Carneiro Jorge Henrique, Rezende João Pedro Andrade, de Almeida Rodrigo, Danes Marina de Arruda Camargo
Department of Animal Science, Universidade Federal de Lavras, Lavras, MG, Brazil, 37200-900.
Department of Animal Science, Universidade Federal do Paraná, Curitiba, PR, Brazil, 81531-990.
JDS Commun. 2024 Oct 29;6(1):65-68. doi: 10.3168/jdsc.2024-0636. eCollection 2025 Jan.
The National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) milk protein yield (MPY) prediction equation includes independent and additive effects of digestible energy intake and absorbed EAA. Our objective was to evaluate the NASEM MPY prediction and EAA use efficiency in Holstein cows in pens from commercial farms. Data collected from 12 Brazilian herds were used. All cows were housed in a freestall or compost barn and fed TMR. For each of the 89 pens (a total of 8,345 cows, 50-325 cows per pen), data on milk production and composition, DMI, DIM, parity, BW, and diet composition were compiled. Data from each pen were entered in NASEM software to predict MPY and efficiency of utilization for each EAA. Pens were divided by observed MPY levels in 3 clusters: low = 970, medium = 1,196, and high = 1,524 g/d MPY, representing the mean values for each cluster. Within each cluster, NASEM MPY prediction was compared with the observed MPY using the coefficient of determination (R), root mean square error (RMSE), and the concordance correlation coefficient (CCC). The MIXED procedure of SAS with the fixed effect of cluster and the random effects of farm and pen nested within farm was used to compare the number of protein sources used in the diets and EAA efficiency by cluster. Overall prediction performance of the NASEM MPY equation was best for the low MPY cluster relative to medium and high ones (CCC = 0.73, 0.37, and 0.35, respectively), with high accuracy (RMSE = 62.9 g/d, 6.5% of the mean) and moderate precision (R = 0.57). On the other hand, despite lower precision (R = 0.39), accuracy was also high for the medium cluster (RMSE = 95.6 g/d, 8% of the mean). Finally, prediction for the high MPY cluster had the highest precision (R = 0.74), but the lowest accuracy (RMSE = 224.7 g/d, 14.7% of the mean). The number of protein sources in the diets was greater in the high and medium productions clusters compared with the low production cluster (4.1, 3.9, and 3.0 sources, respectively; SEM = 0.33). Increasing the production level of the cluster linearly increased the EAA use efficiency of all EAA. The greater pull effect in the higher production groups and the better combination of AA from more protein sources could explain better AA efficiencies.
美国国家科学院、工程院和医学院（NASEM，2021）的乳蛋白产量（MPY）预测方程包括可消化能量摄入量和吸收的必需氨基酸（EAA）的独立和累加效应。我们的目标是评估NASEM的MPY预测以及商业农场栏舍中荷斯坦奶牛的EAA利用效率。使用了从12个巴西牛群收集的数据。所有奶牛都饲养在自由牛舍或堆肥牛舍中，并饲喂全混合日粮（TMR）。对于89个栏舍（共8345头奶牛，每个栏舍50 - 325头奶牛）中的每一个，收集了牛奶产量和成分、干物质采食量（DMI）、泌乳天数（DIM）、胎次、体重（BW）和日粮组成的数据。将每个栏舍的数据输入NASEM软件，以预测MPY和每种EAA的利用效率。栏舍根据观察到的MPY水平分为3组：低 = 970 g/d、中 = 1196 g/d、高 = 1524 g/d MPY，分别代表每组的平均值。在每个组内，使用决定系数（R）、均方根误差（RMSE）和一致性相关系数（CCC）将NASEM的MPY预测值与观察到的MPY进行比较。使用SAS的MIXED过程，以组为固定效应，农场和农场内嵌套的栏舍为随机效应，比较日粮中使用的蛋白质来源数量和各组的EAA效率。相对于中、高MPY组，NASEM的MPY方程对低MPY组的总体预测性能最佳（CCC分别为0.73、0.37和0.35），具有较高的准确性（RMSE = 62.9 g/d，为平均值的6.5%）和中等精度（R = 0.57）。另一方面，尽管精度较低（R = 0.39），但中MPY组的准确性也较高（RMSE = 95.6 g/d，为平均值的8%）。最后，高MPY组的预测精度最高（R = 0.74），但准确性最低（RMSE = 224.7 g/d，为平均值的14.7%）。与低产组相比，高产和中产组日粮中的蛋白质来源数量更多（分别为4.1、3.9和3.0种来源；标准误 = 0.33）。随着组内生产水平的线性增加，所有EAA的利用效率都有所提高。高产组中更大的拉动效应以及更多蛋白质来源中氨基酸的更好组合可以解释更好的氨基酸效率。
