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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Amazônia Oriental; Embrapa Roraima; Embrapa Solos. |
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Data corrente: |
14/10/2015 |
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Data da última atualização: |
16/04/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
POORTER, L.; SANDE, M. T. van der; THOMPSON, J.; ARETS, E. J. M. M.; ALARCÓN, A.; ÁLVAREZ-SÁNCHEZ, J.; ASCARRUNZ, N.; BALVANERA, P.; BARAJAS-GUZMÁN, G.; BOIT, A.; BONGERS, F.; CARVALHO, F. A.; CASANOVES, F.; CORNEJO-TENORIO, G.; COSTA, F. R. C.; CASTILHO, C. V. de; DUIVENVOORDEN, J. F.; DUTRIEUX, L. P.; ENQUIST, J. P.; FERNÁNDEZ-MÉNDEZ, F.; FINEGAN, B.; GORMLEY, L. H. L.; HEALEY, J. R.; HOOSBEEK, M. R.; IBARRA-MANRÍQUEZ, G.; JUNQUEIRA, A. B.; LEVIS, C.; LICONA, J. C.; LISBOA, L. S.; MAGNUSSON, W. E.; MARTÍNEZ-RAMOS, M.; MARTÍNEZ-YRIZAR, A.; MARTORANO, L. G.; MASKELL, L. C.; MAZZEI, L.; MEAVE, J. A.; MORA, F.; MUÑOZ, R.; NYTCH, C.; PANSONATO, M. P.; PARR, T. W.; PAZ, H.; PÉREZ-GARCIA, E. A.; RENTERÍA, L. Y.; RODRÍGUEZ-VELÁZQUEZ, J.; ROZENDAAL, D. M. A.; RUSCHEL, A. R.; SAKSCHEWSKI, B.; SALGADO-NEGRET, B.; SCHIETT, J.; SIMÕES, M.; SINCLAIR, F. L.; SOUZA, P. F.; SOUZA, F. C.; STROPP, J.; STEEGE, H. ter; SWENSON, N. G.; THONICKE, K.; TOLEDO, M.; URIARTE, M.; HOUT, P. van der; WALKER, P.; ZAMORA, N.; PEÑA-CLAROS, M. |
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Afiliação: |
L. POORTER, WAGENINGEN UNIVERSITY; M. T. van der SANDE, WAGENINGEN UNIVERSITY; J. THOMPSON, CENTRE FOR ECOLOGIE & HIDROLOGIE, PENICUIK; E. J. M. M. ARETS; A. ALARCÓN; J. ÁLVAREZ-SÁNCHEZ; N. ASCARRUNZ; P. BALVANERA; G. BARAJAS-GUZMÁN; A. BOIT; F. BONGERS; F. A. CARVALHO, INPA; F. CASANOVES; G. CORNEJO-TENORIO; F. R. C. COSTA, INPA; CAROLINA VOLKMER DE CASTILHO, CPAF-RR; J. F. DUIVENVORDEN; L. P. DUTRIEUX; J. P. ENQUIST; F. FERNÁNDEZ-MÉNDEZ; B. FINEGAN; L. H. L. GORMLEY; J. R. HEALEY; M. R. HOOSBEEK; G. IBARRA-MANRÍQUEZ; A. B. JUNQUEIRA, INPA; C. LEVIS; J. C. LICONA; L. S. LISBOA, ESALQ/USP; W. E. MAGNUSSON; M. MARTÍNEZ-RAMOS; A. MARTÍNEZ-YRIZAR; LUCIETA GUERREIRO MARTORANO, CPATU; L. C. MASKELL; LUCAS JOSE MAZZEI DE FREITAS, CPATU; J. A. MEAVE; F. MORA; R. MUÑOZ; C. NYTCH; M. P. PANSONATO, INPA; T. W. PARR; H. PAZ; E. A. PÉREZ-GARCIA; L. Y. RENTERÍA; J. RODRÍGUEZ-VELÁZQUEZ; D. M. A. ROZENDAAL; ADEMIR ROBERTO RUSCHEL, CPATU; B. SAKSCHEWSKI; B. SALGADO-NEGRET; J. SCHIETT, INPA; MARGARETH SIMOES, CNPS; F. L. SINCLAIR; P. F. SOUZA, INPA; F. C. SOUZA, INPA; J. STROPP, UFAL; H. ter STEEGE; N. G. SWENSON; K. THONICKE; M. TOLEDO; M. URIARTE; P. van der HOUT; P. WALKER; N. ZAMORA; M. PEÑA-CLAROS. |
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Título: |
Diversity enhances carbon storage in tropical forests. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Global Ecology and Biogeography, v. 24, n. 11, p. 1314-1328, Nov. 2015. |
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DOI: |
10.1111/geb.12364 |
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Idioma: |
Inglês |
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Conteúdo: |
