Climate‐Resilient Rice Management Reduces Irrigation and Greenhouse‐Gas Emissions While Maintaining Yield

Abstract

Climate change increases the uncertainty of rice production and makes it more difficult to balance high yield, water saving, soil carbon sequestration, and greenhouse gas (GHGE, combined CH 4 and N 2 O emissions expressed as CO 2 ‐equivalents) mitigation. To address these challenges, this study developed a DNDC–NSGA‐III–TOPSIS framework for optimizing rice management. Based on the field experiment design and DNDC sensitivity analysis, precision water–fertilizer management variables were selected as decision variables to optimize yield, soil organic carbon (SOC), GHGE, and irrigation. The TOPSIS‐selected management strategy was further evaluated under different future climate scenarios from 2030 to 2050 to assess its robustness. The model reproduced the observed patterns with variable performance among indicators; daily‐scale R 2 ranged from 0.58 to 0.61 for CH 4 emissions to 0.74–0.91 for N 2 O emissions, while annual‐scale nRMSE ranged from 1.4% to 10.3% across evaluated indicators. Compared with current practices, the optimal strategy combined a 12.7% reduction in nitrogen application, 10.4% organic manure substitution, and prolonged water‐layer maintenance during the tillering and heading stages, increasing yield by up to 6.2% and SOC by 6.86%, while reducing GHGE emissions by 29.66% and irrigation by 57.09%. Under SSP1‐2.6 to SSP5‐8.5 scenarios, the optimized management reduced interannual variability across objectives, maintaining an average coefficient of variation (CV) of around 20% and a sustainable yield index (SYI) above 80%, indicating improved yield stability and resilience to climate impacts. Overall, the optimization framework developed can provide precise irrigation schedules and stage‐specific fertilization strategies. It further predicts field management performance under diverse future climate scenarios and assesses the stability of optimized farming practices to mitigate climate uncertainties, thereby offering reliable decision support for sustainable rice production.