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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 Crop phenology is a key indicator for assessing agroecosystem responses to environmental changes and informing adaptive management practices. Accurate detection of phenology dynamics is crucial for understanding agroecosystem function and structure at regional scales and supporting policy‐making aimed at sustainable management. This study fused Landsat and MODIS observations with a newly developed Gap Filling and Savitzky‐Golay filtering (GF‐SG) method to derive 30 m major phenological metrics (planting, corn silking, soybean blooming, corn maturity, soybean dropping‐leaves, and harvesting) in the US Midwest from 2000 to 2022. The 30 m metrics were evaluated against PhenoCam observations and ground surveys at field and state scales. Our results suggested that the growth seasons of corn (from planting to maturity date) and of soybeans (from planting to dropping‐leaves date) were lengthened by 0.19 days year⁻¹(p = 0.03) and 0.08 days year⁻¹(p = 0.20), respectively. While planting dates historically advanced in response to rising temperatures, satellite observations indicated a recent trend toward delayed planting in certain regions, shifting more time into the reproductive period. Further analysis suggests that these delays were associated with increased cumulative pre‐season precipitation (p < 0.0001 for corn andp < 0.0001 for soybeans). Our 30 m phenological metrics can strengthen yield gap analysis and regional agricultural assessments and provide science‐based information to facilitate the development of tailored adaptation strategies. 

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. 

Abstract Biochar is well-accepted as a viable climate mitigation strategy to promote agricultural and environmental benefits such as soil carbon sequestration and crop productivity while reducing greenhouse gas emissions. However, its effects on soil microbial biomass carbon (SMBC) in field experiments have not yet been thoroughly explored. In this study, we collected 539 paired globally published observations to study the impacts of biochar on SMBC under field experiments. Our results suggested an overall positive impact of biochar (21.31%) on SMBC, varying widely with different climate conditions, soil types, biochar properties, and management practices. Biochar application exhibits significant impacts under climates with mean annual temperature (MAT) < 15 °C and mean annual precipitation (MAP) between 500 and 1000 mm. Soils of coarse and fine texture, alkaline pH (SPH), soil total organic carbon (STC) content up to 10 g/kg, soil total nitrogen (STN) content up to 1.5 g/kg, and low soil cation exchange capacity (SCEC) content of < 5 cmol/kg received higher positive effects of biochar application on SMBC. Biochar produced from crop residue, specifically from cotton and maize residue, at pyrolysis temperature (BTM) of < 400 °C, with a pH (BPH) between 8 and 9, low application rate (BAP) of < 10 t/ha, and high ash content (BASH) > 400 g/kg resulted in an increase in SMBC. Low biochar total carbon (BTC) and high total nitrogen (BTN) positively affect the SMBC. Repeated application significantly increased the SMBC by 50.11%, and fresh biochar in the soil (≤ 6 months) enhanced SMBC compared to the single application and aged biochar. Biochar applied with nitrogen fertilizer (up to 300 kg/ha) and manure/compost showed significant improvements in SMBC, but co-application with straw resulted in a slight negative impact on the SMBC. The best-fit gradient boosting machines model, which had the lowest root mean square error, demonstrated the relative importance of various factors on biochar effectiveness: biochar, soil, climate, and nitrogen applications at 46.2%, 38.1%, 8.3%, and 7.4%, respectively. Soil clay proportion, BAP, nitrogen application, and MAT were the most critical variables for biochar impacts on SMBC. The results showed that biochar efficiency varies significantly in different climatic conditions, soil environments, field management practices, biochar properties, and feedstock types. Our meta-analysis of field experiments provides the first quantitative review of biochar impacts on SMBC, demonstrating its potential for rehabilitating nutrient-deprived soils and promoting sustainable land management. To improve the efficiency of biochar amendment, we call for long-term field experiments to measure SMBC across diverse agroecosystems. Graphical Abstract 

Abstract Crop phenology regulates seasonal carbon and water fluxes between croplands and the atmosphere and provides essential information for monitoring and predicting crop growth dynamics and productivity. However, under rapid climate change and more frequent extreme events, future changes in crop phenological shifts have not been well investigated and fully considered in earth system modeling and regional climate assessments. Here, we propose an innovative approach combining remote sensing imagery and machine learning (ML) with climate and survey data to predict future crop phenological shifts across the US corn and soybean systems. Specifically, our projected findings demonstrate distinct acceleration patterns—under the RCP 4.5/RCP 8.5 scenarios, corn planting, silking, maturity, and harvesting stages would significantly advance by 0.94/1.66, 1.13/2.45, 0.89/2.68, and 1.04/2.16 days/decade during 2021–2099, respectively. Soybeans exhibit more muted responses with phenological stages showing relatively smaller negative trends (0.59, 1.08, 0.07, and 0.64 days/decade under the RCP 4.5 vs. 1.24, 1.53, 0.92, and 1.04 days/decade under the RCP 8.5). These spatially explicit projections illustrate how crop phenology would respond to future climate change, highlighting widespread and progressively earlier phenological timing. Based on these findings, we call for a specific effort to quantify the cascading effects of future phenology shifts on crop yield and carbon, water, and energy balances and, accordingly, craft targeted adaptive strategies. 

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.