Tropical forests store 25% of global carbon and harbour 96% of the world?s tree species, but it is not clear whether this high biodiversity matters for carbon storage. Few studies have teased apart the relative importance of forest attributes and environmental drivers for ecosystem functioning, and no such study exists for the tropics. We relate aboveground biomass (AGB) to forest attributes (diversity and structure) and environmental drivers (annual rainfall and soil fertility) using data from 144,000 trees, 2050 forest plots and 59 forest sites. The sites span the complete latitudinal and climatic gradients in the lowland Neotropics, with rainfall ranging from 750 to 4350 mm year-1. Relationships were analysed within forest sites at scales of 0.1 and 1 ha and across forest sites along large-scale environmental gradients. We used a structural equation model to test the hypothesis that species richness, forest structural attributes and environmental drivers have independent, positive effects on AGB. Across sites, AGB was most strongly driven by rainfall, followed by average tree stem diameter and rarefied species richness, which all had positive effects on AGB. Our indicator of soil fertility (cation exchange capacity) had a negligible effect on AGB, perhaps because we used a global soil database. Taxonomic forest attributes (i.e. species richness, rarefied richness and Shannon diversity) had the strongest relationships with AGB at small spatial scales, where an additional species can still make a difference in terms of niche complementarity, while structural forest attributes (i.e. tree density and tree size) had strong relationships with AGB at all spatial scales. Biodiversity has an independent, positive effect on AGB and ecosystem functioning, not only in relatively simple temperate systems but also in structurally complex hyperdiverse tropical forests. Biodiversity conservation should therefore be a key component of the UN Reducing Emissions from Deforestation and Degradation strategy. MenosTropical forests store 25% of global carbon and harbour 96% of the world?s tree species, but it is not clear whether this high biodiversity matters for carbon storage. Few studies have teased apart the relative importance of forest attributes and environmental drivers for ecosystem functioning, and no such study exists for the tropics. We relate aboveground biomass (AGB) to forest attributes (diversity and structure) and environmental drivers (annual rainfall and soil fertility) using data from 144,000 trees, 2050 forest plots and 59 forest sites. The sites span the complete latitudinal and climatic gradients in the lowland Neotropics, with rainfall ranging from 750 to 4350 mm year-1. Relationships were analysed within forest sites at scales of 0.1 and 1 ha and across forest sites along large-scale environmental gradients. We used a structural equation model to test the hypothesis that species richness, forest structural attributes and environmental drivers have independent, positive effects on AGB. Across sites, AGB was most strongly driven by rainfall, followed by average tree stem diameter and rarefied species richness, which all had positive effects on AGB. Our indicator of soil fertility (cation exchange capacity) had a negligible effect on AGB, perhaps because we used a global soil database. Taxonomic forest attributes (i.e. species richness, rarefied richness and Shannon diversity) had the strongest relationships with AGB at small spatial scales, where an additional s... Mostrar Tudo |
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Palavras-Chave: |
Escala; Funcionamento dos ecossistemas; Neotropical; Precipitação; REDD+. |
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Thesagro: |
Biodiversidade; Biomassa; Floresta tropical; Solo. |
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Thesaurus Nal: |
Biodiversity; Biomass; Soil; Tropical forests. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 04779naa a2201045 a 4500
001 2026769
005 2018-04-16
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1111/geb.12364$2DOI
100 1 $aPOORTER, L.
