Predicting the excretion of feces,urine and nitrogen using support vector regression:A case study with Holstein dry cows

作     者：Qiang Fu Weizheng Shen Xiaoli Wei Yanling Yin Ping Zheng Yonggen Zhang Zhongbin Su Chunjiang Zhao 

作者机构：College of Electrical and InformationNortheast Agricultural UniversityHarbin 150030China Key Laboratory of Pig-breeding Facilities EngineeringMinistry of AgricultureHarbin 150030China College of Animal Science and TechnologyNortheast Agricultural UniversityHarbin 150030China National Engineering Research Center for Information Technology in AgricultureBeijing 100097China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2020年第13卷第2期

页      面：48-56页

核心收录：

学科分类：0905[农学-畜牧学] 09[农学] 

基　　金：The authors would like to acknowledge the financial support from the National Key R&D Program of China(2016YFD0700204-02) the China Agriculture Research System(CARS-36) the China Postdoctoral Science Foundation(2017M611346) the Natural Science Foundation of Heilongjiang Province of China(C2018018) 

主　　题：cow farming pollution feces/urine excretion prediction nitrogen excretion prediction non-parametric model SVR technique 

摘      要：Predicting the excretion of feces,urine and nitrogen(N)from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled *** traditional prediction methods such as pollutant generation coefficient(PGC)and mathematical model based on linear regression(LR)may be limited by prediction range and regression function assumption,and sometimes may deviate from the actual *** order to solve these problems,the support vector regression(SVR)was applied for predicting the cows feces,urine and N excretions,taking Holstein dry cows as a case *** is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data,and also it can fit the function closest to the actual in most *** evaluate prediction accuracy effectively,the SVR technique was compared with the LR and radial basis function artificial neural network(RBF-ANN)methods,using the required sample data obtained from actual feeding *** prediction results indicate that the proposed technique is superior to the other two conventional(especially LR)methods in predicting the main indicators of feces,urine,and N excretions of Holstein dry cows.