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Svenja Woudstra1
Maya Katrin Gussmann2
Hector Marina3
Ida Hansson3
Carsten Thure Kirkeby2
Volker Krömker1
Per Peetz Nielsen4
Keni Ren5
Lars Rönnegård3,6,7*- 1Department of Veterinary and Animal Sciences, Section for Animal Production, Nutrition and Health, University of Copenhagen, Frederiksberg C, Denmark
- 2Department of Veterinary and Animal Sciences, Section for Animal Welfare and Disease Control, University of Copenhagen, Frederiksberg C, Denmark
- 3Department of Biosciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
- 4Research Institutes of Sweden (RISE), Division of Bioeconomy and Health, Department of Agriculture and Food, Research Institutes of Sweden (RISE) Ideon, Lund, Sweden
- 5Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden
- 6School of Information and Engineering, Dalarna University, Falun, Sweden
- 7The Beijer Laboratory for Animal Science, Swedish University of Agricultural Sciences, Uppsala, Sweden
1 Introduction
Mastitis is the inflammation of mammary glands and (one of) the most costly diseases in dairy cattle herds in developed countries (Halasa et al., 2007). The majority of antibiotics in dairy herds are used in the context of treating intramammary infections, which are the major cause of bovine mastitis (DANMAP, 2022). Current bio-economic simulation models used in the context of mastitis control generally assume that mastitis occurrence and pathogen transmission is a homogeneous process that may either originate from the environment (environmental) or the milking process (cow-associated transmission; e.g. Gussmann et al., 2018). However, research has shown that these assumptions are often wrong and unrealistic, as presumably purely cow-associated pathogens can also have reservoirs in the environment, and those pathogens categorized as environmental can behave in a cow-associated pattern and be transmitted via the milking process (Jørgensen et al., 2016; Zadoks et al., 2003). In addition, exposure to pathogens may not be random but governed by social interactions within the herd and the milking order of the cows. These assumptions may substantially impact the assessment of the cost-effectiveness of mastitis prevention and control strategies. This points out the need for data to properly parametrize these tools for accurate and economic assessment of mastitis prevention and control, to ensure sustainable dairy production.
The availability of technology to record social interactions between animals and the milking order of cows may allow parametrization of decision support tools more realistically, providing farmers with a valuable and accurate tool for decision support making. Precision livestock farming (PLF) opened a myriad of possibilities to monitor disease transmission patterns through cattle positioning systems, computer vision, and data collected via milking machines and robots. With a real-time location system (RTLS), cows can be identified and tracked when being at a feeding trough (Ternman et al., 2019), alleys, or cubicles with high accuracy (Pastell and Frondelius, 2018; Tullo et al., 2016). Also, the order of cows entering the milking parlor can be monitored (Hansson and Woudstra, 2023).
One of the main goals of the research program on cow social interaction and disease transmission (CSI: DT) was to develop tools for minimizing disease transmission within dairy farms based on knowledge gained from continuous monitoring of the milking order of cows and the investigation of transmission patterns of frequently occurring mastitis pathogens. We summarize our publications (Table 1) and experiences from this research program in two opinion papers and give advice for future research within the field. In this opinion paper, we summarize our findings with regard to mastitis pathogen transmission and reservoirs as well as the milking order of cows.
Table 1
Table 1. Scientific publications published during the project.
2 Disease transmission
The disease we focused on in this project was mastitis. While mastitis is one of the most important diseases in dairy cow herds (Ruegg, 2017) and the prevalence of intramammary infections is usually high, direct contacts between individual cows are likely not playing a large role in transmission of mastitis pathogens. Instead, the consecutive use of milking equipment and pre-milking by milkers are thought to play a role in cow-associated transmission of pathogens from infected to uninfected quarters. In addition, there is also a transmission of pathogens that seem to have their primary reservoirs in the environment. Also, the consecutive use of infrastructure (e.g. cubicles) might be related to new infections. As described before, the general assignment of one transmission behavior to a certain pathogen species can be misleading as the transmission behavior within a species can vary between herds (Jørgensen et al., 2016; Zadoks et al., 2003).
Most existing models for spread of cow-associated pathogens assume homogeneous mixing of cows during milking [e.g. Gussmann et al. (2018)]. They do however not take hierarchy and social networks between cows into account which can influence which cows would like to enter a milking parlor together. Furthermore, other factors, such as housing group changes, that would influence transmission in the milking parlor are also not usually considered, as these would further complicate the models. Thus, the question arises how well these models can describe disease dynamics in a herd. Gussmann et al. (2018), for example, showed that infection rate had to be greatly reduced from rates estimated from field studies when some of these factors were taken into consideration more rigorously (such as, e.g., cow-specific susceptibility and decisions). However, they still assumed homogeneous mixing. In this project, we developed a similar simulation model where cow-associated mastitis transmission happens in the milking parlor and infection events are linked to the milking order of cows (Gussmann et al., 2024). This builds on the theory that the risk of infection from an infected cow is highest for the cow that is milked right after in the same place in the milking parlor (i.e. with the same milk liners), and then decreasing for the next cow being milked in the same place and so on. After calibrating the model, we tested if there was a difference when cows were milked in a random order and when cows with a clinical case of mastitis were milked first, or last. Our results showed that in a ‘normal’ dairy herd (i.e. a herd without an acute mastitis problem) there appeared to be no real difference between these three scenarios.
