Ministry of Agriculture

About: Ministry of Agriculture is a government organization based out in Rio de Janeiro, Brazil. It is known for research contribution in the topics: Biology & Chemistry. The organization has 1153 authors who have published 1189 publications receiving 14442 citations.

CORRELAÇÃO ENTRE CONTAMINAÇÃO GASTROINTESTINAL E PRESENÇA DE Salmonella spp. EM CARCAÇAS DE FRANGO INDUSTRIALIZADAS NO RIO GRANDE DO SUL

Abstract: O presente trabalho teve como objetivo avaliar a incidencia de contaminacao gastrointestinal visivel em carcacas industrializadas de frango na etapa de evisceracao e verificar a relacao com a presenca de Salmonella spp . em carcacas apos o sistema de pre-resfriamento. O estudo foi realizado em um abatedouro sob inspecao federal, localizado no Estado do Rio Grande do Sul, no periodo de julho de 2011 a abril de 2012. Foram avaliados registros de ocorrencia de contaminacao gastrointestinal visivel, em um total proximo de 8,5x10 7 carcacas de aves, e a incidencia de Salmonella spp. em 357 amostras. O resultado medio de contaminacao gastrointestinal visivel foi de 3,37% ± 0,61, enquanto o percentual de prevalencia de Salmonella spp. foi de 0,57% ± 0,83. Atraves do teste de correlacao, utilizando o programa Statistica 7.0 (p<0,05), foi possivel constatar que nao houve relacao significativa entre as variaveis estudadas (p= 0,4959). Os resultados apontaram que as operacoes de abate e processamento de aves foram realizadas de forma sanitaria, atraves do controle das Boas Praticas de Fabricacao (BPF), o que propiciou baixos indices de presenca de Salmonella spp. nas amostras.

Cattle trade between and within biomes in the state of Mato Grosso, Brazil

Abstract: The analysis of animal movement patterns may help identify farm premises with a potentially high risk of infectious disease introduction. Farm herd sizes and bovine movement data from 2007 in the state of Mato Grosso, Brazil, were analyzed. There are three different biomes in Mato Grosso: the Amazon, Cerrado, and Pantanal. The analysis of the animal trade between and within biomes would enable characterization of the connections between the biomes and the intensity of the internal trade within each biome. We conducted the following analyses: 1) the concentration of cattle on farm premises in the state and in each biome, 2) the number and relative frequency of cattle moved between biomes, and 3) the most frequent purposes for cattle movements. Twenty percent (20%) of the farm premises had 81.15% of the herd population. Those premises may be important not only for the spread of infectious diseases, but also for the implementation of surveillance and control strategies. Most of the cattle movement was intrastate (97.1%), and internal movements within each biome were predominant (88.6%). A high percentage of movement from the Pantanal was to the Cerrado (48.6%), the biome that received the most cattle for slaughter, fattening and reproduction (62.4%, 56.8%, and 49.1% of all movements for slaughter, fattening, and reproduction, respectively). The primary purposes for cattle trade were fattening (43.5%), slaughter (31.5%), and reproduction (22.7%). Presumably, movements for slaughter has a low risk of disease spread. In contrast, movements for fattening and reproduction purposes (66.2% of all movements) may contribute to an increased risk of the spread of infectious diseases.

Bacteria and yeasts associated to Colonial cheese production chain and assessment of their hydrolytic potential

Abstract: Different types of microorganisms are important in cheese-making because of the contributions their metabolism offers during the process. Few microorganisms present in Colonial cheese are known, in addition to the ones that are introduced to kick-start the processes or the ones that are associated with infections or poisonings. This study aimed to identify, by MALDI-TOF and/or DNA sequencing, the bacteria and yeasts isolated from samples collected in the main stages of Colonial cheese production, i.e., a type of cheese produced in the southern region of Brazil. The lytic capacity of these microorganisms at 5 °C and 30 °C was also evaluated. The 58 bacterial strains were distributed in 10 species among the genera Bacillus, Citrobacter, Klebsiella, Lactococcus, Paenibacillus, Staphylococcus and Raoutella. From the 13 yeasts strains analyzed, three species were identified as following: Candida pararugosa; Meyerozyma guilliermondii; and Rhodotorula mucilaginosa. In three yeasts isolates it was possible to identify only the genus Candida sp. and Trichosporon sp. The species L. lactis (48%) and M. guilliermondii (46%) were, respectively, the predominant bacteria and yeasts species isolated. The highest microbial lytic activity observed was at 30 °C. Lipase activity on isolates was proportionally more observed with yeasts and proteolytic activity with bacteria. Lower caseinase and lipase activity was observed at 5 °C, demonstrating the importance of refrigeration in controlling microbial activity. This research highlighted the cultivation of some microorganisms that are part of the Colonial cheese microbiota as well as that several of them can hydrolyze various compounds present in milk and that could be associated with its maturation or, in uncontrolled circumstances, could be the cause of product deterioration.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

Abstract: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.