Method for Identifying Key Determinants of Meat Inspection Results in Japanese Domestic Pig Farms by Principal Component Analysis

Abstract

Although some municipalities provide meat inspection data to each producer to promote hygienic meat production, few studies relating to the relationship between meat inspection results and rearing environment have been reported. While analysis of the relationship is difficult because both sets of data are multidimensional, principal component analysis (PCA) can overcome the problem as it can reduce the number of dimensions in the data. Therefore, we performed PCA to create three indices related to the condemnation of fattening pigs' organs, number of offspring per sow and pre-weaning mortality rate from multidimensional data containing meat inspection data. Then we conducted multiple regression analysis to examine the relationship between these indices and rearing environments based on questionnaire results. As a result of creating a generalized linear mixed model for the relationship between rearing environments, it confirmed the significant effect on multiple regression analysis and data before PCA, six variables in all-in-all-out, days after the date of birth and tail docking were suggested to be key determinants.