Within this project, we have, also, investigated what role a cow’s parity and lactation stage, characteristics that are usually also associated with a cow’s hierarchy and social interactions within a herd, have on the probability of a cow entering the milking parlor rather early or later during milking (Hansson and Woudstra, 2023). We found that both parity and lactation stage are associated with the order in which cows enter a milking parlor. Cows in early lactation or first parity cows entered the milking parlor earlier than herd mates in later lactation or higher parity. Parity and lactation stage are therefore associated with the risk of getting in contact with liners which were contaminated with cow-associated pathogens during milking by a previous cow and therefore probably also with the risk of getting infected with these pathogens. This influence of hierarchy and social interactions on the milking order means that the milking order is not really random, as it has been assumed in previous simulation models. It is conceivable that such a “structured randomness” could have an influence on the transmission dynamics of mastitis pathogens in dairy herds, particularly as parity and lactation stage are considered risk factors for intramammary infections. We therefore hypothesize that milking first-parity cows, which have a lower risk of being infected with contagious organisms, at the beginning of the milking process would reduce transmission of contagious pathogens during milking. This has, to our knowledge, not been tested yet in simulation models and should be explored in the future.
Due to the high cost associated with strain typing, many studies investigating mastitis epidemiology of different pathogens focused on the species-level only. However, in order to study the actual transmission of pathogens, strain level studies are required to investigate if cows are actually infected with the same strain of a species and to distinguish ongoing from new infections. Within the present project we therefore used strain typing to investigate the transmission routes of different staphylococci and streptococci, namely Staph. aureus, Staph. epidermidis, Staph. haemloyticus, Strep. uberis and Strep. dysgalactiae (Woudstra et al., 2023a, 2023b). Most studies using strain comparisons, including our own, found that Staph. aureus seems to be largely transmitted contagiously from quarter to quarter. Other pathogens such as Strep. dysgalactiae and Strep. uberis however, behave differently from herd to herd (Leelahapongsathon et al., 2020; Wente et al., 2019; Wente and Krömker, 2020).
In this project, a farm with around 200 lactating cows was visited ten times at 14-day intervals and individual quarter foremilk samples were collected at each of the ten visits from all lactating cows. We found few cases of new infections due to contagious pathogen strains making it hard to estimate accurate pathogen transmission parameters, e.g. the rate of transmission through consecutive contact with milking liners. To study the level of importance of the different fomites (e.g. liners, milkers’ hands), a large number of new infections needs to be observed, meaning studies have to be conducted either in herds with high prevalences, or studies have to include a large number of animals. Herds with high prevalences cannot be expected to represent an average herd, while large studies are expensive, and it is hard to get funding for these. Similar limitations are valid when studying the importance of consecutive use of infrastructure (e.g. cubicles) for pathogen transmission. We could, for example, demonstrate that cows of different parities prefer different areas in the barn (Churakov et al., 2021). At least in theory, also milk leakage in the cubicles between milkings could lead to transmission of pathogens from udder to udder.
3 Discussion
Milking order, social interactions and area utilization can play a crucial role in the transmission of infectious diseases. The sequential use of different areas in the barn and the close proximity between certain individuals can facilitate the spread of organisms between animals. In fact, the likelihood of disease transmission between individuals can be computed as a function of contact intensity (de Freslon et al., 2019). In this regard, PLF information, such as RFID tags providing milking order information or UWB sensors providing social interaction and area utilization information, offer the opportunity to automatically monitor the social interactions and area utilization of dairy cows. The use of this information might contribute in the future to facilitating the adoption of preventive measures to minimize the spread of pathogens on dairy farms.
3.1 Ideas and practical advice to farmers
In our study, the prevalence of mastitis pathogens was low, making it hard to estimate accurate parameters that could be used in models. Furthermore, the simulation model we developed showed that in situations, where mastitis was not a big problem in the herd, changes in the milking order did not have a large influence on pathogen transmission. However, with increasing prevalence and transmission, changing the milking order had an increased influence on pathogen spread (Gussmann et al., 2024). We therefore suggest that measures connected to social contacts (induced for instance by milking order) should be considered by farmers depending on the main transmission patterns of pathogens with significant impact on milk quantity and quality in the herd making preventive measures economically meaningful. We also recommend that strain typing to become more widely used in mastitis investigations in the future, as transmission patterns can in some cases not be concluded from a mere species identification.
Author contributions
SW: Writing – original draft, Writing – review & editing. MG: Writing – original draft, Writing – review & editing. HM: Writing – review & editing. IH: Writing – review & editing. CK: Writing – review & editing. VK: Writing – review & editing. PN: Writing – review & editing. KR: Writing – review & editing. LR: Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The knowledge contained in this article was derived from a project funded by Formas – a Swedish Research Council for Sustainable Development, Stockholm, Sweden (ID: 2019–02276 and 2019–02111) and by the Kjell & Märta Beijer Foundation, Stockholm, Sweden. Parts of the microbiological examinations were funded by Steinbeis Research Center Milk Science, Germany, grant number 220.
Acknowledgments
The authors acknowledge the support provided by the resources of the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), which contributed significantly to the execution of the project.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
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References
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Keywords: dairy cows, strain typing, milking order, simulation model, mastitis prevention
Citation: Woudstra S, Gussmann MK, Marina H, Hansson I, Kirkeby CT, Krömker V, Nielsen PP, Ren K and Rönnegård L (2025) Lessons learnt from strain types, milking order, and mastitis pathogen transmission. Front. Anim. Sci. 6:1556831. doi: 10.3389/fanim.2025.1556831
Received: 07 January 2025; Accepted: 10 February 2025;
Published: 24 February 2025.
Edited by:
Bjørg Heringstad, Norwegian University of Life Sciences, NorwayReviewed by:
Olav Reksen, Norwegian University of Life Sciences, NorwayCopyright © 2025 Woudstra, Gussmann, Marina, Hansson, Kirkeby, Krömker, Nielsen, Ren and Rönnegård. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lars Rönnegård, bGFycy5yb25uZWdhcmRAc2x1LnNl