A computer based decision-making model for poultry inspection.

Abstract

In an attempt to develop a predictive model for poultry inspection at processing plants, a systems research design was developed to examine poultry population health dynamics from hatching to processing. Five cooperating Alabama poultry firms with 5 to 7 growers from each firm were identified to form the study unit. The farms were stratified to represent good, average, and poor producers. Via epidemiologic causal diagrams, variables with potential influence on poultry condemnation due to diseases were identified for hatchery, broiler, and processing subsystems. Field and/or laboratory data were generated for each study unit and for each variable. Using stepwise multiple regression and discriminant analysis, a predictor model with a multiple correlation coefficient (R) of 0.91 and multiple coefficient of determination (R2) of 0.82 was developed. A discriminant analysis model for classifying a flock into high or low condemnation group, using 0.1% to 2.0% as the demarcation line, was also developed. This latter model had overall correct classification probabilities ranging from 0.88 to 1.0. The 2 decision-making models were then computerized, using Beginner's All-purpose Symbolic Instruction Code (BASIC). On the day of processing, the grower provides the inspector with selected information, which is entered on a computer. Alternatives for scaled-down inspection or others are then systematically evaluated by the computer, and decision-making information is provided to the user.