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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha : |
21/02/2014 |
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Actualizado : |
21/05/2018 |
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Tipo de producción científica : |
Documentos |
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Autor : |
CUADRO, R. |
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Afiliación : |
WASHINGTON ROBIN CUADRO LOPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Red fertilización de pasturas: fertilización fosfatada en pasturas. Sitio Experimental Glencoe. |
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Fecha de publicación : |
2010 |
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Fuente / Imprenta : |
ln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, Glencoe, 10 setiembre 2010, Paysandú, Uruguay. Pasturas y producción animal. Tacuarembó (Uruguay): INIA, 2010. |
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Páginas : |
p. 7-9 |
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Serie : |
(INIA Serie Actividades de Difusión ; 619) |
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Idioma : |
Español |
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Contenido : |
Objetivos: Seleccionar métodos de análisis según suelo y fuente P. Niveles críticos de P disponible para especie, suelo y profundidad. Niveles críticos para
P total en planta. Relación P agregado - P disponible para suelo, fuentes y profundidad (Equivalente Fertilizante). Evolución P disponible en el tiempo para suelos, fuentes, niveles y profundidad (Tasa de Descenso). Estudiar retención de P por el suelo como indicador de respuesta vegetal y relacionarlo con objetivos 4 y 5. Estudiar el efecto de las condiciones saturación de agua en el suelo en los valores de P disponible para diferentes suelos Estudio de la respuesta al agregado de azufre en diferentes suelos
Efecto de agregar S elemental en la eficiencia de la Fosforita Natural. |
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Thesagro : |
APLICACION DE ABONOS; FORRAJES; PRODUCCION ANIMAL; SUELO BASALTICO. |
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Asunto categoría : |
L01 Ganadería |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9686/1/SAD619p7-9.pdf
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Marc : |
LEADER 01463naa a2200193 a 4500
001 1026065
005 2018-05-21
008 2010 bl uuuu u00u1 u #d
100 1 $aCUADRO, R.
245 $aRed fertilización de pasturas$bfertilización fosfatada en pasturas. Sitio Experimental Glencoe.
260 $c2010
300 $ap. 7-9
490 $a(INIA Serie Actividades de Difusión ; 619)
520 $aObjetivos: Seleccionar métodos de análisis según suelo y fuente P. Niveles críticos de P disponible para especie, suelo y profundidad. Niveles críticos para P total en planta. Relación P agregado - P disponible para suelo, fuentes y profundidad (Equivalente Fertilizante). Evolución P disponible en el tiempo para suelos, fuentes, niveles y profundidad (Tasa de Descenso). Estudiar retención de P por el suelo como indicador de respuesta vegetal y relacionarlo con objetivos 4 y 5. Estudiar el efecto de las condiciones saturación de agua en el suelo en los valores de P disponible para diferentes suelos Estudio de la respuesta al agregado de azufre en diferentes suelos Efecto de agregar S elemental en la eficiencia de la Fosforita Natural.
650 $aAPLICACION DE ABONOS
650 $aFORRAJES
650 $aPRODUCCION ANIMAL
650 $aSUELO BASALTICO
773 $tln: INIA Tacuarembó. Unidad Experimental Glencoe. Día de campo, Glencoe, 10 setiembre 2010, Paysandú, Uruguay. Pasturas y producción animal. Tacuarembó (Uruguay): INIA, 2010.
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Registro original : |
INIA Tacuarembó (TBO) |
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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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