Efficiency and somatic cell count: Unraveling Holstein cow productivity through stochastic frontier modeling

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Pubblicato: Oct 24, 2024
DOI: https://doi.org/10.12681/jhvms.36116
Hİ Tosun
Rumico Livestock Management and Nutrition, Aydın, Türkiye
Abstract

The yield and quality decrease due to high somatic cell counts caused by mastitis, and this also negatively affects the profitability, efficiency, and sustainability of dairy farms. The main objective of this study was to investigate the effects of somatic cell counts on yield, milk chemicals, and the technical efficiency of Holstein dairy cows. A total of 165 lactating cows were involved in the research, and all cows were fed the diets as a total mixed ration three times a day. Milk samples were collected each day during milking and analyzed for chemical composition and somatic cell counts (SCC). The daily milk production of each cow was obtained from the SCR herd management program, which is integrated with the parlor. In conclusion, it was determined that for each group, the efficiency scores, SCC, and milk yield of cows varied between 0.80 and 0.99, 322.000 and 557.857 cells/mL, and 33.13 and 48.90 Kg/d, respectively; they also differed significantly in each group. Considering the findings, milk production can be increased by 7% without changing any input. Additionally, every 1% decrease in SCC will increase the efficiency of milk production by 0.55%. Cows with low technical efficiency (TE) scores produced 2.87 kg/d/cow less milk compared to animals with high TE. Reducing the SCC of the group with a low TE (456.878 cells/mL) to a SCC of high TE (438.869 cells/mL) will increase milk yield by 2.87 kg/d/cow on average. In conclusion, minimizing losses due to mastitis is paramount for enhancing dairy farm efficiency. This research underscores the interplay between TE and udder health, providing a comprehensive understanding of individual cow performance. Addressing inefficiencies and promoting udder health can significantly contribute to sustainable and economically viable dairy farming practices.


Keywords: Dairy farm; Mastitis; Somatic cell count; Stochastic frontier analysis; Technical efficiency

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Riferimenti bibliografici
Ajose D J, Oluwarinde B O, Abolarinwa T O, Fri J, Montso K P, Fayemi
O E, Aremi A O, Ateba C N (2022) Combating bovine mastitis in
the dairy sector in an era of antimicrobial resistance: ethno-veterinary
medicinal option as a viable alternative approach. Frontiers in Veteri
nary Science, 9, 800322. https://www.ncbi.nlm.nih.gov/pmc/articles/
PMC9014217/pdf/fvets-09-800322.pdf
Álvarez A, Del Corral J, Solís D, Pérez J. (2008) Does intensification
improve the economic efficiency of dairy farms. Journal of dairy
science, 91(9), 3693-3698. https://doi.org/https://doi.org/10.3168/
jds.2008-1123
Andrighetto I, Serva L, Fossaluzza D, Marchesini G (2023) Herd Lev
el Yield Gap Analysis in a Local Scale Dairy Farming System: A
Practical Approach to Discriminate between Nutritional and Other
Constraining Factors. Animals, 13(3), 523. https://doi.org/10.3390/
ani13030523
Atzori A, Valsecchi C, Manca E, Masoero F, Cannas A, Gallo A (2021)
Assessment of feed and economic efficiency of dairy farms based on
multivariate aggregation of partial indicators measured on field. Jour
nal of dairy science, 104(12), 12679-12692. https://doi.org/10.3168/
jds.2020-19764
Azooz M, El-Wakeel S A, Yousef H (2020) Financial and economic anal
yses of the impact of cattle mastitis on the profitability of Egyptian
dairy farms. Veterinary World,13(9),1750.https://www.ncbi.nlm.nih.
gov/pmc/articles/PMC7566233/pdf/Vetworld-13-1750.pdf
Berry D (2021) Invited review: Beef on dair - The generation of cross
bred beef x dairy cattle. Journal of dairy science, 104(4), 3789-3819.
https://doi.org/10.3168/jds.2020-19519
Boland F, O’Grady L, More S (2013) Investigating a dilution effect be
tween somatic cell count and milk yield and estimating milk pro
duction losses in Irish dairy cattle. Journal of dairy science, 96(3),
-1484.
https://www.journalofdairyscience.org/article/S0022
(13)00016-7/pdf
Britt J H, Cushman R A, Dechow C D, Dobson H, Humblot P, Hutjens
M F, Jones G A, Mitloehner F M, Ruegg P L, Sheldon I M, Steven
son J S (2021) Review: Perspective on high-performing dairy cows
and herds. Animal, 15 Suppl 1, 100298. https://doi.org/10.1016/j.an
imal.2021.100298
Coelli T, Rao D P, Battese G E, Coelli T, Rao D P, Battese G E (1998)
Efficiency measurement using data envelopment analysis (DEA). In
An introduction to efficiency and productivity analysis (pp. 133-160).
