|Parker Gaddis, Kristen -|
|Cassady, Joe -|
|Maltecca, Christian -|
Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: March 5, 2012
Publication Date: June 28, 2012
Citation: Parker Gaddis, K.L., Cassady, J.P., Cole, J.B., Maltecca, C. 2012. Incidence validation and causal relationship analysis of producer-recorded health event data from on-farm computer systems in the U.S.. Journal of Dairy Science. 95(Suppl. 2):225–226(abstr. 229). 2012. Technical Abstract: Substantial progress has been made in the genetic improvement of production traits in dairy cattle. Due to a negative correlation between production and fitness traits, the health and fitness of dairy cattle have declined as yields have increased. Health and fitness traits are generally difficult and/or expensive to measure, but health event data collected from on-farm computer management systems may provide an effective and low-cost source of health event information. The principle objective of this study was to analyze the reliability of health event data recorded through on-farm recording systems throughout the United States. In order to validate editing methods, incidence rates of on-farm recorded health event data were compared to incidence rates reported in the literature. A second aim of this study was to examine putative causal relationships among common health events using data recorded in on-farm computer systems. Calculated incidence rates ranged from 1.37% for respiratory problems to 7.98% for clinical mastitis. Most health events reported had incidence rates lower than the average incidence rate found in literature. This may represent under-reporting by dairy farmers who record disease events only when a treatment or other intervention is required. Logistic regression was used to examine putative causal relationships among health events within a lactation for three timeframes: 0 to 60 days in milk (DIM), 61 to 90 DIM, and 91 to 150 DIM. Herd, season, parity, breed, and year were included in each model as fixed effects. Health events occurring on average before the health event of interest were included in the model as predictors when significant (P < 0.05). 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. The results of this analysis provide evidence for the usefulness of on-farm recorded health information.