A multi-objective optimization approach to simultaneously halve water consumption, CH₄, and N₂O emissions while maintaining rice yield

Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective, such as increasing crop yield, reducing irrigation water consumption, or minimizing greenhouse gas (GHG) emissions, have proven to be insufficient. Simultaneously optimizing multiple competing rice farming objectives remains less explored. The present study aimed to increase current rice yield, reduce irrigation water consumption, and tackle the dilemma to reduce GHG (CH4 and N2O) emissions at once. Using a heuristic and holistic method, we optimized farm management, focusing on irrigation regimes, sowing window, fertilization rate, tillage depth, and their interactions. We calibrated and validated the process-based DeNitrification-DeComposition (DNDC) model with 5 years of eddy covariance observations in a rice paddy site. The DNDC model was integrated with a Pareto-based non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. Results show that current farm management practices have failed to achieve the potential yield and imposed high environmental costs in the form of water use (604-810 mm/yr) and GHG emissions (CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr). By contrast, our new approach suggested that the optimized management could maintain or even increase current crop yield to its potential (-10 t/ha), while reducing irrigation demand and GHG emissions by >50 %. Our results suggest that earlier sowing, combined with improved irrigation practices, may play a crucial role in maximizing crop yield and potentially providing environmental benefits. We found that the optimal proportion of non-flooded days was around 54 % of the growing season length, and the optimal stage for non-flooding days was the vegetative stage. Our approach could further be applied to evaluate the environmental sustainability of farming systems under various climate and local conditions, and to guide poli-cymaking and farming practices.