ORYZA model development for soil organic carbon dynamics and greenhouse gas emissions: Module description and sensitivity analysis

Rice cultivation contributes substantially to global greenhouse gas (GHG) emissions, accounting for ~10% of anthropogenic methane (CH₄), highlighting the need for process-based mitigation tools. Existing versions of ORYZA simulate crop growth, water and nitrogen dynamics, but do not explicitly represent soil carbon and nitrogen transformation and GHG emissions, limiting their use for mitigation planning and Tier 3 inventories. In this study, ORYZA was extended with soil carbon and nitrogen biogeochemical and GHG emission module. The resulting ORYZA 3.5 version concurrently simulates rice growth, soil organic carbon and nitrogen dynamics, and CH 4 /N 2 O/CO 2 emissions. Using experimental data from irrigated rice systems at IRRI (2023–2024), the model was applied to (i) evaluate crop and soil parameter effects on yield, SOC, and GHG emissions; (ii) quantify sensitivities via Adaptive Sobol’ analysis; and (iii) characterize non-linear parameter effects and interactions using Shapley Additive exPlanations (SHAP). An ensemble was generated using ±25% parameter perturbations, and machine-learning surrogate models were trained to emulate model outputs. Results indicated that soil hydraulic properties exerted the strongest control on yield and biomass. Seasonal CH₄ emissions were primarily influenced by methanogenesis potential, soil bulk density, and decomposition parameters, whereas SOC dynamics were dominated by initial SOC/soil organic nitrogen (SON) stratification and crop carbon allocation. SHAP analysis revealed substatial nonlinearities and parameter interactions. Trade-off analysis showed that 22–38% of parameter combinations achieved higher yield with lower CH₄ emissions with statistically robust frequencies (SE = 0.0022–0.0026). The ORYZA v3.5 provides a basis for Tier 3 GHG estimation, carbon accounting, and climate-smart rice management.