The objectives of this study were to evaluate the energy partition patterns of growing pigs fed diets with different net energy (NE) levels based on machine learning methods, and to develop prediction models for the NE requirement of growing pigs. Twenty-four Duroc × Landrace × Yorkshire crossbred barrows with an initial body weight of 24.90 ± 0.46 kg were randomly assigned to 3 dietary treatments, including the low NE group (2,325 kcal/kg), the medium NE group (2,475 kcal/kg), and the high NE group (2,625 kcal/kg). The total feces and urine produced from each pig during each period were collected, to calculate the NE intake, NE retained as protein (NEp), and NE retained as lipid (NEl). A total of 240 sets of data on the energy partition patterns of each pig were collected, 75% of the data in the dataset was randomly selected as the training dataset, and the remaining 25% was set as the testing dataset. Prediction models for the NE requirement of growing pigs were developed using algorithms including multiple linear regression (MR), artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF), and the prediction performance of these models was compared on the testing dataset. The results showed pigs in the low NE group showed a lower average daily gain, lower average daily feed intake, lower NE intake, but greater feed conversion ratio compared to pigs in the high NE group in most growth stages. In addition, pigs in the 3 treatment groups did not show a significant difference in NEp in all growth stages, while pigs in the medium and high NE groups showed greater NEl compared to pig in the low NE group in growth stages from 25 to 55 kg (P < 0.05). Among the developed prediction models for NE intake, NEp, and NEl, the ANN models demonstrated the most optimal prediction performance with the smallest root mean square error (RMSE) and the largest R2, while the RF models had the worst prediction performance with the largest RMSE and the smallest R2. In conclusion, diets with varied NE concentrations within a certain range did not affect the NEp of growing pigs, and the models developed with the ANN algorithm could accurately achieve the NE requirement prediction in growing pigs.

中文翻译：

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基于机器学习对饲喂不同净能级日粮的生长猪的能量分配模式进行建模


本研究的目的是基于机器学习方法评估饲喂不同净能量 （NE） 水平的日粮的生长猪的能量分配模式，并开发生长猪 NE 需求的预测模型。将 24 头初始体重为 24.90 ± 0.46 kg 的杜洛克×长白×约克郡杂交牛随机分配到 3 种日粮处理，包括低 NE 组 （2,325 kcal/kg）、中 NE 组 （2,475 kcal/kg） 和高 NE 组 （2,625 kcal/kg）。收集每头猪在每个时期产生的总粪便和尿液，以计算 NE 摄入量、NE 保留为蛋白质 （NEp） 和 NE 保留为脂质 （NEl）。共收集了 240 组关于每头猪能量分配模式的数据，数据集中 75% 的数据被随机选择作为训练数据集，其余 25% 的数据被设置为测试数据集。使用多元线性回归 （MR） 、人工神经网络 （ANN）、k 最近邻 （KNN） 和随机森林 （RF） 等算法开发了生长猪 NE 需求的预测模型，并在测试数据集上比较了这些模型的预测性能。结果表明，与高 NE 组的猪相比，低 NE 组的猪在大多数生长阶段表现出较低的平均日增重、较低的平均日采食量、较低的 NE 摄入量，但更高的饲料转化率。此外，3 个处理组猪在所有生长阶段的 NEp 均未表现出显著差异，而中高 NE 组猪在 25 至 55 kg 的生长阶段与低 NE 组猪相比表现出更高的 NEl （P < 0.05）。 在开发的 NE 摄入量、 NEp 和 NEl 预测模型中，ANN 模型表现出最佳的预测性能，均方根误差 （RMSE） 最小，R2 最大，而 RF 模型的预测性能最差，RMSE 最大，R2 最小。综上所述，在一定范围内不同 NE 浓度的日粮对生长猪的 NEp 没有影响，使用 ANN 算法开发的模型可以准确实现生长猪的 NE 需求预测。