Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models

作     者：Li Wang Qile Hu Lu Wang Huangwei Shi Changhua Lai Shuai Zhang Li Wang;Qile Hu;Lu Wang;Huangwei Shi;Changhua Lai;Shuai Zhang

作者机构：State Key Laboratory of Animal NutritionCollege of Animal Science and TechnologyChina Agricultural UniversityBeijing 100193P.R.China 

出 版 物：《Journal of Animal Science and Biotechnology》 (畜牧与生物技术杂志（英文版）)

年 卷 期：2022年第13卷第6期

页      面：1932-1944页

核心收录：

学科分类：0905[农学-畜牧学] 09[农学] 

基　　金：funded by the National Natural Science Foundation of China(32072764, 31702121) the 2115 Talent Development Program of China Agricultural University National Key Research and Development Program of China (2019YFD1002605) 

主　　题：Multiple regression model Neural networks Pig Prediction 

摘      要：Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are *** regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction ***,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this ***:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate *** the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR ***,the“over-fittingoccurred in MR models but not in ANN *** validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the ***:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR ***,it is promising to use ANN models in related swine nutrition studies in the future.