1,720,968 research outputs found
Compost Barns: What Have We Learned So Far?
No full textEndres, Marcia. (2006). Compost Barns: What Have We Learned So Far?. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/109615
TRANSITION COW NUTRITION UPDATE
No full textEndres, Marcia I.. (2002). TRANSITION COW NUTRITION UPDATE. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/108752
Feeding Practices on Dairy Farms with Automatic Milking Systems
Full text linkSalfer, Jim; Endres, Marcia. (2014). Feeding Practices on Dairy Farms with Automatic Milking Systems. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/204513
CALCIUM METABOLISM, PATHOLOGY, TREATMENT REVIEW AND UPDATES TRANSITION COWS
No full textEndres, Marcia I.. (2002). CALCIUM METABOLISM, PATHOLOGY, TREATMENT REVIEW AND UPDATES TRANSITION COWS. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/108751
Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders with supplemental tables
No full textSupplemental tables that can't be included with the Journal of Dairy Science publication are added to this copy of the article.In 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.Perttu, Rielle K; Peiter, Mateus; Bresolin, Tiago; Dórea, Joao R R; Endres, Marcia I. (2023). Predictive models for disease detection in group-housed preweaning dairy calves using data collected from automated milk feeders with supplemental tables. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/257784
Assessment of animal welfare in cross-ventilated vs. naturally ventilated freestall barns using survival analysis
No full textLobeck, Karen Marie; Endres, Marcia I.; Godden, Sandra; Fetrow, John. (2010). Assessment of animal welfare in cross-ventilated vs. naturally ventilated freestall barns using survival analysis. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/118881
Barn Environment Study
No full textEndres, Marcia I.; Lobeck, Karen; Janni, Kevin; Godden, Sandra; Fetrow, John. (2011). Barn Environment Study. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/118898
Studies in Dairy Cow Calving Behavior
No full textCarrier, Jerome; Godden, Sandra; Fetrow, John; Stewart, Steve; Rapnicki, Paul; Endres, Marcia; Mertens, Petra. (2005). Studies in Dairy Cow Calving Behavior. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/109538
Does Moderating Amount Fed and Forage Type in Dry Cow Diets Affect Pre- and Postpartum Feeding and Lying Behavior?
No full textWeich, William Douglas; Kmicikewycz, Alanna Daria; Endres, Marcia I.; Litherland, N.B.. (2010). Does Moderating Amount Fed and Forage Type in Dry Cow Diets Affect Pre- and Postpartum Feeding and Lying Behavior?. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/118887
Effects of prepartum grouping strategy on health, reproductive, and productive parameters of dairy cows.
No full textSilva, P.R.B.; Moraes, J.G.N.; Mendonça, L.G.D.; Scanavez, A.L.A.; Nakagawa, G.; Endres, Marcia I.; Fetrow, John; Ballou, M.A.; Chebel, R.C.. (2012). Effects of prepartum grouping strategy on health, reproductive, and productive parameters of dairy cows.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/141757
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