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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
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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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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Registro original : |
INIA La Estanzuela (LE) |
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
15/11/2015 |
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Actualizado : |
15/11/2015 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Nacionales |
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Circulación / Nivel : |
B - 2 |
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Autor : |
BERRUETA, C.; DOGLIOTTI, S.; FRANCO, J. |
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Afiliación : |
MARIA CECILIA BERRUETA MOREIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Análisis y jerarquización de factores determinantes del rendimiento de tomate para industria en Uruguay. |
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Fecha de publicación : |
2012 |
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Fuente / Imprenta : |
Agrociencia Uruguay, 2012, v.16, no.2, p. 39-48. |
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Idioma : |
Español |
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Contenido : |
RESUMEN.
El rendimiento potencial del cultivo de tomate para industria en el Uruguay según experimentos en el país es de 90 Mg ha-1. En contraposición, la productividad promedio del cultivo a nivel comercial no supera los 50 Mg ha-1. Este trabajo tiene por objetivo determinar cuales son las causas principales que determinan las diferencias de rendimiento entre productores de tomate para industria, estableciendo un orden jerárquico de factores determinantes. Para esto, se realizó un análisis de los factores que afectaron el rendimiento en los sistemas de producción en la zafra 2007/08. La metodología se basó en un muestreo estratificado de productores. Se formó una muestra de 22 productores, en los cuales se relevaron variables relacionadas al sistema de producción, al sistema de manejo y se midió el rendimiento. Dichas variables se clasificaron en niveles para realizar el análisis de varianza y las que resultaron significativas se incluyeron en un modelo mixto. A partir del análisis, se concluyó que la variable que explicó en mayor medida las diferencias en rendimiento para la zafra en estudio fue el agua disponible (43% de la variación total). La aplicación de cama de pollo siguió en importancia y explicó el 21% de la variación de rendimiento. Otras variables significativas fueron la densidad de plantas y la variedad.
SUMMARY. Analysis and Hierarchy of Yield Determinant Factors on Tomato for Processing in Uruguay.
The potential yield of tomato crops grown for processing in Uruguay is 90 Mg ha-1, according to experiments in the country. In contrast, the average productivity of commercial farmers does not exceed 50 Mg ha-1. This study aims to explain the main causes of the differences in yield among growers of tomato for processing, establishing a hierarchical order of the determinant factors. For this, we performed an analysis of factors affecting performance in production systems in 2007/08 harvest. The methodology was based on a stratified sample of producers. Within this sample of 22 farmers, we measured and collected information on several variables related to the farming systems, the crop management systems and the performance of the crop. These variables were classified into levels for the analysis of variance, and the ones that were significant were included in a mixed model. From this analysis, we concluded that the variable that explained further the differences in yield for the crop under study was the water available (43% of total variation). The application of poultry litter followed in importance and explained 21% of yield variation. Other significant variables were plant density and variety. MenosRESUMEN.
El rendimiento potencial del cultivo de tomate para industria en el Uruguay según experimentos en el país es de 90 Mg ha-1. En contraposición, la productividad promedio del cultivo a nivel comercial no supera los 50 Mg ha-1. Este trabajo tiene por objetivo determinar cuales son las causas principales que determinan las diferencias de rendimiento entre productores de tomate para industria, estableciendo un orden jerárquico de factores determinantes. Para esto, se realizó un análisis de los factores que afectaron el rendimiento en los sistemas de producción en la zafra 2007/08. La metodología se basó en un muestreo estratificado de productores. Se formó una muestra de 22 productores, en los cuales se relevaron variables relacionadas al sistema de producción, al sistema de manejo y se midió el rendimiento. Dichas variables se clasificaron en niveles para realizar el análisis de varianza y las que resultaron significativas se incluyeron en un modelo mixto. A partir del análisis, se concluyó que la variable que explicó en mayor medida las diferencias en rendimiento para la zafra en estudio fue el agua disponible (43% de la variación total). La aplicación de cama de pollo siguió en importancia y explicó el 21% de la variación de rendimiento. Otras variables significativas fueron la densidad de plantas y la variedad.
SUMMARY. Analysis and Hierarchy of Yield Determinant Factors on Tomato for Processing in Uruguay.
The potential yield of tomato crops grown for processing ... Presentar Todo |
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Thesagro : |
BRECHAS DE RENDIMIENTO; CAMA DE POLLO; DEFICIT HIDRICO; SISTEMAS DE CULTIVO; SOLANUM LYCOPERSICUM; TOMATE. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/5207/1/Berrueta-C.-2012.-Agrociencia-v.162-p.39-48.pdf
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Marc : |
LEADER 03307naa a2200217 a 4500
001 1053904
005 2015-11-15
008 2012 bl uuuu u00u1 u #d
100 1 $aBERRUETA, C.
245 $aAnálisis y jerarquización de factores determinantes del rendimiento de tomate para industria en Uruguay.
260 $c2012
520 $aRESUMEN. El rendimiento potencial del cultivo de tomate para industria en el Uruguay según experimentos en el país es de 90 Mg ha-1. En contraposición, la productividad promedio del cultivo a nivel comercial no supera los 50 Mg ha-1. Este trabajo tiene por objetivo determinar cuales son las causas principales que determinan las diferencias de rendimiento entre productores de tomate para industria, estableciendo un orden jerárquico de factores determinantes. Para esto, se realizó un análisis de los factores que afectaron el rendimiento en los sistemas de producción en la zafra 2007/08. La metodología se basó en un muestreo estratificado de productores. Se formó una muestra de 22 productores, en los cuales se relevaron variables relacionadas al sistema de producción, al sistema de manejo y se midió el rendimiento. Dichas variables se clasificaron en niveles para realizar el análisis de varianza y las que resultaron significativas se incluyeron en un modelo mixto. A partir del análisis, se concluyó que la variable que explicó en mayor medida las diferencias en rendimiento para la zafra en estudio fue el agua disponible (43% de la variación total). La aplicación de cama de pollo siguió en importancia y explicó el 21% de la variación de rendimiento. Otras variables significativas fueron la densidad de plantas y la variedad. SUMMARY. Analysis and Hierarchy of Yield Determinant Factors on Tomato for Processing in Uruguay. The potential yield of tomato crops grown for processing in Uruguay is 90 Mg ha-1, according to experiments in the country. In contrast, the average productivity of commercial farmers does not exceed 50 Mg ha-1. This study aims to explain the main causes of the differences in yield among growers of tomato for processing, establishing a hierarchical order of the determinant factors. For this, we performed an analysis of factors affecting performance in production systems in 2007/08 harvest. The methodology was based on a stratified sample of producers. Within this sample of 22 farmers, we measured and collected information on several variables related to the farming systems, the crop management systems and the performance of the crop. These variables were classified into levels for the analysis of variance, and the ones that were significant were included in a mixed model. From this analysis, we concluded that the variable that explained further the differences in yield for the crop under study was the water available (43% of total variation). The application of poultry litter followed in importance and explained 21% of yield variation. Other significant variables were plant density and variety.
650 $aBRECHAS DE RENDIMIENTO
650 $aCAMA DE POLLO
650 $aDEFICIT HIDRICO
650 $aSISTEMAS DE CULTIVO
650 $aSOLANUM LYCOPERSICUM
650 $aTOMATE
700 1 $aDOGLIOTTI, S.
700 1 $aFRANCO, J.
773 $tAgrociencia Uruguay, 2012$gv.16, no.2, p. 39-48.
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