文献类型： 外文期刊

作者： Zeng, Yuan ¹ ; Chen, Sujuan ¹ ; Li, Yunpeng ¹ ; Xiong, Li ² ; Liu, Cheng ³ ; Azeem, Muhammad ⁴ ; Jie, Xiaoting ¹ ; Chen, Mei ¹ ; Zhang, Longjiang ¹ ; Sun, Jianfei ¹ ;

作者机构： 1.Minist Ecol & Environm, Nanjing Inst Environm Sci, Nanjing 210042, Peoples R China

2.Jiangxi Acad Agr Sci, Inst Soil & Fertilizer & Resources & Environm, Nanchang 330200, Peoples R China

3.Zhejiang Univ Sci & Technol, Inst Ecoenvironm Res, Hangzhou 310023, Peoples R China

4.Chinese Acad Sci, Inst Urban Environm, State Key Lab Ecol Secur Reg & Cities, Ningbo Observat & Res Stn, Xiamen 361021, Peoples R China

5.Pir Mehr Ali Shah Arid Agr Univ, Inst Soil & Environm Sci, Rawalpindi 46300, Pakistan

关键词： biochar-based fertilizers; N2O; crop yield; greenhouse gas reduction; machine learning models

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： 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 (R-2: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R-2, 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.