Sustainability, Volume 17, Issue 13
2025 July-1 - 565 articles
Cover Story: Machine learning models are valuable in assessing how agricultural practices influence greenhouse gas emissions. We identified soil moisture thresholds associated with soil CO2 emissions under climate-smart practices. Using a classification and regression tree approach, we determined a critical moisture level below which CO2 emissions were significantly lower. Generalized additive and multilinear regression models (GAMs and MLR) were used to predict short-term emissions from sweet corn plots supplemented with biochar, chicken, and dairy manure. The use of biochar resulted in the greatest reduction in soil CO2 emissions, and GAMs outperformed MLR. Identifying these thresholds provides support for more effective mitigation strategies and sustainable soil management to reduce emissions and maintain yields under changing climate conditions. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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