Characterization of milk production systems in the county of Alegrete, RS, Brazil, based on productive indexes

This study aimed to characterize dairy production systems in Alegrete, RS, Brazil, based on productive indices, management practices, and technification. The present study was conducted on 43 farms distributed in 22 localities of the county. The collection of data on milk production systems was carried out through visits to the properties, using a semi-structured guide questionnaire. The data obtained with the questionnaires were tabulated in Excel and with the aid of the IBM SPSS Statistics 20.0 software, through multivariate statistics, data were submitted to main component analysis (MCA) and hierarchical clusters analysis (HCA), allowing the division of 43 production units into homogeneous groups. The studied variables were summarized through the MCA in two main components (1 and 2), which clarified 71.53% of the explained variance. The alpha-Cronbach values observed for the two main components totaled 0.977, a result that confirms the reliability of the questionnaire used and reveals the high correlation between the answers obtained. From the hierarchical classification analysis, the dataset of the 43-farm studied was reduced to six groups (G1, G2, G3, G4, G5, and G6). The quadrants obtained from the insertion of the axes of the main components 1 and 2 allowed the interpretation of the groups of systems, according to the characteristics related to milk production. G2 presented the highest number of farms of the six systems formed, representing 41.86% of the establishments studied. These are characterized by being a more productive farm, an average 881-1 L day, with greater technological adoption of production and greater area destined to milk production, corresponding to the average of 78 hectares. The productive aspects that define the characteristics of milk production systems in the county were related to the structure of the herd, pasture area, daily production, disposal criteria, and milking management. The main differences found in the different groups are related to the productive indexes, suggesting that the technical assistance and rural extension actions in the dairy production systems in the county of Alegrete should be directed according to the individual need of each group formed.

Calf rearing profile in agrarian reform settlements in Santana of Livramento, RS, Brazil / Perfil da criação de bezerras nos assentamentos de reforma agrária de Santana do Livramento, RS, Brazil

In agrarian reform settlements there are obstacles in the rearing of calves; however, the main aspects that cause this obstacle are not known. Thus, the aim of this study was to diagnose the main obstacles in the management of these dairy heifers to target extension actions.For this, semi-structured guided questionnaires were applied to 40 families from 20 settlements. The questionnaire contained questions about the characteristics of the family, property and general management, feeding and sanitation of calves. The data were tabulated in Excel, transformed into variables and studied by means of multivariate analysis of main components after the farms were grouped into clusters according to similarity. The variables that most affected the heifer rearing system in dairy farms in the agrarian reform settlements of Santana do Livramento, RS, Brazil, were related to diet and occurrence of diarrhoea in calves. The 40 farms studied were grouped into six clusters, which differed especially in relation to who was responsible for the management of the heifers inside the property, regarding the infrastructure for the calves, time in milk production and concern regarding the care in the breeding of the calves. Based on these differences between the clusters being correctly identified, the technicians who work in these farms can establish strategies to work with settled families, emphasising the points where the obstacles are known to trigger the survival of heifers that will replace the dairy herds.

Em assentamentos da reforma agrária há entraves na criação das bezerras, porém, não se conhece os principais aspectos que causam esses entraves. Assim, objetivou-se com este estudo, diagnosticar os principais entraves no manejo destas bezerras leiterias para direcionamento de ações de extensão. Para tal, foram aplicados questionários guia semiestruturados à 40 famílias de 20 assentamentos. O questionário continha questões sobre as características da família, propriedade e manejo geral, alimentar e sanitário das bezerras. Os dados foram tabulados em Excel, transformados em variáveis e estudados por meio de análise multivariada de componentes principais. Após as propriedades foram agrupadas em clusters segundo a similaridade. As varáveis que mais afetam o sistema de criação de bezerras em propriedades leiteiras nos assentamentos da reforma agrária de Santana do Livramento, RS, estão relacionadas à alimentação e ocorrência de diarreia nas bezerras. As 40 propriedades estudadas foram agrupadas em seis clusters, que se diferiram especialmente em relação à com quem fica a responsabilidade pelo manejo das bezerras dentro da propriedade, quanto à infraestrutura destinada às bezerras, tempo na produção de leite e preocupação em relação aos cuidados na criação das bezerras. Com base nestas diferenças existentes entre os clusters e corretamente identificadas, os técnicos que atuam nestas propriedades poderão estabelecer estratégias para trabalhar com as famílias assentadas, enfatizando os pontos onde sabidamente estão os entraves para deslanchar a criação das bezerras que irão substituir as matrizes leiteiras.

