A proposal for the evaluation of the bioeconomic efficiency of beef cattle production systems

• Autor(a) principal: Marques,Pedro Rocha
• Data de Publicação: 2017
• Outros Autores: Peripolli,Vanessa, Lampert,Vinícius do Nascimento, Dias,Eduardo Antunes, Pereira,Gabriel Ribas, Oliveira,Tamara Esteves de, Rocha,Marcela Kuczynski da, Barcellos,Júlio Otávio Jardim
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Zootecnia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017000100065
• topic: beef cattle farmers; clusters analysis; efficiency; evaluation methods

ABSTRACT The objective of this study was to identify types of production system and their main indicators on bioeconomic efficiency, using qualitative and quantitative methods to evaluate beef cattle farms in the western region of the state of Rio Grande do Sul. A survey was carried out with 43 farmers operating in the western region of that state. All farms operated with complete cycle production systems in areas larger or equal to 900 ha. A qualitative questionnaire with binary answers and a quantitative questionnaire with numerical answers were applied. Technology and Management drivers were used for the calculation of the efficiency index of farmers obtained by both questionnaires. Farmers were divided into three clusters: low-efficiency level (LEL), intermediate-efficiency level (IEM), or high-efficiency level (HEL), as a result of the comparison of the scores obtained for the analyzed parameters. Subfactors resulting from each comparison (LEL × IEL; LEL × HEL, and IEL × HEL) were different as a function of the comparison and of the methods applied. Low-efficiency level farmers need to improve essential production processes, such as technology and management, as well as health management practices together with the financial management of the production system. Intermediate-efficiency level farmers need to improve their routine animal management, pasture management, and calculation of financial indicators to become highly efficient. The quantitative method allowed to identify underestimation (39.3%) or overestimation (24.2%) when farmers were are classified in clusters. Different methods may be used, but those based on quantitative information have stronger discrimination power to identify different types of farmers.