Associations between weather conditions during the first 45 days after feedlot arrival and daily respiratory disease risks in autumn-placed feeder cattle in the United States1

Abstract

Data on associations between weather conditions and bovine respiratory disease (BRD) morbidity in autumn-placed feedlot cattle are sparse. The goal of our study was to quantify how different weather variables during corresponding lag periods (considering up to 7 d before the day of disease measure) were associated with daily BRD incidence during the first 45 d of the feeding period based on a post hoc analysis of existing feedlot operational data. Our study population included 1,904 cohorts of feeder cattle (representing 288,388 total cattle) that arrived to 9 US commercial feedlots during September to November in 2005 to 2007. There were 24,947 total cases of initial respiratory disease (animals diagnosed by the feedlots with BRD and subsequently treated with an antimicrobial). The mean number of BRD cases during the study period (the first 45 d after arrival) was 0.3 cases per day per cohort (range = 0 to 53.0), and cumulative BRD incidence risks ranged from 0 to 36% within cattle cohorts. Data were analyzed with a multivariable mixed-effects binomial regression model. Results indicate that several weather factors (maximum wind speed, mean wind chill temperature, and temperature change in different lag periods) were significantly (P < 0.05) associated with increased daily BRD incidence, but their effects depended on several cattle demographic factors (month of arrival, BRD risk code, BW class, and cohort size). In addition, month and year of arrival, sex of the cohort, days on feed, mean BW of the cohort at entry, predicted BRD risk designation of the cohort (high or low risk), cohort size, and the interaction between BRD risk code and arrival year were significantly (P < 0.05) associated with daily BRD incidence. Our results demonstrate that weather conditions are significantly associated with BRD risk in populations of feedlot cattle. Defining these conditions for specific cattle populations may enable cattle health managers to predict and potentially manage these effects more effectively; further, estimates of effects may contribute to the development of quantitative predictive models for this important disease syndrome.