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Registro Completo |
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Biblioteca(s): |
Embrapa Arroz e Feijão. |
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Data corrente: |
17/04/2015 |
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Data da última atualização: |
15/05/2015 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LIMA, D. C.; ABREU, A. de F. B.; FERREIRA, R. A. D. C.; RAMALHO, M. A. P. |
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Afiliação: |
DAYANE CRISTINA LIMA, UFLA; ANGELA DE FATIMA BARBOSA ABREU, CNPAF; RICARDO AUGUSTO DINIZ CABRAL FERREIRA, UFLA; MAGNO ANTONIO PATTO RAMALHO, UFLA. |
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Título: |
Breeding common bean populations for traits using selection index. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Scientia Agricola, Piracicaba, v. 72, n. 2, p. 132-137, Mar./Apr. 2015. |
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DOI: |
10.1590/0103-9016-2014-0130 |
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Idioma: |
Inglês |
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Conteúdo: |
A common bean (Phaseolus vulgaris L.) cultivar must combine desirable genotypes for several traits in order to be accepted by producers and consumers. This study aimed to evaluate selection efficiency when segregating bean populations for traits, by means of a selection index, in order to obtain superior progenies for traits considered. A total of 16 populations from the F4 and F5 generations were evaluated in 2011 and 2012, respectively. The traits evaluated were plant architecture, plant disease, grain type and yield. Using standard scores (Z), the sum of the four traits (ΣZ) was obtained and, based on this information, the best populations were identified. The evaluation of selection effectiveness was performed on 31 progenies from each population. The 496 progenies plus eight controls were evaluated in the F5:6 and F5:7 generations for the same traits in July and November 2012, respectively. The selection, using the index based on the sum of standardized variables (ΣZ), was efficient for identifying populations with superior progenies for all the traits considered. |
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Thesagro: |
Doença de planta; Feijão; Melhoramento genético vegetal; Phaseolus vulgaris; População de planta. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/122496/1/CNPAF-2015sa.pdf
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Marc: |
LEADER 01811naa a2200229 a 4500
001 2013787
005 2015-05-15
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1590/0103-9016-2014-0130$2DOI
100 1 $aLIMA, D. C.
245 $aBreeding common bean populations for traits using selection index.$h[electronic resource]
260 $c2015
520 $aA common bean (Phaseolus vulgaris L.) cultivar must combine desirable genotypes for several traits in order to be accepted by producers and consumers. This study aimed to evaluate selection efficiency when segregating bean populations for traits, by means of a selection index, in order to obtain superior progenies for traits considered. A total of 16 populations from the F4 and F5 generations were evaluated in 2011 and 2012, respectively. The traits evaluated were plant architecture, plant disease, grain type and yield. Using standard scores (Z), the sum of the four traits (ΣZ) was obtained and, based on this information, the best populations were identified. The evaluation of selection effectiveness was performed on 31 progenies from each population. The 496 progenies plus eight controls were evaluated in the F5:6 and F5:7 generations for the same traits in July and November 2012, respectively. The selection, using the index based on the sum of standardized variables (ΣZ), was efficient for identifying populations with superior progenies for all the traits considered.
650 $aDoença de planta
650 $aFeijão
650 $aMelhoramento genético vegetal
650 $aPhaseolus vulgaris
650 $aPopulação de planta
700 1 $aABREU, A. de F. B.
700 1 $aFERREIRA, R. A. D. C.
700 1 $aRAMALHO, M. A. P.
773 $tScientia Agricola, Piracicaba$gv. 72, n. 2, p. 132-137, Mar./Apr. 2015.
