Machine learning-driven prediction of nitrogen loss in organic solid waste composting

doi:10.1016/j.jia.2025.09.002

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Haoran Mi¹, Dawei Gao², Deling Yuan¹, Xiao Liu³, Lili Gao⁴, Shengping Li^5#, Yuanwang Liu¹^#

¹Hebei Key Laboratory of Heavy Metal Deep-Remediation in Water and Resource Reuse, School of Environmental and Chemical Engineering/State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, China

²School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

³Earth & Environment Strasbourg (EES), University of Strasbourg, Strasbourg 67084, France

⁴State Key Laboratory of Efficient Utilization of Agricultural Water Resource, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China

⁵State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Highlights

l Machine learning algorithms were utilized to predict nitrogen loss during manure composting.

l The adaptive boosting model achieved an R² of 0.847 for nitrogen loss prediction.

l Model performance enhanced following Bayesian optimization of hyperparameters.

l Redundant features (e.g., scale and C/N) were eliminated to optimize input variables.

l Shapley additive explanation (SHAP) analysis revealed time stages and bulking agents as critical factors.

Abstract
References

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摘要

堆肥是可持续废弃物管理的关键环节，能够显著实现资源回收并带来环境效益。然而，氮素损失始终是堆肥过程中的主要难题，亟需建立氮损失预测模型。本研究基于307组涵盖堆肥策略、理化性质及堆肥时间阶段的实验数据，采用五种机器学习算法对有机固废堆肥过程中的氮损失进行预测。结果表明，AdaBoost 算法在剔除冗余特征（规模和碳氮比）后表现最优，决定系数达到 0.847。进一步通过Shapley分析发现，堆肥时间阶段、膨松剂、原料类型及铵态氮含量是影响氮损失的关键因素。其中，堆肥初期是氮损失最敏感的时期；以锯末、稻壳和玉米秸秆为膨松剂可提升氮保留率；采用静态通风并辅以化学添加剂亦能有效降低氮损失。上述发现为优化堆肥条件、最大限度减少氮损失提供了科学依据，并为实际操作中的最佳实践给予了明确指导。

Abstract

Composting represents a crucial component of sustainable waste management, providing significant resource recovery and environmental advantages. However, nitrogen loss during composting remains a significant challenge, necessitating the development of a predictive model for nitrogen loss during the composting process. This investigation implemented five machine learning models, utilizing 307 data points encompassing composting strategies, physicochemical properties, and composting time stages, to predict nitrogen loss during organic solid waste composting. The findings demonstrated that the adaptive boosting (AdaBoost) algorithm achieved optimal performance with a coefficient of determination of 0.847 after eliminating redundant features (scale and C/N). Moreover, Shapley additive explanation analysis identified several key factors significantly influencing nitrogen losses during composting, including composting time stages, bulking agents, raw materials, and ammonium nitrogen levels. Notably, the initial phase of composting emerged as the most critical period for nitrogen loss. The utilization of sawdust, rice husk, and corn stalk as bulking agents enhanced nitrogen retention in compost. Furthermore, implementing static aeration for ventilation and applying chemical additives effectively reduced nitrogen losses during the composting process. These results provide a scientific foundation for identifying optimal composting conditions to minimize nitrogen loss, thereby offering practical guidance for effective composting operations.

Keywords: machine learning composting adaptive boosting nitrogen loss feature selection

Online: 04 September 2025

Fund:

This study was financially supported by the National Key R&D Program of China (2021YFD1900700), the National Natural Science Foundation of China (52400188), and the Youth Innovation Program of the Chinese Academy of Agricultural Sciences (Y2025QC09).

About author: Haoran Mi, Mobile: +86-13643122038, Email: mhr1120800322@163.com; #Correspondence Shengping Li, Mobile: +86-18511339881, E-mail: lishengping@caas.cn; Yuanwang Liu, Mobile: +86-15624955445, E-mail: liuyuanwang@ysu.edu.cn

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Haoran Mi, Dawei Gao, Deling Yuan, Xiao Liu, Lili Gao, Shengping Li, Yuanwan Liu. 2025. Machine learning-driven prediction of nitrogen loss in organic solid waste composting. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2025.09.002

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