Genetic Algorithms Optimization for Water Management in Irrigated Paddy Fields

Abstract

Abstract Water management in paddy fields is main key to produce more yield and mitigate greenhouse gas (GHG) emissions at the same time. Commonly, Indonesian farmers apply continuous flooding irrigation to combat weed growth and gain maximum yield as well. However, this method is less efficient in water use and releases more GHG emissions as represented by Global Warming Potential (GWP) value. The System of Rice Intensification (SRI), as alternative rice farming, applies intermittent irrigation that has possibility produce more yields with minimum GHG emissions. However, optimum water management for these purposes is not clear yet. The objective of this study was to search optimal soil moisture and water depth in each growth stage using genetic algorithms (GA) model for SRI rice farming. GA model was developed based on one rice season experiment that was conducted during January to May 2018 with three water management regimes, i.e., flooded (FL), moderate (MD) and dry (DR) regimes, respectively. Based on the experiment, MD regime produced highest yield by 5.26% and 10.89% higher than those FL and DR regimes, respectively. So this was the best regime among others. However, this regime release more GHG emissions than that DR regime in which its GWP value was 87.85% higher than that DR regime. So, the GA model was used to find the better regime than that MD regime. Based on those empirical data, GA model found optimal soil moisture and water depth in four growth stages. Based on GA optimal scenario, the yield can be increased up to 1.31% higher than that MD regime and GWP can be reduced up to 8.62% lower than that MD regime. More field experiments are needed to validate the model under various climate conditions.