Effects of Environmental Temperature and Humidity on Milk Composition, Microbial Load, and Somatic Cells in Milk of Holstein Dairy Cows in the Northeast Regions of Iran

Exploring the impact of biosecurity measures on somatic cell score in mountain dairy farms considering the CLASSYFARM system

Biosecurity plays a crucial role in preventing the introduction of infectious diseases to a herd as well as the spread of diseases within or between animals and herds. In particular, biosecurity measures are crucial for maintaining animal health and reducing the need for the application of antibiotic substances for fighting the rising antibiotic resistance. The object of this cross-sectional study was to investigate the presence of biosecurity measures and their association with milk quality parameters, with a special focus on somatic cell count (SCC) - an indicator for udder health -, in small-scale mountain dairy farms. Therefore, for the very first time, the CLASSYFARM system was considered, which is a computer platform integrated into the Italian national veterinary portal, that processes a significant amount of data from various sources collected in the field or from other information systems (e.g. animal welfare, health status, biosecurity, antimicrobial use, slaughterhouse information). A total of 169 dairy farms were included in the study. Biosecurity measures, based on 15 questions required in the CLASSYFARM welfare assessment protocol, as well as information about husbandry systems, milking systems and pasture practices were gathered and combined with milk yield data, provided by the South Tyrolean dairy association. Farms only scored 44.00 points on average in a scoring system from 0.00 to 100.00 points that was be able to summarize 15 different biosecurity measures in one index. Our results show a clear negative correlation (-0.713) between the biosecurity index and somatic cell score (SCS) indicating that a higher level of biosecurity, which reflects the presence of biosecurity measures within a farm, is associated with lower SCC levels. Furthermore, we found significant correlations between SCS and milk production (-0.629), confirming that udder health is linked to higher milk production. Fat, protein, and the fat-to-protein ratio showed a positive correlation with SCS (0.281, 0.146, 0.106), likely to be caused by a concentration shift effect (dilution effect). Husbandry system, breed, milking system, and pasture practices seem to have an impact as well, but the main factor was the biosecurity score. This study highlights the importance of implementing biosecurity measures for ensuring animal health and thus productivity and quality in milk production, even in small-scale farms, which are characterized by limited structure availability and smaller herds compared to big dairy enterprises in the lowlands.

Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

Quantitative Associations between Season, Month, and Temperature-Humidity Index with Milk Yield, Composition, Somatic Cell Counts, and Microbial Load: A Comprehensive Study across Ten Dairy Farms over an Annual Cycle

This current study addresses the knowledge gap regarding the influence of seasons, months, and THI on milk yield, composition, somatic cell counts (SCC), and total bacterial counts (TBC) of dairy farms in northeastern regions of Iran. For this purpose, ten dairy herds were randomly chosen, and daily milk production records were obtained. Milk samples were systematically collected from individual herds upon delivery to the dairy processing facility for subsequent analysis, including fat, protein, solids-not-fat (SNF), pH, SCC, and TBC. The effects of seasons, months, and THI on milk yield, composition, SCC, and TBC were assessed using an analysis of variance. To account for these effects, a mixed-effects model was utilized with a restricted maximum likelihood approach, treating month and THI as fixed factors. Our investigation revealed noteworthy correlations between key milk parameters and seasonal, monthly, and THI variations. Winter showed the highest milk yield, fat, protein, SNF, and pH (p < 0.01), whereas both SCC and TBC reached their lowest values in winter (p < 0.01). The highest values for milk yield, fat, and pH were recorded in January (p < 0.01), while the highest protein and SNF levels were observed in March (p < 0.01). December marked the lowest SCC and TBC values (p < 0.01). Across the THI spectrum, spanning from -3.6 to 37.7, distinct trends were evident. Quadratic regression models accounted for 34.59%, 21.33%, 4.78%, 20.22%, 1.34%, 15.42%, and 13.16% of the variance in milk yield, fat, protein, SNF, pH, SCC, and TBC, respectively. In conclusion, our findings underscore the significant impact of THI on milk production, composition, SCC, and TBC, offering valuable insights for dairy management strategies. In the face of persistent challenges posed by climate change, these results provide crucial guidance for enhancing production efficiency and upholding milk quality standards.

