Statistical Modelling of Agricultural Insurance Uptake and Loss Claims in the North West Zone of Nigeria

Agricultural insurance remains severely underutilised among Nigerian smallholder farmers despite growing climate-related production risks, and statistical modelling of uptake determinants and loss claim patterns provides evidence critical for scheme redesign and expansion in the North West zone. This study statistically models agricultural insurance uptake and indemnity loss claims using data from the Nigeria Agricultural Insurance Corporation (NAIC) North West regional office for Kano, Kaduna, and Kebbi States from 2019 to 2022. Logistic regression modelled insurance uptake as a function of farm size, crop type, credit access, extension service contact, and loss experience. Loss amount was modelled by gamma regression given its right-skewed distribution. Time series analysis characterised annual claim frequency trends. Insurance uptake was 8.4 percent among eligible farmers in the dataset. Logistic regression identified farm size above 2 hectares (OR 3.8), prior year loss experience (OR 4.1), and extension service contact (OR 2.6) as the strongest uptake predictors. Gamma regression showed that rainfall deficit events were associated with 2.4 times higher mean claim amounts than pest-related losses. Annual claim frequency showed a statistically significant increasing trend from 2019 to 2022 (Kendall's tau = 0.89, p = 0.041). The study recommends NAIC introduce bundled credit-insurance products, claims processing digitisation, and extension-led enrolment campaigns in the North West zone. Keywords: agricultural insurance, logistic regression, loss claims, North West Nigeria, NAIC