267 Predicting dry matter intake in lactating beef and dairy crossbred suckler cows using novel animal measurements. (7th December 2018) - Williams, M; McCabe, S; Prendiville, R
Abstract: The ability to predict grass dry matter intake (GDMI) in grazing suckler cows using grazing behaviour, infrared thermography (IRT), linear body measurements and linear body scoring in addition to milk yield and body weight was investigated. The dependent variable, GDMI was estimated for 105 spring calving suckler cows on three occasions (56, 123, 190 DIM) in 2015 using the n-alkane technique. Milk yield was estimated for all cows using the weigh-suckle-weigh technique, the week prior to each GDMI estimation. Cows were weighed and body condition scored every three weeks throughout the experimentation period. Grazing behaviour was measured once for each animal using Institute of Grassland and Environmental Research behaviour (IGER) recorders. Linear measurements were conducted twice, once immediately after grazing and once when cows were indoors without access to food for four hours. Images of each animal's eye, rib, front and hind hooves and udder were captured using IRT. Linear body scoring was independently performed by the Irish Cattle Breeding Federation (ICBF) and the Irish Holstein Friesian Association (IHFA). Multivariate models were constructed for 101 independent variables using SAS PROC REG, which included adjustment variables (breed, genotype and parity) and known energy sinks (body weight and milk yield). Independent variables with p-values less than 0.25 were included in a backwards linear regression model constructed using SAS PROC REG. Variables were retained in this model based on a p-value of 0.05 or less and the same adjustment variables and known energy sinks were included. Rumination mastications (p=0.0056), full body depth (p=0.0019) and width at pins as measured by the ICBF (p<0.0001) were retained in the model, all had positive associations with GDMI. The R-squared increased from 0.41 to 0.60 when these three variables were included, compared to adjustment variables and known energy sinks alone, increasing the predictability of GDMI by 19%.