Use of differential somatic cell count, somatic cell score, and milk mid-infrared spectral analysis for monitoring mastitis in dairy cows during routine milk recording

Rienesl, Lisa; Marginter, Mathias; Stückler, Peter; Köck, Astrid; Egger-Danner, C.; Sölkner, Johann

doi:10.1016/j.livsci.2022.105050

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Therefore, test-day records were linked to the respective mastitis or ketosis diagnoses which were detected in the period of 21 days prior and 21 days after the test day. The specific time window of ±21 days was chosen, so that almost any diagnosis could be assigned to a test-day [ 14 ]. If a cow had no recorded mastitis or ketosis diagnosis in this particular time window, the test-day result was identified as healthy.…”

Section: Methodsmentioning

confidence: 99%

“…Other studies focused on the prediction of methane emission [ 6 ], daily energy intake, feed intake or efficiency [ 7 , 8 ], milk differentiation regarding feeding systems [ 9 , 10 ], or pregnancy status [ 11 , 12 ]. Recently, quite some research has been conducted addressing predictive models based on MIR spectra to detect economically relevant diseases, including prognosis of clinical mastitis [ 13 , 14 ], ketosis [ 15 ], ketone bodies [ 16 ], or lameness [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows

Gruber

Rienesl

Köck³

et al. 2023

Animals

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Mid-infrared (MIR) spectroscopy is routinely applied to determine major milk components, such as fat and protein. Moreover, it is used to predict fine milk composition and various traits pertinent to animal health. MIR spectra indicate an absorbance value of infrared light at 1060 specific wavenumbers from 926 to 5010 cm−1. According to research, certain parts of the spectrum do not contain sufficient information on traits of dairy cows. Hence, the objective of the present study was to identify specific regions of the MIR spectra of particular importance for the prediction of mastitis and ketosis, performing variable selection analysis. Partial least squares discriminant analysis (PLS-DA) along with three other statistical methods, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and random forest (RF), were compared. Data originated from the Austrian milk recording and associated health monitoring system (GMON). Test-day data and corresponding MIR spectra were linked to respective clinical mastitis and ketosis diagnoses. Certain wavenumbers were identified as particularly relevant for the prediction models of clinical mastitis (23) and ketosis (61). Wavenumbers varied across four distinct statistical methods as well as concerning different traits. The results indicate that variable selection analysis could potentially be beneficial in the process of modeling.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows

Gruber

Rienesl

Köck³

et al. 2023

Animals

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“…Mastitis is one of the most important inflammatory diseases in dairy farming as it causes reduced milk production, premature discarding of animals, increased medication costs, and losses in the quality of milk and its derivatives, leading to significant economic losses within this industry (Carvalho-Sombra et al, 2021). There is a growing global concern regarding increasing health problems in dairy farming (Masia et al, 2022) and declining milk quality (Stocco et al, 2020), with the somatic cell count (SCC) being internationally recognized as a standard for monitoring mammary gland health and milk quality (Bobbo et al, 2017;Rienesl et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

Reitz,

Pelegrini,

Molinari

et al. 2024

Sem. Ci. Agr.

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This study assessed the effect of subclinical mastitis on the productivity and inflammatory markers in Jersey dairy cows. Blood, milk, and milk yield data were collected from 59 Jersey dairy cows reared under a semi-extensive system. Milk samples were collected from individual collectors and evaluated for their somatic cell count (SCC), lactose (Lact), protein, fat, total and defatted dry extracts (DDE), casein, freezing point, and milk urea nitrogen (MUN) levels. After milking, blood was collected by puncturing the coccygeal arteriovenous complex. In the serum samples, the inflammatory biomarkers paraoxonase-1, albumin, and total plasma protein levels were analyzed using colorimetric methods. Samples of the entire diet provided and pastures were submitted for bromatological analysis. Additionally, the body condition score, number of lactations, milk yield, days of lactation, calving interval, number of inseminations until conception, calving-conception interval, and days of pregnancy were analyzed. The cows were categorized based on their SCC into a subclinical mastitis group (SubG), with SCC levels greater than or equal to 200,000 cells/mL, and a control group (CG), with SCC levels below 200,000 cells/mL. Blood metabolic variables, milk quality, and milk production data were analyzed using multivariate regression, analysis of variance, and logistic regression using the SAS® program. The multivariate regression analysis revealed that several markers, such as Lact, total protein, and casein, influenced milk composition, predicting over 97% of the data. The SubG showed lower concentrations of Lact (4.37 vs. 4.47%; P = 0.0002), MUN (20.55 vs. 23.85 mg/dL; P = 0.02), and DDE (9.24 vs. 9.50%; P = 0.02) compared with the CG; moreover, animals in the SubG had a higher number of lactations compared with those in the CG (4.30 vs. 2.69; P = 0.0039). Cows with a Lact content lower than 4.265% (quartile 25%), compared with those with Lact content greater than 4.565% (quartile 100%), were 52 times more likely to have subclinical mastitis. Jersey dairy cows with subclinical mastitis have lower Lact, urea, and defatted dry extract levels in their milk as well as a higher number of lactations. Therefore, Lact levels in milk serve as a good diagnostic marker of subclinical mastitis in Jersey cows. Subclinical mastitis in Jersey dairy cows did not decrease milk yield nor effect reproductive performance.

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“…In the era of precision livestock farming, however, farmers are expected to improve detection accuracy of quarters/cows with inflammation(s) to be monitored or treated [ 13 ], particularly in local endangered populations. The recent European limitations on the antimicrobial use, in addition, have changed the routine of dairy farmers, making the analysis of SCC trend within lactation indispensable for a smart and correct application of selective dry-cow therapy protocol.…”

Section: Introductionmentioning

confidence: 99%

Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition

Magro

Costa

Marchi

2023

Animals

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Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.

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Use of differential somatic cell count, somatic cell score, and milk mid-infrared spectral analysis for monitoring mastitis in dairy cows during routine milk recording

Cited by 10 publications

References 38 publications

Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows

Importance of Mid-Infrared Spectra Regions for the Prediction of Mastitis and Ketosis in Dairy Cows

Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition

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