Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N2O Emissions in China - Yuan Zeng; Sujuan Chen; Yunpeng Li; Li Xiong; Cheng Liu; Muhammad Azeem; Xiaoting Jie; Mei Chen; Longjiang Zhang; Jianfei Sun (Agronomy)
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’ potential to increase yields and mitigate emissions in China’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: 0.98–0.99). 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’s major crop yields by 4.3–5.0% and reduce N2O emissions by 3.7–6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials.