Interpretive Summary: Modeling net energy partition patterns of growing–finishing pigs using nonlinear regression and artificial neural networks

By: Li Wang, Huangwei Shi, Qile Hu, Wenjun Gao, Lu Wang, Changhua Lai, Shuai Zhang

Net energy (NE) is the most refined energy system in animal nutrition, and understanding the NE partition patterns of pigs can help us to develop suitable feeding strategies to improve the growth performance and carcass traits of pigs. However, it is time-consuming, laborious, and expensive to directly measure the NE; thus, establishing a predicted model is more efficient. In research on the energy nutrition of pigs, regression is the most used tool to develop models, but little literature has focused on the application of artificial neural networks (ANN) models. In this study, we measured the NE partition patterns of pigs, and our results show that the proportion of NE for maintenance stayed within the range of 42.0% to 48.6%, while the proportion of NE retained as a lipid kept increasing as pig grows (pigs body weight: 30 to 90 kg). The value of NE retained as protein increased to its maximum value and then stayed in a certain range of 4.64 to 4.88 MJ/d, but with a decreased proportion of NE intake. Additionally, we applied the corresponding nonlinear regression (NLR) and ANN models and made comparisons between them. The ANN models exhibited better performance than NLR models for all the target outputs.