Browsing by Author "Peiter, Mateus"
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Item Association between daily cow data and milk production in dairy herds milked with automatic milking systems(2021-04) Peiter, MateusAutomatic milking systems (AMS) for dairy cows and its associated technologies can continuously monitor individual production, behavior, and physiological parameters, allowing for real-time decision making on these farms. Furthermore, the data recorded automatically in these systems allow dairy advisors to extrapolate some of the findings to cows milked with systems other than AMS. The main objective of this dissertation was to investigate cow daily behavior associated with milk production on AMS farms. Data from 47 AMS dairy farms located in Minnesota and Wisconsin in the U.S. were collected for a period of 12 months. RStudio was used for all the data management and statistical analyses. Multiparous cows with greater average rumination time and greater rumination time increase in the immediate postpartum period produced more milk at lactation peak, while the same association was absent for primiparous cows. Various behavior variables recorded during the time cows are in the AMS to be milked were associated with daily milk production, such as average daily milking interval, concentrate intake, number of failures, milking speed and time, and pre- and post-treatment time. Moreover, a quadratic relationship was found between body weight change in early lactation and 90-d milk yield, where cows with the ability to maintain or lose less than 10% of their body weight from DIM 1 presented greater total milk production over the first 90 DIM. Findings reported in this dissertation may be used to make milk production predictions and serve as benchmark data for AMS producers in the U.S.Item Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders with supplemental tables(2023-10-26) Perttu, Rielle K; Peiter, Mateus; Bresolin, Tiago; Dórea, Joao R R; Endres, Marcia I; miendres@umn.edu; Endres, Marcia IIn the United States, dairy calves are typically housed individually due to the perception of reduced risk of spreading infectious diseases between calves and the ability to monitor health on an individual calf basis. However, automated milk feeders (AMF) can provide individual monitoring of group-housed calves while allowing them to express more natural feeding behaviors and interact with each other. Research has shown that feeding behaviors recorded by AMF can be a helpful screening tool for detecting disease in dairy calves. Altogether, there is an opportunity to use the data from AMF to create a more robust and efficient model to predict disease, reducing the need for visual observation. Therefore, the objective of this observational study was to predict disease in preweaning dairy calves using AMF feeding behavior data and machine learning (ML) algorithms. This study was conducted on a dairy farm located in the Upper Midwest United States and visited weekly from July 2018 to May 2019. During farm visits, AMF data and calves’ treatment records were collected, and calves were visually health scored for attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, and cough score. The final datasets used for the analyses consisted of 740 and 741 calves, with 1,007 (healthy = 594 and sick = 413) and 1,044 (healthy = 560 and sick = 484) observations (health events) for Data 1 and Data 2, respectively. Data 1 included only the weekly calf health scores observed by research personnel, whereas Data 2 included primarily daily calf treatment records by farm staff in addition to weekly health scores. Calf visit-level feeding behaviors from AMF data included milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per d), and the interval between visits (min). Three approaches were used to predict health status: generalized linear model, random forest, and gradient boosting machine. A total of 16 models were built using different combinations of behavior parameters, including the number of rewarded visits, number of unrewarded visits, visit duration, the interval between visits, intake, intake divided by rewarded visits, drinking speed, and drinking speed divided by rewarded visits, and also calf age at the sick day as predictor variables. Of all algorithms, random forest and gradient boosting had the best performance predicting the health status of dairy calves. The results indicated that weekly health scores were not enough to predict calf health status. However, daily treatment records and AMF data were sufficient for creating predictive algorithms (e.g., F1-scores of 0.775 and 0.784 for random forest and gradient boosting, Data 2). This study suggests that ML was effective in determining the specific visit-level feeding behaviors that can be used to predict disease in group-housed preweaning dairy calves. Implementing these ML algorithms could reduce the need for visual calf observation on farms, minimizing labor time and improving calf health. Supplemental tables are included herein.