This study investigated the effects of heat stress on milk production in Korean Holstein cows using large-scale data. Heat stress was assessed using the temperature-humidity index (THI). Weather records (2016 to 2020) were collected from 70 regional weather stations using an installed automated surface observing system (ASOS). A dataset of 2,094,436 milk production records from 215,276 Holstein cows obtained from the Dairy Cattle Genetic Improvement Center was analyzed. Stepwise selection was used to select the input variables, including the daily maximum THI (THI_max). Least-squares means were calculated for milk yield, fat and protein corrected milk (FPCM), fat and protein yield, fat-to-protein ratio, solids not fat, and lactation persistency. Segmented linear regression analysis determined the break points (BPs) of the THI_max. Over the five years, heat stress exposure increased, particularly from May to September. This study identified BPs around THI_max of 80–82 for milk yield and FPCM. Similar patterns for other milk traits were observed, which significantly decreased beyond their respective BPs. These findings indicate that THI variations adversely affect milk yield and composition in dairy cows, highlighting the importance of appropriate feeding management strategies to ensure the optimal productivity of Holstein cows under varying climatic conditions.

This study investigates the effect of probiotics supplementation on tight junction protein (TJP) expression in animal models by meta-analysis. We estimated the effect of probiotics administration in an animal inflammatory bowel disease model based on 47 collected articles from the databases, including Sciencedirect, Pubmed, Scopus, and Google Scholar. The effect size was analyzed with the standardized mean difference, and the heterogeneity of the effect sizes was assessed using Cochran’s Q test. To explain the heterogeneity, moderate analyses, such as meta-ANOVA and meta-regression, were performed using the mixed effects model. Finally, publication bias was assessed using Egger’s linear regression test. Among the evaluated items, zonula occluden (ZO)-1 showed the highest Q statistics value, and the effect sizes of all items were positive with high significance (p < 0.0001). The I² value of all items reflected high heterogeneity (in excess of 80%). From the results of the meta-ANOVA, the factors of the heterogeneity found in the probiotics strains were investigated. Lactobacillus reuteri was identified as having the greatest effect on claudin and ZO-1 expression. The publication bias was detected by the Egger’s linear regression test, though it revealed that the occludin and ZO-1 had larger sample sizes than the claudin. In sum, this meta-analysis reveals that probiotics are effective at improving TJP expression in a gut environment of inflammatory bowel disease (IBD)-induced animal model. Our findings will interest IBD patients, as they suggest an area warranting future study.

(1) Background: This study explores the correlation between volatile fatty acid (VFA) concentrations and methanogenesis in ruminants, focusing on how the nutritional composition of their diets affects these processes. (2) Methods: We developed predictive models using multiple linear regression, artificial neural networks, and k-nearest neighbor algorithms. The models are based on data extracted from 31 research papers and 16 ruminal in vitro fermentation tests to predict VFA concentrations from nutrient intake. Methane production estimates were derived by converting and clustering these predicted VFA values into molar ratios. (3) Results: This study found that acetate concentrations correlate significantly with neutral detergent fiber intake. Conversely, propionate and butyrate concentrations are highly dependent on dry matter intake. There was a notable correlation between methane production and the concentrations of acetate and butyrate. Increases in neutral detergent fiber intake were associated with higher levels of acetate, butyrate, and methane production. Among the three methods, the k-nearest neighbor algorithm performed best in terms of statistical fitting. (4) Conclusions: It is vital to determine the optimal intake levels of neutral detergent fiber to minimize methane emissions and reduce energy loss in ruminants. The predictive accuracy of VFA and methane models can be enhanced through experimental data collected from diverse environmental conditions, which will aid in determining optimal VFA and methane levels.

Journal of Animal Science and Technology (JAST) is a peer-reviewed, open access journal publishing original research, review articles and notes in all fields of animal science. Topics covered by the journal include: genetics and breeding, physiology, nutrition of monogastric animals, nutrition of ruminants, animal products (milk, meat, eggs and their by-products) and their processing, grasslands and roughages, livestock environment, animal biotechnology, animal behavior and welfare.

