Evaluation of National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) feed evaluation model on predictions of milk protein yield on Québec commercial dairy farms

Abstract

A recent study assessed the ability of 4 feed evaluation models to predict milk protein yield (MPY) in a commercial context, with data of 541 cows from 23 dairy herds in the province of Québec, Canada. However, the recently published Nutrient Requirements of Dairy Cattle from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) was not released at that time. Thus, the current study evaluated NASEM using the same dataset. To be consistent with the previous study, predicted DMI was used. Therefore, MPY was predicted using the 2 estimations of DMI proposed by NASEM: one based on animal characteristics only (DMI_Ao) and one also including ration characteristics (DMI_A&R). For each type of DMI estimates, 2 MPY predictions were made, using (1) the multivariate equation directly published in NASEM and (2) a variable efficiency of utilization of MP predicted using inputs and outputs from NASEM, published a posteriori. With the 2 approaches, multivariate and variable efficiency, the DMI_A&R yielded the best MPY predictions. The multivariate equation showed a regression bias between observed and predicted MPY with both DMI estimations. The estimated variable efficiency allowed for MPY predictions without mean and regression biases. With DMI_A&R, concordance correlation coefficients (CCC) were 0.72 and 0.78 for MPY predicted using the multivariate and variable efficiency equations, respectively. In comparison, DMI_Ao CCC were 0.60 and 0.71, respectively. In conclusion, on commercial farms, where dairy rations are usually optimized for a group of cows, estimates of DMI based on animal and rations characteristics yielded the best MPY predictions. The multivariate equation from NASEM predicted MPY with a regression bias, whereas the variable efficiency of utilization of MP based on MP and energy supplies resulted in no bias in MPY predictions.