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

Abstract

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 balance, and nitrogen dynamics, but do not explicitly represent soil carbon turnover and GHG emissions, limiting their use for mitigation planning and Tier 3 inventories. In this study, ORYZA was extended with mechanistic modules for soil organic matter (SOM) decomposition, GHG fluxes, and vertical solute transport, enabling coupled simulation of carbon dynamics and emissions. The model was applied to (i) evaluate crop and soil parameter effects on grain yield, soil organic carbon (SOC), and GHG emissions; (ii) quantify sensitivities via Adaptive Sobol’ analysis; and (iii) examine non-linear parameter interactions using Shapley Additive exPlanations (SHAP). Simulations were constrained using experimental data from irrigated rice systems at IRRI (2023–2024). An ensemble was generated using ±25% parameter perturbations, and a machine-learning surrogate was trained to emulate model outputs for sensitivity analysis. Results indicate that soil hydraulic properties exert strong control on yield and biomass. Seasonal CH₄ emissions were mainly influenced by methanogenesis potential, soil bulk density, and decomposition parameters, while SOC dynamics were influenced by initial SOC/soil organic nitrogen (SON) stratification and crop shoot allocation. SHAP analysis indicated non-linearities and parameter interactions consistent with established biogeochemical processes. Trade-off analysis suggests that 22–38% of parameter combinations achieved higher yield with lower CH₄ emissions. The framework provides a basis for improved Tier 3 GHG estimation and mitigation in rice systems.