{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:07:37Z","timestamp":1761174457995,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cattle Information Service"},{"name":"National Bovine Data Centre"},{"name":"UK Research Innovation"},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/Y00597X\/1"],"award-info":[{"award-number":["EP\/Y00597X\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance (R2 = 0.85 for milk yield and R2 = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH3) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact.<\/jats:p>","DOI":"10.3390\/a18100670","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:36:31Z","timestamp":1761060991000},"page":"670","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Engineering-Oriented Explainable Machine Learning and Digital Twin Framework for Sustainable Dairy Production and Environmental Impact Optimisation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5358-5186","authenticated-orcid":false,"given":"Ruiming","family":"Xing","sequence":"first","affiliation":[{"name":"Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK"}]},{"given":"Baihua","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6182-4124","authenticated-orcid":false,"given":"Shirin","family":"Dora","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Loughborough University, Loughborough LE11 3TU, UK"}]},{"given":"Michael","family":"Whittaker","sequence":"additional","affiliation":[{"name":"Cattle Information Service, Scope House, Hortonwood 33, Telford TF1 7EX, UK"}]},{"given":"Janette","family":"Mathie","sequence":"additional","affiliation":[{"name":"Cattle Information Service, Scope House, Hortonwood 33, Telford TF1 7EX, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., and Roser, M. (2023, October 02). Meat and Dairy Production. Our World in Data, Available online: https:\/\/ourworldindata.org\/meat-production."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3923\/ajava.2011.1.19","article-title":"Evaluation of milk urea concentration as useful indicator for dairy herd management: A review","volume":"6","author":"Roy","year":"2011","journal-title":"Asian J. Anim. Vet. Adv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big data in smart farming\u2014A review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agric. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cockburn, M. (2020). Review: Application and prospective discussion of machine learning for the management of dairy farms. Animals, 10.","DOI":"10.3390\/ani10091690"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lopez-Suarez, M., Armengol, E., Calsamiglia, S., and Castillejos, L. (2018, January 25\u201327). Using decision trees to extract patterns for dairy culling management. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Rhodes, Greece.","DOI":"10.1007\/978-3-319-92007-8_20"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.3168\/jds.2015-10254","article-title":"A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques","volume":"99","author":"Williams","year":"2016","journal-title":"J. Dairy Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1017\/S0021859613000178","article-title":"Mastitis detection in dairy cows: The application of support vector machines","volume":"151","author":"Miekley","year":"2013","journal-title":"J. Agric. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ebrahimi, M., Mohammadi-Dehcheshmeh, M., Ebrahimie, E., and Petrovski, K.R. (2019). Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models. Comput. Biol. Med., 114.","DOI":"10.1016\/j.compbiomed.2019.103456"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105032","DOI":"10.1016\/j.compag.2019.105032","article-title":"Predicting first test day milk yield of dairy heifers","volume":"166","author":"Dallago","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.biosystemseng.2022.02.013","article-title":"A machine learning framework to predict the next month\u2019s daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm","volume":"216","author":"Ji","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.56093\/ijans.v90i8.109314","article-title":"Artificial insemination for milk production in India: A statistical insight","volume":"90","author":"Saha","year":"2020","journal-title":"Indian J. Anim. Sci."},{"key":"ref_12","first-page":"120","article-title":"Effect of introducing weather parameters on the accuracy of milk production forecast models","volume":"7","author":"Zhang","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1016\/j.asoc.2006.07.002","article-title":"Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling","volume":"7","author":"Sharma","year":"2007","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11585","DOI":"10.3168\/jds.2020-18870","article-title":"A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra","volume":"103","author":"Soyeurt","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"429","DOI":"10.5455\/javar.2020.g438","article-title":"Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models","volume":"7","author":"Radwan","year":"2020","journal-title":"J. Adv. Vet. Anim. