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| Registros recuperados : 328 | |
| 105. |  | JUSTINO, L. F.; BATTISTI, R.; STONE, L. F.; HEINEMANN, A. B. Avaliação de doses e datas de aplicação de nitrogênio no feijão-comum por meio de modelo de simulação. In: CONGRESSO NACIONAL DE PESQUISA DE FEIJÃO, 13., 2021, Goiânia. Conectividade tecnológica, intensificação sustentável: resumos. Brasília, DF: Embrapa; Santo Antônio de Goiás: Embrapa Arroz e Feijão, 2021. p. 172. Evento online.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 106. | | STONE, L. F.; MOREIRA, J. A. A.; SILVEIRA, P. M. da; HEINEMANN, A. B. Curva de retenção da água do solo: centrifugação versus câmara de pressão de Richards. In: CONGRESSO BRASILEIRO DE ENGENHARIA AGRÍCOLA, 38., 2009, Juazeiro, Petrolina. Planejamento da bacia hidrográfica e o desenvolvimento da agricultura: anais. Juazeiro, Petrolina: UNIVASF: Embrapa Semi-árido: CEFET: SBEA, 2009. 1 CD-ROM.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 107. | | VAN OORT, P. A. J.; ZHANG, T.; VRIES, M. E. de; HEINEMANN, A. B.; MEINKE, H. Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agricultural and Forest Meteorology, Amsterdam, v. 151, n. 12, p. 1545-1555, Dec. 2011.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 112. | | SILVA, W. da; VILELA, D.; COBUCCI, T.; HEINEMANN, A. B. e outros. Avaliação da eficiência de herbicidas no controle de plantas daninhas em alfafa. CNPq/Embrapa Gado de Leite - C.P. 179 - CEP 75375-000 - Santo antopnio de Goiás,, GO. Embrapa Gado de Leite, Dom Bosco - 36038-330 - Juiz de Fora, MG, vilela@cnpgl.embrapa.br, alexbh@cnpaf.embrapa.br. Embrapa Arroz e Feijão, Caixa Postal 179- 74001 -970- Goiánia, GO. cobucci@cnpaf.embrapa.br. Ciência e Agrotecnologia. Lavras, v.28, n.4, p.729-735, jul./ago. / 2004.| Biblioteca(s): Embrapa Rondônia. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Arroz e Feijão. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
17/07/2022 |
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Data da última atualização: |
22/08/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
HEINEMANN, A. B.; COSTA-NETO, G; FRITSCHE-NETO, R.; MATTA, D. H. da; FERNANDES, I. K. |
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Afiliação: |
ALEXANDRE BRYAN HEINEMANN, CNPAF; GERMANO COSTA-NETO, Cornell University, Ithaca-NY; ROBERTO FRITSCHE-NETO, ESALQ; DAVID HENRIQUES DA MATTA, UFG; IGOR KUIVJOGI FERNANDES, UFG. |
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Título: |
Enviromic prediction is useful to define the limits of climate adaptation: a case study of common bean in Brazil. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Field Crops Research, v. 286, 108628, Oct. 2022. |
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ISSN: |
0378-4290 |
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DOI: |
https://doi.org/10.1016/j.fcr.2022.108628 |
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Idioma: |
Inglês |
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Conteúdo: |
Ongoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our approach it was possible to pinpoint the effects of the region- or season-specific impacts, which helped identify the ?climate limits? and critical development phases for each possible production scenario. This could allow breeders to design crop ideotypes while directing efforts to develop climate-smart varieties. Furthermore, enviromics prediction is a cost-effective way to use EC as a data analytics tool to support the visualization of regional breeding gaps for specific growing conditions. MenosOngoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our ap... Mostrar Tudo |
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Palavras-Chave: |
Generalized Additive Models (GAM). |
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Thesagro: |
Clima; Feijão; Melhoramento; Phaseolus Vulgaris. |
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Thesaurus NAL: |
Beans; Breeding; Climate; Environment; Plant adaptation. |
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Categoria do assunto: |
-- |
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Marc: |
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100 1 $aHEINEMANN, A. B.
245 $aEnviromic prediction is useful to define the limits of climate adaptation$ba case study of common bean in Brazil.$h[electronic resource]
260 $c2022
520 $aOngoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an ?enviromic prediction? approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our approach it was possible to pinpoint the effects of the region- or season-specific impacts, which helped identify the ?climate limits? and critical development phases for each possible production scenario. This could allow breeders to design crop ideotypes while directing efforts to develop climate-smart varieties. Furthermore, enviromics prediction is a cost-effective way to use EC as a data analytics tool to support the visualization of regional breeding gaps for specific growing conditions.
650 $aBeans
650 $aBreeding
650 $aClimate
650 $aEnvironment
650 $aPlant adaptation
650 $aClima
650 $aFeijão
650 $aMelhoramento
650 $aPhaseolus Vulgaris
653 $aGeneralized Additive Models (GAM)
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700 1 $aMATTA, D. H. da
700 1 $aFERNANDES, I. K.
773 $tField Crops Research$gv. 286, 108628, Oct. 2022.
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