Multistage Stochastic Optimization for Agricultural Operations in Arid and Semi-Arid Regions

Abstract

Extreme weather events, particularly droughts, pose significant risks to agriculturalproductivity and economic stability in arid and semi-arid regions. Addressing these challenges requires a comprehensive approach that integrates advanced optimization techniques and machine learning to enhance decision-making under uncertainty. This dissertation develops multistage stochastic optimization models to improve agricultural operations, like water irrigation, crop rotation, harvesting, machinery scheduling, resource allocation, and yield prediction, contributing to more resilient and sustainable farming systems. With developed multistage stochastic optimization models for planting planningand harvest scheduling, decision-making tools are designed to minimize costs, maximize profit, and minimize the risk of water scarcity and extreme weather events like drought in arid and semi-arid regions. These models incorporate multi-method irrigation strategies, machinery allocation, and harvesting logistics, ensuring efficient resource utilization and economic viability under drought scenarios. To complement these optimization strategies, machine learning is utilized to enhancepredictive capabilities for crop yields. By integrating weather and soil data, machine learning techniques, including hybrid deep learning architectures, are employed to capture complex relationships between environmental variables and agricultural output. Comparative analyses across different climatic regions provide insights into yield variability, improving forecasting accuracy, and informing adaptive management practices. Additionally, precision agriculture techniques, such as precision irrigation, offer targeted solutions for managing limited water resources in arid and semi-arid regions. By integrating data from soil moisture sensors and weather forecasts, machine learning models with multistage stochastic optimization can generate adaptive andcost-effective irrigation schedules under uncertain weather conditions, ensuring efficient resource allocation while maintaining crop productivity. This research advances decision-making for agricultural operations by integratingmultistage stochastic optimization and machine learning. The findings offer actionable strategies for farmers, policymakers, and stakeholders to enhance sustainability, mitigate economic losses, and improve resilience against climate uncertainties.