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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
13/12/2016 |
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Actualizado : |
09/06/2022 |
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Autor : |
BAUMGARDT, B.R.; MARTIN, M.A. (Ed.). |
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Afiliación : |
BILL R. BAUMGARDT; MARSHALL A. MARTIN. |
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Título : |
Agricultural biotechnology: issues and choices. |
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Fecha de publicación : |
1991 |
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Fuente / Imprenta : |
Indiana (USA): Purdue University, 1991 |
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Páginas : |
181p. |
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Serie : |
Information for Decision Makers |
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ISBN : |
0-931682-28-2 |
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Idioma : |
Inglés |
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Notas : |
Uno de los ejemplares es una carpeta con los resúmenes de las exposiciones. |
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Thesagro : |
ANALISIS DE COSTOS Y BENEFICIOS; AUMENTO DE PRODUCCION; BIOTECNOLOGIA ANIMAL; BIOTECNOLOGIA VEGETAL; CAMBIO TECNOLOGICO; COOPERACION; DESARROLLO ECONOMICO Y SOCIAL; ETICA; FIJACION DEL NITROGENO; FITOMEJORAMIENTO; FOTOSINTESIS; GENETICA; INGENIERIA GENETICA; INNOVACION; INVESTIGACION; MANEJO DEL CULTIVO; MEJORAMIENTO ANIMAL; ORDENACION DE CULTIVOS; PAISES EN DESARROLLO; PATENTES; SISTEMAS DE VALORES; TRANSFERENCIA DE TECNOLOGIA. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 01258nam a2200421 a 4500
001 1056271
005 2022-06-09
008 1991 bl uuuu u00u1 u #d
020 $a0-931682-28-2
100 1 $aBAUMGARDT, B.R.
245 $aAgricultural biotechnology$bissues and choices.
260 $aIndiana (USA): Purdue University$c1991
300 $a181p.
490 $aInformation for Decision Makers
500 $aUno de los ejemplares es una carpeta con los resúmenes de las exposiciones.
650 $aANALISIS DE COSTOS Y BENEFICIOS
650 $aAUMENTO DE PRODUCCION
650 $aBIOTECNOLOGIA ANIMAL
650 $aBIOTECNOLOGIA VEGETAL
650 $aCAMBIO TECNOLOGICO
650 $aCOOPERACION
650 $aDESARROLLO ECONOMICO Y SOCIAL
650 $aETICA
650 $aFIJACION DEL NITROGENO
650 $aFITOMEJORAMIENTO
650 $aFOTOSINTESIS
650 $aGENETICA
650 $aINGENIERIA GENETICA
650 $aINNOVACION
650 $aINVESTIGACION
650 $aMANEJO DEL CULTIVO
650 $aMEJORAMIENTO ANIMAL
650 $aORDENACION DE CULTIVOS
650 $aPAISES EN DESARROLLO
650 $aPATENTES
650 $aSISTEMAS DE VALORES
650 $aTRANSFERENCIA DE TECNOLOGIA
700 1 $aMARTIN, M.A.
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Registro original : |
INIA Treinta y Tres (TT) |
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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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INIA La Estanzuela (LE) |
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