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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. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
17/06/2024 |
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Data da última atualização: |
18/06/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
HEINEMANN, A. B.; COSTA-NETO, G.; MATTA, D. H. da; FERNANDES, I. K.; STONE, L. F. |
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Afiliação: |
ALEXANDRE BRYAN HEINEMANN, CNPAF; GERMANO COSTA-NETO, CORNELL UNIVERSITY; DAVID HENRIQUES DA MATTA, UNIVERSIDADE FEDERAL DE GOIÁS; IGOR KUIVJOGI FERNANDES, UNIVERSIDADE FEDERAL DE GOIÁS; LUIS FERNANDO STONE, CNPAF. |
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Título: |
Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Field Crops Research, v. 315, 109452, July 2024. |
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ISSN: |
0378-4290 |
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DOI: |
https://doi.org/10.1016/j.fcr.2024.109452 |
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Idioma: |
Inglês |
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Conteúdo: |
Problem: Tropical ecosystems are essential for irrigated rice production and are vital for ensuring food security in countries like Brazil. However, the uncertainty stemming from numerous environmental constraints limits current productivity levels and presents challenges for plant breeding research in developing productive and stable cultivars. Objective: To overcome these challenges, environmental characterization is essential to support breeding decision-making and help design and develop superior cultivars capable of capitalizing on genotype x environment interactions (GxE). Methods: In this study, we characterize the tropical rice zone in Brazil using crop modeling outcomes, machine learning tools (unsupervised clustering and supervised classification), envirotyping, and long-term observed yield trials from the irrigated rice-breeding program. Results: Three target population of environments (TPE) were detected across diverse geographic regions and planting dates, named after LFE (Least Favorable Environments, 44 % of occurrence), FE (Favorable Environments, 33 %), and HFE (Highly Favorable Environments, 23 %). For each TPE, we identified the key climate drivers for achieving higher yields, which involved a combination of lower temperatures (average maximum and minimum temperature: 30 ◦C and 19 ◦C), while higher radiation in vegetative and reproductive phases (692 and 723 MJ m 2) and precipitation (663 mm). The inclusion of TPE information to support the GXE analysis spanning 18 years of advanced yield trials for cultivar targeting increased the model’s heritability, even reducing the number of trials by 40 %. Conclusions: Environmental characterization pipelines in tropical rice could be leveraged by combining crop models and machine learning. Virtual simulations of historical climate trends across locations and planting dates could extend the spectrum of growing conditions screened to compose target population of environments for breeding. This is vital to support the design of new breeding programs, exploring alternative planting dates, or when breeders want to characterize a new growing region without any prior field trial data. For an extensive region, this helps breeders either improve cultivar testing analysis or identify the most prone locations to optimize seed production at commercial phases. MenosProblem: Tropical ecosystems are essential for irrigated rice production and are vital for ensuring food security in countries like Brazil. However, the uncertainty stemming from numerous environmental constraints limits current productivity levels and presents challenges for plant breeding research in developing productive and stable cultivars. Objective: To overcome these challenges, environmental characterization is essential to support breeding decision-making and help design and develop superior cultivars capable of capitalizing on genotype x environment interactions (GxE). Methods: In this study, we characterize the tropical rice zone in Brazil using crop modeling outcomes, machine learning tools (unsupervised clustering and supervised classification), envirotyping, and long-term observed yield trials from the irrigated rice-breeding program. Results: Three target population of environments (TPE) were detected across diverse geographic regions and planting dates, named after LFE (Least Favorable Environments, 44 % of occurrence), FE (Favorable Environments, 33 %), and HFE (Highly Favorable Environments, 23 %). For each TPE, we identified the key climate drivers for achieving higher yields, which involved a combination of lower temperatures (average maximum and minimum temperature: 30 ◦C and 19 ◦C), while higher radiation in vegetative and reproductive phases (692 and 723 MJ m 2) and precipitation (663 mm). The inclusion of TPE information to support the GXE analysis sp... Mostrar Tudo |
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Thesagro: |
Arroz Irrigado; Clima; Interação Genética; Melhoramento Genético Vegetal. |
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Thesaurus Nal: |
Climate; Crop models; Environmental factors; Genotype-environment interaction; Plant breeding. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03308naa a2200301 a 4500
001 2164897
005 2024-06-18
008 2024 bl uuuu u00u1 u #d
022 $a0378-4290
024 7 $ahttps://doi.org/10.1016/j.fcr.2024.109452$2DOI
100 1 $aHEINEMANN, A. B.