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107393","DOI":"10.1016\/j.dib.2021.107393","article-title":"CowNflow: A dataset on nitrogen flows and balances in dairy cows fed maize forage or herbage-based diets","volume":"38","author":"Ferreira","year":"2021","journal-title":"Data Brief"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gr\u00e6sb\u00f8ll, K., Kirkeby, C., Nielsen, S.S., Halasa, T., Toft, N., and Christiansen, L.E. (2017). A robust statistical model to predict the future value of the milk production of dairy cows using herd recording data. Front. Vet. Sci., 4.","DOI":"10.3389\/fvets.2017.00013"},{"key":"ref_18","first-page":"1","article-title":"Mathematical modeling of lactation curves: A review of parametric models","volume":"1","author":"Bouallegue","year":"2020","journal-title":"Lact. Farm Anim.-Biol. Physiol. Basis Nutr. Requir. Model."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gr\u00e6sb\u00f8ll, K., Kirkeby, C., Nielsen, S.S., Halasa, T., Toft, N., and Christiansen, L.E. (2016). Models to estimate lactation curves of milk yield and somatic cell count in dairy cows at the herd level for the use in simulations and predictive models. Front. Vet. Sci., 3.","DOI":"10.3389\/fvets.2016.00115"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.2754\/avb201180010073","article-title":"Analysis of lactation curves, milk constituents, somatic cell count and urea in milk of cows by the mathematical model of Wood","volume":"80","author":"Brzozowski","year":"2011","journal-title":"Acta Vet. Brno"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compag.2006.08.004","article-title":"Methods of predicting milk yield in dairy cows\u2014Predictive capabilities of Wood\u2019s lactation curve and artificial neural networks (ANNs)","volume":"54","author":"Grzesiak","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3352","DOI":"10.3168\/jds.2013-7451","article-title":"Comparison of modelling techniques for milk-production forecasting","volume":"97","author":"Murphy","year":"2014","journal-title":"J. Dairy Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Grzesiak, W., Zaborski, D., Szatkowska, I., and Kr\u00f3laczyk, K. (2021). Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood\u2019s model. Anim. Biosci., 34.","DOI":"10.5713\/ajas.19.0939"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.livsci.2012.04.002","article-title":"Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle","volume":"147","author":"Dongre","year":"2012","journal-title":"Livest. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11250-009-9468-7","article-title":"Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein\u2013Friesian dairy cows","volume":"42","author":"Njubi","year":"2010","journal-title":"Trop. Anim. Health Prod."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.3168\/jds.2022-22454","article-title":"Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle","volume":"106","author":"Giannuzzi","year":"2023","journal-title":"J. Dairy Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Giannuzzi, D., Mota, L.F.M., Pegolo, S., Gallo, L., Schiavon, S., Tagliapietra, F., Katz, G., Fainboym, D., Minuti, A., and Trevisi, E. (2022). In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-11799-0"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"321","DOI":"10.3168\/jds.2009-2263","article-title":"Milk urea concentration as an indicator of ammonia emission from dairy cow barn under restricted grazing","volume":"94","author":"Smits","year":"2011","journal-title":"J. Dairy Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12741","DOI":"10.3168\/jds.2021-20659","article-title":"Genetic analysis of milk urea concentration and its genetic relationship with selected traits of interest in dairy cows","volume":"104","author":"Chen","year":"2021","journal-title":"J. Dairy Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1023\/A:1009747109538","article-title":"Ammonia volatilization from dairy farming systems in temperate areas: A review","volume":"51","author":"Bussink","year":"1998","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1111\/sum.12203","article-title":"Ammonia emissions from cattle dung, urine and urine with dicyandiamide in a temperate grassland","volume":"32","author":"Fischer","year":"2016","journal-title":"Soil Use Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5499","DOI":"10.3168\/jds.2007-0299","article-title":"Prediction of ammonia emission from dairy cattle manure based on milk urea nitrogen: Relation of milk urea nitrogen to urine urea nitrogen excretion","volume":"90","author":"Burgos","year":"2007","journal-title":"J. Dairy Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.3168\/jds.2009-2415","article-title":"Prediction of ammonia emission from dairy cattle manure based on milk urea nitrogen: Relation of milk urea nitrogen to ammonia emissions","volume":"93","author":"Burgos","year":"2010","journal-title":"J. Dairy Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/ACCESS.2020.3048415","article-title":"Machine learning