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Abstract：
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.
Net energy (NE) can unify the energy value of the feed with the energy requirements of the pig more accurately and is the optimal system for accurately predicting the growth performance of pigs. The evaluation of the NE partition pattern is difficult and costly, thus, establishing a predicted model is a more efficient way. This study was conducted to evaluate the energy partition patterns of growing pigs fed diets with different NE levels based on machine learning methods. Diets with varied NE concentrations within a certain range did not affect the growth performance and NE requirement for lipid deposition in growing pigs. Among the 4 models developed to predict NE requirements, the artificial neural networks model had the highest accuracy, while the multiple linear regression model had the highest interpretability.
Net energy (NE) can unify the energy value of the feed with the energy requirements of the pig more accurately and is the optimal system for accurately predicting the growth performance of pigs. The evaluation of the NE partition pattern is difficult and costly, thus, establishing a predicted model is a more efficient way. This study was conducted to evaluate the energy partition patterns of growing pigs fed diets with different NE levels based on machine learning methods. Diets with varied NE concentrations within a certain range did not affect the growth performance and NE requirement for lipid deposition in growing pigs. Among the 4 models developed to predict NE requirements, the artificial neural networks model had the highest accuracy, while the multiple linear regression model had the highest interpretability.
摘要：
这项研究的目的是评估基于机器学习方法的不同净能量（NE）水平的生长猪饲喂饮食的能量分配模式，并建立生长猪NE需求量的预测模型。将24只初始体重为24.90±0.46kg的杜洛克×长白兰×约克郡杂交手推车随机分配到3种饮食处理中，包括低NE组(2,325kcal/kg)，中等NE组(2,475千卡/千克)，和高NE组(2,625kcal/kg)。收集每头猪在每个时期产生的粪便和尿液总量,为了计算NE的摄入量，NE保留为蛋白质（NEp），和NE保留为脂质(NEl)。共收集了每头猪能量分区模式的240组数据,数据集中75％的数据被随机选择作为训练数据集，剩下的25%设置为测试数据集。使用包括多元线性回归（MR）在内的算法开发了生长猪的NE需求的预测模型，人工神经网络(ANN)，k-最近邻(K-NN)，和随机森林(RF)，并在测试数据集上比较了这些模型的预测性能。结果表明，低NE组的猪平均日增重较低，较低的平均每日采食量，较低的NE摄入量，但在大多数生长阶段，与高NE组的猪相比，饲料转化率更高。此外,三个处理组中的猪在所有生长阶段的NEp均未显示出显着差异，而中和高NE组的猪在25至55kg的生长阶段显示出比低NE组的猪更高的NEl(P<0.05)。在已开发的NE摄入量预测模型中，NEp,和NEl,ANN模型具有最小的均方根误差（RMSE）和最大的R2，而RF模型具有最差的预测性能，具有最大的RMSE和最小的R2。总之,在一定范围内不同NE浓度的饮食不会影响生长猪的NEp，用人工神经网络算法开发的模型可以准确地实现生长猪的NE需求预测。
