%0 Journal Article
%T Modeling energy partition patterns of growing pigs fed diets with different net energy levels based on machine learning.
%A Yang Y
%A Hu Q
%A Wang L
%A Wang L
%A Xiao N
%A Dong X
%A Liu S
%A Lai C
%A Zhang S
%J J Anim Sci
%V 102
%N 0
%D 2024 Jan 3
%M 39121178
%F 3.338
%R 10.1093/jas/skae220
%X 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.<BR>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.