Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China

Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N<sub>2</sub>O Emissions in China

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),...

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Bibliographic Details
Main Authors: Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Agronomy
Subjects:
biochar-based fertilizers
N<sub>2</sub>O
crop yield
greenhouse gas reduction
machine learning models
Online Access:https://www.mdpi.com/2073-4395/15/5/1238
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Summary: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 N<sub>2</sub>O 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 N<sub>2</sub>O 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 N<sub>2</sub>O emissions (R<sup>2</sup>: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R<sup>2</sup>, 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 N<sub>2</sub>O emission variation, respectively. BBFs could increase China’s major crop yields by 4.3–5.0% and reduce N<sub>2</sub>O 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.
ISSN:2073-4395

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