Análise multivariada na caracterização de indicadores econômicos e produtivos de fazendas de bovinocultura de corte no Brasil

Abstract

This study aims to characterize, through multivariate statistical methods, the main patterns of variability and to cluster beef cattle farms focused on rearing and fattening according to similar productive and economic characteristics. The research used data from 118 farms and 35 indicators from the 2021/2022 season, standardized by the Agricultural Metrics Institute (INTTEGRA). The analysis was based on Principal Component Analysis (PCA) and Cluster Analysis (CA), followed by Tukey’s test (p < 0.05) and frequency analysis.The results of the PCA indicated that three principal components explained 63.3% of the data variability. PC1 (27.8%) reflects production costs, PC2 (20.5%) is related to profitability, including indicators such as profit per arroba and final margin, and PC3 (15%) is associated with farm size and number of animals.Cluster Analysis identified four distinct groups. Tukey’s test showed that group 1 had low costs and low production, with modest profitability (126.66 ± 102.91 US$/ha). Group 2 presented a balance between cost and production, generating profit (166.49 ± 226.58 US$/ha). Group 3, on the other hand, showed significant losses (–110.89 ± 390.85 US$/ha), highlighting that merely reducing costs or increasing spending does not guarantee better results. Group 4, despite high costs, achieved high profitability due to high production levels (1161.95 ± 1003.50 US$/ha).The study identified the variables related to profitability and emphasized that the combination of cost management and productive efficiency is essential to increase the profitability of rearing and fattening beef cattle farms.