Laktasyon Dönemindeki Süt İneklerinde Somatik Hücre Sayısı ve Süt Verimi Etkileşiminin Süt Kompozisyonu Üzerindeki Etkileri: Sinerjik Bir Analiz

Year 2024, Volume: 65 Issue: 2, 109 - 118, 31.12.2024

Halil İbrahim Tosun

https://doi.org/10.29185/hayuretim.1528073

Abstract

Amaç: Bu çalışmanın amacı, somatik hücre sayısı ile süt verimi arasındaki etkileşimin süt ineklerinde süt bileşenleri üzerindeki etkisini araştırmak olmuştur.

Materyal ve Metot: Çalışma, ortalama 1,76 doğum sayısına ve ortalama 221 sağım gün sayısına sahip 165 adet klinik olarak sağlıklı laktasyon dönemindeki Holstein süt ineğini kapsamaktadır. İnekler, somatik hücre sayısı ve süt verimine göre K-means kümeleme analizi kullanılarak gruplandırılmıştır. Süt örnekleri, 30 günlük araştırma süresi boyunca günlük olarak toplanmış ve kompozisyonu analiz edilmiştir. Somatik hücre sayısı ve süt veriminin süt bileşenleri üzerindeki ana etki ve sinerjik etkisini incelemek için 2x2 faktöriyel tasarım methodu kullanılmıştır.

Bulgular: Etkileşim, süt bileşenlerini etkilemiştir. Özellikle, yüksek somatik hücre sayısı ile yüksek süt verimine sahip inek sütlerinin kuru maddesi %12.70 ± 0.02, süt yağı %3.76 ± 0.02, süt proteini %3.26 ± 0.01, süt kazeini %2.42 ± 0.01 ve süt üre azotu 10.84 mg/dL ± 0.13 olduğu tespit edilmiştir. Süt laktoz konsantrasyonu anlamlı şekilde artarak %5.06 ± 0.01 olduğu tespit edilmiştir (P=0.01). Özellikle, etkileşimin laktoz konsantrasyonunda anlamlı bir artışa neden olduğu tespit edilmiştir (P=0.01).

Sonuç: Çalışma, somatik hücre sayısı ile süt verimi arasındaki etkileşimin süt bileşenleri üzerine etkisini doğrulamakta ve süt kalitesini optimize etmek için her iki faktörün de dikkate alınması gerektiğini vurgulamaktadır. Etkileşim nedeniyle gözlenen laktoz miktarındaki artış, süt bileşenlerinin dinamiklerini öne çıkarmakta olup meme sağlığı ve yönetimsel uygulamalar için potansiyel sonuçları göstermektedir.

Anahtar sözcükler: somatik hücre sayısı, süt verimi, süt içeriği, süt ineği, meme sağlığı, süt kalitesi

Keywords

somatik hücre sayısı, süt verimi, süt içeriği, süt ineği, meme sağlığı, süt kalitesi

Ethical Statement

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.

Supporting Institution

Rumico Livestock Management and Nutrition (RUMICO) has funded this study.

Interaction Effects of Somatic Cell Count and Milk Yield on Milk Composition in Lactating Dairy Cows: A Synergistic Analysis*

Abstract

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.

Keywords

somatic cell count, milk yield, milk composition, dairy cows, udder health, milk quality

