Cow-calf operation management clusters, Argentina

Abstract

This research was conducted to gain more insight into the productivity and good management practice (GMP) adoption of cow-calf operations in Río Negro Province, Argentina. The objectives were to characterize productivity and management profiles according to GMP adoption, identify herd clusters, and describe the performance and productivity within each specific cluster. A survey sample of 142 out of 1,229 cow-calf and cow-calf to-finish operations from Avellaneda and Pichi Mahuida counties provided data on productivity, herd structure, and GMP adoption. The productivity variables, calves per 100 cows (C/100 c) and calves per 100 ha (C/100 ha), were described statistically using quartiles, and differences by county according to the three-month breeding season adoption were explored. Moreover, herd structure and GMP-relatedvariables were subjected to multiple correspondence analysis (MCA) with complete-link hierarchical cluster analysis to typify the operations. A total of 127 out of 142 farmers provided productivity data, showing median values of 79.00 C/100 c (Q1= 67.00; Q3= 85.00) and 4.58 C/100 ha (Q1= 2.78; Q3= 8.00). A Significant difference was found between three-month and year-round breeding operations for both variables (82.00 C/100 c vs 75.50 C/100 c; P=0.0006 and 5.60 C/100 ha vs 3.97 C/100 ha; P=0.0072). Three farm clusters (Cl) were identified: Cl 1 (low adoption), Cl 2 (moderate transition), and Cl 3 (GMP-oriented). Cl 3 showed the highest GMP adoption level, and a significant difference was found in the C/100 c variable (85.00 C/100 c vs. 76.00 and 80.00 for Cl 1 and 2, respectively; P=0.0233). Cluster profiling enhances our understanding of the cow-calf operation segment and highlights the importance of GMP adoption for improving productivity in cow-calf operations. The results should be interpreted with caution due to the limitations of a cross-sectional study showing correlational associations. Nevertheless, it provides insights for designing science-based and targeted interventions to improve the performance of the beef cattle industry in Río Negro, Argentina, and may be the basis for prospective or interventions studies in the future.