The Dairy Sector of Brazil: A Country Study

Agribusiness, International Development, International Relations/Trade,

SUBSTITUIÇÃO DE FONTE DE AMIDO POR FIBRA SOLÚVEL EM DETERGENTE NEUTRO NA DIETA DE VACAS

Conduziu-se este trabalho com o objetivo de avaliar os efeitos da substituição do milho por polpa cítrica sobre o desempenho de vacas leiteiras. Utilizaram-se nove vacas (três canuladas no rúmen) da raça Holandesa, pluríparas, com 80 ± 24 dias de lactação e produção média diária de 20 ± 0,58 kg de leite. Os animais foram confinados em tie stall, com cocho e bebedouro individuais. Utilizou-se um quadrado latino 3 x 3. Os períodos experimentais tiveram duração de 21 dias, sendo 14 de adaptação e sete de coleta. Os tratamentos foram constituídos de 100% milho grão (MG), 50% milho grão e 50% polpa cítrica (MP) e 100% polpa cítrica (PC). Não houve diferenças significativas na ingestão dos nutrientes (MS, MO, FDN, FDA, PB, amido) entre dietas. O tratamento com polpa cítrica produziu maior proporção de acetato em relação aos demais, bem como maior relação acetato/propionato. As médias de pH situaram-se entre 5,86 e 7,35. As médias de N-NH3 apresentaram maiores diferenças nos tempos 2 e 3 horas pós-alimentação. Não houve diferenças entre as dietas para produção total de leite corrigido para 4% de gordura, proteína, lactose, extrato seco total (EST) e nitrogênio uréico do leite (NUL). Palavras-chave: ingestão de nutrientes; milho grão; polpa cítrica; vacas leiteiras

Proposal of a non-linear model to adjust in vitro gas production at different incubation times

This work aims to propose a new model named Gompertz-Von Bertalanffy bicompartmental (GVB), a combination of the models Gompertz and Von Bertalanffy. The GVB models is applied to fit the kinetic curve of cumulative gas production (CGP) of four foods (SS – sunflower silage; CS – corn silage; and the mixtures 340SS – 660 gkg-1 of corn silage and 340 gkg-1 of sunflower silage; and 660SS – 340 gkg-1 of corn silage and 660 gkg-1 of sunflower silage). The GVB fit is compared to models Logistic-Von Bertalanffy bicompartmental (LVB) and bicompartmental logistic (BL). All the process studied employed the semi-automatic “in vitro” technique of producing gases used in ruminant nutrition. The gas production readout was performed at times 2, 4, 6, 8, 10, 12, 15, 19, 24, 30, 48, 72, and 96 h. The data generated were used to estimate the models’ parameters by the least squared method with the iterative Gauss-Newton process. The data fit quality of the models was verified using the adjusted coefficient of determination criterion (), mean residual square (MRS), Akaike information criterion (AIC), and mean absolute deviation (MAD). Among the analyzed models, the LVB model presented the best quality of fit evaluators for CS. In contrast, the GVB model showed better quality of fit to describe CGP over time for 340SS, 660SS, and SS, presenting the highest values of () and the lowest values of MSR, AIC, and MAD

Avaliação de Alimentos pela Técnica Semi-Automática In Vitro de Produção de Gases: uma revisão

Procurou-se mostrar, no presente estudo, a aplicação da técnica semi-automática in vitro de produção de gases para avaliação de alimentos. � uma técnica não invasiva que reproduz eficientemente o modelo de fermentação da microbiota do trato digestório. Além disso é barata, acessível, de fácil execução e pode, com mínimos recursos, ser implantada com rapidez e eficiência. Apesar de recente, essa técnica vem se difundindo com boa aceitação para simulação laboratorial da cinética da digestão e da qualidade dos alimentos

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

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