References

• 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
• Costa A, Neglia G, Campanile G, De Marchi M. 2020. Milk somatic cell count and its relationship with milk yield and quality traits in Italian water buffaloes. Journal of Dairy Science, 103(6), 5485-5494. https://doi.org/10.3168/jds.2019-18009
• Ebrahimie E, Ebrahimi F, Ebrahimi M, Tomlinson S, Petrovski KR. 2018. A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity. Journal of Dairy Research, 85(2), 193-200. https://doi.org/10.1017/S0022029918000249
• Goncalves JL, Kamphuis C, Vernooij H, Araujo JP, Grenfell RJ, Juliano L, Anderson KL, Hogeveen H, Dos Santos MV. 2020. Pathogen effects on milk yield and composition in chronic subclinical mastitis in dairy cows. The Veterinary Journal, 262, 105473. https://doi.org/10.1016/j.tvjl.2020.105473
• Grace D, Wu F, Havelaar AH. 2020. Foodborne diseases from milk and milk products in developing countries-Review of causes and health and economic implications. Journal of Dairy Science, 103(11), 9715-9729. https://doi.org/10.3168/jds.2020-18323
• Gorelik OV, Galushina PS, Knysh IV, Bobkova EY, Grigoryants IA. 2021. Relationship between cow milk yield and milk quality indicators. Earth and Environmental Science, Vol. 677, No. 3, p. 032013. https://doi.org/10.1088/1755-1315/677/3/032013
• Gussmann M, Denwood M, Kirkeby C, Farre M, Halasa T. 2019. Associations between udder health and culling in dairy cows. Preventive Veterinary Medicine, 171, 104751. https://doi.org/10.1016/j.prevetmed.2019.104751
• Hall MB. 2023. Corrected milk: Reconsideration of common equations and milk energy estimates. Journal of Dairy Science, 106(4): p. 2230-2246.
• Hennessy D, Delaby L, Van den Pol-Van Dasselaar A, Shalloo L. 2020. Increasing grazing in dairy cow milk production systems in Europe. Sustainability, 12(6), 2443. https://doi.org/10.3390/su12062443
• Leitner G, Merin U, Silanikove N. 2004. Changes in milk composition as affected by subclinical mastitis in goats. Journal of Dairy Science, 87(6), 1719-1726. https://doi.org/10.3168/jds.S0022-0302(04)73325-1
• Lim DH, Mayakrishnan V, Lee HJ, Ki KS, Kim TI, Kim Y. 2020. A comparative study on milk composition of Jersey and Holstein dairy cows during the early lactation. Journal of Animal Science Technologhy, 62(4), 565-576. https://doi.org/10.5187/jast.2020.62.4.565
• Malek dos Reis CB, Barreiro JR, Mestieri L, Porcionato MA, Dos Santos MV. 2013. Effect of somatic cell count and mastitis pathogens on milk composition in Gyr cows. BMC Veterinary Research, 9, 67. https://doi.org/10.1186/1746-6148-9-67
• Nainggolan R, Perangin-Angin R, Simarmata E, Tarigan AF. 2019. Improved the performance of the K-means cluster using the sum of squared error (SSE) optimized by using the Elbow method. Journal of Physics: Conference Series.
• National Academies of Sciences Engineering and Medicine. 2021. Nutrient requirements of dairy cattle: Eighth revised edition. The National Academies Press. https://doi.org/doi:10.17226/25806
• Neculai-Valeanu AS, Ariton AM. 2022. Udder health monitoring for prevention of bovine mastitis and improvement of milk quality. Bioengineering (Basel), 9(11), 608. https://doi.org/10.3390/bioengineering9110608
• Ndahetuye JB, Artursson K, Bage R, Ingabire A, Karege C, Djangwani J, Persson Y. 2020. Microbiological quality and safety of milk from farm to milk collection centers in Rwanda. Journal of Dairy Science, 103(11), 9730-9739. https://doi.org/10.3168/jds.2020-18302
• Odorcic M, Rasmussen MD, Paulrud CO, Bruckmaier RM. 2019. Milking machine settings, teat condition and milking efficiency in dairy cows. Animal, 13(S1), s94-s99. https://doi.org/10.1017/S1751731119000417
• Pakrashi A, Ryan C, Gueret C, Berry DP, Corcoran MT, Keane MT, Mac Namee B. 2023. Early detection of subclinical mastitis in lactating dairy cows using cow-level features. Journal of Dairy Science, 106(7), 4978-4990. https://doi.org/10.3168/jds.2022-22803
• Pegolo S, Giannuzzi D, Bisutti V, Tessari R, Gelain M, Gallo L, Schiavon S, Tagliapietra F, Trevisi E, Marsan PA. 2021. Associations between differential somatic cell count and milk yield, quality, and technological characteristics in Holstein cows. Journal of Dairy Science, 104(4), 4822-4836. https://doi.org/10.3168/jds.2020-19084
