Stochastic Multi-Seasonal Optimization of Sesame Cultivation in Chad: A Nonlinear Programming Approach

Sesame cultivation in Chad has witnessed substantial expansion, driven by increasing global demand. However, the absence of advanced decision-support tools among local farmers has led to suboptimal resource utilization and economic inefficiencies. This study extends classical linear programming models by integrating stochastic rainfall variability, non-linear irrigation cost structures, and a multi-seasonal decision-making framework. The proposed stochastic multi-seasonal optimization model strategically allocates land between early- and late-maturing sesame varieties while accounting for uncertainty in precipitation patterns and market price fluctuations. A nonlinear irrigation cost function is employed to capture diminishing returns on water investment, enhancing the realism of the model. By leveraging multi-period optimization, this approach evaluates the cumulative impact of seasonal decisions, providing a rigorous decision-support framework for optimizing productivity and economic returns under stochastic climatic and market conditions.