Adaptive Management of Crop Rotation Systems Under Climate Uncertainty in Nigerian Farming: A Dynamic Optimisation Approach

This study develops a dynamic optimisation framework for adaptive management of crop rotation systems under climate uncertainty in Nigerian farming, contributing an original decision theory approach to rotation management that explicitly accounts for inter-seasonal rainfall variability and climate change trends. Crop rotation decisions are multi-period agricultural management choices where the optimal sequence of crops depends on anticipated weather, market prices, soil state, and weed and pest dynamics, all of which are uncertain. Standard rotation recommendations assume stable conditions that do not reflect the climate variability reality of Nigerian farming. This study formulates a stochastic dynamic programming model of rotation management under uncertain seasonal rainfall, crop prices, and soil fertility states, calibrated to six representative farming systems across all geopolitical zones. Rainfall scenario generation uses historical climate data augmented by CORDEX-Africa climate projections. Agronomic response functions are derived from multi-environment trial data. Model solutions identify adaptive rotation policies that maximise expected long-term income while maintaining soil fertility above minimum thresholds. Field validation compares model-recommended adaptive policies against farmer practice and static expert recommendations across three growing seasons. Findings reveal that adaptive rotation policies responding to accumulated seasonal rainfall information in real time outperform static expert recommendations by 18 to 32 percent in expected income across climate scenarios. The performance advantage is largest in the most climate-variable northern zones. The study contributes an original stochastic dynamic programming rotation framework for Nigeria and recommends its integration into seasonal climate-based agricultural advisory services.

Keywords: adaptive management, crop rotation, stochastic optimisation, climate uncertainty, Nigeria.