Interaction Effects of Somatic Cell Count and Milk Yield on Milk Composition in Lactating Dairy Cows: A Synergistic Analysis*
Öz
Objective: This study aimed to investigate the interaction effect between somatic cell count and milk yield on the composition of milk components in dairy cows.
Material and Methods: The study involved 165 clinically healthy lactating Holstein cows with an average parity of 1.76 and an average of 221 days in milk. Cows were grouped using K-means clustering analysis based on somatic cell count and milk yield. Milk samples were collected daily during the 30-day experimental period and analyzed for composition. A 2x2 factorial design was employed to examine the main and interaction effects of somatic cell count and milk yield on milk components.
Results: The interaction affected various milk components. Specifically, a higher somatic cell count combined with increased milk yield was associated with higher levels of solids at 12.70% ± 0.02, fat at 3.76% ± 0.02, true protein at 3.26% ± 0.01, casein at 2.42% ± 0.01, and milk urea nitrogen at 10.84 mg/dL ± 0.13. Lactose concentration significantly increased to 5.06% ± 0.01 (P=0.01). Notably, this interaction effect resulted in a significant increase in lactose concentration (P=0.01).
Conclusion: The study confirms an interaction effect between somatic cell count and milk yield on milk composition, emphasizing the need to consider both factors for optimizing milk quality. The observed increase in lactose concentration due to the interaction effect underscores the complexity of somatic cell count and milk yield dynamics, suggesting potential implications for udder health and dairy management practices.
Keywords: somatic cell count, milk yield, milk composition, dairy cows, udder health, milk quality.
Anahtar Kelimeler
somatic cell count, milk yield, milk composition, dairy cows, udder health, milk quality
Destekleyen Kurum
Rumico Livestock Management and Nutrition (RUMICO) has funded this study.
Etik Beyan
DECLARATION OF COMPETING INTEREST
The author/s declared that there is no conflict of interest.
DATA AVAILABILITY STATEMENT
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Kaynakça
- Alhussien MN, Dang AK. 2018. Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview. Veterinary World, 11(5), 562. https://doi: 10.14202/vetworld.2018.562-577
- Antanaitis R, Juozaitienė V, Jonike V, Baumgartner W, Paulauskas A. 2021. Milk lactose as a biomarker of subclinical mastitis in dairy cows. Animals, 11(6), 1736. https://doi.org/10.3390/ani11061736
- Ataallahi M, Cheon SN, Park GW, Nugrahaeningtyas E, Jeon JH, Park KH. 2023. Assessment of stress levels in lactating cattle: Analyzing cortisol residues in commercial milk products in relation to the temperature-humidity index. Animals (Basel), 13(15). 2407. https://doi.org/10.3390/ani13152407
- Azooz MF, El-Wakeel SA, Yousef HM. 2020. Financial and economic analyses of the impact of cattle mastitis on the profitability of Egyptian dairy farms. Veterinary World, 13(9), 1750-1759. https://doi.org/10.14202/vetworld.2020.1750-1759
- Bach A, Terre M, Vidal M. 2020. Decomposing efficiency of milk production and maximizing profit. Journal of Dairy Science, 103(6), 5709-5725. https://doi.org/10.3168/jds.2019-17304
- Bozic M, Wolf CA. 2022. Negative producer price differentials in federal milk marketing orders: Explanations, implications, and policy options. Journal of Dairy Science, 105(1), 424-440. https://doi.org/10.3168/jds.2021-20664
- Brito LF, Bedere N, Douhard F, Oliveira HR, Arnal M, Penagaricano F, Schinckel AP, Baes CF, Miglior F. 2021. Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world. Animal, 15 Suppl 1, 100292. https://doi.org/10.1016/j.animal.2021.100292
- Bronzo V, Lopreiato V, Riva F, Amadori M, Curone G, Addis MF, Cremonesi P, Moroni P, Trevisi E, Castiglioni B. 2020. The role of innate immune response and microbiome in resilience of dairy cattle to disease: The mastitis model. Animals, 10(8), 1397. https://doi.org/10.3390/ani10081397
- Carvalho-Sombra TCF, Fernandes DD, Bezerra BMO, Nunes-Pinheiro DCS. 2021. Systemic inflammatory biomarkers and somatic cell count in dairy cows with subclinical mastitis. Veterinary Animal Science, 11, 100165. https://doi.org/10.1016/j.vas.2021.100165
- Cohen J. 1992. Statistical power analysis. Current Directions in Psychological Science, 1(3), 98-101. https://doi.org/10.1111/1467-8721.ep10768