Prediction of cow's fertility based on data recorded by automatic milking system during the periparturient period

Kliś, Piotr; Sawa, Anna; Piwczyński, Dariusz; Sitkowska, Beata; Bogucki, M.

doi:10.1111/rda.13981

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Furthermore, to optimize animal reproduction, it is crucial to associate management strategies that can promote better efficiency in reproductive animal management. In this regard, there are already several studies proposing the use of AAM systems and exploring machine learning algorithms to enhance the prediction of health and fertility disorders in dairy cows [ 51 , 52 , 53 ]. A previous study has also shown that routinely collected farm data and milk production records on the test day are valuable for predicting the success of insemination in dairy cows [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

Marques,

Fernandes

et al. 2024

Animals

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Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings—8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature–humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

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Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

Marques,

Fernandes

et al. 2024

Animals

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“…Piwczy ński et al [101] demonstrated that the DT method, by analyzing the graphical model, allows herd managers to identify the factors that influence specific productive traits of animals. More recently, a Classification and Regression Trees (CART) Decision Tree algorithm was employed to predict lactation milk yield based on information recorded during the periparturient period [102]. CART is a ML technique that has been shown to be particularly valuable when analyzing nonlinear relationships and interactions, and identifying the variables that automatically affect and reduce the complexity of the data [103].…”

Section: Productionmentioning

confidence: 99%

A Literature Review on the Use of Modeling Approaches Applied to Automatic Milking Systems

Ozella¹,

Rebuli²,

Forte³

et al. 2023

Preprint

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Automatic milking systems (AMSs) are among the earliest Precision Livestock Farming developments that have transformed dairy farming worldwide. This review aims to gather, evaluate, and summarize papers that focus on the use of modeling approaches in the context of AMS. We provided a review of 60 articles with a specific focus on cows’ health, production, and behavior/management. The most used modeling approach was Machine Learning (ML, present in 63% of the studies), followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic mod-els (7%), and detection algorithms (7%). Most of the reviewed studies (82%) focused on the detection of cows' health, specifically mastitis, while only 11% were concerned with milk production. Accurate forecasting of dairy cow milk yield and knowledge on the deviation between expected and observed milk yields of individual cows would be beneficial in dairy cow management. Likewise, the study of cows’ behavior and the herd management in AMSs is under-explored (7%). Despite the increasing use of ML techniques in this field there is still a lack of a robust methodology for their application. In particular, we identified a significant gap in the systematic balancing of positive and negative classes for health prediction models.

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“…Piwczyński et al [ 101 ] demonstrated that the DT method, by analyzing the graphical model, allows herd managers to identify the factors that influence specific productive traits of animals. More recently, a Classification and Regression Trees (CART) Decision Tree algorithm was employed to predict lactation milk yield based on information recorded during the periparturient period [ 102 ]. CART is a ML technique that has been shown to be particularly valuable when analyzing nonlinear relationships and interactions, and identifying the variables that automatically affect and reduce the complexity of the data [ 103 ].…”

Section: Application Of Modeling Approaches In Amsmentioning

confidence: 99%

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

Ozella

Rebuli

Forte

et al. 2023

Animals

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Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows’ health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows’ health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows’ behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

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Prediction of cow's fertility based on data recorded by automatic milking system during the periparturient period

Cited by 3 publications

References 22 publications

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

A Literature Review on the Use of Modeling Approaches Applied to Automatic Milking Systems

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

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