- 3.1Impact Factor
- 4.9CiteScore
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Dairy, Volume 6, Issue 5
October 2025 - 14 articles
Cover Story: In this graphical concept, we envision a next-generation breeding pipeline that unites genomic data (SNPs, omics), sensor-derived phenotypes (e.g., body temperature, rumination, imaging), and environmental metadata as inputs into machine learning and deep learning models. These models yield refined genomic estimated breeding values (GEBVs), enabling selection not only for productivity, but for disease resistance, heat tolerance, and reduced methane emissions. The schematic highlights a feedback loop: as new phenotypes are collected in the field and fed back into the learning system, prediction accuracy improves over time. This approach illustrates how AI-augmented genomic selection can drive sustainable, climate-smart dairy systems with enhanced animal health and environmental performance. View this paper
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Dairy - ISSN 2624-862X