12 research outputs found
Comparison of methods for the evaluation of adaptability and stability for yield in cotton genotypes
O objetivo deste trabalho foi comparar diferentes mĂ©todos de estimação da adaptabilidade e estabilidade produtiva em 17 genĂłtipos de algodoeiro avaliados em 23 ambientes do Cerrado brasileiro. Os efeitos de genĂłtipos e ambientes e a interação genĂłtipos x ambientes foram significativos. Os modelos da ecovalĂŞncia e AMMI indicaram a cultivar BRS Cedro como a mais estável, enquanto as cultivares Delta Penta e BRS IpĂŞ foram identificadas como as mais instáveis; nenhuma delas esteve entre as mais produtivas. De acordo com o mĂ©todo de Eberhart & Russell, Lin & Binns e Annicchiarico, as cultivares BRS 269 – Buriti e FMT 701 e o genĂłtipo CNPA GO 2001-999 foram os mais indicados para plantio no Cerrado, e se destacaram entre as cinco mais produtivas na mĂ©dia dos ambientes. A identificação de adaptabilidades especĂficas, proporcionada pela análise AMMI, Ă© de grande relevância no estudo do comportamento dos genĂłtipos. Pelo conjunto de informações obtidas e pela facilidade de uso e interpretação, recomenda-se o emprego do mĂ©todo de Lin & Binns , que pode ser complementado pela análise AMMI.The objective of this work was to compare different methods used to estimate adaptability and stability of 17 cotton genotypes evaluated in 23 locations of the Brazilian savannah. Genotype and environment effects and genotype x environment interaction were significant. According to ecovalence and AMMI models, cultivar BRS Cedro showed the best stability. Varieties Delta Penta and BRS IpĂŞ were among the most unstable genotypes, but not among the most productive. Using the methods of Eberhart & Russel, Lin & Binns and Annicchiarico, genotypes BRS 269 – Buriti, FMT 701 and CNPA GO 2001-999 were the most stable and among the five most productive on average. The evaluation of the specific adaptabilities provided by the AMMI analysis is of great importance in the study of the behavior of genotypes. The amount of information generated and the facilities of interpretation favors Lin & Binns method, which can be complemented by an AMMI analysis
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
Comparação de métodos para avaliar a adaptabilidade e estabilidade produtiva em algodoeiro Comparison of methods for the evaluation of adaptability and stability for yield in cotton genotypes
O objetivo deste trabalho foi comparar diferentes mĂ©todos de estimação da adaptabilidade e estabilidade produtiva em 17 genĂłtipos de algodoeiro avaliados em 23 ambientes do Cerrado brasileiro. Os efeitos de genĂłtipos e ambientes e a interação genĂłtipos x ambientes foram significativos. Os modelos da ecovalĂŞncia e AMMI indicaram a cultivar BRS Cedro como a mais estável, enquanto as cultivares Delta Penta e BRS IpĂŞ foram identificadas como as mais instáveis; nenhuma delas esteve entre as mais produtivas. De acordo com o mĂ©todo de Eberhart & Russell, Lin & Binns e Annicchiarico, as cultivares BRS 269 - Buriti e FMT 701 e o genĂłtipo CNPA GO 2001-999 foram os mais indicados para plantio no Cerrado, e se destacaram entre as cinco mais produtivas na mĂ©dia dos ambientes. A identificação de adaptabilidades especĂficas, proporcionada pela análise AMMI, Ă© de grande relevância no estudo do comportamento dos genĂłtipos. Pelo conjunto de informações obtidas e pela facilidade de uso e interpretação, recomenda-se o emprego do mĂ©todo de Lin & Binns , que pode ser complementado pela análise AMMI.The objective of this work was to compare different methods used to estimate adaptability and stability of 17 cotton genotypes evaluated in 23 locations of the Brazilian savannah. Genotype and environment effects and genotype x environment interaction were significant. According to ecovalence and AMMI models, cultivar BRS Cedro showed the best stability. Varieties Delta Penta and BRS IpĂŞ were among the most unstable genotypes, but not among the most productive. Using the methods of Eberhart & Russel, Lin & Binns and Annicchiarico, genotypes BRS 269 - Buriti, FMT 701 and CNPA GO 2001-999 were the most stable and among the five most productive on average. The evaluation of the specific adaptabilities provided by the AMMI analysis is of great importance in the study of the behavior of genotypes. The amount of information generated and the facilities of interpretation favors Lin & Binns method, which can be complemented by an AMMI analysis