245 $aHarnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil.$h[electronic resource]
260 $c2024
520 $aProblem: Tropical ecosystems are essential for irrigated rice production and are vital for ensuring food security in countries like Brazil. However, the uncertainty stemming from numerous environmental constraints limits current productivity levels and presents challenges for plant breeding research in developing productive and stable cultivars. Objective: To overcome these challenges, environmental characterization is essential to support breeding decision-making and help design and develop superior cultivars capable of capitalizing on genotype x environment interactions (GxE). Methods: In this study, we characterize the tropical rice zone in Brazil using crop modeling outcomes, machine learning tools (unsupervised clustering and supervised classification), envirotyping, and long-term observed yield trials from the irrigated rice-breeding program. Results: Three target population of environments (TPE) were detected across diverse geographic regions and planting dates, named after LFE (Least Favorable Environments, 44 % of occurrence), FE (Favorable Environments, 33 %), and HFE (Highly Favorable Environments, 23 %). For each TPE, we identified the key climate drivers for achieving higher yields, which involved a combination of lower temperatures (average maximum and minimum temperature: 30 ◦C and 19 ◦C), while higher radiation in vegetative and reproductive phases (692 and 723 MJ m 2) and precipitation (663 mm). The inclusion of TPE information to support the GXE analysis spanning 18 years of advanced yield trials for cultivar targeting increased the model’s heritability, even reducing the number of trials by 40 %. Conclusions: Environmental characterization pipelines in tropical rice could be leveraged by combining crop models and machine learning. Virtual simulations of historical climate trends across locations and planting dates could extend the spectrum of growing conditions screened to compose target population of environments for breeding. This is vital to support the design of new breeding programs, exploring alternative planting dates, or when breeders want to characterize a new growing region without any prior field trial data. For an extensive region, this helps breeders either improve cultivar testing analysis or identify the most prone locations to optimize seed production at commercial phases.
650 $aClimate
650 $aCrop models
650 $aEnvironmental factors
650 $aGenotype-environment interaction
650 $aPlant breeding
650 $aArroz Irrigado
650 $aClima
650 $aInteração Genética
650 $aMelhoramento Genético Vegetal
700 1 $aCOSTA-NETO, G.
700 1 $aMATTA, D. H. da
700 1 $aFERNANDES, I. K.
700 1 $aSTONE, L. F.
773 $tField Crops Research$gv. 315, 109452, July 2024.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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| Registros recuperados : 53 | |
| 2. |  | BARROS, M. A. L. Perspectivo do algodao brasileiro. In: CONGRESSO BRASILEIRO DE ALGODAO, 2., 1999. Ribeirao Preto-SP. o Algodao no seculo XX, perspetivas para o seculo XXI - Anais. Campina Grande: EMBRAPA-CNPA, 1999. p. 142-143.| Biblioteca(s): Embrapa Algodão. |
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| 8. | | BARROS, M. A. L.; BELTRÃO, N. E. de M. Comercialização do algodão In: BELTRÃO, N. E. de M. O Agronegócio do algodão no Brasil. Campina Grande: Embrapa Algodão; Brasília: Embrapa Comunicação para Transferência de Tecnologia, 1999. v. 2 p. 1015-1023| Tipo: Capítulo em Livro Técnico-Científico | Circulação/Nível: -- - -- |
| Biblioteca(s): Embrapa Algodão. |
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| 9. | | BARROS, M. A. L.; BELTRÃO, N. E. de M. Comercialização. In: BELTRÃO, N. E. de M.; AZEVEDO, D. M. P. de. (Ed.). O Agronegócio do Algodão no Brasil. Brasília, DF: Embrapa Informação Tecnológica, 2008. v. 2 p. 1271-1277| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Algodão. |
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| Registros recuperados : 53 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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