Credit Preferences and Technology Adoption Constraints among Vegetable Farmers in Kerala, India
Arpith M. Johny *
Department of Agricultural Economics, College of Agriculture, Vellayani, India.
F. Thasnimol
Department of Agricultural Economics, College of Agriculture, Vellayani, India.
Anil Kuruvila
Department of Agricultural Economics, College of Agriculture, Vellayani, India.
A. R. Durga
Department of Basic and Allied Science, College of Forestry, Vellanikkara, India.
S. K. Nisha
Department of Vegetable Science, College of Agriculture, Vellayani, India.
Ajan S. Raj
Department of Agricultural Economics, College of Agriculture, Vellayani, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Vegetable farmers in Kerala face several challenges, including limited access to agricultural credit, high production costs, and barriers to adopting modern farming technologies. Understanding the relationship between farmers’ credit preferences and technology adoption constraints is essential for developing effective financial and agricultural support strategies.
Aims: To determine the primary structural challenges to modern technology adoption, to rank the criteria that vegetable producers consider before obtaining an agricultural loan, and to examine the association between farmers' credit priorities and their technology adoption barriers.
Study Design: Cross-sectional primary survey with a descriptive-analytical ranking design.
Place and Duration of Study: Alappuzha and Thiruvananthapuram vegetable-growing regions of Kerala, India, during the 2025–2026 growing season.
Methodology: A pre-tested structured schedule was used to interview 180 randomly selected vegetable growers (n = 180; 100% response rate). Respondents ranked five technology adoption barriers (N = 5) and six credit factors (N = 6). These rankings were transformed into numerical scores using the Garrett ranking method. Kendall's coefficient of concordance (W), contingency chi-square tests, 95% confidence intervals (CI), and the non-parametric Friedman chi-square test were employed in the analysis.
Results: Farmers showed a statistically significant ordering of borrowing criteria (Friedman χ² = 359.92, df = 5, Kendall's W = 0.400, P < .001). “Easy and quick access” ranked first (mean score: 64.88), followed by “Low interest rate” (61.47) and “Less procedure” (52.76), whereas “Previous loan experience” ranked last (31.47). Among technology adoption constraints (Friedman χ² = 62.51, df = 4, W = 0.087, P < .001), agreement among farmers was weak: “Lack of capital” ranked first (56.08), followed by “Difficulty in operation or maintenance” (53.33) and “High risk or uncertainty” (52.83), with overlapping 95% confidence intervals for the three leading constraints. A moderate association was found between farmers' main technology constraint and their main credit priority (Pearson χ² = 65.27, df = 20; Monte Carlo P < .001; Cramér's V = 0.30), with capital-constrained farmers primarily prioritising low interest rates and risk-constrained farmers prioritising quick access.
Conclusion: Farmers’ stated credit preferences centre on the speed and cost of borrowing. Capital shortage was the highest-ranked barrier to technology adoption. These patterns suggest that quicker agricultural loan processing, direct maintenance assistance via Krishi Bhavans, and collateral-free Joint Liability Group (JLG) finance for growers limited by leased-in land are worth considering as policy options.
Keywords: Agricultural credit, credit preferences, Garrett ranking, Kerala agriculture, technology adoption, tenant farming, vegetable cultivation