Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes

Bobbo, Tania; Matera, Roberta; Pedota, G; Manunza, Arianna; Cotticelli, Alessio; Neglia, Gianluca; Biffani, Stefano

doi:10.3168/jds.2022-22292

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…Neural Network, Random Forest, and linear methods best predicted udder health classes based on recorded milk traits. The findings suggest that machine learning algorithms can be a promising tool to improve farmers' decisionmaking in identifying cows with high somatic cell counts, which can improve surveillance methods and potentially reduce the economic and health impact of bovine mastitis in dairy farms [67,68]. The use of machine learning and data mining techniques in genomic and phenotypic udder evaluation for dairy cattle selection has become an essential tool in improving udder health.…”

Section: Genomic Genotypic and Phenotypic Udder Traits: Impact On Mam...mentioning

confidence: 99%

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

Gutiérrez-Reinoso

Aponte

García-Herreros

2023

Animals

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The traditional point of view regarding dairy cattle selection has been challenged by recent genomic studies indicating that livestock productivity prediction can be redefined based on the evaluation of genomic and phenotypic data. Several studies that included different genomic-derived traits only indicated that interactions among them or even with conventional phenotypic evaluation criteria require further elucidation. Unfortunately, certain genomic and phenotypic-derived traits have been shown to be secondary factors influencing dairy production. Thus, these factors, as well as evaluation criteria, need to be defined. Owing to the variety of genomic and phenotypic udder-derived traits which may affect the modern dairy cow functionality and conformation, a definition of currently important traits in the broad sense is indicated. This is essential for cattle productivity and dairy sustainability. The main objective of the present review is to elucidate the possible relationships among genomic and phenotypic udder evaluation characteristics to define the most relevant traits related to selection for function and conformation in dairy cattle. This review aims to examine the potential impact of various udder-related evaluation criteria on dairy cattle productivity and explore how to mitigate the adverse effects of compromised udder conformation and functionality. Specifically, we will consider the implications for udder health, welfare, longevity, and production-derived traits. Subsequently, we will address several concerns covering the application of genomic and phenotypic evaluation criteria with emphasis on udder-related traits in dairy cattle selection as well as its evolution from origins to the present and future prospects.

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Section: Genomic Genotypic and Phenotypic Udder Traits: Impact On Mam...mentioning

confidence: 99%

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

Gutiérrez-Reinoso

Aponte

García-Herreros

2023

Animals

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“…Inflammation of the internal tissues in the udder of dairy cows, commonly known as mastitis (Fatmawati et al 2019) is considered a local inflammation caused by the invasion of exogenous pathogens, resulting in microbiota and metabolite dysbiosis in milk (Wang et al 2022). Mastitis is one of the most commonly found diseases in dairy cows worldwide, affecting milk production and quality (Velasco-Bolanos et al 2021), livestock health (Bobbo et al 2023), well-being, longevity and performance (Pakrashi et al 2023), thus leading to long-term and challenging-to-control economic losses (Wang et al 2023). Economic losses resulting from decreased milk quantity and quality, discarded milk, compromised conception, premature culling, recurring cases and the costs of disease treatment create a significant economic burden for dairy farmers each year (Gonçalves et al 2018;Mohsin et al 2022;Meçaj et al 2023).…”

Section: Introductionmentioning

confidence: 99%

Prevalence of Subclinical Mastitis in Holstein-Friesian Cow Dairy Among Small-Scale Farms in Batu, Indonesia

2024

Int J Vet Sci

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Subclinical mastitis continues to be a common issue for small-scale dairy cow farmers in Indonesia. This disease poses significant challenges for farmers as it can lead to a reduction in milk production, resulting in severe economic losses. This study involved 126 dairy cows with lactation periods ranging from 1 to 5, sourced from 29 different farmers, totalling 504 quarters examined. The research utilized a case study method with field exploration conducted in Toyomerto Village, Batu Sub District, Batu City, East Java, Indonesia. The analysis of subclinical mastitis was carried out using the California Mastitis Test (CMT). The findings of the study revealed a prevalence of subclinical mastitis in Toyomerto Village at 69.84% (n=88). Among all the tested teats, 41.47% of them were detected with subclinical mastitis, with the highest prevalence observed in the right hind teats, totalling 27.27%. The prevalence of subclinical mastitis in dairy cows from small farms in Toyomerto Village remains notably high. The continued lack of attention to sanitation and cleanliness remains a primary factor in the high prevalence of mastitis.

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“…Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows (1,7,26), an opportunity exists to extract greater value from cow-level data to inform cow-and group-based decision-making based on the risk of intramammary infection. Machine learning methods have been explored to predict IMI status at subsequent milk recording in both dairy cows (21) and buffaloes alongside climate data (22). These analyses were undertaken on small datasets and not externally validated to assess generalizability for external farms.…”

Section: Introductionmentioning

confidence: 99%

The use of machine learning to predict somatic cell count status in dairy cows post-calving

Thompson,

Green,

Hyde

et al. 2023

Front. Vet. Sci.

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Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow’s risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

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Exploiting machine learning methods with monthly routine milk recording data and climatic information to predict subclinical mastitis in Italian Mediterranean buffaloes

Cited by 8 publications

References 31 publications

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

Prevalence of Subclinical Mastitis in Holstein-Friesian Cow Dairy Among Small-Scale Farms in Batu, Indonesia

The use of machine learning to predict somatic cell count status in dairy cows post-calving

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