ADOPTION OF CLIMATE-SMART AGRICULTURE PRACTICES AMONG SMALL HOLDER FARMERS IN NORTHERN TARABA STATE, NIGERIA

Abstract

This study assessed the adoption of Climate-Smart Agriculture practices among smallholder farmers in Taraba State, Nigeria, a region where approximately 90% of agricultural production is rain-fed and highly vulnerable to climate variability. The specific objectives of the study were to describe the socioeconomic characteristics of smallholder farmers, evaluate their awareness and technical knowledge of CSA practices, examine the extent of adoption (identifying the most and least adopted practices), determine the influence of socioeconomic factors on adoption decisions, and analyze the primary institutional, financial, and socio-cultural constraints limiting widespread implementation. Utilizing a multi-stage sampling procedure, a sample size of 108 respondents was determined using Cochran’s formula and selected across three agricultural Local Government Areas (Karim-Lamido, Zing, and Lau). Primary data were gathered using structured questionnaires and analyzed through both descriptive statistics (frequencies, percentages, weighted means) and inferential statistics (binary logistic regression). The findings revealed a predominantly middle-aged (35% aged 31–45) and male-dominated (57%) farming population operating on small landholdings (74% under 3.0 hectares) with low annual cash reserves (80% earning under 300,000 NGN). While general conceptual awareness of climate adaptation was moderate (56.5%), a significant knowledge-adoption divide was observed between traditional agronomic methods and capital-intensive practices. Farmers demonstrated high knowledge and strong implementation of low-cost, immediate-benefit practices such as intercropping (83% frequent/full adoption) and crop rotation (64%). Conversely, advanced or high-cost techniques including drip irrigation (66.7% low/never adopted), soil testing (64.8%), and compost making suffered from low technical knowledge and negligible adoption rates. The binary logistic regression model demonstrated a highly robust fit (Nagelkerke R² = 0.78), establishing that CSA adoption is significantly driven by formal educational level (p < 0.01), access to agricultural credit (p < 0.10), age (p < 0.10), farm size (p < 0.05), and gender (p < 0.05). Systemic constraints hindering widespread scaling included severely understaffed agricultural extension networks (81.5% rating as very serious), acute credit constraints (79.6%), and poor road infrastructure. The study concludes that human capital, credit access, and institutional support are critical determinants of CSA uptake. It is recommended that policymakers establish targeted, low-interest microcredit schemes, strengthen extension networks through bundled practice demonstrations, and implement gender-sensitive initiatives to ensure equitable access to productive resources and land titles.