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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 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. 

IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field. MethodsData were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield. ResultsThe exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data. DiscussionFurther work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.