Tipologia de sistemas de produção de leite na região do Arenito Caiuá, PR

• Ano de defesa: 2015
• Autor(a) principal: Tramontini, Rita de Cássia Menchon
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Maringá; Brasil; Departamento de Zootecnia; Programa de Pós-Graduação em Zootecnia; UEM; Maringá, PR; Centro de Ciências Agrárias
• Palavras-chave em Português: Pecuária de leite; Qualidade do Leite; Produção de leite; Umuarama; Paraná (Estado); Brasil.; Management; Dairy farming; Quality; Paraná (State); Brazil.; Ciências Agrárias; Zootecnia
• Link de acesso: http://repositorio.uem.br:8080/jspui/handle/1/1636

The object of this study was to characterize and analyze the Dairy Production Systems (DPS) in the region of Umuarama, PR. A total of 105 semi-structured forms were applied from March/2013 to May/2014 in order to collect information about managing posts management and structural characteristics of DPS and social characteristics of its managers, dairy farmers and the milk price received by the marketed. There were used techniques of descriptive statistics, mean test (t test), proportions test (p test) and multivariate statistics. To the last one, the techniques of factor analysis and hierarchical cluster analysis were employed. Two papers have been written. In the first, from management variables and factor analysis (FA) three factors were defined: F1: nutrition; F2: technical assistance and F3: milking practices. From these factors, it was used the analysis of hierarchical clusters. Two groups were identified: Group 1 - G1 (n=84 DPS) and Group 2 - G2 (n=21 DPS). G2 was formed by DPS that nutrition was a priority between management posts of management and G1 by DPS on that nutrition did not represent an important strategy for managing the DPS. Group 2 differed from group 1 (P<0.05) due to its higher overall area and area for milk production, larger herd, higher amount of animals in milk, increased production (liters of milk per day) and higher productivity (liters of milk / animal-1.day-1) and largest area of grassland and area forage production of fodder. For socioeconomic variables, the groups did not differ (P>0.05). The results indicated that the highest efficiency of Group 2 relates to the combination of factors like herd production strategies for use of nutritional management and forage surface. In the second article, the DPS were grouped (hierarchical cluster) from average prices received by producers in 2014, compared to the average price in the state (US$ 0.92) paid by the liter of milk in the same year. Identified the groups, there was a medium analysis of the structural characteristics, management and milk quality. The G1 group was defined by farmers who received lower values (R$ 0.88 milk liter-1) the average of the state and G2, for producers who received (R$ 0.98 milk liter-1), which is higher than the State average. It was found that the G1 group which had lowest structural and productive characteristics had the highest income when compared to the G2. The G2 group had higher total area dedicated to milk production, larger herd and milking cows as well as increased daily production and productivity liters/cow-1.day-1, indicating more specialized flock. As for the milk quality produced, the G1 and G2 groups were statistically similar. Among the DPS analyzed the best value paid by the milk marketed was directly linked to the productive and structural issues, which resulted, among other factors, to a greater volume of milk produced. It was verified that the milk production in a region is from a set of DPS with different profiles with respect to their structure and management strategies. The results of these characteristics also reflect in different prices of milk depending of the production variation during the year. The study of the DPS diversity, considering the production scale, management and socio-economic aspects, enabled us to understand the diversity of the DPS driving strategies and the different results arising from these strategies. In addition, it allowed to identified variables that most highlight the differences among the groups. From these results, it is possible to adjust more easily the approach of the technical assistance offered to farmers, whether public or private.