1. Introduction
One of the most expensive and frequent disorders on a dairy cattle farm is the inflammation of the udder or mastitis. The prevalence of mastitis could occur in a subclinical or clinical form, including a set of various changes in the animal’s udder and the deteriorating overall health status of an animal. This could be caused by bacterial infections, mechanical injuries or irritation (inadequate milking), inadequate hygiene, etc. Furthermore, mastitis prevalence is correlated with the use of antibiotics and considerable financial loss due to a decline in the quality and quantity of milk [
1]. In addition, mastitis prevalence has a negative effect on the environment by increasing GHG emissions from dairy farms [
2]. The application of various mastitis detection methods and the prevention of mastitis prevalence represents an efficient way of enabling economically and environmentally satisfactory dairy farming. Study [
3] stated that mastitis prevalence damages the udder tissue, resulting in a raised somatic cell count (SCC). Therefore, SCC, as an integral parameter in regular milk recording, could be used as an accurate indicator of mastitis prevalence without any additional cost [
4,
5].
Due to the increasing significance of the prevention of various disorders/diseases in dairy cattle, this research aimed to determine mastitis prevalence and its consequences on milk production in the Holstein population considering farm size.
2. Methods Section
After logical control, the analyzed data set consisted of 3,953,637 test-day records of Holsteins referring to the period 01/2005 to 12/2022. The daily somatic cell count (SCC) was used as a mastitis indicator (healthy animals (SCC < 200,000/mL) for cows at mastitis risk (SCC = 200,000–400,000/mL) and cows with mastitis (SCC > 400,000/mL)). The prevalence was defined as the percentage (%) of cows in each class from the total population (analysis was performed considering herd size). For the analysis of the mastitis consequence, only cows with determined mastitis (SCC > 400,000/mL) were considered. The daily milk yield on the day when mastitis was detected was taken as a reference value. The mastitis index was defined concerning the number of days after mastitis as follows: D-0 (detecting date), A-1 (within 35 days), A-2 (36–70 days), A-3 (71–105 days), and A-4 (>105 days). The effect of mastitis on successive milk production was tested using the MIXED procedure of SAS [
6] with a statistical model that included the effects of lactation stage, parity, age at first calving, the milk recording season, and mastitis index. The statistical analysis was performed separately for each herd size class (<5, 5–10, 10–50, 50–200, 200–500, >500 cows).
3. Results and Discussion
The prevalence of healthy cows at mastitis risk and cows with mastitis concerning the farm size is presented in
Figure 1. The highest percentage of healthy animals was observed for the largest farms with more than 500 cows in lactation at the amount of 73.6%, while the highest percentage of animals at risk and with mastitis was observed in farms with less than 5 cows (16.5%; 27.8%). Furthermore, there was a visible trend of an increase in the risk of mastitis and mastitis prevalence depending on the number of animals in lactation, i.e., with increasing farm size as the prevalence decreased. The observed trend could be explained by significantly better management strategies (higher level of investments in equipment, knowledge, feeding quality, etc.) and the higher genetic potential of animals in production on larger farms.
Estimated differences in the quantity (kg) and value (euro) of milk at successive milk recordings after the detection of mastitis (SCC > 400,000/mL) depending on herd size are shown in
Table 1. The highest difference was recorded at the first successive milk recording after mastitis detection (A-1 milk recording) regardless of the farm size, with the highest difference observed on farms with 200–500 cows (64.239 kg; 33.40 euro) and the lowest on farms with less than 5 cows. In the other analyzed periods between successive milk recordings, the differences varied but were mainly negative (indicating a decrease in milk production). The highest total estimated difference was recorded in herds with 200 to 500 cows (66.654 kg; 34.66 euro), while the lowest total estimated difference was recorded in herds with less than 5 cows (22.30 kg; 11.60 euro).
A determined increase in daily production for successive milk recordings indicates the potential for recovery of animals after the prevalence of mastitis. From the point of view of the total difference in the analyzed period (four successive milk recordings after mastitis prevalence), the highest increase in the daily productivity was determined in herds with 200–500 cows in lactation, indicating the highest recovery potential of animals at those farms. Furthermore, it was observed that the recovery from mastitis risk varied regarding farm size, with the lowest observed in small farms with less than five cows in production.
Ref. [
7] states that herd size affects the prevalence of any disorder/disease within the herd, including mastitis, and that a higher prevalence was found in smaller herds (30–99 cows). Similarly, Ref. [
8] also reports a higher frequency of subclinical mastitis in small herds compared to medium and large herds and explains the same with less attention paid to cow management when the farm is small. Furthermore, Ref. [
7] states that an increase in herd size is associated with increased milk production and productivity. Refs. [
9,
10] suggest that season, herd management, average production, somatic cell counts, and herd size could be related to mastitis prevalence rate in dairy herds.
4. Conclusions
The obtained analyses indicate differences in mastitis prevalence and consequences on successive milk production depending on herd size. The lowest mastitis prevalence was observed on the largest farms (>500), while the most pronounced recovery potential was observed at farms with 200–500 cows. Higher mastitis prevalence and lower recovery potential observed in smaller farms indicate the necessity of education and knowledge transfer to those farms.
Author Contributions
Conceptualization, V.G. and I.J.; methodology, V.G.; software, V.G.; validation, V.G., I.J., and R.G.; formal analysis, V.G.; investigation, V.G. and I.J.; resources, V.G. and R.G.; data curation, V.G., I.J. and M.G.; writing—original draft preparation, V.G.; writing—review and editing, R.G. and Z.S; visualization, V.G. and M.G.; supervision, V.G.; project administration, R.G. and Z.S.; funding acquisition, V.G. and R.G. All authors have read and agreed to the published version of the manuscript.
Funding
Research and dissemination were supported by the Fund for Bilateral Relations within the Financial Mechanism of the European Economic Area and Norwegian Financial Mechanism for the period 2014–2021 (Grant number: 04-UBS-U-0031/23-14).
Institutional Review Board Statement
This study did not require ethical approval.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data used in this research are unavailable due to privacy restrictions.
Conflicts of Interest
The authors declare no conflicts of interest.
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