Abstract
Automated milk feeders (AMF) are an attractive option for producers interested in adopting practices that offer greater behavioral freedom for calves and can potentially improve labor management. These feeders give farmers the opportunity to have a more flexible labor schedule and more efficiently feed group-housed calves. However, housing calves in group systems can pose challenges for monitoring calf health on an individual basis, potentially leading to increased morbidity and mortality. Feeding behavior recorded by AMF software could potentially be used as an indicator of disease. Therefore, the objective of this observational study was to investigate the association between feeding behaviors and disease in preweaning group-housed dairy calves fed with AMF. The study was conducted at a dairy farm located in the Upper Midwest United States and included a final data set of 599 Holstein heifer calves. The farm was visited on a weekly basis from May 2018, to May 2019, when calves were visually health scored and AMF data were collected. Calf health scores included calf attitude, ear position, ocular discharge, nasal discharge, hide dirtiness, cough score, and rectal temperatures. Generalized additive mixed models (GAMM) were used to identify associations between feeding behavior and disease. The final quasibinomial GAMM included the fixed (main and interactions) effects of feeding behavior at calf visit-level including milk intake (mL/d), drinking speed (mL/min), visit duration (min), rewarded (with milk being offered) and unrewarded (without milk) visits (number per day), and interval between visits (min), as well as the random effects of calf age in regard to their relationship with calf health status. Total milk intake (mL/d), drinking speed (mL/min), interval between visits (min) to the AMF, calf age (d), and rewarded visits were significantly associated with dairy calf health status. These results indicate that as total milk intake and drinking speed increased, the risk of calves being sick decreased. In contrast, as the interval between visits and age increased, the risk of calves being sick also increased. This study suggests that AMF data may be a useful screening tool for detecting disease in dairy calves. In addition, GAMM were shown to be a simple and flexible approach to modeling calf health status, as they can cope with non-normal data distribution of the response variable, capture nonlinear relationships between explanatory and response variables and accommodate random effects.
Original language | English (US) |
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Pages (from-to) | 1206-1217 |
Number of pages | 12 |
Journal | Journal of Dairy Science |
Volume | 106 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2023 |
Bibliographical note
Funding Information:This study was partially supported by Department of Animal Science (University of Minnesota, St. Paul) and USDA-Hatch funding, CFANS Alumni Society Professional Development Fund Research Grant (University of Minnesota, St. Paul), the Alexander & Lydia Anderson Grant (Graduate School, University of Minnesota, Minneapolis) and the CFANS Hueg-Harrison Fellowship (University of Minnesota, St. Paul). We thank Heather Johnson and Alfalawn Farm (Menomonie, WI) for providing us with investigation space and calves to conduct this research. The authors have not stated any conflicts of interest.
Publisher Copyright:
© 2023 American Dairy Science Association
Keywords
- automatic milk feeder
- dairy calf
- feeding behavior
PubMed: MeSH publication types
- Observational Study
- Observational Study, Veterinary
- Journal Article