Assessment of Stress Levels in Lactating Cattle: Analyzing Cortisol Residues in Commercial Milk Products in Relation to the Temperature-Humidity Index

Chronic stress in the dairy cattle industry has negative impacts on animal health, productivity, and welfare. It has been confirmed that cortisol transfers to milk and resists the high temperature during milk processing. This study evaluated the relationship between the milk cortisol concentration (MCC) in commercial milk products and the temperature-humidity index (THI) at the time of milk production. Eleven commercially produced pasteurized and sterilized milk products, purchased in Chuncheon, Korea, with production dates ranging from July to October 2021 were analyzed. The MCC was extracted using diethyl ether and analyzed using an enzyme immunoassay. The average THI values based on microclimate data provided by the Korea Meteorological Administration were 77 ± 0.8, 75 ± 1.4, 69 ± 1.4, and 58 ± 1.8, in July, August, September, and October, respectively. The average MCC levels were 211.9 ± 95.1, 173.5 ± 63.8, 109.6 ± 53.2, and 106.7 ± 33.7 pg/mL in July, August, September, and October, respectively. The MCC in July was higher than in August, September, and October (p < 0.05), while it was lower in September and October than in August (p < 0.05). Significant variations in the MCC were observed in commercial milk products across the four production months (p < 0.05), except for two milk products. Overall, monitoring the cortisol residue in commercial dairy milk products can be an alternative indicator of stress in dairy cattle of farms.

Evaluation of milk yield and composition, feed intake, chewing activities, and clinical variables in dairy cows under hot-humid climate of tropical zone

Dairy cows increase heat loads when the temperature-humidity index (THI) value is elevated in the ambient environments. This condition often occurs in the tropical areas due to a higher THI rate throughout seasons. The major objective of the study was to investigate the different responses in milk yield and composition, chewing activities, and health parameters in dairy cows under the dry and wet seasons of tropical climate zone in Indonesia. Twenty mid-lactating Indonesian Holstein-Friesian cows (139.3 ± 24.63 DIM; 10 primiparous and 10 multiparous; 441 ± 21.5 kg BW) were randomly subjected to 2 groups, dairy cows under dry (n = 10) and wet season (n = 10). Both groups received the same diets throughout the experiment. To determine the heat stress condition, the THI values were recorded daily. Overall, a higher number of THI was more pronounced in wet season. A lower dry matter intake (DMI) and milk yield were observed in wet season group. A tendency towards higher milk protein contents was found in dairy cows under dry season compared to cows under wet season. The other milk compositions such as fat, lactose, and SNF remained unchanged in both dry and wet season groups. The comparison between both groups at several time points of eating and ruminating time revealed significantly higher in cows under dry season. Overall, a higher chewing per bolus was observed in cows under dry season than their counterparts. Furthermore, a tendential greater extent rectal temperature pointed in the wet season group compared to the dry season group relatively. Data suggest that a stronger heat stress condition in wet season was more pronounced compared to dry season, with adversely affecting stronger declined DMI, milk yield, and chewing activities of dairy cows.

Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality

To maximize milk production, efficiency, and profits, modern dairy cows are genetically selected and bred to produce more and more milk and are fed copious quantities of high-energy feed to support ever-increasing milk volumes. As demands for increased milk yield and milking efficiency continue to rise to provide for the growing world population, more significant stress is placed on the dairy cow's productive capacity. In this climate, which is becoming increasingly hotter, millions of people depend on the capacity of cattle to respond to new environments and to cope with temperature shocks as well as additional stress factors such as solar radiation, animal crowding, insect pests, and poor ventilation, which are often associated with an increased risk of mastitis, resulting in lower milk quality and reduced production. This article reviews the impact of heat stress on milk production and quality and emphasizes the importance of udder health monitoring, with a focus on the use of emergent methods for monitoring udder health, such as infrared thermography, biosensors, and lab-on-chip devices, which may promote animal health and welfare, as well as the quality and safety of dairy products, without hindering the technological flow, while providing significant benefits to farmers, manufacturers, and consumers.