Modeling the co-benefits and trade-offs of rice production interventions for mitigation and adaptation to climate change

Climate change is a global threat, where rice production emits significant greenhouse emissions. Therefore, rising interest is in reducing agricultural greenhouse gas (GHG) emissions while facilitating agricultural systems' adaptation to climate change. Much of the combined agricultural mitigation and adaptation research centers on managing soil carbon and nutrient cycling processes, particularly for rice-based farming systems. We summarized the preliminary results from two international projects in Bangladesh and Vietnam - to evaluate agricultural soil-water-crop interactions and discuss implications for climate mitigation and adaptation by crop modeling technology. Modeling results confirmed the appropriate water, fertilizer, and system of rice intensification management practices could adapt to future climates for maintaining or enhancing co-benefits and minimizing the tradeoffs, for example, increasing or maintaining the rice grain yield and soil organic carbon storage, but saving irrigation water and reducing GHG emissions from the field site to region scales. However, these results also suggest no uniform solutions among field sites across region scale due to the various biophysical conditions. The field experimental results obtained from one to a few field sites could not be directly applied to different field sites over region scales. The same technology combinations could not gain the optimal cobenefits and minimal trade-offs under current and future climates conditions because of the various biophysical conditions and complex interactions of crop, soil, climate, and cropping management. In these studies, we tried to build up the modeling system as a time- and cost-efficient approach to quantify the complex interactions, adjust cropping management from field experiments for specific sites, maximize co-benefits, and minimize the trade-offs in the mitigation and adaptation to climate change.