Model evaluation using data from calves fed several types of milk replacers and calf starters under different environmental conditions is important to identify whether the model can be useful for dairy nutritionists. Following our previous studies, 501 sets of individual calf data from 16 studies carried out at University of São Paulo (ESALQ) in Brazil were used to evaluate the updates published by the Agriculture Modeling and Training Systems (AMTS) calf model (AMTS.Cattle.Pro). These studies provided all inputs required by the model. Descriptive statistics were generated using SAS. Analyses for model adequacy were performed with R and the Model Evaluation System. The average daily gain (ADG) observed at weaning was used to evaluate the model (7 or 8 weeks of age). The mean square error of prediction (MSPE), mean bias, concordance correlation coefficient (CCC), and analysis of linear regression were calculated. Calves (36.3 ± 6.3 kg BW at birth; 60.7 ± 14.2 kg BW at weaning) were fed different amounts of milk replacer (4.5 ± 0.9 L/d, 12.5% DM, average 21 ± 1.9% CP and 17 ± 2.0% fat in dry solids) or whole milk (5.3 ± 0.9 L/d), and were weaned at 57 ± 3 d of age (ADG 0.745 ± 0.383 kg/d). Calf starters contained 21.3 ± 1.1% CP. The ADG predicted by dietary metabolizable protein (MP) showed lower values of mean bias and MSPE and higher CCC compared with the ADG predicted by dietary metabolizable energy (ME, Table 1). The model showed to be more accurate than precise (Cb > r value). The low precision of the model may be related to variation in starter intake (0.820 ± 0.549 kg/d) of young calves before weaning. The updates published by the AMTS model can be used in the evaluation of early nutrition programs for young calves to make predictions with good accuracy but a low precision. Table 1.

Variable	ME allowable gain	MP allowable gain
Model-predicted ADG (kg/d)	0.729	0.748
Mean bias (Y – X)	0.016	− 0.003
MSPE (kg × kg)	0.138	0.099
MSPE decomposition
Mean bias (%)	0.189	0.007
Systematic bias (%)	4.623	1.181
Random errors (%)	95.188	98.811
CCC	0.256	0.446
Pearson r	0.31	0.57
Cb	0.82	0.78
R2	0.10	0.33
P-value (a = 0)	0.00001	0.015
P-value (b = 1)	0.00001	0.014