{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T13:02:02Z","timestamp":1792155722382,"version":"4.4.0"},"reference-count":145,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["R37424"],"award-info":[{"award-number":["R37424"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["NB2425-0025"],"award-info":[{"award-number":["NB2425-0025"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"New Brunswick Department of Agriculture, Aquaculture and Fisheries","award":["R37424"],"award-info":[{"award-number":["R37424"]}]},{"name":"New Brunswick Department of Agriculture, Aquaculture and Fisheries","award":["NB2425-0025"],"award-info":[{"award-number":["NB2425-0025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010\u20132025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework\u2014spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity\u2014to provide a coherent comparative lens across diverse DT implementations. Hybrid edge\u2013cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15\u201320% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management\u2014an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.<\/jats:p>","DOI":"10.3390\/s25164899","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T13:20:09Z","timestamp":1754659209000},"page":"4899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework"],"prefix":"10.3390","volume":"25","author":[{"given":"Shreya","family":"Rao","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0990-0235","authenticated-orcid":false,"given":"Suresh","family":"Neethirajan","sequence":"additional","affiliation":[{"name":"Faculty of Agriculture, Dalhousie University, Truro, NS B3H 4R2, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108952","DOI":"10.1109\/ACCESS.2020.2998358","article-title":"Digital Twin: Enabling Technologies, Challenges and Open Research","volume":"8","author":"Fuller","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/JPROC.2020.2998530","article-title":"Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models","volume":"108","author":"Minerva","year":"2020","journal-title":"Proc. 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