Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Nørstebø, Håvard; Dalén, Gunnar; Rachah, Amira; Heringstad, B.; Whist, Anne Cathrine; Nødtvedt, Ane; Reksen, O.

doi:10.1016/j.prevetmed.2019.104786

Cited by 27 publications

(21 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When OCC measured in AMS was compared with test-day SCC from milk recordings, the correlation between them was strong (Sørensen et al, 2016;Nørstebø et al, 2019). The current study did confirm a strong genetic agreement between OCC and LSCS, which supports in-line-measured cell count as an indicator of udder health.…”

Section: Implications and Further Recommendationssupporting

confidence: 72%

A genetic study of new udder health indicator traits with data from automatic milking systems

Wethal

Svendsen²,

Heringstad

2020

Journal of Dairy Science

View full text Add to dashboard Cite

The current study aimed to investigate new udder health traits based on data from automatic milking systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each milking, whereas OCC was measured per milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11-0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.

show abstract

Section: Implications and Further Recommendationssupporting

confidence: 72%

A genetic study of new udder health indicator traits with data from automatic milking systems

Wethal

Svendsen²,

Heringstad

2020

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…In automatic milking systems (AMS), fully automated online analysis equipment is available for on-farm analysis of SCC at every milking [ 92 ]. In addition, from online cell counter results, an array of additional cow level and quarter-level factors considered important for udder health is recorded in these systems [ 93 ]. The SCC can, to some extent, be used for the surveillance of intramammary infection, and the industry has advanced toward developing new sensors that are designed explicitly for udder health surveillance.…”

Section: Welfare Of Dairy Cows and Precision Livestock Farmingmentioning

confidence: 99%

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Silva

Araújo

Guedes

et al. 2021

Animals

View full text Add to dashboard Cite

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

show abstract

“…linked to fluctuations in milk yield or milking interval). Physiological variation has been described to account for a large proportion of the variation of composite milk SCC within individual dairy cows (Nørstebø et al, 2019;Deng et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Regularly fluctuating somatic cell count pattern in dairy herds

Deng

Lam

Hogeveen

et al. 2021

Journal of Dairy Science

View full text Add to dashboard Cite

Online somatic cell count (SCC) measurement is widely used in dairy herds milked with automatic milking systems (AMS) and gives the opportunity to closely monitor individual cow udder health. Using automated SCC data, we observed cows displaying a remarkably regularly fluctuating SCC (rfSCC) pattern, which is described in this study. We aimed to (1) estimate the prevalence of rfSCC in cows milked by AMS, (2) characterize the rfSCC pattern, and (3) identify factors potentially associated with the rfSCC pattern. We analyzed 30-d episodes of composite SCC recordings of 1,000 cows from 55 dairy herds from 6 countries using an AMS with automated SCC measurement, and we identified the rfSCC pattern in 4.7% (95% CI: 3.5-6.2%) of these episodes. The rfSCC episodes had a median SCC of 701 × 1,000 cells/mL (2.5-97.5% quantile: 539-1,162), a median amplitude of 552 × 1,000 cells/mL (2.5-97.5% quantile: 409-886), and a median cycle length of 4.1 d (2.5-97.5% quantile: 3.7-4.9). Bacteriological culture data from quarter-milk samples collected every 2 wk in 1 Dutch AMS herd were analyzed, yielding no clear association between pathogen species and the rfSCC pattern found in that herd. Altogether, we described an intriguing phenomenon, present in almost 5% of the cows during a 1-mo study period. Further work is needed to quantify its importance in terms of udder health, but also to elucidate the mechanism behind this remarkable SCC pattern.

show abstract

Factors associated with milking-to-milking variability in somatic cell counts from healthy cows in an automatic milking system

Cited by 27 publications

References 34 publications

A genetic study of new udder health indicator traits with data from automatic milking systems

A genetic study of new udder health indicator traits with data from automatic milking systems

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Regularly fluctuating somatic cell count pattern in dairy herds

Contact Info

Product

Resources

About