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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
27/08/2001 |
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Data da última atualização: |
09/11/2017 |
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Autoria: |
SANTOS, C. A. F. |
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Afiliação: |
CARLOS ANTONIO FERNANDES SANTOS, CPATSA. |
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Título: |
Biometrical studies and quantitative trait loci associated with major products of the carotenoid pathway of carrot (Daucus carota L.) |
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Ano de publicação: |
2001 |
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Fonte/Imprenta: |
2001. |
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Páginas: |
265 f. |
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Idioma: |
Inglês |
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Notas: |
Thesis (Doctor of Philosophy) - University of Wisconsin, Madison. |
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Conteúdo: |
Orange carrots are a top ranked vegetable in terms ofpro-vitamin A content. Carotenoid pathway products were investigated in F2 populations from two different carrot crosses: orange Brasilia x dark orange HCM and orange B493 x white wild QAL. Broad sense heritabilities va1ues for a1l carotenoids were greater than 90% in the B493 x QAL cross and from 35% to 70% among different carotenoid,s in the Brasilia x HCM cross. The estimated number of factors was 4 for a-carotene, 3 for ~-carotene and total carotenes and one for ?- carotene, lycopene and phytoene in the orange x dark orange cross, and 4 for a-carotene, 1-2 for lycopene and tota1 carotenes and 1 for the other carotenes in the orange x white cross. In comparison to the known biochemical pathways the correct order of substrates and products, phytoene-+? -carotene-+ lycopene, was identified in the path analysis of f3-carotene in the cross Brasilia x HCM but not in the correlation analysis. Linkage grouping ana1ysis assigned 287 and 250 scored molecular markers to the nine chromosomes of carrots, at LOD scores ranging from 3.0 to 7.0 and the average marker spacing was 4.78,4.80,5.54 and 5.13 cM in 11 the Brasilia-, HCM-, B493 and QAL-coupling phase maps, respectively. Interval mapping performed with the orange x dark orange cross detected four, eight, three, one, five and three putative QTL associated with accumulation of ?-carotene, a-carotene, 13-carotene, lycopene, phytoene and total carotenoids, respectively, with major QTL explaining from 10.2 to 13.0% of total phenotypic variation. In the B493 x QAL population single marker analysis identified loci explaining 13.8%,6.8%, 19.3%,5.7%, 17.5% and 20.2% of total phenotypic variation for ?-carotene, a-carotene, 13-carotene, lycopene, phytoene and tota1 carotenoids content, respectively. Overall analysis showed clustered loci affecting the phenotypic variation of carotenoidpathway suggesting clusters of related-pathway loci as an evolutionary mechanism and supporting an adaptive evolutionary model suggested by H. A. Orr. Path analysis and QTL studies suggested that phytoene biosynthesis, perhaps associated with a root specific signal, are the two key factors limiting the carotenoid pathway in roots of white carrots. MenosOrange carrots are a top ranked vegetable in terms ofpro-vitamin A content. Carotenoid pathway products were investigated in F2 populations from two different carrot crosses: orange Brasilia x dark orange HCM and orange B493 x white wild QAL. Broad sense heritabilities va1ues for a1l carotenoids were greater than 90% in the B493 x QAL cross and from 35% to 70% among different carotenoid,s in the Brasilia x HCM cross. The estimated number of factors was 4 for a-carotene, 3 for ~-carotene and total carotenes and one for ?- carotene, lycopene and phytoene in the orange x dark orange cross, and 4 for a-carotene, 1-2 for lycopene and tota1 carotenes and 1 for the other carotenes in the orange x white cross. In comparison to the known biochemical pathways the correct order of substrates and products, phytoene-+? -carotene-+ lycopene, was identified in the path analysis of f3-carotene in the cross Brasilia x HCM but not in the correlation analysis. Linkage grouping ana1ysis assigned 287 and 250 scored molecular markers to the nine chromosomes of carrots, at LOD scores ranging from 3.0 to 7.0 and the average marker spacing was 4.78,4.80,5.54 and 5.13 cM in 11 the Brasilia-, HCM-, B493 and QAL-coupling phase maps, respectively. Interval mapping performed with the orange x dark orange cross detected four, eight, three, one, five and three putative QTL associated with accumulation of ?-carotene, a-carotene, 13-carotene, lycopene, phytoene and total carotenoids, respectively, with major... Mostrar Tudo |
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Palavras-Chave: |
AFLP; Carotene; Carotenoid content; Conteudo; Correlacao fenotipa; Dissecacao genetica; Genetic crosses; Genetic dissection; Heranca; Inheritance; Mapeamento; Mapping; Melhoramento genético; Molecular markers. |
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Thesagro: |
Carotenóide; Cenoura; Cruzamento; Daucus Carota; DNA; Genética; Marcador Genético; População; Variedade. |
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Thesaurus Nal: |
carotenes; carrots; genetic markers; phenotypic correlation; population; varieties. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 03550nam a2200481 a 4500
001 1134339
005 2017-11-09
008 2001 bl uuuu m 00u1 u #d
100 1 $aSANTOS, C. A. F.
