Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N2O Emissions in China
2025
Yuan Zeng | Sujuan Chen | Yunpeng Li | Li Xiong | Cheng Liu | Muhammad Azeem | Xiaoting Jie | Mei Chen | Longjiang Zhang | Jianfei Sun
The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, enhance nutrient efficiency, boost crop yields, and reduce N2O emissions. However, comprehensive field studies on BBF impacts remain limited. This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N2O emissions, and to assess BBFs&rsquo: potential to increase yields and mitigate emissions in China&rsquo:s major crops. The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N2O emissions (R2: 0.99: EF: 0.99), while all models showed high accuracy for crop yields (R2, EF: 0.98&ndash:0.99). Variable importance analysis revealed that BBF C/N and BBF N/Mineral N explained 4.25% and 3.95% of yield variation, and 3.19% and 0.55% of N2O emission variation, respectively. BBFs could increase China&rsquo:s major crop yields by 4.3&ndash:5.0% and reduce N2O emissions by 3.7&ndash:6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials.
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