Field-Scale Improvement of Water Allocation for Maize Cultivation Using Grey Wolf Optimization Algorithm

It is necessary to develop methods to improve soil moisture capacity and agricultural productions in arid and semiarid areas. This study was conducted to evaluate the impact of optimal water allocation to improve the yield production in Gotvand Plain, Khuzestan Province, south-western Iran. A field-scale experiment with three scenarios of sugarcane bagasse compost application (0, 15 and 30 ton/ha) and four levels of water supply (50%, 75%, 100% and 125% of total allowable water) was performed in three replications (March–July 2019). Porous media texture, infiltration rate and irrigation demand were simulated at daily time steps to provide the root zone moisture content using real-time analysis of soil water balance. Furthermore, the maximization of readily available water based on the agricultural demand were considered using grey wolf optimization algorithm under deep percolation and runoff constraints. The results showed that the water allocation strategies and compost scenarios can improve water use efficiency and soil moisture.