Optimizing swine manure composting parameters with integrated CatBoost and XGBoost models: nitrogen loss mitigation and mechanism

In this study, machine learning was used to optimize the aerobic composting process of swine manure to enhance nitrogen retention and compost maturity in order to meet the demand for high-quality organic fertilizers in sustainable agriculture. In this paper, multidimensional parameter data of swine...

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Vydané v:Journal of environmental management Ročník 388; s. 125995
Hlavní autori: Wu, Xuan, Ren, Ying, Wu, Weilong, Yang, Xu, Yi, Guorong, Zhou, Shunxi, Tang, Kuok Ho Daniel, Huang, Lvwen, Li, Ronghua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.07.2025
Predmet:
Agriculture
Animals
Boosting Machine Learning Algorithms
Composting
Fertilizers
Machine Learning
Manure
Mechanism
Nitrogen
Nitrogen retention
Soil
Swine
Swine manure composting
Visualization of models
Nitrogen retention
Swine manure composting
Visualization of models
Mechanism
Machine learning
ISSN:0301-4797, 1095-8630, 1095-8630
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Shrnutí:In this study, machine learning was used to optimize the aerobic composting process of swine manure to enhance nitrogen retention and compost maturity in order to meet the demand for high-quality organic fertilizers in sustainable agriculture. In this paper, multidimensional parameter data of swine manure composting were collected, six machine learning models (including CatBoost and XGBoost) were constructed, and the model parameters were optimized by genetic algorithm. Through model interpretation analysis (SHapley Additive exPlanations and Partial Dependency Plots), experimental validation and mechanism study, the significant effects of operating parameters on composting process and nitrogen loss were revealed. The results showed that optimal control of moisture content, compost temperature and aeration could effectively improve compost quality (GI nearly 198 %), reduce NH3-N and N2O-N emissions by 35.17 % and 9.70 %, and promote nitrogen conversion by increasing microbial community activity. This approach provides a new way for the efficient resource utilization of agricultural waste, which can help reduce the dependence on chemical fertilizers. [Display omitted] •CatBoost and XGBoost models excel in predicting compost maturity indicators.•Genetic algorithms enhance model accuracy, reducing errors in compost predictions.•Parameter tuning boosts GI to 198 % and cuts NH3-N emissions by 35 %.•Parameter control realize nitrogen loss to 13.50 %, improving compost quality.
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2025.125995