245 $aDiversity enhances carbon storage in tropical forests.$h[electronic resource]
260 $c2015
520 $aTropical forests store 25% of global carbon and harbour 96% of the world?s tree species, but it is not clear whether this high biodiversity matters for carbon storage. Few studies have teased apart the relative importance of forest attributes and environmental drivers for ecosystem functioning, and no such study exists for the tropics. We relate aboveground biomass (AGB) to forest attributes (diversity and structure) and environmental drivers (annual rainfall and soil fertility) using data from 144,000 trees, 2050 forest plots and 59 forest sites. The sites span the complete latitudinal and climatic gradients in the lowland Neotropics, with rainfall ranging from 750 to 4350 mm year-1. Relationships were analysed within forest sites at scales of 0.1 and 1 ha and across forest sites along large-scale environmental gradients. We used a structural equation model to test the hypothesis that species richness, forest structural attributes and environmental drivers have independent, positive effects on AGB. Across sites, AGB was most strongly driven by rainfall, followed by average tree stem diameter and rarefied species richness, which all had positive effects on AGB. Our indicator of soil fertility (cation exchange capacity) had a negligible effect on AGB, perhaps because we used a global soil database. Taxonomic forest attributes (i.e. species richness, rarefied richness and Shannon diversity) had the strongest relationships with AGB at small spatial scales, where an additional species can still make a difference in terms of niche complementarity, while structural forest attributes (i.e. tree density and tree size) had strong relationships with AGB at all spatial scales. Biodiversity has an independent, positive effect on AGB and ecosystem functioning, not only in relatively simple temperate systems but also in structurally complex hyperdiverse tropical forests. Biodiversity conservation should therefore be a key component of the UN Reducing Emissions from Deforestation and Degradation strategy.
650 $aBiodiversity
650 $aBiomass
650 $aSoil
650 $aTropical forests
650 $aBiodiversidade
650 $aBiomassa
650 $aFloresta tropical
650 $aSolo
653 $aEscala
653 $aFuncionamento dos ecossistemas
653 $aNeotropical
653 $aPrecipitação
653 $aREDD+
700 1 $aSANDE, M. T. van der
700 1 $aTHOMPSON, J.
700 1 $aARETS, E. J. M. M.
700 1 $aALARCÓN, A.
700 1 $aÁLVAREZ-SÁNCHEZ, J.
700 1 $aASCARRUNZ, N.
700 1 $aBALVANERA, P.
700 1 $aBARAJAS-GUZMÁN, G.
700 1 $aBOIT, A.
700 1 $aBONGERS, F.
700 1 $aCARVALHO, F. A.
700 1 $aCASANOVES, F.
700 1 $aCORNEJO-TENORIO, G.
700 1 $aCOSTA, F. R. C.
700 1 $aCASTILHO, C. V. de
700 1 $aDUIVENVOORDEN, J. F.
700 1 $aDUTRIEUX, L. P.
700 1 $aENQUIST, J. P.
700 1 $aFERNÁNDEZ-MÉNDEZ, F.
700 1 $aFINEGAN, B.
700 1 $aGORMLEY, L. H. L.
700 1 $aHEALEY, J. R.
700 1 $aHOOSBEEK, M. R.
700 1 $aIBARRA-MANRÍQUEZ, G.
700 1 $aJUNQUEIRA, A. B.
700 1 $aLEVIS, C.
700 1 $aLICONA, J. C.
700 1 $aLISBOA, L. S.
700 1 $aMAGNUSSON, W. E.
