title: Benchmarking dairy herd health status using routinely recorded herd summary data.
abstract: Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.
authors.affiliation: Department of Animal Sciences, University of Florida, Gainesville 32611. Electronic address: klpgaddis@ufl.edu.
authors.forename: K L
authors.initials: KL
authors.lastname: Parker Gaddis
authors.affiliation: Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
authors.forename: J B
authors.initials: JB
authors.lastname: Cole
authors.affiliation: Dairy Records Management Systems, Raleigh, NC 27603.
authors.forename: J S
authors.initials: JS
authors.lastname: Clay
authors.affiliation: Department of Animal Science, North Carolina State University, Raleigh 27695.
authors.forename: C
authors.initials: C
authors.lastname: Maltecca
mesh_terms: D000818:Animals; D019985:Benchmarking; D002417:Cattle / Q000502:physiology*; D002418:Cattle Diseases / Q000453:epidemiology; D003612:Dairying / Q000379:methods*; D004777:Environment; D005260:Female; D006304:Health Status; D016015:Logistic Models; D012098:Reproduction
keywords: benchmarking; herd health status; prediction; producer-recorded data