Precision feeding using real-time models to estimate daily tailored diets can potentially increase nutrient utilization efficiency. However, to improve the estimation of amino acid requirements for growing–finishing pigs, it is necessary to accurately estimate the real-time body protein (BP) mass. The aim of this study was to predict individual BP over time in order to obtain individual daily protein content of the gain (i.e., protein deposition/daily gain, PD/DG) to be integrated into a real-time model used for precision feeding. Two databases were used in this study: one for the development of the equations for the model and the other for model evaluation. For the equations, data from 79 barrows (25 to 144 kg BW) were used to estimate the parameters for a Gompertz function and a mixed linear-quadratic regression. Individual BP predictions obtained by dual X-ray absorptiometry were regressed as a function of BW. Individual pig BP estimates were obtained by linear-quadratic regression using the MIXED procedure of SAS, considering pig measurements repeated in time. Individual Gompertz curves were obtained using the NLMIXED procedure of SAS. Both procedures generate an average or a general model, which was assessed for accuracy with the database used to generate the equations. Coefficients of concordance and determination were both 0.99, and the RMSE was 0.21 kg for the linear-quadratic regression. The Gompertz curve coefficients of concordance and determination were both 0.99, and the RMSE was 0.36 kg. In sequence, the linear-quadratic regression and Gompertz curve were evaluated in an independent data set (488 observations; 21 to 126 kg BW). The linear-quadratic regression to predict BP mass was accurate (mean absolute percentage error (MAPE) = 2.5%; bias = 0.03); the Gompertz model performed worse (MAPE = 3.9%; bias = 0.04) than the linear-quadratic regression. When using the derivative of these equations to predict PD/DG, the linear-quadratic regression was more accurate (MAPE = 4.8%, bias = 0.17%) compared to the Gompertz (MAPE = 10.6%, bias = −0.99%) mainly due to the linear decrease in PD/DG in the observed data. Further analysis using individual pig data showed that the goodness of fit of PD/DG curve depends on the individual shape of the growth curve, with either the Gompertz or the linear-quadratic regression being more accurate for specific individuals. Therefore, both approaches are provided to allow end users to select the model that best fits their needs. The proposed update of the empirical component of the original model, using either linear-quadratic regression or the Gompertz function, is able to predict BP in real-time with good accuracy.

使用实时模型估算每日定制饮食的精确喂养可以潜在地提高营养利用效率。但是，为了改善对生长肥育猪的氨基酸需求量的估算，有必要准确估算实时体蛋白（BP）的质量。这项研究的目的是预测一段时间内的个体血压，以获取个体每日增重的蛋白质含量（即蛋白质沉积/每日增重，PD / DG）集成到用于精确进给的实时模型中。这项研究使用了两个数据库：一个用于模型方程式的开发，另一个用于模型评估。对于这些方程式，使用来自79个手推车（25至144 kg BW）的数据来估计Gompertz函数和混合线性二次回归的参数。通过双X线吸收法获得的单个BP预测作为BW的函数回归。考虑到时间上重复进行的猪的测量，使用SAS的MIXED方法通过线性二次回归获得各个猪的BP估计值。使用SAS的NLMIXED程序获得单个Gompertz曲线。两种方法均会生成平均值模型或通用模型，并使用用于生成方程式的数据库对其准确性进行评估。线性和二次回归的一致性和确定性系数均为0.99，RMSE为0.21 kg。一致性和确定性的Gompertz曲线系数均为0.99，RMSE为0.36 kg。依序，在独立的数据集中评估了线性二次回归和Gompertz曲线（488个观测值； 21至126 kg BW）。线性二次回归预测BP质量是准确的（平均绝对百分比误差（玛普）= 2.5％；偏差= 0.03）；Gompertz模型的表现比线性二次回归更差（MAPE = 3.9％；偏差= 0.04）。与Gompertz（MAPE = 10.6％，bias = -0.99％）相比，使用这些方程式的导数预测PD / DG时，线性二次回归更为准确（MAPE = 4.8％，偏差= 0.17％）。观察数据中PD / DG的线性下降。使用猪的个体数据进行的进一步分析表明，PD / DG曲线的拟合优度取决于生长曲线的个体形状，对于特定个体而言，Gompertz或线性二次方回归更为准确。因此，提供了两种方法以允许最终用户选择最适合其需求的模型。建议对原始模型的经验部分进行更新，