Abstract Biochar is a promising climate-smart agriculture (CSA) solution to sustainably support food security while mitigating the adverse impacts of climate change on agroecosystems. Yet, its effectiveness across diverse environments is not well quantified. We developed a process-based biochar model and used it to evaluate biochar’s impacts on agroecosystem production and the dynamics of soil biogeochemical cycles (e.g., key CSA indicators such as crop yield, soil organic carbon (SOC), and greenhouse gas (GHG) emissions) across 48 globally distributed field experiment sites. The biochar model was calibrated and evaluated in maize, wheat, and soybean cropping systems, with an average root mean square error of 1878.9 kg ha − 1 ( R 2 = 0.78) for crop yield, 4129.3 kg C ha − 1 ( R 2 = 0.72) for SOC, and 1995.7 kg CO 2 ha − 1 ( R 2 = 0.91) for GHG emissions. The model accuracy varied across environments, with yield predictions performing better in tropical ( R 2 = 0.90) and temperate ( R 2 = 0.81) zones and on medium-textured soils ( R 2 = 0.87), but declining in arid regions ( R 2 = 0.55) and on coarse soils ( R 2 = 0.65). Simulation accuracy of SOC and CO 2 was higher in maize than in soybean systems. Biochar application rates also influenced model performance, with medium rates best for crop yield and high rates optimal for SOC and CO 2 emissions. These results highlight the need for robust modeling tools to optimize biochar application across diverse soil and climate conditions. These tools can be important for stakeholders, from farmers to policymakers, and can help refine biochar management strategies and advance global goals of sustainable intensification and net-zero agricultural systems. Highlights A process-based biochar model was developed and calibrated using observational data from 48 global field studies. Model’s performance was evaluated across a range of climate conditions, soil types, cropping systems, and biochar application rates. The model serves as a robust tool for optimizing site-specific biochar application and advancing climate-smart agriculture. Graphical Abstract
Ren et al. (Fri,) studied this question.