Milk production units characterized by sanitary, nutritional and infrastructure variables in the northwest, center western and southwest mesoregions of Rio Grande do Sul, Brazil / Caracterização de unidades de produção de leite caracterizadas por variáveis ​​sanitárias, nutricionais e de infraestrutura nas mesorregiões noroeste, centro ocidental e sudoeste do Rio Grande do Sul, Brasil

Evaluating and characterizing production systems using farm characteristics allows the diagnosis of failing points. This diagnosis can be used to improve the productive and zootechnical indices. Little is known about the milk production systems in the state of Rio Grande do Sul, therefore, the aim of this study was to characterize the milk production systems of the Northwest, Center Western and Southwest mesoregions of Rio Grande do Sul, considering the infrastructure, milk handling, milk quantity and composition, and nutritional intake of the cattle. To conduct this study, 38 Milk Production Units (MPUs) registered at the Municipal Secretaries of Agriculture and Emater/Ascar-RS were randomly selected. After being randomly selected, the dairy farms were visited and a semi-structured guide questionnaire was applied and milk samples were collected from expansion tanks. The milk was analyzed for somatic cell counts (SCC) and total bacterial counts (TBC). Data were evaluated through principal component analysis and cluster analysis. Multivariate analysis allowed the investigated variables to be reduced into two main components (CP1 and CP2). These two showed eigenvalues greater than 1 (alpha> 1) and together explained 55.05% of the characteristics variability of the 38 MPUs studied. CP1 contemplated productive capacity and factors related to nutritional management of the MPUs, interfering directly with reproductive performance. CP2 comprised milk handling and daily production. Using these main variables, the data set generated from the 38 MPUs studied were adjusted and classified into five groups (G1, G2, G3, G4, and G5). The characteristics of these groups differed statistically especially in infrastructure and nutritional management of the cattle. Due to their particularities, each of these five groups of MPUs requires strategic technical interventions to improve their productive indexes. KEYWORDS: Management. Production Systems. Milk Quality. INTRODUCTIONThe milk activity is developed in milk production units (MPU) distributed throughout the Brazilian territory (WILLERS et al., 2014). These units have different characteristics regardless of the technological level adopted. The daily production and monthly remuneration play a fundamental role in the generation of jobs and income, moving the economy of the regions where they are inserted. However, the activity is developed in the MPUs under production systems with a wide range of organizational, technological, and production scales (BODENMÜLLER FILHO et al., 2010; WINCK; THALER NETO, 2012). Thus, each productive system has peculiar characteristics, which directly affect the productive efficiency of the MPUs. Rio Grande do Sul is the state in Brazil that produces the second largest volume of milk. In this Received: 08/04/19 Accepted: 30/12/19

Avaliar e caracterizar sistemas de produção utilizando as características das propriedades permite diagnosticar os pontos falhos, visando melhorar os índices produtivos e zootécnicos, desse modo pouco se sabe sobre os sistemas de produção de leite no estado do Rio Grande do Sul. Assim, objetivou-se com este estudo caracterizar os sistemas de produção de leite das mesorregiões Noroeste, Centro-Oeste e Sudoeste do Rio Grande do Sul, considerando a infra-estrutura, a quantidade, qualidade e composição do leite e o manejo nutricional dos rebanhos. Para conduzir este estudo, 38 Unidades de Produção de Leite (UPLs) registradas nas Secretarias Municipais de Agricultura e Emater/Ascar-RS foram selecionadas aleatoriamente. Depois, as fazendas leiteiras foram visitadas, um questionário semi-estruturado foi aplicado e amostras de leite do tanque foram coletadas. O leite foi analisado quanto à contagem de células somáticas (CCS) e à contagem bacteriana total (CBT). Os dados foram avaliados através de análise de componentes principais e análise de cluster. A análise multivariada permitiu que as variáveis investigadas fossem reduzidas em dois componentes principais (CP1 e CP2). Estes apresentaram autovalores maiores que 1 (alfa> 1) e juntos explicaram 55,05% da variabilidade das características das 38 UPLs estudadas. O CP1 contemplou a capacidade produtiva e os fatores relacionados ao manejo nutricional das UPLs, interferindo diretamente no desempenho produtivo. CP2 compreendeu manipulação de leite e produção diária. Utilizando essas variáveis principais, os dados gerados a partir das 38 UPLs estudadas foram ajustados e estas classificadas em cinco grupos (G1, G2, G3, G4 e G5). As características desses grupos diferiram estatisticamente, especialmente em infraestrutura e manejo nutricional do rebanho. Devido a suas particularidades, cada um desses cinco grupos de MPUs requer intervenções técnicas estratégicas para melhorar seus índices produtivos.