{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T09:29:04Z","timestamp":1787218144758,"version":"build-2736575974"},"reference-count":107,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Reliable and accurate farm-level forecasting of greenhouse gas concentrations from dairy cattle is important to the formulation of climate change mitigation strategies and policy planning in livestock systems. This study proposed an uncertainty-aware deep learning model that integrated data from multiple sources, including remote sensing and ground-based sensors, to forecast hourly concentrations of nitrous oxide, methane, and carbon dioxide in a controlled zero-grazing dairy system. The forecasting was implemented by formulating a causal multivariate time series using a 24-h lookback window with direct one-step-ahead prediction. A two-stage model evaluation protocol, model hyperparameter tuning via rolling cross-validation, and holdout Test Evaluation were implemented during model development. The model was evaluated using the coefficient of determination, root-mean-square error, and mean squared error. To evaluate model reliability, the study employed a dual-output gated recurrent unit to estimate the conditional mean and heteroscedastic variance, and Monte Carlo dropout and Gaussian Negative Log-Likelihood to quantify epistemic and aleatoric uncertainty. Results indicate stable generalisation across temporal folds and strong probabilistic calibration, with empirical 95% coverage ranging from 93.6 to 94.8% on the holdout test set. Feature selection indicates that rainfall, normalised difference vegetation index, humidity, temperature, trend, and season influence the prediction of concentration. The study shows that incorporating exogenous variables improves model performance. The proposed framework demonstrates proof of concept for controlled zero-grazing systems, with potential for broader application following multi-site validation.<\/jats:p>","DOI":"10.3389\/frai.2026.1782333","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T05:39:59Z","timestamp":1782106799000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Gated recurrent unit model for forecasting greenhouse gas concentrations with uncertainty quantification"],"prefix":"10.3389","volume":"9","author":[{"given":"Erica Hargety","family":"Kimei","sequence":"first","affiliation":[{"name":"School of Computation and Communication Science and Engineering, The Nelson Mandela African, Institution of Science and Technology","place":["Arusha, Tanzania"]},{"name":"Informatics and Technical Education, National Institute of Transport","place":["Dar-Es-Salam, Tanzania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devotha Godfrey","family":"Nyambo","sequence":"additional","affiliation":[{"name":"School of Computation and Communication Science and Engineering, The Nelson Mandela African, Institution of Science and Technology","place":["Arusha, Tanzania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neema","family":"Mduma","sequence":"additional","affiliation":[{"name":"School of Computation and Communication Science and Engineering, The Nelson Mandela African, Institution of Science and Technology","place":["Arusha, Tanzania"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shubi Felix","family":"Kaijage","sequence":"additional","affiliation":[{"name":"School of Computation and Communication Science and Engineering, The Nelson Mandela African, Institution of Science and Technology","place":["Arusha, Tanzania"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2510","DOI":"10.1007\/s11356-024-35764-8","article-title":"An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models","volume":"32","author":"Ajala","year":"2025","journal-title":"Environ. 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Inf.","author":"Hastomo","year":"2022"},{"key":"ref44","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2307\/2953682","article-title":"Postwar U.S. business cycles: an empirical investigation","volume":"29","author":"Hodrick","year":"1997","journal-title":"J. Money Credit Bank."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","article-title":"Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not","volume":"15","author":"Hodson","year":"2022","journal-title":"Geosci. 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Syst."},{"key":"ref8800","volume-title":"2006 IPCC Guidelines for National Greenhouse Gas Inventories.","year":"2006"},{"key":"ref50","year":"2019"},{"key":"ref51","volume-title":"Guide to the Expression of Uncertainty in Measurement","year":"1993"},{"key":"ref52","first-page":"109","article-title":"The augmented Dickey-Fuller test for the stationarity of the final public consumption and GDP time series of the republic of North Macedonia","volume":"15","author":"Ivanovski","year":"2024","journal-title":"UTMS J. Econ."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"323","DOI":"10.3390\/atmos15030323","article-title":"A novel approach for predicting anthropogenic CO2 emissions using machine learning based on clustering of the CO2 concentration","volume":"15","author":"Ji","year":"2024","journal-title":"Atmos."},{"key":"ref54","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","volume-title":"Adv. Neural Inf. Process. 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Ser."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.3390\/su17114787","article-title":"Uncertainty analysis of provincial carbon emission inventories: a comparative assessment of emission factors sources","volume":"17","author":"Liu","year":"2025","journal-title":"Sustainability"},{"key":"ref62","doi-asserted-by":"publisher","first-page":"11103","DOI":"10.3390\/app142311103","article-title":"Stability of feature selection in multi-omics data analysis","volume":"14","author":"\u0141ukaszuk","year":"2024","journal-title":"Appl. Sci."},{"key":"ref63","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.3390\/stats7040072","article-title":"Prepivoted augmented Dickey-Fuller test with bootstrap-assisted lag length selection","volume":"7","author":"Maitra","year":"2024","journal-title":"Stat"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.snb.2014.11.032","article-title":"Approach for quantification of metal oxide type semiconductor gas sensors used for ambient air quality monitoring","volume":"208","author":"Masson","year":"2015","journal-title":"Sensors Actuators B Chem."