SciELO - Scientific Electronic Library Online

vol.48Typology of dairy production systems that meet Brazilian standards for milk quality author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand




Related links


Revista Brasileira de Zootecnia

Print version ISSN 1516-3598On-line version ISSN 1806-9290


KOERICH, Guilherme et al. Influence of forage production area, concentrate supply, and workforce on productive results in milk production systems. R. Bras. Zootec. [online]. 2019, vol.48, e20170177.  Epub Feb 25, 2019. ISSN 1806-9290.

This study aimed to investigate the influence of forage production area, concentrate supply, and farm labor on characterization of milk production systems (MPS) and their productive results. Milk volume data provided in 2014 by 110 dairy farms located in the eastern region of Santa Catarina State, Brazil, were obtained from a dairy industry. Forty-four farms with different production levels were selected, in which interviews were carried out aiming to characterize the management practices related to forage production area, concentrate supply, and farm labor. A principal component analysis (PCA) was performed. Then, regressions were made between the principal components (PC) and indicator variables of productive response (annual milk production, milk production per area, milk production per cow, milk production per worker on farm, and number of cows per hectare). Finally, we performed a hierarchical agglomerative cluster analysis based on the PCA, followed by comparison of the means between clusters. Three PC were generated: indicator of scale and intensification, indicator of age of the manager and his experience on dairy farming, and indicator of specialization and permanent family labor available. The three PC were influencing the production results, especially PC1. It was possible to form five clusters: cluster 1, characterized by the highest value in PC1, showed the highest production results, followed by the cluster 2, with intermediate values in PC1; clusters 3, 4, and 5, characterized by lower values in PC1 and distinguished by PC2 and PC3, had the lowest productive results. Aspects related to forage production area, concentrate supply, and workforce are important for MPS characterization and have significant influence on productive results.

Keywords : cluster; multivariate analysis; principal component analysis.

        · text in English     · English ( pdf )