applications for precision agriculture: A comprehensive review","volume":"9","author":"Sharma","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"31866","DOI":"10.1109\/ACCESS.2023.3262138","article-title":"Machine learning operations (mlops): Overview, definition, and architecture","volume":"11","author":"Kreuzberger","year":"2023","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Grzesiak, W., Kowalski, Z., and B\u0142aszczyk, P. (2025). The Use of Selected Machine Learning Methods in Dairy Cattle Farming. Animals, 15.","DOI":"10.3390\/ani15142033"},{"key":"ref_37","first-page":"109234","article-title":"A Machine Learning Framework for Precision Prediction of Lactation Performance in Large Dairy Herds","volume":"227","author":"Liu","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","first-page":"1124","article-title":"Development of a Machine Learning Tool for the Enhancement of Carbon Footprint Prediction for Cattle Milk Production","volume":"30","author":"Foschi","year":"2025","journal-title":"Int. J. Life Cycle Assess."},{"key":"ref_39","first-page":"82","article-title":"A review of statistical outlier methods","volume":"30","author":"Walfish","year":"2006","journal-title":"Pharm. Technol."},{"key":"ref_40","first-page":"1","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_41","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased boosting with categorical features. Proceedings of the 32nd International Conference on Neural Information Processing System, Montr\u00e9al, QC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1093\/mnras\/staa2236","article-title":"A study on the statistical significance of mutual information between morphology of a galaxy and its large-scale environment","volume":"497","author":"Sarkar","year":"2020","journal-title":"Mon. Not. R. Astron. Soc."},{"key":"ref_43","unstructured":"Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Salciccioli, J.D., Crutain, Y., Komorowski, M., and Marshall, D.C. (2016). Sensitivity analysis and model validation. Secondary Analysis of Electronic Health Records, Springer.","DOI":"10.1007\/978-3-319-43742-2_17"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shapley, L.S. (1953). A value for n-person games. Classics in Game Theory, Princeton University Press.","DOI":"10.1515\/9781400881970-018"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"120108","DOI":"10.1016\/j.envres.2024.120108","article-title":"Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization","volume":"263","author":"Zhang","year":"2024","journal-title":"Environ. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"119929","DOI":"10.1016\/j.envres.2024.119929","article-title":"Examining the importance of neighborhood natural, and built environment factors in predicting older adults\u2019 mental well-being: An XGBoost-SHAP approach","volume":"262","author":"Liu","year":"2024","journal-title":"Environ. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4851","DOI":"10.3168\/jds.2024-25656","article-title":"Assessing milk urea nitrogen as an indicator of protein nutrition and nitrogen utilization efficiency: A meta-analysis","volume":"108","author":"Zhao","year":"2025","journal-title":"J. Dairy Sci."},{"key":"ref_50","first-page":"7173","article-title":"Considerations for nutritional grouping in dairy farms","volume":"105","author":"White","year":"2022","journal-title":"J. Dairy Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1017\/S1751731119000235","article-title":"Genetic variation in milk urea nitrogen concentration of dairy cattle and its implications for reducing urinary nitrogen excretion","volume":"13","author":"Beatson","year":"2019","journal-title":"Animal"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4690","DOI":"10.3168\/jds.2011-4476","article-title":"Evaluation of milk urea nitrogen as a management tool to reduce ammonia emissions from dairy farms","volume":"94","author":"Powell","year":"2011","journal-title":"J. Dairy Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4310","DOI":"10.3168\/jds.2012-6265","article-title":"Prediction of urinary nitrogen and urinary urea nitrogen excretion by lactating dairy cattle in northwestern Europe and North America: A meta-analysis","volume":"96","author":"Spek","year":"2013","journal-title":"J. Dairy Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, B., Thompson, M., Partridge, T., Xing, R., Cutler, J., Alhnaity, B., and Meng, Q. (2024, January 3\u20135). AI-powered digital twin for sustainable agriculture and greenhouse gas reduction. Proceedings of the 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET), Doha, Qatar.","DOI":"10.1109\/HONET63146.2024.10822980"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/670\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:07:41Z","timestamp":1761116861000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/670"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":54,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["a18100670"],"URL":"https:\/\/doi.org\/10.3390\/a18100670","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}