Tratamento de sementes sobre a germinação, o vigor e o desenvolvimento do trigo

Seed treatment (ST) is an important tool in the management of diseases in the wheat crop, although few products are indicated for this purpose. The research must carry out studies related to the ST effectiveness using different products and the possible phytotoxic effect. The aim of this study was to perform physiological tests on seeds and evaluate the development of two wheat cultivars (BRS Guamirim and TBIO Toruk), submitted to 13 different ST. Germination and vigor were analyzed for each treatment. The treated seeds were also sown to the field, where the field emergence and fresh root and shoot mass were determined. In the germination test, the seeds treated azoxystrobin and Bacillus amyloliquefaciens presented a decrease in the cultivar TBIO Toruk. Vigor analyzes indicated differences between treatments in the two cultivars, and the dimethomorph treatment presented the lowest vigor for TBIO Toruk and azoxystrobin for the BRS Guamirim. The seeds submitted to the Carboxamide A treatment showed seedlings the highest values of fresh root and shoot mass. The results presented allow the selection of products with low or no phytotoxicity for incorporating in wheat disease management.O tratamento de sementes (TS) é uma importante ferramenta no manejo de doenças na cultura do trigo, embora poucos produtos sejam indicados para esse fim. Por isso, a pesquisa deve realizar trabalhos relacionados à efetividade de diferentes produtos no TS e também o possível efeito fitotóxico. Os objetivos deste trabalho foram realizar testes fisiológicos em sementes e avaliar o desenvolvimento de duas cultivares de trigo (BRS Guamirim e TBIO Toruk), submetidas a 13 diferentes tratamentos de sementes. Para cada tratamento foram analisados a germinação e o vigor. As sementes tratadas também foram semeadas a campo, onde se determinou a emergência a campo e massa fresca de raiz e parte aérea. As sementes tratadas com azoxistrobina e Bacillus amyloliquefaciens resultaram em decréscimo na germinação da cultivar TBIO Toruk. Na análise do vigor houve diferença entre os tratamentos testados para as duas cultivares, sendo que houve redução do vigor em sementes tratadas com dimetomorfe para TBIO Toruk e azoxistrobina para a cultivar BRS Guamirim. As sementes submetidas ao tratamento Carboxamida A resultaram em plântulas com maiores valores de massa fresca de raiz e parte aérea. Os resultados apresentados permitem a seleção de produtos com baixa ou ausência de fitotoxidade para incorporação no manejo de doenças no trigo

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