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Registro original: |
Embrapa Arroz e Feijão (CNPAF) |
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Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
29/06/2020 |
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Data da última atualização: |
03/07/2020 |
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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: |
MACIEL, D. A.; SILVA, V. A.; ALVES, H. M. R.; VOLPATO, M. M. L.; BARBOSA, J. P. R. A. de; SOUZA, V. C. O.; SANTOS, M. O.; SILVEIRA, H. R. DE O.; DANTAS, M. F.; FREITAS, A. F. de; SANTOS, J. O. DOS. |
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Afiliação: |
DANIEL ANDRADE MACIEL, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; VÂNIA APARECIDA SILVA, EPAMIG; HELENA MARIA RAMOS ALVES, CNPCa; MARGARETE MARIN LORDELO VOLPATO, EPAMIG; JOÃO PAULO RODRIGUES ALVES DE BARBOSA, UNIVERSIDADE FEDERAL DE LAVRAS; VANESSA CRISTINA OLIVEIRA SOUZA, UNIVERSIDADE FEDERAL DE ITAJUBÁ; MELINE OLIVEIRA SANTOS, EPAMIG; HELBERT REZENDE DE OLIVEIRA SILVEIRA, EPAMIG; MAYARA FONTES DANTAS, EPAMIG; ANA FLÁVIA DE FREITAS, EPAMIG; JACQUELINE OLIVEIRA DOS SANTOS, EPAMIG. |
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Título: |
Leaf water potential of coffee estimated by landsat-8 images. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Plos One, v. 15, n. 3, e031019, Mar. 2020. |
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DOI: |
https://doi.org/10.1371/journal.pone.0230013 |
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Idioma: |
Português |
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Conteúdo: |
Traditionally, water conditions of coffee areas are monitored by measuring the leaf water potential throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the WW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the WW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate WW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers. |
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Thesagro: |
Área Foliar; Café; Potencial Hídrico; Produção Agrícola. |
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Thesaurus NAL: |
Coffee beans; Leaf water potential; Plantations; Remote sensing. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/214268/1/Leaf-water-petential.pdf
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Marc: |
LEADER 02267naa a2200349 a 4500
001 2123511
005 2020-07-03
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0230013$2DOI
100 1 $aMACIEL, D. A.
245 $aLeaf water potential of coffee estimated by landsat-8 images.$h[electronic resource]
260 $c2020
520 $aTraditionally, water conditions of coffee areas are monitored by measuring the leaf water potential throughout a pressure pump. However, there is a demand for the development of technologies that can estimate large areas or regions. In this context, the objective of this study was to estimate the WW by surface reflectance values and vegetation indices obtained from the Landsat-8/OLI sensor in Minas Gerais-Brazil Several algorithms using OLI bands and vegetation indexes were evaluated and from the correlation analysis, a quadratic algorithm that uses the Normalized Difference Vegetation Index (NDVI) performed better, with a correlation coefficient (R2) of 0.82. Leave-One-Out Cross-Validation (LOOCV) was performed to validate the models and the best results were for NDVI quadratic algorithm, presenting a Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. Subsequently, the NDVI quadratic algorithm was applied to Landsat-8 images, aiming to spatialize the WW estimated in a representative area of regional coffee planting between September 2014 to July 2015. From the proposed algorithm, it was possible to estimate WW from Landsat-8/OLI imagery, contributing to drought monitoring in the coffee area leading to cost reduction to the producers.
650 $aCoffee beans
650 $aLeaf water potential
650 $aPlantations
650 $aRemote sensing
650 $aÁrea Foliar
650 $aCafé
650 $aPotencial Hídrico
650 $aProdução Agrícola
700 1 $aSILVA, V. A.
700 1 $aALVES, H. M. R.
700 1 $aVOLPATO, M. M. L.
700 1 $aBARBOSA, J. P. R. A. de
700 1 $aSOUZA, V. C. O.
700 1 $aSANTOS, M. O.
700 1 $aSILVEIRA, H. R. DE O.
700 1 $aDANTAS, M. F.
700 1 $aFREITAS, A. F. de
700 1 $aSANTOS, J. O. DOS
773 $tPlos One$gv. 15, n. 3, e031019, Mar. 2020.
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Registro original: |
Embrapa Café (CNPCa) |
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