Abstract
Dairy cows are known to mobilise body fat to achieve their genetic potential for milk production, which can have a detrimental impact on the health, fertility and survival of the cow. Better monitoring of cows with poor body condition (low or high body fat) will lead to improvements in production efficiencies and less wasted resources when producing milk from dairy cows. The aim of this study was to compare different methods for monitoring the body condition (body fat) of dairy cows. The methods used to measure body condition were: ultrasound scanner, manual observation, and a still digital image of the cow. For comparison, each measure was expressed as a body condition score (BCS) on a scale of extremely thin (1) to very fat (5) in quarter intervals. A total of 209 cows at various stages of lactation were assessed. Lin’s concordance correlation coefficient (CCC) and the root mean square prediction error (RMSPE) were used to compare the accuracy of methods. The average BCS across cows was 2.10 for ultrasound, 2.76 for manual and 2.41 for digital methods. The study found that both manual (r = 0.790) and digital (r = 0.819) approaches for monitoring cow body condition were highly correlated with ultrasound BCS measurements. After adjusting correlation coefficients for prediction bias relative to a 45 line through the origin, the digital BCS had a higher CCC of 0.789 when compared to the ultrasound BCS than the manual BCS with a CCC of 0.592. The digital BCS also had a lower prediction error (RMSPE = 28.3%) when compared with ultrasound BCS than the manual BCS (RMSPE = 42.7%). The prediction error for digital and manual BCS methods were similar for cows with a BCS of 2.5 or more (RMSPE = 20.5% and 19.0% respectively) but digital BCS was more accurate for cows of less than 2.5 BCS (RMSPE = 35.5% and 63.8% respectively). Digital BCS can provide a more accurate assessment of cow body fat than manual BCS observations, with the added benefit of more automated and frequent monitoring potentially improving the welfare and sustainability of high production systems.
Highlights
Body condition scoring was developed for management in sheep during the 1960s, before being adopted for use with cattle in the 1970s (Earle, 1976; Bewley and Schutz, 2008)
While Holstein dairy cows are a popular breed for producing high volumes of milk, they are characterized by having lower body condition score (BCS), and reduced fertility and survival compared to other breeds (Dillon et al, 2006)
The ultrasound BCS was compared to the digital measurements of tail head to pin bones and hook bones dimensions
Summary
Body condition scoring was developed for management in sheep during the 1960s, before being adopted for use with cattle in the 1970s (Earle, 1976; Bewley and Schutz, 2008). Monitoring individual cow body fat and maintaining adequate body condition is essential to maintain a productive animal that has appropriate nutrition and fertility, whilst producing acceptable amounts of milk. While Holstein dairy cows are a popular breed for producing high volumes of milk, they are characterized by having lower body condition score (BCS), and reduced fertility and survival compared to other breeds (Dillon et al, 2006). Bell and Wilson (2018) identified body condition as an important phenotypic trait, along with feed utilization, enteric methane emissions, health, fertility, and survival, associated with more sustainable milk production in UK dairy herds. The cost of poor survival in the UK (each percentage increase in cows culled or died within a herd) is estimated at about £13.50 per percentage of cows lost from a herd, with each percentage change resulting in an increase in CO2-eq emissions of about 50 kg per cow and 91 kg per kilogram milk solids due to resources required by replacement animals (Bell and Wilson, 2018)
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