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"9713","DOI":"10.3168\/jds.2022-22117","article-title":"Mapping the carbon footprint of milk production from cattle: a systematic review","volume":"105","author":"Mazzetto","year":"2022","journal-title":"J. Dairy Sci."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"73","DOI":"10.2134\/jeq2011.0266","article-title":"Dairy farm methane emissions using a dispersion model","volume":"41","author":"McGinn","year":"2012","journal-title":"J. Environ. Qual."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"978","DOI":"10.3168\/jds.2023-23577","article-title":"Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples","volume":"107","author":"McParland","year":"2024","journal-title":"J. Dairy Sci."},{"key":"ref68","first-page":"30709","article-title":"Improved greenhouse gas emission factors for smallholder livestock systems in East Africa","author":"Merbold","year":"2019","journal-title":"AgriGHG Berlin"},{"key":"ref70","first-page":"2247","article-title":"Forecasting Enhancement using a Hodrick-Prescott Filter","volume-title":"Int. J. Nonlinear Anal. 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Lett."},{"key":"ref74","first-page":"55","author":"Nix","year":"1994"},{"key":"ref75","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1007\/978-3-030-45106-6_43","volume-title":"African Handbook of Climate Change Adaptation","author":"Ntinyari","year":"2021"},{"key":"ref76","doi-asserted-by":"publisher","first-page":"109423","DOI":"10.1016\/j.compag.2024.109423","article-title":"Animal-based CO2, CH4, and N2O emissions analysis: machine learning predictions by agricultural regions and climate dynamics in varied scenarios","volume":"226","author":"Pence","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.5194\/bg-21-1867-2024","article-title":"Exploring temporal and spatial variation of nitrous oxide flux using several years of peatland forest automatic chamber data","volume":"21","author":"Rautakoski","year":"2024","journal-title":"Biogeosciences"},{"key":"ref79","doi-asserted-by":"publisher","first-page":"skae219","DOI":"10.1093\/jas\/skae219","article-title":"Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning","volume":"102","author":"Ross","year":"2024","journal-title":"J. Anim. Sci."},{"key":"ref80","doi-asserted-by":"publisher","first-page":"179713","DOI":"10.1016\/j.scitotenv.2025.179713","article-title":"Modeling enteric methane emission from dairy cows using deep learning approach","volume":"984","author":"Sahraei","year":"2025","journal-title":"Sci. Total Environ."},{"key":"ref81","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1093\/biomet\/71.3.599","article-title":"Testing for unit roots in autoregressive-moving average models of unknown order","volume":"71","author":"Said","year":"1984","journal-title":"Biometrika"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1145\/3706598.3713725","article-title":"More forecasts, more (decision) problems: how uncertainty representations for multiple forecasts impact decision-making","author":"Sarma","year":"2025","journal-title":"CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems"},{"key":"ref83","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.5194\/essd-16-2669-2024","article-title":"Estimating the uncertainty of the greenhouse gas emission accounts in global multi-regional input\u2013output analysis","volume":"16","author":"Schulte","year":"2024","journal-title":"Earth Syst. Sci. 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China Earth Sci.","author":"Uluocak","year":"2025"},{"key":"ref96","volume-title":"Tanzania\u2019s Nationally Determined Contribution","year":"2021"},{"key":"ref97","doi-asserted-by":"publisher","first-page":"3054","DOI":"10.1111\/gcb.16692","article-title":"Spatial insurance against a heatwave differs between trophic levels in experimental aquatic communities","volume":"29","author":"Vad","year":"2023","journal-title":"Global Change Biol."},{"key":"ref98","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1029\/2019JG005167","article-title":"Relationships between methane and carbon dioxide fluxes in a temperate cattail-dominated freshwater wetland","volume":"124","author":"Villa","year":"2019","journal-title":"J. Geophys. Res. Biogeo."},{"key":"ref99","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s10651-024-00615-9","article-title":"A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems","volume":"31","author":"Vitale","year":"2024","journal-title":"Environ. Ecol. Stat."},{"key":"ref100","article-title":"From aleatoric to epistemic: exploring uncertainty quantification techniques in artificial intelligence","author":"Wang","year":"2025","journal-title":"arXiv"},{"key":"ref101","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.3390\/s100302088","article-title":"Metal oxide gas sensors: sensitivity and influencing factors","volume":"10","author":"Wang","year":"2010","journal-title":"Sensors"},{"key":"ref102","doi-asserted-by":"publisher","first-page":"118907","DOI":"10.1016\/j.envres.2024.118907","article-title":"Global responses of wetland methane emissions to extreme temperature and precipitation","volume":"252","author":"Xu","year":"2024","journal-title":"Environ. Res."},{"key":"ref103","first-page":"3958","author":"Yoon","year":"2017"},{"key":"ref104","doi-asserted-by":"publisher","first-page":"378","DOI":"10.54097\/zy49qb44","article-title":"Time series analysis of greenhouse gas emission based on ARIMA and LSTM","volume":"76","author":"Zhang","year":"2023","journal-title":"Highl. Sci. Eng. Technol."},{"key":"ref105","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s12302-025-01056-2","article-title":"Impact of agricultural emissions on goal 13 of the sustainable development agenda: in east African strategy for climate action","volume":"37","author":"Zhao","year":"2025","journal-title":"Environ. Sci. Eur."},{"key":"ref106","doi-asserted-by":"publisher","first-page":"1386","DOI":"10.1109\/tpwrs.2021.3105101","article-title":"Generative probabilistic wind speed forecasting: a variational recurrent autoencoder based method","volume":"37","author":"Zheng","year":"2021","journal-title":"IEEE Trans. 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