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Abstract In agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning. 

By influencing soil organic carbon (SOC), cover crops play a key role in shaping soil health and hence the system's long‐term sustainability. However, the magnitude by which cover crops impacts SOC depends on multiple factors, including soil type, climate, crop rotation, tillage type, cover crop growth, and years under management. To elucidate how these multiple factors influence the relative impact of cover crops on SOC, we conducted a meta‐analysis on the impacts of cover crops within rotations that included corn (Zea maysL.) on SOC accumulation. Information on climatic conditions, soil characteristics, management, and cover crop performance was extracted, resulting in 198 paired comparisons from 61 peer‐reviewed studies. Over the course of each study, cover crops on average increased SOC by 7.3% (95% CI, 4.9%–9.6%). Furthermore, the impact of cover crop–induced increases in percent change SOC was evaluated across soil textures, cover crop types, crop rotations, biomass amounts, cover crop durations, tillage practices, and climatic zones. Our results suggest that current cover crop–based corn production systems are sequestering 5.5 million Mg of SOC per year in the United States and have the potential to sequester 175 million Mg SOC per year globally. These findings can be used to improve carbon footprint calculations and develop science‐based policy recommendations. Taken altogether, cover cropping is a promising strategy to sequester atmospheric C and hence make corn production systems more resilient to changing climates.