Improvement of Dairy Cattle Health Through the Utilization of Producer-Recorded Data and Genomic Methods

The overall objective of this study was to investigate utilization of producer-recorded data in order to improve dairy cattle health. Producer-recorded dairy cattle data were obtained for production and health traits, as well as overall herd characteristics. Initial analyses determined the plausibility of health data recorded through on-farm recording systems throughout the United States. There were originally over 8 million health records available from 1996 through 2009. Original production data consisted of 1.8 million records from over 450,000 cows. Editing criteria were developed and implemented. In order to validate editing methods, incidence rates of on-farm recorded health event data were compared to incidence rates reported in literature. Calculated incidence rates ranged from 1.37% for respiratory problems to 12.32% for mastitis. Most health events had incidence rates lower than the average incidence rate found in literature. Path diagrams developed using odds ratios calculated from logistic regression models for each of 13 common health events allowed putative relationships to be examined. The greatest odds ratios were estimated to be the influence of ketosis on displaced abomasum (15.5) and the influence of retained placenta on metritis (8.37), and were consistent with earlier reports. Additional data were obtained and variance components and heritabilities were estimated for health traits most commonly experienced by dairy cows with pedigree as well as genomic data. Single-step analyses were conducted to estimate genomic variance components and heritabilities for common health events. A blended H-matrix was constructed for a threshold model with fixed effects of parity and year-season and random effects of herd-year and sire. The single-step genomic analysis produced heritability estimates that ranged from 0.02 (SD = 0:005) for lameness to 0.36 ( SD = 0:08) for retained placenta. Significant genetic correlations were found between lameness and cystic ovaries, displaced abomasum and metritis, and retained placenta and metritis. Sire reliabilities increased, on average, approximately 30% with incorporation of genomic data. Implementations of single-trait and two-trait models were compared based on predictive ability using BayesA and single-step methods for mastitis and somatic cell score with a restricted dataset. Estimated sire breeding values were used to estimate number of daughters expected to experience mastitis. Predictive ability of each model was assessed using sum of chi2 and proportion of wrong predictions. Depending on model implemented, heritability of liability to mastitis ranged from 0.05 (SD = 0:02) to 0.11 (SD = 0:03) and heritability of somatic cell score ranged from 0.08 (SD = 0:01) to 0.18 (SD = 0:03). Posterior mean of genetic correlation between mastitis and somatic cell score was 0.63 (SD = 0:17). The single-step method had best predictive ability among univariate analyses of mastitis. Conversely, the BayesA univariate model had the smallest number of wrong predictions. Best model fit was found for single-step and pedigree-based models. Bivariate single-step analysis had a better predictive ability than bivariate BayesA; however, bivariate BayesA analysis had the smallest number of wrong predictions. Lastly, over 1,000 herd characteristic variables were utilized to benchmark herd health status. Health events were grouped into three categories for analyses: mastitis, reproductive, and metabolic. Herd incidence was calculated for each category based on individual cow records and converted to a binary indicator of either low or high incidence. Models implemented included stepwise logistic regression, support vector machines, and random forest. Stepwise regression models had the poorest predictive performance with accuracy ranging from 0.42 for reproductive events up to 0.46 for metabolic events when splitting data based on year. Highest accuracy was estimated for random forest models for all health event categories; however, this was not statistically different from accuracy obtained with support vector machine models. Highly significant variables and key words from logistic regression and random forest models were also investigated. Combined results from these analyses provide evidence for the value of data recorded by producers on-farm and the possibility of utilizing these data for benchmarking herd health status. A wealth of information is gathered regularly that can be used for improvement of dairy cattle health.