Dairy, Volume 6, Issue 5 (October 2025) – 4 articles

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 continuous pH monitoring (Moonsyst Ltd., Kilkenny, Republic of Ireland) and particle separation by 19, 8, 4 mm sieves and pad using the Wasserbauer particle separator, along with regular milk and DMI measurements. Data were analyzed by IBM SPSS 26.0 with ANOVA, Pearson correlations and statistically significant differences between tertiles by post hoc Tukey HSD test were performed (p < 0.05). Tertiles by frequency analysis were used to categorize particle size proportions into three groups, each containing an equal number of observations. Principal component analysis (PCA) and heatmaps by SRplot were generated. Moderate particle size distributions (second tertiles of 19 mm, 8 mm, 4 mm sieves, and pad as the fraction of TMR particles that pass through the all sieves and are collected in the bottom pan) optimized rumen pH stability, reducing time below 6.2 (SARA risk) or above 6.8, and correlated with milk β-hydroxybutyrate (BHB), oleic acid, and acetone levels. Moreover, milk production was maximized with a combination of coarser (19 mm and 8 mm, third tertiles) and finer (4 mm, first tertile) particles, milk fat peaked in both the finest pad fraction (third tertile) and coarsest larger sieves (first tertiles), and milk protein in the first tertiles of 19 mm and 8 mm sieves. Similarly, DMI positively correlated with coarser particles, but sometimes negatively with milk quality. In addition, PCA showed fine particle groups clustering with higher milk fat-to-protein ratios, somatic cell counts, and urea. In conclusion, mid-range TMR particle sizes (second tertiles) consistently provided the most benefits across ruminal, metabolic, and production parameters, underscoring TMR structure as a crucial precision feeding tool. Full article

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. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems. Full article

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 provide an approach to analysis and the resulting management solutions using a real-life case within a 500-cow herd. The initial assessment, involving the collection of data on the level of production, animal health and behaviour, and metabolic indicators, as well as management and housing key indicators, revealed key risk factors, including overcrowding, suboptimal feeding strategies, inadequate water supply, and insufficient disease monitoring. These factors contributed to increased cases of metabolic disorders such as hypocalcemia (annual incidence 7.8%), excessive lipomobilisation, and displaced abomasum (annual incidence 5.2%). A holistic approach combining feeding adjustments, disease monitoring, facility improvements, and long-term management strategies was implemented to address these challenges. Short-term interventions, such as optimizing the dietary cation–anion balance and enhancing disease detection protocols, led to noticeable improvements. However, structural constraints and external factors, such as extreme weather conditions (heat stress) and economic limitations, created significant hurdles in achieving immediate and sustained success. The farm ultimately opted for infrastructural improvements, including a new transition cow facility, to provide a long-term solution to these recurring issues. This case highlights the complexity of transition cow management, demonstrating that long-term success depends on continuous monitoring, interdisciplinary collaboration, and adaptability in response to evolving challenges in dairy production. Full article

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 dairy cows) are responsible for 62% of livestock-produced GHGs. Compared to beef cattle, continuous lactation in dairy cows requires sustained energy intake to drive rumen fermentation and CH₄ production, making it a key mitigation target for balancing dairy production and environmental sustainability. Determining how to safely and efficiently reduce CH₄ emissions from dairy cows is essential to promote the sustainable development of animal husbandry and environmental friendliness and plays an important role in improving feed conversion, reducing environmental pollution, and improving the performance of dairy cows. Combined with the factors influencing CH₄ emissions from dairy cows and previous research reports, this paper reviews the research progress on reducing the enteric CH₄ emissions (EMEs) of dairy cows from the perspectives of the CH₄ generation mechanism and emission reduction strategies, and it summarizes various measures for CH₄ emission reduction in dairy cows, mainly including accelerating genetic breeding, improving diet composition, optimizing feeding management, and improving fecal treatment. Future research should focus on optimizing the combination of strategies, explore more innovative methods, reduce EME without affecting the growth performance of dairy cows and milk safety, and scientifically and effectively promote the sustainable development of animal husbandry. Full article