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

Mostafa Bokharaeian; Abdolhakim Toghdory; Taghi Ghoorchi; Jalil Ghassemi Nejad; Iman Janghorban Esfahani

doi:10.3390/ani13203205

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

Animals (Basel). 2023 Oct 13;13(20):3205. doi: 10.3390/ani13203205.

Authors

Mostafa Bokharaeian¹, Abdolhakim Toghdory¹, Taghi Ghoorchi¹, Jalil Ghassemi Nejad², Iman Janghorban Esfahani³

Affiliations

¹ Department of Animal and Poultry Nutrition, Animal Science Faculty, Gorgan University of Agricultural Science and Natural Resources, Gorgan 49189-43464, Iran.
² Department of Animal Science and Technology, Sanghuh College of Life Sciences, Konkuk University, Seoul 05029, Republic of Korea.
³ Glopex Co., Ltd., R&D Center, GeumGang Penterium IX Tower A2801, Dongtancheomdansaneop 1-ro 27, Hwaseong-si 18469, Gyeonggi-do, Republic of Korea.

Abstract

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.

Keywords: heat stress; microbial load; milk production; somatic cell count; temperature–humidity index.

Grants and funding

The funding for this work was provided by Glopex through the research grant for innovation (project no. GP-BioMed2023). The authors would like to convey their sincere appreciation to Glopex (https://glopex.kr/) accessed on 8 October 2023, whose kind financial assistance made this study feasible. We would also like to thank our coworkers and collaborators for their suggestions and criticism throughout the course of this project. We also thank the editorial staff and reviewers for their insightful criticism and recommendations, which considerably raised the caliber of this work. We warmly thank Glopex for their dedication to science and their conviction that this research is crucial.