Performance of Young Nellore Bulls Grazing Marandu Grass Pasture at Different Heights

Brazil is one of the largest beef cattle producers in the world with approximately 200 M head. The Industry relies predominantly on warm-season grass pastures, with approximately 90% of animals finished on pastures. One of the main factors for the intensification of animal production systems based on pasture is appropriate management. Adjustment of stocking rate to maintain optimum forage allowance is essential. Studies on forage allowance have resulted in a better understanding of the response of forage crops and animals to changes in grazing intensity. The purpose of this study was to evaluate management strategies for beef cattle systems grazed at different heights (15, 25 and 35 cm) in Brachiaria brizantha cv. Marandu in terms of pasture production and animal performance

Effect of Grazing Height on Marandu Pasture Production and Performance of Soybean Grain-Supplemented Nellore Bulls

The Brazilian beef cattle industry is primarily based on the use of pastures. Grasses belonging to the genus Brachiaria are extremely important, regardless of whether the production system used is intensive or extensive. Appropriate management of the system is vital for obtaining a high efficiency of resource usage. Adjustment of stocking rate to maintain optimum forage allowance and feeding of supplements are strategies for achieving these aims. Feeding of concentrates on pasture can result in increased carrying capacity and higher weight gains over unsupplemented systems. The purpose of this study was to evaluate the effects of 3 different grazing heights on pasture production and performance of young Nellore bulls grazing Brachiaria brizantha cv. Marandu. To enable increased pasture carrying capacity and support additional liveweight gains, the bulls were supplemented with soybean grain as an unconventional lipid source

A construção do mercado para o café em Alto Paraíso de Goiás.

Este estudo apresenta os principais resultados da análise de como habitantes de Alto Paraíso de Goiás estão buscando alternativas para o desenvolvimento sustentável do município, por meio da implantação, pela Embrapa, de projeto relativo ao resgate do café. A mineração e as atividades agropecuárias foram exercidas na região até os anos 60, quando a atividade principal passou a ser o turismo, a partir da criação do Parque Nacional da Chapada dos Veadeiros e da inauguração de Brasília. Em 2000, o fluxo de turistas decaiu devido a problemas relativos à saúde pública, acarretando estagnação da economia local. Nos últimos anos, produtores familiares despertaram para a existência, ali, de um café que pode ser comercializado em nichos de mercado de grãos especiais: orgânicos e de origem definida, e buscaram, na Embrapa, o desenvolvimento de projeto de pesquisa. O estudo na região revelou que por meio do desvelamento de valores - história, cultura e tradições - é possível estabelecer estratégia mais eficiente de busca de mercado, a partir da experiência revelada no trabalho concreto e na cultura dos produtores. E que o desenvolvimento rural deve ser buscado por meio do desenvolvimento de atividades da nova ruralidade e da aplicação de abordagem territorial de desenvolvimento

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

2 nd Brazilian Consensus on Chagas Disease, 2015

Abstract Chagas disease is a neglected chronic condition with a high burden of morbidity and mortality. It has considerable psychological, social, and economic impacts. The disease represents a significant public health issue in Brazil, with different regional patterns. This document presents the evidence that resulted in the Brazilian Consensus on Chagas Disease. The objective was to review and standardize strategies for diagnosis, treatment, prevention, and control of Chagas disease in the country, based on the available scientific evidence. The consensus is based on the articulation and strategic contribution of renowned Brazilian experts with knowledge and experience on various aspects of the disease. It is the result of a close collaboration between the Brazilian Society of Tropical Medicine and the Ministry of Health. It is hoped that this document will strengthen the development of integrated actions against Chagas disease in the country, focusing on epidemiology, management, comprehensive care (including families and communities), communication, information, education, and research

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost