PARAMETERIZING THE INTEGRATED EFFECT OF BIOCHAR ADDITION ON SOIL GREENHOUSE GAS EMISSION AND CARBON SEQUESTRATION USING ARTIFICIAL INTELLIGENCE AND LAND SURFACE MODEL

Biochar addition is a potential climate-smart agriculture practice due to biochar’s capacity to sequestrate carbon, hold water, and increase nutrient availability. However, these biochar benefits to agriculture and environments still showed large uncertainty depending on the physical and chemical characterization of biochar, and diverse soil environments where biochar is applied. To date, increased model efforts have outlined long-term single characterized biochar addition to a few sites, whereas there is still a lack of clear understanding about the effect of biochar addition on soil chemical and physical processes and the consequent impact on greenhouse gas (GHG) emission and carbon sequestration, much less a comprehensive upscaling impact over diverse environments. To address this knowledge gap, we explored the heterogeneity of biochar’s effect by integrating global-scale biochar addition experiments with process-based and artificial intelligence (AI) models. We incorporated biochar decomposition dynamics into the Community Land Model (CLM5.0). We integrated a total of 359 biochar addition experiments from 69 diverse locations across the globe to train an AI-based surrogate model for parameterizing the biochar effect on the decomposition of natural soil organic matter (SOM). We then applied the coupled CLM-AI model to investigate the heterogeneity of biochar’s effect on GHG emissions and carbon sequestration. Our results indicated that the carbon benefit of biochar addition would be more obvious in nutrient-deficient and water-limited soils due to increased SOM and inorganic nutrients adsorption and water holding capacity of biochar, extra carbon input, and improved nutrient availability resulting from biochar-derived nitrogen mineralization.