Long-Term Monitoring of Greenhouse Gas Emissions and Interpretable Machine Learning Models for Methane Prediction in Dairy Cattle Slurry Storage.
- 1Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam14469, Germany.
- 2Institute of Animal Hygiene and Environmental Health, Department of Veterinary Medicine, Freie Universität Berlin, Berlin14163, Germany.
- 3Faculty of Civil Engineering, Architecture and Environmental Engineering, University of Zielona Góra, Zielona Góra65-516, Poland.
Abstract
Greenhouse gas (GHG) emissions from continuously filled dairy cattle slurry storage remain difficult to estimate under practical conditions. This study monitored methane (CH4), nitrous oxide (N2O) emissions, and slurry physicochemical properties for 15 months in laboratory-scale storage tanks designed to reproduce key features of on-farm storage, including continuous filling, farm-derived temperature dynamics, and periodic emptying events. N2O concentrations remained below 0.5 ppm and contributed negligibly to measured direct GHG emissions. In contrast, CH4 showed pronounced seasonal variability: 94.5% of annual CH4 emissions occurred during summer and autumn, whereas only 5.5% occurred during winter and spring. Summer emptying caused a sharp decline in the CH4 flux followed by partial recovery, whereas emptying during colder periods had limited effects. Machine learning (ML) models achieved robust performance during temporal external validation (R2 = 0.82) and identified pH, temperature, and volatile fatty acids as key nonlinear predictors of CH4 emissions. By combining cross-season monitoring with interpretable ML models, this study highlights the importance of long-term observations for characterizing CH4 emissions from continuously filled dairy cattle slurry storage estimation and targeted mitigation.
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BibTeX
@article{song2026longterm,
title = {{Long-Term Monitoring of Greenhouse Gas Emissions and Interpretable Machine Learning Models for Methane Prediction in Dairy Cattle Slurry Storage.}},
author = {Zhenhua Song and Aditya Rawat and Tina Kabelitz and Barbara Amon and Qianying Yi and David Janke and Thomas Amon},
journal = {Environmental science \& technology},
year = {2026},
month = sep,
doi = {10.1021/acs.est.6c06417},
url = {https://europepmc.org/article/MED/42915415},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Long-Term Monitoring of Greenhouse Gas Emissions and Interpretable Machine Learning Models for Methane Prediction in Dairy Cattle Slurry Storage.
AU - Zhenhua Song
AU - Aditya Rawat
AU - Tina Kabelitz
AU - Barbara Amon
AU - Qianying Yi
AU - David Janke
AU - Thomas Amon
PY - 2026
DA - 2026/09/01
JO - Environmental science & technology
DO - 10.1021/acs.est.6c06417
UR - https://europepmc.org/article/MED/42915415
AB - Greenhouse gas (GHG) emissions from continuously filled dairy cattle slurry storage remain difficult to estimate under practical conditions. This study monitored methane (CH4), nitrous oxide (N2O) emissions, and slurry physicochemical properties for 15 months in laboratory-scale storage tanks designed to reproduce key features of on-farm storage, including continuous filling, farm-derived temperature dynamics, and periodic emptying events. N2O concentrations remained below 0.5 ppm and contributed negligibly to measured direct GHG emissions. In contrast, CH4 showed pronounced seasonal variability: 94.5% of annual CH4 emissions occurred during summer and autumn, whereas only 5.5% occurred during winter and spring. Summer emptying caused a sharp decline in the CH4 flux followed by partial recovery, whereas emptying during colder periods had limited effects. Machine learning (ML) models achieved robust performance during temporal external validation (R2 = 0.82) and identified pH, temperature, and volatile fatty acids as key nonlinear predictors of CH4 emissions. By combining cross-season monitoring with interpretable ML models, this study highlights the importance of long-term observations for characterizing CH4 emissions from continuously filled dairy cattle slurry storage estimation and targeted mitigation.
SN - 0013-936X
SN - 1520-5851
ER -