245 $aBiometrical studies and quantitative trait loci associated with major products of the carotenoid pathway of carrot (Daucus carota L.)
260 $a2001.$c2001
300 $a265 f.
500 $aThesis (Doctor of Philosophy) - University of Wisconsin, Madison.
520 $aOrange carrots are a top ranked vegetable in terms ofpro-vitamin A content. Carotenoid pathway products were investigated in F2 populations from two different carrot crosses: orange Brasilia x dark orange HCM and orange B493 x white wild QAL. Broad sense heritabilities va1ues for a1l carotenoids were greater than 90% in the B493 x QAL cross and from 35% to 70% among different carotenoid,s in the Brasilia x HCM cross. The estimated number of factors was 4 for a-carotene, 3 for ~-carotene and total carotenes and one for ?- carotene, lycopene and phytoene in the orange x dark orange cross, and 4 for a-carotene, 1-2 for lycopene and tota1 carotenes and 1 for the other carotenes in the orange x white cross. In comparison to the known biochemical pathways the correct order of substrates and products, phytoene-+? -carotene-+ lycopene, was identified in the path analysis of f3-carotene in the cross Brasilia x HCM but not in the correlation analysis. Linkage grouping ana1ysis assigned 287 and 250 scored molecular markers to the nine chromosomes of carrots, at LOD scores ranging from 3.0 to 7.0 and the average marker spacing was 4.78,4.80,5.54 and 5.13 cM in 11 the Brasilia-, HCM-, B493 and QAL-coupling phase maps, respectively. Interval mapping performed with the orange x dark orange cross detected four, eight, three, one, five and three putative QTL associated with accumulation of ?-carotene, a-carotene, 13-carotene, lycopene, phytoene and total carotenoids, respectively, with major QTL explaining from 10.2 to 13.0% of total phenotypic variation. In the B493 x QAL population single marker analysis identified loci explaining 13.8%,6.8%, 19.3%,5.7%, 17.5% and 20.2% of total phenotypic variation for ?-carotene, a-carotene, 13-carotene, lycopene, phytoene and tota1 carotenoids content, respectively. Overall analysis showed clustered loci affecting the phenotypic variation of carotenoidpathway suggesting clusters of related-pathway loci as an evolutionary mechanism and supporting an adaptive evolutionary model suggested by H. A. Orr. Path analysis and QTL studies suggested that phytoene biosynthesis, perhaps associated with a root specific signal, are the two key factors limiting the carotenoid pathway in roots of white carrots.
650 $acarotenes
650 $acarrots
650 $agenetic markers
650 $aphenotypic correlation
650 $apopulation
650 $avarieties
650 $aCarotenóide
650 $aCenoura
650 $aCruzamento
650 $aDaucus Carota
650 $aDNA
650 $aGenética
650 $aMarcador Genético
650 $aPopulação
650 $aVariedade
653 $aAFLP
653 $aCarotene
653 $aCarotenoid content
653 $aConteudo
653 $aCorrelacao fenotipa
653 $aDissecacao genetica
653 $aGenetic crosses
653 $aGenetic dissection
653 $aHeranca
653 $aInheritance
653 $aMapeamento
653 $aMapping
653 $aMelhoramento genético
653 $aMolecular markers
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Registro original: |
Embrapa Semiárido (CPATSA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Meio Ambiente. |
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Data corrente: |
15/09/2023 |
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Data da última atualização: |
15/09/2023 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
CUADRA, S. V.; VICTORIA, D. de C.; PELLEGRINO, G. Q.; BOLFE, E. L.; MONTEIRO, J. E. B. de A.; ASSAD, E. D.; OLIVEIRA, A. F. de; FASIABEN, M. do C. R.; MARTHA JÚNIOR, G. B.; BATISTELLA, M.; BARIONI, L. G.; NAKAI, A. M.; SILVA, F. C. da; MATSUURA, M. I. da S. F. |
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Afiliação: |
SANTIAGO VIANNA CUADRA, CNPTIA; DANIEL DE CASTRO VICTORIA, CNPTIA; GIAMPAOLO QUEIROZ PELLEGRINO, CNPTIA; EDSON LUIS BOLFE, CNPTIA; JOSE EDUARDO B DE ALMEIDA MONTEIRO, CNPTIA; EDUARDO DELGADO ASSAD, CNPTIA; ARYEVERTON FORTES DE OLIVEIRA, CNPTIA; MARIA DO CARMO RAMOS FASIABEN, CNPTIA; GERALDO BUENO MARTHA JUNIOR, CNPTIA; MATEUS BATISTELLA, CNPTIA; LUIS GUSTAVO BARIONI, CNPTIA; ALAN MASSARU NAKAI, CNPTIA; FABIO CESAR DA SILVA, CNPTIA; MARILIA IEDA DA S F MATSUURA, CNPMA. |
