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| 7. |  | FERREIRA, F. A. Situacao do reflorestamento ate julho de 1976, nas regioes de maior ocorrencia do cancro do Eucalyptus, nos Estados de Espirito Santo e Minas Gerais, em, termos de escolha de especies e procedencias de Eucalyptus. Revista Arvore, Vicosa, v.2, n.1, p.104-110, 1978| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
02/06/2022 |
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Data da última atualização: |
01/12/2022 |
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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: |
FERREIRA, F. M.; LEITE, R. V.; MALIKOUSKI, R. G.; PEIXOTO, M. A.; BERNARDELI, A.; ALVES, R. S.; MAGALHAES JUNIOR, W. C. P. de; ANDRADE, R. G.; BHERING, L. L.; MACHADO, J. C. |
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Afiliação: |
FILIPE MANOEL FERREIRA, Universidade Federal de Viçosa; RODRIGO VIEIRA LEITE, Universidade Federal de Viçosa; RENAN GARCIA MALIKOUSKI, Universidade Federal de Viçosa; MARCO ANTONIO PEIXOTO, Universidade Federal de Viçosa; ARTHUR BERNARDELI, Universidade Federal de Viçosa; RODRIGO SILVA ALVES, Universidade Federal de Lavras; WALTER COELHO P DE MAGALHAES JUNIOR, CNPGL; RICARDO GUIMARAES ANDRADE, CNPGL; LEONARDO LOPES BHERING, Universidade Federal de Viçosa; JUAREZ CAMPOLINA MACHADO, CNPGL. |
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Título: |
Bioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Journal of Cleaner Production, v. 363, 132286, 2022. |
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DOI: |
https://doi.org/10.1016/j.jclepro.2022.132286 |
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Idioma: |
Inglês |
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Conteúdo: |
The burning of fossil fuels contributes to global warming. Using renewable energy sources such as elephant grass biomass mitigates anthropogenic impact on nature. The genetic selection of high-yield elephant grass genotypes is important to increase the use of this forage for energy generation. Unmanned aerial vehicles have been used for data collection and optimization of the selection of genotypes. However, statistical tests should be conducted to study the suitability of vegetation indices for predicting morphological traits. In addition, spatial sources of variation, such as soil structure heterogeneity, can disturb the selection process. This study compared the correlation between morphological traits and vegetation indices of elephant grass clones using basic linear mixed and spatial linear mixed models. In addition, we evaluated the magnitude and contribution of each index to explain the variations in traits and identify the best index for this forage. There was significant genetic variability in some morphological traits that enabled selection. Spatial models (autoregressive correlation among rows and columns) were more suitable for modeling some of the evaluated traits. There were changes in the magnitude of the correlation between traits when we considered the best-fit model instead of the non-spatial model. The increase in efficiency using the best-fitted model instead of the non-spatial model was 15.39% for heritability and 9.54% for accuracy. The total dry biomass was the only morphological trait significantly correlated with some vegetation indices, allowing for indirect selection. The coincidence index, heritability, and gains from indirect selection indicated that the normalized difference red-edge index was the best for selecting superior elephant grass high-yielding genotypes. The spatial modeling leveraged the genetic selection of high yield elephant grass genotypes for bioenergetic purposes. MenosThe burning of fossil fuels contributes to global warming. Using renewable energy sources such as elephant grass biomass mitigates anthropogenic impact on nature. The genetic selection of high-yield elephant grass genotypes is important to increase the use of this forage for energy generation. Unmanned aerial vehicles have been used for data collection and optimization of the selection of genotypes. However, statistical tests should be conducted to study the suitability of vegetation indices for predicting morphological traits. In addition, spatial sources of variation, such as soil structure heterogeneity, can disturb the selection process. This study compared the correlation between morphological traits and vegetation indices of elephant grass clones using basic linear mixed and spatial linear mixed models. In addition, we evaluated the magnitude and contribution of each index to explain the variations in traits and identify the best index for this forage. There was significant genetic variability in some morphological traits that enabled selection. Spatial models (autoregressive correlation among rows and columns) were more suitable for modeling some of the evaluated traits. There were changes in the magnitude of the correlation between traits when we considered the best-fit model instead of the non-spatial model. The increase in efficiency using the best-fitted model instead of the non-spatial model was 15.39% for heritability and 9.54% for accuracy. The total dry biomas... Mostrar Tudo |
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Palavras-Chave: |
Forage breeding; Genetic selection; Seleção gênica. |
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Thesagro: |
Bioenergia; Capim Elefante; Seleção Fenótipa; Seleção Genética; Sensoriamento Remoto. |
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Thesaurus NAL: |
Phenomics; Remote sensing. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
LEADER 03055naa a2200361 a 4500
001 2143669
005 2022-12-01
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jclepro.2022.132286$2DOI
100 1 $aFERREIRA, F. M.
245 $aBioenergy elephant grass genotype selection leveraged by spatial modeling of conventional and high-throughput phenotyping data.$h[electronic resource]
260 $c2022
520 $aThe burning of fossil fuels contributes to global warming. Using renewable energy sources such as elephant grass biomass mitigates anthropogenic impact on nature. The genetic selection of high-yield elephant grass genotypes is important to increase the use of this forage for energy generation. Unmanned aerial vehicles have been used for data collection and optimization of the selection of genotypes. However, statistical tests should be conducted to study the suitability of vegetation indices for predicting morphological traits. In addition, spatial sources of variation, such as soil structure heterogeneity, can disturb the selection process. This study compared the correlation between morphological traits and vegetation indices of elephant grass clones using basic linear mixed and spatial linear mixed models. In addition, we evaluated the magnitude and contribution of each index to explain the variations in traits and identify the best index for this forage. There was significant genetic variability in some morphological traits that enabled selection. Spatial models (autoregressive correlation among rows and columns) were more suitable for modeling some of the evaluated traits. There were changes in the magnitude of the correlation between traits when we considered the best-fit model instead of the non-spatial model. The increase in efficiency using the best-fitted model instead of the non-spatial model was 15.39% for heritability and 9.54% for accuracy. The total dry biomass was the only morphological trait significantly correlated with some vegetation indices, allowing for indirect selection. The coincidence index, heritability, and gains from indirect selection indicated that the normalized difference red-edge index was the best for selecting superior elephant grass high-yielding genotypes. The spatial modeling leveraged the genetic selection of high yield elephant grass genotypes for bioenergetic purposes.
650 $aPhenomics
650 $aRemote sensing
650 $aBioenergia
650 $aCapim Elefante
650 $aSeleção Fenótipa
650 $aSeleção Genética
650 $aSensoriamento Remoto
653 $aForage breeding
653 $aGenetic selection
653 $aSeleção gênica
700 1 $aLEITE, R. V.
700 1 $aMALIKOUSKI, R. G.
700 1 $aPEIXOTO, M. A.
700 1 $aBERNARDELI, A.
700 1 $aALVES, R. S.
700 1 $aMAGALHAES JUNIOR, W. C. P. de
700 1 $aANDRADE, R. G.
700 1 $aBHERING, L. L.
700 1 $aMACHADO, J. C.
773 $tJournal of Cleaner Production$gv. 363, 132286, 2022.
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