Development of irrigation schedule and management model for sustaining optimal crop production under agricultural drought

Abstract

Agriculture is vulnerable to drought indicating that the increasing climate crisis requires the necessity of sustainable crop production. In this study, we developed the Irrigation Schedule and Management (ISM) model based on a simulation–optimization (Soil Water Atmosphere Plant-SWAP model with Genetic Algorithm-GA) framework. The ISM model finds an optimal combination of Irrigation Water Amount (IWA) and Irrigation Interval (II) by adjusting Water Stress (WS) responding to environmental conditions (weather, soils, crops and bottom boundary conditions) throughout growing periods. By conditioning the crop (WS) and water management (IWA and II) variables, ISM improves the sustainability of optimal crop productions under different climatic-land surface conditions. The Regional Agromet Center (RAC) site in Faisalabad (at Punjab, Pakistan) was selected to test the proposed ISM model for the field validation/synthetic numerical experiments with various crops (Wheat, Corn and Potato) and soils. We demonstrated that the ISM model that reflects the relationship between crop and water management variables improved the sustainability of crop productions and Water Productivity (WP) compared to those of the conventional irrigation method at the RAC site under various environment conditions. Additionally, the ISM-based long-term crop productions showed the variations along the yearly precipitation changes indicating that optimal combinations of the crop and water management variables are considerably influenced by environmental conditions. Although uncertainties exist, our proposed ISM model can contribute to the establishment of efficient irrigation schedule/management plans under agricultural drought.