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Dott. Enrico Ponzo

Phd thesis


Automatic milking systems data for early mastitis detection in dairy cows

Scientific background:

Mastitis is an infectious disease that affects the mammary gland of cattle, causing a
decrease in milk production (Maity, et al., 2020). This infection is mainly caused by bacteria (e.g., Staphylococcus aureus, Escherichia coli) that penetrate the mammary gland via teat canal (Jashari, et al., 2016). Due to its high frequency, to the management costs, and to the elevated antibiotic use, mastitis is considered one of the costliest diseases in dairy cow farms (Ruegg, 2017). Furthermore, this widespread use of antibiotics in adult dairy cows contributes largely to the increased antimicrobial resistance issue (Oliver, et al., 2011). Early detection is therefore fundamental for a better mastitis management. Automatic Milking Systems(AMS) caused a profound transformation in dairy cows’ management and can record a large amount
of animal-based information (Zucali, et al., 2021). Among the varieties of variables recorded by AMS, Somatic Cells Count (SCC) and milk conductivity (MC) are probably the most important and innovative ones. While the former has been largely used as a mastitis diagnostic tool (Koivula, et al., 2005), the latter has been suggested as a potential proxy for mastitis detection only recently (Paudyal, et al., 2020). So far, there is a lack of knowledge regarding the correlation between the mastitis etiology and AMS recorded parameters, and therefore the diagnosis is mainly based on microbiological analysis.

Aims and innovative aspects: aim of this project is to investigate the parameters recorded by AMS, to be used as tools for early mastitis detection. The study will focus on SCC and MC but will also include other parameters both recorded from the AMS (e.g., rumination time, daily milk yield, milking time), and collected from the farmer (e.g., animal age, parity, dates of last calving events). Combining SCC and MC would give an innovative point of view over the mastitis detection since there few information is found in scientific literature. Furthermore, since AMS continuously record data, changes in variables of interest could be evaluated even in short time intervals, which leads to a fine and punctual analysis of mastitis-related
variation. A further aim of this project would be to investigate the potential correlation between the AMS recorded parameters and the results from microbiological analysis. Preliminary data suggests that this correlation exists, and this could help veterinarians to identify the best suitable therapy (avoiding, e.g., the use of antibiotics when bacteria are not involved).

Main techniques: The project will be structured in three main phases: data collection, data analysis, and statistical modelling training and validation. Dairy farms will be involved in the first phase of the project based on the availability of an AMS already on-farm, with the final number of farms depending on the total number of available animals. AMS built-in software will be used to gather data and a dedicated database will be prepared accordingly. Since a large amount of data is expected, different data management options will be
evaluated. Microbiological analysis will be performed on milk samples when mastitis is diagnosed to identify the etiology. The third phase will involve statistical modelling techniques to evaluate different models (simple regression, logistic regression, mixed models) based on the dependent variable to be studied. Data analysis and model implementation will both involve the use of R statistical environment for basic descriptive analyses and different specific R packages to perform more in-depth statistical analyses. Other R packages, related to graphical presentation of data, will be used to create reports, plots, and presentations for results dissemination. Ad hoc scripts will be produced as needed.

Expected results and possible developments: preliminary results suggest that SCC and MC could be used as a tool for mastitis early detection. Expected results will therefore include statistically significant associations between the variables analysed in this project and mastitis. This could lead to a future innovative approach to mastitis management. According to the results of the project, veterinarians would have an additional tool to help them identify mastitis in an early stage, and to help them choose the best treatment for the situation,
possibly reducing the use of antibiotics when not strictly necessary. Moreover, a possible development ofthis project would be to identify animals that are either resistant or resilient to mastitis infection. This would be of great importance, especially from a one-health approach point of view: a better management with a reduced use of antibiotics will impact not only the animal welfare, but will also benefit both human and environment, because of the reduced amount of medicated milk discarded as waste product.

Automatic milking system; dairy cows; antimicrobic resistance; somatic cell count; milk conductivity.

Jashari, R., Piepers, S. & De Vliegher, S., 2016. Evaluation of the composite milk somatic cell count as a predictor of intramammary infection in dairy cattle. Journal of Dairy Science, Volume 99, pp. 9271-9286.
Koivula, M., Mäntysaari, E., Negussie, E. & Serenius, T., 2005. Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis. Journal of Dairy Science, Volume 88, pp. 827-833.
Maity, S., Das, D. & Ambatipudi, K., 2020. Quantitative alterations in bovine milk proteome from healthy, subclinical and clinical mastitis during S. aureus infection. Journal of Proteomics, Volume 223, p. 103815.
Oliver, S., Murinda, S. & Jayarao, B., 2011. Impact of antibiotic use in adult dairy cows on antimicrobial resistance of veterinary and human pathogens: a comprehensive review. Foodborne Pathogens and Disease, Volume 8, pp. 337-355.
Paudyal, S. et al., 2020. Use of milk electrical conductivity for the differentiation of mastitis causing pathogens in Holstein cows. Animal, Volume 14, pp. 588-596.
Ruegg, P., 2017. A 100-Year Review: Mastitis detection, management, and prevention. Journal of Dairy Science, Volume 100, pp. 10381-10397.
Zucali, M. et al., 2021. Association between udder and quarter level indicators and milk somatic cell count in

Research activities

Co supervisor

Paola Sacchi

Poster presentation: 

Moretti R., Ponzo E., Sartore S., Chessa S., Sacchi P. (2023). Heritability of milk conductivity measured by automatic milking system in Italian Holstein cows. In ITALIAN JOURNAL OF ANIMAL SCIENCE vol. 22 (S1), ASPA 25th Congress, Monopoli (BARI – ITALY), June 13–16, 2023

Last update: 07/11/2023 13:53

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