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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
18/11/2016 |
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Actualizado : |
29/11/2016 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
VICENTE, E.; ARES, G.; RODRIGUEZ, G.; VARELA, P.; BOLOGNA, F.; LADO, J. |
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Afiliación : |
CARLOS ESTEBAN VICENTE CASTRO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN ARES, Universidad de la República (UdelaR)/ Facultad de Química; GUSTAVO ROBERTO RODRIGUEZ LAGOUTTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO NICOLAS VARELA PESSOLANO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FRANCO DAMIAN BOLOGNA FERNANDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOANNA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Selection of promising sweet potato clones using projective mapping. |
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Complemento del título : |
Research article. |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of the Science of Food & Agriculture, 2017, v.97, no.1, p.158-164. |
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DOI : |
10.1002/jsfa.7704 |
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Idioma : |
Inglés |
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Notas : |
Article information: Issue online: 7 November 2016 // Version of record online: 13 April 2016 // Accepted manuscript online: 9 March 2016 // Manuscript Accepted: 3 March 2016
Manuscript Revised: 29 February 2016 // Manuscript Received: 22 December 2015 |
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Palabras claves : |
BREEDING; PROJECTIVE MAPPING; SENSORY ANALYSIS; SWEETPOTATO. |
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Thesagro : |
BONIATO; FITOMEJORAMIENTO; IPOMOEA BATATA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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Marc : |
LEADER 01022naa a2200277 a 4500
001 1056096
005 2016-11-29
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1002/jsfa.7704$2DOI
100 1 $aVICENTE, E.
245 $aSelection of promising sweet potato clones using projective mapping.$h[electronic resource]
260 $c2016
500 $aArticle information: Issue online: 7 November 2016 // Version of record online: 13 April 2016 // Accepted manuscript online: 9 March 2016 // Manuscript Accepted: 3 March 2016 Manuscript Revised: 29 February 2016 // Manuscript Received: 22 December 2015
650 $aBONIATO
650 $aFITOMEJORAMIENTO
650 $aIPOMOEA BATATA
653 $aBREEDING
653 $aPROJECTIVE MAPPING
653 $aSENSORY ANALYSIS
653 $aSWEETPOTATO
700 1 $aARES, G.
700 1 $aRODRIGUEZ, G.
700 1 $aVARELA, P.
700 1 $aBOLOGNA, F.
700 1 $aLADO, J.
773 $tJournal of the Science of Food & Agriculture, 2017$gv.97, no.1, p.158-164.
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Registro original : |
INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
04/01/2018 |
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Actualizado : |
30/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
GONZALEZ-BARRIOS, P.; CASTRO, M.; PÉREZ, O.; VILARÓ, D.; GUTIÉRREZ, G. |
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Afiliación : |
PABLO GONZALEZ-BARRIOS,; MARINA CASTRO DERENYI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OSVALDO MARTIN PÉREZ GONZÁLEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DIEGO VILARÓ; LUCÍA GUTIÉRREZ. |
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Título : |
Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Spanish Journal of Agricultural Research, v.15. n.4, e0705, 2017. |
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DOI : |
10.5424/sjar/2017154-11016 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 06 Jan 2017, Accepted: 01 Dec 2017. |
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Contenido : |
Abstract:
Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars. MenosAbstract:
Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and f... Presentar Todo |
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Palabras claves : |
GENOTYPE BY ENVIRONMENT INTERACTION; MULTI-ENVIRONMENT TRIALS; NETWORK EFFICIENCY; SUNFLOWER; YIELD STABILITY. |
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Thesagro : |
GIRASOL; INTERACCIÓN GENOTIPO AMBIENTE. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/8628/1/SJAR.2017.v.15.n.4.pdf
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Marc : |
LEADER 02709naa a2200277 a 4500
001 1057950
005 2020-01-30
008 2017 bl uuuu u00u1 u #d
024 7 $a10.5424/sjar/2017154-11016$2DOI
100 1 $aGONZALEZ-BARRIOS, P.
245 $aGenotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: 06 Jan 2017, Accepted: 01 Dec 2017.
520 $aAbstract: Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.
650 $aGIRASOL
650 $aINTERACCIÓN GENOTIPO AMBIENTE
653 $aGENOTYPE BY ENVIRONMENT INTERACTION
653 $aMULTI-ENVIRONMENT TRIALS
653 $aNETWORK EFFICIENCY
653 $aSUNFLOWER
653 $aYIELD STABILITY
700 1 $aCASTRO, M.
700 1 $aPÉREZ, O.
700 1 $aVILARÓ, D.
700 1 $aGUTIÉRREZ, G.
773 $tSpanish Journal of Agricultural Research$gv.15. n.4, e0705, 2017.
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