Abstract
The "Precoce MS" program, established by the Brazilian government in Mato Grosso do Sul in 2017, aims to encourage beef producers to harvest animals at younger ages to enhance carcass quality. About 40% of the beef produced in the state now comes from this program, which offers tax refunds ranging from 49% to 67% based on carcass classification and production system. Despite the program success, with participants delivering younger animals (with a maximum of 4 incisors), there remains significant variability in carcass quality. This paper investigates management practices and environmental factors affecting farm performance regarding carcass quality. Data from all animals harvested between the beginning of 2017 and the end of 2018 were analyzed, totaling 1,107 million animals from 1,470 farms. Farm performance was assessed based on the percentage of animals achieving grades "AAA" and "AA." Each batch of harvested cattle from each farm was categorized into two groups: high farm performance (HFP, with more than 50% of animals classified as "AAA" or "AA") and low farm performance (LFP, with less than 50% classified as such). A predictive logistic model was developed to forecast farm performance (FP) using 14 continuous and 15 discrete pre-selected variables. The most effective model, obtained through backward stepwise variable selection, had an R (2) of 0.18, accuracy of 71.5%, and AUC of 0.715. Key predictors included animal category, production system type, carcass weight, individual identification, traceability system, presence of a feed plant, location, and the Normalized Difference Vegetation Index (NDVI) from the 12-mo average before harvest. Developing predictive models of carcass quality by integrating data from commercial farms with other sources of information (animal, production system, and environment) can improve our understanding of production systems, optimize resource allocation, and advance sustainable animal production. Additionally, they offer valuable insights for designing and implementing better sectorial, social, and environmental policies by public administrations, not only in Brazil but also in other tropical and subtropical regions worldwide.