Predicting the environmental fate of biodegradable mulch films: A machine learning approach for sustainable agriculture

文献类型： 外文期刊

第一作者： Chen, Shan

作者： Chen, Shan;Xu, Guohailin;Chen, Jingwen;Jiang, Xizhi;Liu, Ziwen;Lin, Zhiwei;Xu, Lei;Xu, Guohailin;Zhang, Hui;Zhang, Congzhi;Zhang, Jiabao

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关键词： Machine learning; Biodegradable plastic mulch film; Meta-analysis; Prediction model; Plastic pollution

期刊名称：JOURNAL OF HAZARDOUS MATERIALS （ 影响因子：11.3； 五年影响因子：12.4 ）

ISSN： 0304-3894

年卷期： 2025 年 492 卷

页码：

收录情况： SCI

摘要： This cover image depicts the key factors influencing the degradation of biodegradable plastic mulch films (BDM) in agricultural settings. Various environmental, experimental, soil, microbial, and material variables, such as temperature, pH, microbial diversity, and BDM composition, are shown with their respective influences. The integrated approach of meta-analysis and machine learning is applied to quantify these factors and predict BDM degradation across regions. This visualization encapsulates the complex interactions that contribute to BDM degradation and highlights the model's capacity to predict degradation rates, providing crucial insights for sustainable agricultural practices. Biodegradable plastic mulch films (BDM) have been proposed as one of the dominant strategies for plastic pollution prevention in agriculture. As the BDM degradation is a complex process affected by multiple factors, the degradation cycle of BDM has significant regional differences ranging from months to years, resulting in its mismatch to the crop cycle. Existing works focus on only a few influencing factors as it is too laborious to elaborate on all the factors by experiments, limiting our comprehensive understanding of BDM degradation. Here, we integrated meta-analysis with a machine-learning approach to quantify the impacts of multiple factors and develop a prediction model on BDM degradation. 24 influencing factors, including material composition, weather, soil properties, microbial activity, and other factors, were reorganized systematically and quantified for the first time. The established machine learning model enables the prediction of the regional BDM degradation rate for over 2800 counties/districts in China, which has a vast geographical distribution and diverse climatic characteristics. In this work, our study provides deeper insights into current understanding of BDM degradation and paves the way for future investigations into the environmental degradation of biodegradable materials, offering critical insights for environmental management and policy-making in agriculture.

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