Non-invasive diagnostics and finding biomarkers of disease in humans have been a very active research area. Some of the analytical technologies used for finding biomarkers of human disease are finding their use in livestock. Non-invasive sample collection from diseased cattle using breath and headspace of fecal samples have been reported. In this work, we explore the use of volatile organic compounds (VOCs) emitted from bovine nasal secretions and serum for finding biomarkers for bovine respiratory disease (BRD). One hundred nasal swabs and 100 serum samples (n = 50 for both ‘sick’ and ‘healthy’) were collected at the time of treatment for suspected BRD. Solid-phase microextraction (SPME) was used to collect headspace samples that were analyzed using gas chromatography-mass spectrometry (GC-MS). It was possible to separate sick cattle using non-invasive analyses of nasal swabs and also serum samples by analyzing and comparing volatiles emitted from each group of samples. Four volatile compounds were found to be statistically significantly different between ‘sick’ and ‘normal’ cattle nasal swabs samples. Five volatile compounds were found to be significantly different between ‘sick’ and ‘normal’ cattle serum samples, with phenol being the common marker. Future studies are warranted to improve the extraction efficiency targeting VOCs preliminarily identified in this study. These findings bring us closer to the long-term goal of real-time, animal-side detection and separation of sick cattle.

Developing and raising replacement heifers requires a large capital investment for producers. Therefore, it is imperative to discover traits and management practices to eliminate subfertile heifers prior to breeding and pregnancy determination. In this study, four years of data was analyzed from a centralized beef heifer development yard in the Midwest of the United States. The objective of this study was to analyze various heifer physical characteristics and management practices in order to quantify their impact on pregnancy and date of conception. Logistic regression models were built to investigate risk factors associated with conception to artificial insemination (AI), pregnancy by natural service after AI exposure, and pregnancy in the first 21-days of the breeding season. Age at entry, average daily gain from entry to breeding, pelvic width, and year were associated with AI pregnancy (p < 0.05). On the second model, average daily gain from entry to yearling weight, weight at breeding, weight at pregnancy diagnosis, and age at AI were significantly associated with pregnancy. There were no associations with reproductive tract score with any of the response variables analyzed. These results indicate there are physical measurements that can be used to improve the ability to select and develop heifers for improved reproductive performance.