategies to optimize productivity. Banks and financial institutions should intensify loan monitoring activities both within the office and through field visits for detecting and addressing undesirable repayment patterns promptly.