ArticleJDS communications2024
Understanding the dynamics of mastitis in milk yield: Decoding onset and recovery patterns in response to mastitis occurrence.
Article in JDS communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Genomic Characterization of Multidrug-ResistantMicroorganisms · 2026Article
- Development of a health subindex for genetic selection of bulls and cows in Canadian dairy operations.JDS communications · 2026Article
- AI-Driven Multimodal Sensing for Early Detection of Health Disorders in Dairy Cows.Animals : an open access journal from MDPI · 2026Article
- The impact of highly pathogenic avian influenza H5N1 virus infection on dairy cows.Nature communications · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
No recent study has attempted to model daily milk losses before and after mastitis onset and the moment when it begins. Thus, we aimed to describe the impact of mastitis on milk production based on mastitis level and moment of occurrence. We used data from 11 dairy farms, and the dataset consisted of 885,759 daily individual milk test records from 3,508 cows in different lactations, with an average milk yield (MY) from January 2017 to December 2022. We modeled the impact of mastitis severity (i.e., 1 [mild] and 2 [severe]) based on the drop and recovery of MY following 3 steps. First, we removed milk recorded on the day of diagnosis of mastitis from the dataset and fitted a Wood's curve for each cow and parity. Second, we returned the mastitis data to the dataset and estimated the residual milk loss due to mastitis from 15 d before to 30 d after the mastitis event. Third, we used generalized additive mixed effect models to estimate the residual milk loss, including farm as a random effect. In addition to the random effect of the farm, we also included the predicted milk yield (by Wood's curve) over the influence of mastitis, the day effect before and after mastitis incidence, and the interaction between the predicted value of mastitis and days. On average, mastitis level 2 resulted in a more severe MY drop in all represented stages of lactation (80, 170, and 260 DIM), suggesting a higher loss close to the lactation peak, approximately 130 kg more than mastitis level 1. Moreover, the occurrence of mastitis case level 1 during the early phase of lactation (DIM 80) can cause an average milk loss of 158 L and mastitis level 2, an average loss of 288 L. The estimations suggest that milk drop occurs 14 to 4 d before mastitis onset and can last until 15 to 25 d from the diagnosis, which would be the necessary time for a cow to re-establish their predicted MY. Therefore, our study brings new perspectives to investigate MY drop and recovery due to mastitis infections and how much mastitis can deplete and impair milk production.
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