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Título: |
Agroenvironmental modeling and the digital transformation of agriculture. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 3, p. 51-70. |
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ISBN: |
978-65-89957-72-0 |
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Idioma: |
Inglês |
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Conteúdo: |
Introduction. The evolution of agroenvironmental modelling. Agroenvironmental modeling products to support decision-making. Databases for agricultural and environmental research: Agritempo, Conprees. Risk assessments and climate resilience evaluation: Agricultural Climate Risk Zoning (ZARC), Plantio Certo (Sure Sowing). Support for agricultural planning and monitoring: Invernada, WebAgritec. Climate change impact assessments and agricultural adaptation based on agroenvironmental models. Climatic projections. Agricultural impacts. Simulation of future agricultural scenarios. Territorial planning and land use: Agroideal, DINACER. Applications of agroenvironmental models for the conservation of ecosystem services: WebAmbiente, Hydric resources, Integration of socio-economic analysis in agro-environmental modeling, Applications for quantification and mitigation strategies for GHG emissions. Final considerations. |
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Palavras-Chave: |
Modelagem agroambiental; Pesquisa agrícola e ambiental; Transformação digital da agricultura. |
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Thesagro: |
Agricultura. |
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Thesaurus NAL: |
Agricultural research; Agriculture; Environmental models; Research and development. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1156700/1/LV-Digital-agriculture-2023-cap3.pdf
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Marc: |
LEADER 02288naa a2200385 a 4500
001 2156700
005 2023-09-15
008 2023 bl uuuu u00u1 u #d
020 $a978-65-89957-72-0
100 1 $aCUADRA, S. V.
245 $aAgroenvironmental modeling and the digital transformation of agriculture.$h[electronic resource]
260 $c2023
520 $aIntroduction. The evolution of agroenvironmental modelling. Agroenvironmental modeling products to support decision-making. Databases for agricultural and environmental research: Agritempo, Conprees. Risk assessments and climate resilience evaluation: Agricultural Climate Risk Zoning (ZARC), Plantio Certo (Sure Sowing). Support for agricultural planning and monitoring: Invernada, WebAgritec. Climate change impact assessments and agricultural adaptation based on agroenvironmental models. Climatic projections. Agricultural impacts. Simulation of future agricultural scenarios. Territorial planning and land use: Agroideal, DINACER. Applications of agroenvironmental models for the conservation of ecosystem services: WebAmbiente, Hydric resources, Integration of socio-economic analysis in agro-environmental modeling, Applications for quantification and mitigation strategies for GHG emissions. Final considerations.
650 $aAgricultural research
650 $aAgriculture
650 $aEnvironmental models
650 $aResearch and development
650 $aAgricultura
653 $aModelagem agroambiental
653 $aPesquisa agrícola e ambiental
653 $aTransformação digital da agricultura
700 1 $aVICTORIA, D. de C.
700 1 $aPELLEGRINO, G. Q.
700 1 $aBOLFE, E. L.
700 1 $aMONTEIRO, J. E. B. de A.
700 1 $aASSAD, E. D.
700 1 $aOLIVEIRA, A. F. de
700 1 $aFASIABEN, M. do C. R.
700 1 $aMARTHA JÚNIOR, G. B.
700 1 $aBATISTELLA, M.
700 1 $aBARIONI, L. G.
700 1 $aNAKAI, A. M.
700 1 $aSILVA, F. C. da
700 1 $aMATSUURA, M. I. da S. F.
773 $tIn: MASSRUHÁ, S. M. F. S.; LEITE, M. A. de A.; OLIVEIRA, S. R. de M.; MEIRA, C. A. A.; LUCHIARI JUNIOR, A.; BOLFE, E. L. (ed.). Digital agriculture: research, development and innovation in production chains. Brasília, DF: Embrapa, 2023. cap. 3, p. 51-70.
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Embrapa Agricultura Digital (CNPTIA) |
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