NÍVEIS DE RACTOPAMINA E SUA INFLUÊNCIA SOBRE O DESEMPENHO E CARACTERÍSTICAS DE CARCAÇA DE SUÍNOS EM TERMINAÇÃO

Aumentar a quantidade de carne na carcaça de suínos tem sido o objetivo não somente da indústria, como também do produtor de suínos, uma vez que melhora a rentabilidade e diminui os custos de produção. Neste sentido, objetivou-se com essa pesquisa avaliar o desempenho e as características de carcaça de suínos em terminação suplementados com diferentes níveis de ractopamina na dieta. Foram utilizados 60 suínos (30 machos castrados e 30 fêmeas), com peso inicial médio de 75,0 Kg alojados em baias de piso parcialmente ripado. O delineamento experimental foi em blocos casualizados com três níveis de ractopamina – 0, 5 e 10 ppm, totalizando três tratamentos e dez repetições, com dois animais (um macho e uma fêmea) por baia (parcela experimental). Foram realizadas análises de desempenho (peso final, ganho de peso médio diário, consumo de ração médio diário e conversão alimentar) e de qualidade de carcaça (rendimento de carcaça, rendimento de carne na carcaça, espessura de toucinho e profundidade de lombo). A suplementação de 10 ppm de ractopamina na dieta de suínos em terminação aumentou o rendimento de carne na carcaça e reduziu a espessura de toucinho. Assim, conclui-se que a suplementação de 10 ppm de ractopamina na dieta de suínos em terminação melhora as características de carcaça

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

Digestible and metabolizable energy of crude glycerin for finishing pigs

The aim of this study was to determine the values of apparent digestible energy (DE) and metabolizable energy (ME) for crude glycerin derived of biodiesel based on pork fat for finishing pigs. The diets consisted of a basal diet and four levels of crude glycerin (0, 5, 10 and 15%). Twelve pigs were housed individually in metabolic cages and after seven days of adaptation, total collections of urine and feces for four consecutive days were performed. Gross energy (GE) of crude glycerin, diets, urine and fecal samples from each animal was determined. The crude glycerin used in this experiment presented 74.74% glycerin and 6,500 kcal kg-1 gross energy. The values of digestible energy (DE) and metabolizable energy (ME) were estimated by difference in the DE, and content of the basal diet was subtracted from the test diets containing the ingredient. The amount of GE, DE and ME for finishing pigs was 6,500, 5,839 and 5,509 kcal kg-1, respectively, with a coefficient of 91.0% of DE and 94.0% of ME. The energy of crude glycerin is based on the levels of fatty acids and GE depends on the concentration of fatty acids and glycerin, ME being a percentage of GE averaging is 84.75%.

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data