Ketosis, a metabolic disorder in dairy cows, poses a risk of substantial economic losses, particularly when it progresses to clinical forms. Previous prediction models relied on smart farming data and binary classification, without incorporating risk factors such as calf birth weight. Therefore, we aimed to develop a multiclass classification model to differentiate non- (NK), subclinical (SCK), and clinical (CK) ketosis in Holstein cows by integrating behavioral indicators, cow-specific traits, and environmental variables. We hypothesized that integrating these diverse data sources would improve the ability of the model to accurately classify ketosis severity during the transition period. A total of 132 Holsteins were monitored for 21 d after calving using automatic monitoring (HR-TAG). Input features included activity, rumination time, calving age, calf birth weight, and calving season. Blood β-hydroxybutyrate concentrations were measured at eight time points, and cows were classified into NK (<1.2 mmol/L), SCK (1.2-2.9 mmol/L), or CK (≥3.0 mmol/L) groups based on the highest BHBA value recorded across the sampling period. Five machine-learning algorithms-K-nearest neighbors, decision tree, random forest, support vector machine, and extreme gradient boosting (XGBoost)-were trained on 70 % of the dataset and optimized using 10-fold cross-validation, and final model performance was evaluated on the remaining 30 % test set. XGBoost performed best, achieving an accuracy, sensitivity, specificity, F-measure, kappa, and an area under the curve of 0.959, 0.935, 0.966, 0.951, 0.918, and 0.950, respectively. Feature importance analysis identified calving age, calf birth weight, and calving season as key predictors for ketosis severity. These results demonstrate that sensor-based behavioral traits, together with cow-specific characteristics and environmental factors, enable accurate classification of ketosis severity and support the application of precision dairy technologies for early detection and tailored herd management.

Objective: Subclinical mastitis decreases milk production and quality, despite the normal appearance of the mammary glands and milk. Herein, we aimed to investigate changes in factors monitored via automatic milking systems (AMS) prior to subclinical mastitis onset and identify differences in hematological and biochemical parameters and milk composition at subclinical mastitis onset.Methods: Thirty-two Holstein cows were divided into two groups according to somatic cell counts (SCC) from AMS and milk composition analysis and the California mastitis test (CMT): healthy cows (controls [CON], n = 16, SCC <500×103 cells/mL and negative for CMT) and cows with subclinical mastitis (SCM, n=16, SCC ≥500×103 cells/mL and positive for CMT). Eventually, 121 milk samples from the CON ([mCON], n = 60) and SCM ([mSCM], n = 61) groups were obtained; SCM samples were categorized as those from non-inflamed (mNQ) or subclinically-inflamed (mIQ) quarters. We evaluated AMS factors; hematological, biochemical, and milk composition parameters; and bacterial isolation.Results: In cows with SCM, milk yield decreased, and electrical conductivity (EC) changed before disease onset. Milk EC decreased in mNQ although increased in mIQ (p<0.05). The SCM group had higher globulin levels and lower basophil counts; albumin-to-globulin ratio; and total cholesterol, albumin, and blood urea nitrogen levels than the CON group (p<0.05). The mIQ group had higher SCC but lower levels of lactose and milk solids-notfat than those in the mCON and mNQ groups (p<0.05). The mCON group had higher levels of milk non-protein nitrogen than the mNQ group (p<0.05). Opportunistic mastitis pathogens were isolated in the mIQ group.Conclusion: Changes in milk yield and EC measured using AMS occurred prior to subclinical mastitis, which may be associated with variation in basophil counts; albumin-toglobulin ratio; and total cholesterol, albumin, blood urea nitrogen, globulin, SCC, milk lactose, and milk solids-not-fat levels at disease onset. These findings provide new insights into early-stage subclinical mastitis.