700 1 $aMARTÍNEZ-RAMOS, M.
700 1 $aMARTÍNEZ-YRIZAR, A.
700 1 $aMARTORANO, L. G.
700 1 $aMASKELL, L. C.
700 1 $aMAZZEI, L.
700 1 $aMEAVE, J. A.
700 1 $aMORA, F.
700 1 $aMUÑOZ, R.
700 1 $aNYTCH, C.
700 1 $aPANSONATO, M. P.
700 1 $aPARR, T. W.
700 1 $aPAZ, H.
700 1 $aPÉREZ-GARCIA, E. A.
700 1 $aRENTERÍA, L. Y.
700 1 $aRODRÍGUEZ-VELÁZQUEZ, J.
700 1 $aROZENDAAL, D. M. A.
700 1 $aRUSCHEL, A. R.
700 1 $aSAKSCHEWSKI, B.
700 1 $aSALGADO-NEGRET, B.
700 1 $aSCHIETT, J.
700 1 $aSIMÕES, M.
700 1 $aSINCLAIR, F. L.
700 1 $aSOUZA, P. F.
700 1 $aSOUZA, F. C.
700 1 $aSTROPP, J.
700 1 $aSTEEGE, H. ter
700 1 $aSWENSON, N. G.
700 1 $aTHONICKE, K.
700 1 $aTOLEDO, M.
700 1 $aURIARTE, M.
700 1 $aHOUT, P. van der
700 1 $aWALKER, P.
700 1 $aZAMORA, N.
700 1 $aPEÑA-CLAROS, M.
773 $tGlobal Ecology and Biogeography$gv. 24, n. 11, p. 1314-1328, Nov. 2015.
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Registro original: |
Embrapa Roraima (CPAF-RR) |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
10/01/2023 |
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Data da última atualização: |
10/01/2023 |
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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: |
ANDRADE, L. R. B. de; SOUSA, M. B. e; WOLFE, M.; JANNINK, J. L.; RESENDE, M. D. V. de; AZEVEDO, C. F.; OLIVEIRA, E. J. de. |
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Afiliação: |
LUCIANO ROGÉRIO BRAATZ DE ANDRADE, UNIVERSIDADE FEDERAL DE VIÇOSA; MASSAINE BANDEIRA E SOUSA, EMBRAPA MANDIOCA E FRUTICULTURA; MARNIN WOLFE, AUBURN UNIVERSITY; JEAN-LUC JANNINK, CORNELL UNIVERSITY; MARCOS DEON VILELA DE RESENDE, CNPCa; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; EDER JORGE DE OLIVEIRA, CNPMF. |
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Título: |
Increasing cassava root yield: additive-dominant genetic models for selection of parents and clones. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v. 13, article 1071156, 2022. |
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DOI: |
https://doi.org/10.3389/fpls.2022.1071156 |
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Idioma: |
Inglês |
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Conteúdo: |
Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cr, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties. MenosGenomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cr, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability w... Mostrar Tudo |
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Thesaurus NAL: |
Breeding value; Cassava; Clones; Genomics; Natural selection; Plant breeding. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1150823/1/Increasing-cassava-root-yield.pdf
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Marc: |
LEADER 03028naa a2200277 a 4500
001 2150823
005 2023-01-10
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2022.1071156$2DOI
100 1 $aANDRADE, L. R. B. de
245 $aIncreasing cassava root yield$badditive-dominant genetic models for selection of parents and clones.$h[electronic resource]
260 $c2022
520 $aGenomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cr, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties.
650 $aBreeding value
650 $aCassava
650 $aClones
650 $aGenomics
650 $aNatural selection
650 $aPlant breeding
700 1 $aSOUSA, M. B. e
700 1 $aWOLFE, M.
700 1 $aJANNINK, J. L.
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aOLIVEIRA, E. J. de
773 $tFrontiers in Plant Science$gv. 13, article 1071156, 2022.
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