Optimizing Non-flooded Irrigation Regime under System of Rice Intensification Crop Management using Genetic Algorithms

Abstract

In this study, an optimal non-flooded irrigation regime that maximizes both the yield and water productivity of System of Rice Intensification (SRI) crop management was simulated by genetic algorithms (GAs) model. Here, the field was classified into wet (W). medium (M) and dry (D) conditions in each growth stage, namely initial, crop development, mid-season and late season stages according to the soil moisture level. The simulation was performed based on the identification process according to the empirical data during three cropping seasons. As the results, the optimal combination was 0.622 (W), 0.563 (W), 0.522 (M), and 0.350 cm3/cm3(D) for initial, crop development, mid-season and late season growth stages, respectively. The wet conditions in the initial and crop development growth stages should be achieved to provide enough water for the plant to develop root, stem and tiller in the vegetative stage, and then the field can be drained into medium condition with the irrigation threshold of field capacity to avoid spikelet fertility in mid-season stage and finally, let the field dry to save more water in the late season stage. By this scenario, it was simulated that the yield can be increased up to 6.33% and water productivity up to 25.09% with saving water up to 12.71% compared to the empirical data.