Regression Analysis of Factors Affecting Crop Yield Among Smallholder Farmers in Benue State

Benue State, known as Nigeria's food basket and located in the North Central geopolitical zone, faces declining agricultural productivity driven by land tenure insecurity, climate variability, and limited access to improved inputs. This study applies multiple regression analysis to identify and quantify the key determinants of crop yield among 250 smallholder yam and cassava farmers in Makurdi, Gwer West, and Logo Local Government Areas. Primary data are collected through structured questionnaires and direct field measurements during the 2023 planting season, capturing yield per hectare, fertiliser application rate, improved variety adoption, land size, years of farming experience, access to extension services, and rainfall adequacy score. Ordinary least squares regression is estimated separately for yam and cassava yield models after confirming the absence of severe multicollinearity using variance inflation factors. The yam yield model explains 67 percent of observed variance, with fertiliser rate, improved variety adoption, and extension service access as the three most significant positive predictors. Land fragmentation, measured as the number of non-contiguous plots, is identified as a significant negative predictor for both crops. The study recommends consolidating farmer input subsidy programmes with variety improvement initiatives to achieve multiplicative productivity gains. These findings have direct relevance to the Benue State Agricultural Development Programme's strategic planning cycle. Keywords: crop yield regression, smallholder farming, Benue State, agricultural productivity, OLS estimation.