Análise de antimicrobianos e análise físico-química do leite na região central do estado de Rondônia/ Antimicrobial analysis and physical chemical analysis of milk in the central region of the state of Rondônia

O Twin Sensor Test ou teste rápido é utilizado para detecção simultânea de resíduos de antimicrobianos, por exemplo, dos grupos betalactâmicos e tetraciclinas em leite. Logo, define-se a quantidade de antimicrobianos que poderá ser detectado em determinado lote. Neste estudo, sobre a importância do acompanhamento do leite para garantir a qualidade do leite e identificar o percentual de nutrientes e resíduos de antibióticos, foram coletadas 500 amostras de leite, oriundos dos municípios de Ouro Preto D’Oeste, Teixeirópolis, Nova União, Urupá e Monte Negro, estado de Rondônia. Deste total de amostras coletadas, foram selecionadas ao acaso120 para serem submetidas ao Twin Sensor Test. As 500 amostras de leite foram submetidas a análise físico-química para determinação dos teores de gordura, proteína, lactose, sólidos totais e extrato seco desngordurado. O estudo da qualidade do leite na Região Central de Rondônia apresentou menos de 1% das amostras contendo resíduos de antimicrobianos, 46,6% apresentavam contagem de células somáticas (CCS) acima de 4X105 células/mL e 39,6% contagem bacteriana total (CBT) acima de 2x105 UFC/mL A análise físico-química do leite revelou que 8,6% das amostras apresentavam valores inferiores de sólidos totais,  10,8% de gordura, 4,20% de proteína, 4,60% de lactose e 7,60% de ESD, portanto, não atendendo os valores mínimos necessários estabelecidos na IN 76 de 26 de novembro de 2018

Desempenho zootécnico e financeiro de bovinos confinados com acesso a diferentes áreas de sombreamento e a pleno sol / Zootecnical and financial development of confined bovines with access to different areas of shading and exposed to the sun

Estudos indicam que o fornecimento de sombra para bovinos de corte durante o período de confinamento favorece o aumento de produtividade, mesmo em zebuínos, como o nelore. Desta forma, foram avaliados o desempenho zootécnico e financeiro de bovinos mestiços machos não-castrados confinados no estado de Rondônia entre os meses de junho e setembro de 2017. Os animais foram apartados em três lotes de 130 animais cada. Os lotes identificados como F1 e F2 receberam, respectivamente, acesso à área de 400m² e 200m² de malha de sombrite com 80% de bloqueio solar. O lote F3, escolhido como testemunha, foi mantido a pleno sol. O período de confinamento foi de 89 dias, com fornecimento controlado de dieta total composta por ureia, núcleo mineral aditivado, farelo de soja, soja em grão moída, milho em grão seco moído e silagem de planta inteira de milho. As variáveis climáticas obtidas na estação meteorológica local e junto ao Instituto Nacional de Meteorologia, indicaram que os animais sofreram estresse térmico durante o período de confinamento. O acesso à maior área de sombra por animal melhorou o índice de rendimento de carcaça, mas ocasionou queda no consumo e piorou o desempenho de eficiência biológica. Como consequência, o custo por arroba produzida dos lotes com acesso à sombra foi superior quando comparado ao lote mantido a pleno sol, tornando economicamente inviável a implantação da estrutura de sombrite sob essas circunstâncias. O investimento feito foi viabilizado diante de uma perspectiva ética, considerando a melhoria no conforto térmico proporcionada pelo acesso à sombra no decorrer do período de confinamento, altamente estressante para os animais. Do ponto de vista comercial, o investimento foi viabilizado quando consideradas as cobranças crescentes do mercado consumidor por melhorias no bem-estar animal durante toda a cadeia produtiva

Qualitative characteristics of meat from confined crossbred heifers fed with lipid sources

Lipids have been used in ruminant feed to replace high amounts of grain for increasing the diet energy density, performance and meat quality. This study evaluated the qualitative characteristics of meat from feedlot heifers fed with sources of lipid supplements. Twenty-one crossbred heifers (1/4Nelore × 1/4Santa Gertrudis × 1/2Braunvieh) were used. Each heifer received 60 % forage with a base of corn silage and 40 % concentrate, resulting in 5.8 % lipid content in the total diet. The following sources of lipids were used: soybeans, protected fat and soybean oil. There were no differences on physical characteristics of meat samples from heifers fed with the lipid sources. Soybeans increased the concentration of linoleic acid, content of polyunsaturated fatty acid and activity of the Δ9-desaturase C16 enzyme in the Longissimus muscle. The use of soybean oil in the diet increased the oleic acid, monounsaturated fatty acid, total cis- and trans-fatty acids (C18:0) and the activity of the Δ9-desaturase C16 enzyme in the subcutaneous fat. Diets with soybean grain had greater deposition of linoleic and linolenic acids than diets with fat protected and greater presence of these essential fatty acids are associated to a better composition and meat quality

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

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