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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(R² = 0.78) for crop yield, 4129.3 kg C ha⁻¹(R² = 0.72) for SOC, and 1995.7 kg CO₂ ha⁻¹(R² = 0.91) for GHG emissions. The model accuracy varied across environments, with yield predictions performing better in tropical (R² = 0.90) and temperate (R² = 0.81) zones and on medium-textured soils (R² = 0.87), but declining in arid regions (R² = 0.55) and on coarse soils (R² = 0.65). Simulation accuracy of SOC and CO₂was 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 CO₂ 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. HighlightsA 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 

Abstract Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture. 

Abstract Fertilizer use enhances crop yields but exacerbates nitrate leaching, threatening water quality in farming systems. This study optimizes nitrogen fertilization strategies by integrating numerical modeling and machine learning to balance corn yield and nitrate leaching in the US Midwest, 1979–2100. We evaluate the economic optimum nitrogen rate under climate‐smart agricultural practices like no‐tillage and cover crops. Findings show that the economic optimum nitrogen rate sustains yields but increases nitrate leaching, especially under future scenarios. In contrast, optimized strategies—such as a lower rate (−30%) than the economic optimum nitrogen rate combined with cover crops and no‐tillage—could reduce yield‐scaled nitrate leaching by over 60% from 2020 to 2100. The study underscores the synergistic benefits of integrated management in mitigating trade‐offs between productivity and environmental impacts. Further predictions offer adaptive strategies for achieving sustainable, high yields while minimizing nitrate leaching under various climate scenarios. 

Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices.