Multiobjective Simulation–Optimization of Crop Allocation and Water Resources in Canal Command Areas Using Genetic Algorithms

This study develops an integrated simulation–optimization framework for sustainable crop allocation and water resource management in the Bargarh Canal Command (BCC), eastern India. Efficient irrigation allocation remains a critical challenge due to competing demands, groundwater–surface water interactions and environmental constraints. Existing studies often address crop water demand, groundwater dynamics or optimization independently, limiting real‐world applicability. To overcome this gap, the study integrates CROPWAT 8.0 for estimating crop water requirements, GMS‐MODFLOW for simulating groundwater availability and interactions and a multiobjective genetic algorithm (MOGA) for optimizing crop allocation. The model simultaneously maximizes agricultural production and economic returns while minimizing labour requirements and satisfying constraints such as groundwater sustainability, soil salinity, carbon emissions and nutritional crop needs. The results show that the optimized cropping pattern increases economic returns from 867.31 to 987.65 billion rupees (14%) compared to existing practices. The solution promotes crop diversification by reducing water‐intensive paddies and expanding pulses and vegetables. Water utilization reached 99% for surface water and 100% for groundwater, indicating efficient conjunctive use. The proposed framework provides a practical decision‐support tool for improving productivity, resource efficiency and sustainability in canal command systems.