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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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Articles (14)

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Article

1,297 Views

19 Pages

Effect of TMR Physical Structure and Ruminal pH Environment on Production and Milk Quality

Ondrej Hanušovský,
Milan Šimko,
Michal Rolinec,
Branislav Gálik,
Mária Kapusniaková,
Stanislava Drotárová,
Matúš Džima,
Luboš Zábranský and
Miroslav Juráček

Dairy2025, 6(5), 51;https://doi.org/10.3390/dairy6050051

11 September 2025

Total Mixed Ration (TMR) particle size significantly impacts dairy cow health and productivity. This study investigated the effects of TMR particle size tertiles on rumen pH, dry matter intake (DMI), and milk characteristics in Simmental cows by cont...

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Review

5,061 Views

27 Pages

Enhancing Genomic Selection in Dairy Cattle Through Artificial Intelligence: Integrating Advanced Phenotyping and Predictive Models to Advance Health, Climate Resilience, and Sustainability

Karina Džermeikaitė,
Monika Šidlauskaitė,
Ramūnas Antanaitis and
Lina Anskienė

Dairy2025, 6(5), 50;https://doi.org/10.3390/dairy6050050

1 September 2025

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. Thi...

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Case Report

1,642 Views

26 Pages

Metabolic Disorders in Transition Dairy Cows in a 500-Cow Herd—Analysis, Prevention and Follow-Up

Melanie Schären-Bannert,
Benno Waurich,
Fanny Rachidi,
Adriana Wöckel,
Wolf Wippermann,
Julia Wittich,
Guntram Hermenau,
Erik Bannert,
Peter Hufe and
Detlef May
+ 3 authors

Dairy2025, 6(5), 49;https://doi.org/10.3390/dairy6050049

1 September 2025

Managing transition cows and preventing diseases related to this period is challenging due to the latter’s multifactorial nature. The aim of this applied observational case study is to illustrate and discuss the different aspects involved and p...

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Review

2,568 Views

19 Pages

Research Progress on Methane Emission Reduction Strategies for Dairy Cows

Yu Wang,
Kuan Chen,
Shulin Yuan,
Jianying Liu,
Jianchao Guo and
Yongqing Guo

Dairy2025, 6(5), 48;https://doi.org/10.3390/dairy6050048

26 August 2025

Methane (CH4) is the second largest greenhouse gas (GHG) after carbon dioxide (CO2), and ruminant production is an important source of CH4 emissions. Among the six types of livestock animal species that produce GHGs, cattle (including beef cattle and...

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