Development of a performance model for classifying broiler farms

Broiler meat is the second world's most consumed meat, and the increase in consumption by 2027 is fore-casted to be near 35 kg/capita/year. Brazil ranks third in broiler production globally and is the world's largest exporter of chicken meat. To reach proper rearing conditions, broiler farms need to meet good practices of husbandry and welfare. The present study aimed to develop a performance classification model using data mining to evaluate broiler farmers based on detailed flock housing and performance information. The input dataset from 49 broiler farms from a cooperative in Northeastern Brazil was organized with details on the housing characteristics, rearing environment, management, and performance data from flocks. We also added the cooperative technical classification retrieved from the housing conditions and the production index. The input classification had weights attributed to each housing feature. The output variable (target) was defined as the performance classification (PC) index. The dataset was processed using Rapidminer (R) software using 80% of training and 20% for implementing the random forest algorithm. The prominent variables in classifying the performance were the feed conversion, the daily weight gain, the productivity index, and the cooperative classification criteria. The developed model pointed out a way to auto-classify farms and allow the cooperative to evaluate the farmers' production based on the broiler production and management practices. It was possible to create 'If-Then' rules that enable appropriate decision-making by broiler farmers to comply with good practices' norms.

Automated summary

This study aims to develop a performance classification model using data mining to evaluate broiler farmers based on detailed flock housing and performance information. By analyzing key variables, it can help farmers comply with good practice guidelines and make appropriate decisions.