Coelli T J (1996) A guide to FRONTIER version 4.1: a computer program
for stochastic frontier production and cost function estimation.
Ebi K L, Anderson C L, Hess J J, Kim S H, Loladze I, Neumann R B,
Singh D, Ziska L, Wood R (2021) Nutritional quality of crops in a
high CO2 world: an agenda for research and technology develop
ment. Environmental Research Letters, 16(6), 064045. https://doi.
org/10.1088/1748-9326/abfcfa
Fernandez-Novo A, Pérez-Garnelo S S, Villagrá A, Pérez-Villalobos N,
Astiz S (2020) The effect of stress on reproduction and reproductive
technologies in beef cattle - A review. Animals, 10(11), 2096. https://
doi.org/10.3390/ani10112096
Girdhar A, Kapur H, Kumar V (2022) Classification of White blood cell
using Convolution Neural Network. Biomedical Signal Processing
and Control, 71, 103156. https://doi.org/10.1016/j.bspc.2021.103156
Guth M, Smędzik-Ambroży K (2020) Economic resources versus the ef
f
iciency of different types of agricultural production in regions of the
European Union. Economic research-Ekonomska istraživanja, 33(1),
-1051.
Hall M B (2023) Invited review: Corrected milk: Reconsideration of com
mon equations and milk energy estimates. Journal of Dairy Science,
(4), 2230-2246. https://doi.org/10.3168/jds.2022-22219
Hisira V, Zigo F, Kadaši M, Klein R, Farkašová Z, Vargová M, Mudroň
P. (2023) Comparative Analysis of Methods for Somatic Cell Count
ing in Cow’s Milk and Relationship between Somatic Cell Count and
Occurrence of Intramammary Bacteria. Veterinary Science. 2023 Jul
;10(7):468. https://10.3390/vetsci10070468 PMID: 37505872;
PMCID: PMC10384197.
Hogeveen H, Huijps K, Lam T J (2011) Economic aspects of mastitis: new
developments. New Zealand Veterinary Journal, 59(1), 16-23. https://
doi.org/10.1080/00480169.2011.547165
Hogeveen H, Steeneveld W, Wolf C A (2019) Production diseases reduce
the efficiency of dairy production: A review of the results, methods,
and approaches regarding the economics of mastitis. Annual Review
of Resource Economics, 11, 289-312. https://doi.org/10.1146/an
nurev-resource-100518-093954
Jehan S, Ullah I, Khan S, Muhammad S, Khattak S A, Khan T (2020)
Evaluation of the Swat River, Northern Pakistan, water quality us
ing multivariate statistical techniques and water quality index (WQI)
model. Environmental Science and Pollution Research, 27(31),
- 38558. https://doi.org/10.1007/s11356-020-09688-y
Kaskous S (2021) Physiological aspects of milk somatic cell count in
dairy cattle. International Journal of Livestock Research, Vol. 11(10).
eISS: 2277-1964. https://dx.doi.org/10.5455/ijlr.2021051
Kimura S, Sauer J (2015) Dynamics of dairy farm productivity
growth: Cross-country comparison. OIECDiLibrary. https://doi.
org/10.1787/18156797
Krampe C, Fridman A (2022) Oatly, a serious ‘problem’for the dairy indus
try? A case study. International Food and Agribusiness Management
Review, 25(1), 157-171. https://doi.org/10.22434/IFAMR2021.0058
Lahari S (2023) Economic Losses Due to Mastitis in Dairy Farms of
Hyderabad, Telangana, India: Estimation and Implications. American
Journal Of Agriculture And Horticulture Innovations, 3(07), 15-18.
Mor S, Sharma S (2012) Technical efficiency and supply chain practices
in dairying: The case of India. Agricultural Economics, 58(2), 85-91.
Mordenti A L, Giaretta E, Campidonico L, Parazza P, Formigoni A (2021)
A Review Regarding the Use of Molasses in Animal Nutrition.
Animals, 11(1), 115. https://mdpi-res.com/d_attachment/animals/
animals-11-00115/article_deploy/animals-11-00115-v3.pdf?ver
sion=1610539396
Nainggolan R, Perangin-angin R, Simarmata E, Tarigan A F (2019) Im
proved 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.