• Puerto MA, Shepley E, Cue RI, Warner D, Dubuc J, Vasseur E. 2021. The hidden cost of disease: Impact of the first incidence of mastitis on production and economic indicators of primiparous dairy cows. Journal of Dairy Science, 104(7), 7932-7943. https://doi.org/10.3168/jds.2020-19584
• Pyorala S. 2003. Indicators of inflammation in the diagnosis of mastitis. The Veterinary Research, 34(5), 565-578. https://doi.org/10.1051/vetres:2003026
• Rowe S, House JK, Zadoks RN. 2024. Milk as diagnostic fluid for udder health management. Australian Veterinary Journal, 102(1-2), 5-10. https://doi.org/10.1111/avj.13290
• Santman-Berends I, Van den Heuvel KWH, Lam T, Scherpenzeel CGM, Van Schaik G. 2021. Monitoring udder health on routinely collected census data: Evaluating the short- to mid-term consequences of implementing selective dry cow treatment. Journal of Dairy Science, 104(2), 2280-2289. https://doi.org/10.3168/jds.2020-18973
• Schwarz D, Santschi DE, Durocher J, Lefebvre DM. 2020. Evaluation of the new differential somatic cell count parameter as a rapid and inexpensive supplementary tool for udder health management through regular milk recording. Preventive Veterinary Medicine, 181, 105079. https://doi.org/10.1016/j.prevetmed.2020.105079
• Sehested J, Gaillard C, Lehmann JO, Maciel GM, Vestergaard M, Weisbjerg MR, Mogensen L, Larsen LB, Poulsen NA, Kristensen T. 2019. Extended lactation in dairy cattle. Animal, 13(S1), s65-s74. https://doi.org/10.1017/S1751731119000806
• Sharun K, Dhama K, Tiwari R, Gugjoo MB, Iqbal Yatoo M, Patel SK, Pathak M, Karthik K, Khurana SK, Singh R, Puvvala B, Amarpal Singh R, Singh KP, Chaicumpa W. 2021. Advances in therapeutic and managemental approaches of bovine mastitis: A comprehensive review. Veterinary Quarterly, 41(1), 107-136. https://doi.org/10.1080/01652176.2021.1882713
• Singla A, Karambir M. 2012. Comparative analysis & evaluation of euclidean distance function and manhattan distance function using k-means algorithm. International Journal of Advanced Research in Computer Science and Software Engineering (IJARSSE), 2(7), 298-300.
• Stocco G, Summer A, Cipolat-Gotet C, Zanini L, Vairani D, Dadousis C, Zecconi A. 2020. Differential somatic cell count as a novel indicator of milk quality in dairy cows. Animals, 10(5), 753. https://doi.org/10.3390/ani10050753
• Soufleri A, Banos G, Panousis N, Fletouris D, Arsenos G, Kougioumtzis A, Valergakis GE. 2021. Evaluation of factors affecting colostrum quality and quantity in Holstein dairy cattle. Animals (Basel), 11(7), 2005. https://doi.org/10.3390/ani11072005
• SPSS Inc. 2011. IBM SPSS Statistics Base 20. Chicago, IL: SPSS Inc.
• Yalçın H, Çakmak T. 2022. İnek Sütlerinde Somatik Hücre Sayısı ve Bazı Parametrelerin Araştırılması. MJAVL Sciences. 11 (2) 81-88. https://doi.org/10.53518/mjavl.1092994
• Tan PN, Steinbach M, Kumar V. 2006. Data mining introduction. People’s Posts and Telecommunications Publishing House, Beijing.
• Tosun HI. 2021. TRCI bölgesinde süt sığırcılığı işletmelerinin karlılık ve etkinlik analizi Ondokuz Mayıs Universitesi. PhD Thesis
• Tosun HI, Ceyhan V. 2015. Current situation in dairy industry and feed efficiency of professional dairy farms of Turkey. Sustainable Agriculture and Environment Proceeding Book, 175.
• Tricarico JM, Kebreab E, Wattiaux MA. 2020. Sustainability of dairy production and consumption in low-income countries with emphasis on productivity and environmental impact. Journal of Dairy Science, 103(11), 9791-9802. https://doi.org/10.3168/jds.2020-18269
• Waller KP, Lundberg A, Nyman AK. 2020. Udder health of early-lactation primiparous dairy cows based on somatic cell count categories. Journal of Dairy Science, 103(10), 9430-9445. https://doi.org/10.3168/jds.2020-18346
• Zigo F, Vasil M, Ondrasovicova S, Vyrostkova J, Bujok J, Pecka-Kielb E. 2021. Maintaining optimal mammary gland health and prevention of mastitis. Frontier Veterinary Science, 8, 607311. https://doi.org/10.3389/fvets.2021.607311.