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 (K-NN), 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 three 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量水平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 (NE) 水平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。 24头杜洛克×长白×约克夏杂交阉公猪，初始体重为24.90±0.46 kg，随机分为3个饲料处理组，包括低NE组（2,325 kcal/kg）、中NE组（2,475 kcal/kg） ，以及高 NE 组（2,625 kcal/kg）。收集各时期每头猪产生的粪便和尿液总量，计算NE摄入量、以蛋白质形式保留的NE（NEp）和以脂质形式保留的NE（NEl）。共收集了240组每头猪的能量分配模式数据，随机选择数据集中的75%作为训练数据集，剩余25%作为测试数据集。采用多元线性回归（MR）、人工神经网络（ANN）、k-近邻（K-NN）和随机森林（RF）等算法建立了生长猪NE需求的预测模型，并且预测性能这些模型在测试数据集上进行了比较。结果显示，与高NE组猪相比，在大多数生长阶段，低NE组猪的平均日增重、平均日采食量和NE摄入量均较低，但饲料转化率较高。此外，三个处理组的猪在所有生长阶段的NEp均未表现出显着差异，而在25至55 kg的生长阶段，中和高NE组的猪比低NE组的猪表现出更高的NEl （P< 0.05）。 在已开发的 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，具有最小的均方根误差 (RMSE) 和最大的 R2，而 RF 模型的预测性能最差，最大RMSE 和最小的 R2。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，采用ANN算法建立的模型能够准确地实现生长猪的NE需求预测。