Nikkhah A, Alimirzaei M (2023) Management Updates on Prepartal
Stress Effects on Transition Cow and Calf Health. World, 13(2), 250
https://doi.org/10.54203/scil.2023.wvj27
Novac C S, Andrei S (2020) The Impact of Mastitis on the Biochemi
cal Parameters, Oxidative and Nitrosative Stress Markers in Goat’s
Milk: A Review. Pathogens, 9(11), 882. https://www.mdpi.com/2076
/9/11/882
Pakrashi A, Ryan C, Guéret C, Berry D P, Corcoran M, Keane M T, Mac
Namee B (2023) Early detection of subclinical mastitis in lactating
dairy cows using cow-level features. Journal of Dairy Science. https://
www.journalofdairyscience.org/article/S0022-0302(23)00297-7/full
text
Sadat A, Farag A M M, Elhanafi D, Awad A, Elmahallawy E K, Also
wayeh N, El-khadragy M F, Elshopakey G E (2023) Immunological
and Oxidative Biomarkers in Bovine Serum from Healthy, Clinical,
and Sub-Clinical Mastitis Caused by Escherichia coli and Staphy
lococcus aureus Infection. Animals (Basel), 13(5), 892. https://doi.
org/10.3390/ani13050892
Schuster J C, Barkema H W, De Vries A, Kelton D F, Orsel K (2020) Invit
ed review: Academic and applied approach to evaluating longevity in
dairy cows. Journal of Dairy Science, 103(12), 11008-11024. https://
doi.org/10.3168/jds.2020-19043
Schwarz D, Diesterbeck U S, Failing K, König S, Bruegemann K,
Zschöck M, Wolter W, Czerny C P (2010) Somatic cell counts and
bacteriological status in quarter foremilk samples of cows in Hesse,
Germany - A longitudinal study. Journal of Dairy Science, 93(12),
-5728. https://doi.org/10.3168/jds.2010-3223
Shinde S, Mahesh K, Venkanna B (2022) Evaluation of surf field test and
california mastitis test for diagnosis of sub clinical mastitis in Cross
bred Cows. Journal of Krishi Vigyan, 11(si), 37-42.
Sørensen L P, Bjerring M, Løvendahl P (2016) Monitoring individual
cow udder health in automated milking systems using online somat
ic cell counts. Journal of Dairy Science, 99(1), 608-620. https://doi.
org/10.3168/jds.2014-8823
SPSS (2011) IBM SPSS Statistics 20.0 for Windows. Armonk, N.Y: IBM
Corp.
Tan P N, Steinbach M, Kumar V (2006) Data mining introduction. Peo
ple’s Posts and Telecommunications Publishing House, Beijing.
Tosun H I, Ceyhan V (2015) Current situation in dairy industry and feed
efficiency of professional dairy farms of Turkey. 2nd International
Conference on Sustainable Agriculture and Environment (2nd IC
SAE), September 30 - October 3, 2015, Konya, Turkey.
Valle-Aguilar M, López-González F, Sainz-Ramírez A, Arriaga-Jordán C
M (2020) Prevalence subclinical mastitis in small-scale dairy farms
under grazing or in total confinement in the central highlands of Mex
ico. Indian Journal of Dairy Science, 73, 73-76.
Van Amburgh M E, Collao-Saenz E A, Higgs R J, Foskolos A (2015) The
Cornell Net Carbohydrate and Protein System: Updates to the mod
el and evaluation of version 6.5. Journal of Dairy Science 98:6361
https://doi.org/10.3168/jds.2015 -9378
Veerkamp R (1998) Selection for economic efficiency of dairy cattle us
ing information on live weight and feed intake: a review. Journal of
Dairy Science, 81(4), 1109-1119. https://doi.org/10.3168/jds.S0022
(98)75673-5
Viguier C, Arora S, Gilmartin N, Welbeck K, O’Kennedy R (2009) Mas
titis detection: current trends and future perspectives. Trends in Bio
technology, 27(8), 486-493.
Wani S A, Haq U, Parray O R, Ul Q, Nazir A, Mushtaq M, Bhat RA, Par
rah J, Chakraborty S, Dhama K (2022) A Brief Analysis of Economic
Losses Due to Mastitis in Dairy Cattle. Indian Veterinary Journal, 90,
-31.
Xue M Y, Xie Y Y, Zhong Y, Ma X J, Sun H Z, Liu J X (2022) Integrated
meta-omics reveals new ruminal microbial features associated with
feed efficiency in dairy cattle. Microbiome, 10(1), 32. https://doi.
org/10.1186/s40168-022-01228-9
Zigo F, Elecko J, Farkasova Z, Zigova M, Vasil M, Ondrasovicova S, Len
ka K (2019) Preventive methods in reduction of mastitis pathogens in
dairy cows. Journal of Microbiology, Biotechnology and Food Scienc
es, 9(1), 121-126. https://doi.org/10.15414/jmbfs.2019.9.1.121-126
Zigo F, Vasil M, Ondrasovicova S, Vyrostkova J, Bujok J, Pecka-Kielb
E (2021) Maintaining Optimal Mammary Gland Health and Preven
tion of Mastitis. Frontier Veterinary Science, 8, 607311. https://doi.
org/10.3389/fvets.2021.607311
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