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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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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
30/05/2023 |
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Actualizado : |
30/05/2023 |
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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 : |
CAZZULI, F.; SÁNCHEZ, J.; HIRIGOYEN, A.; ROVIRA, P.J.; BERETTA, V.; SIMEONE, A.; JAURENA, M.; DURANTE, M.; SAVIAN, J.V.; POPPI, D.; MONTOSSI, F.; LAGOMARSINO, X.; LUZARDO, S.; BRITO, G.; VELAZCO, J.I.; BREMM, C.; LATTANZI, F. |
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Afiliación : |
FIORELLA CARLA CAZZULI ALBA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAVIER SÁNCHEZ, Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PEI, Canada; ANDRES EDUARDO HIRIGOYEN DOMINGUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PABLO JUAN ROVIRA SANZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VIRGINIA BERETTA, Animal Science Department, Universidad de la República, Paysandú, Uruguay; ÁLVARO SIMEONE, Animal Science Department, Universidad de la República, Paysandú, Uruguay; MARTIN ALEJANDRO JAURENA BARRIOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTÍN DURANTE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Estación Experimental Agropecuaria Concepción del Uruguay, Concepción del Uruguay, Argentina; JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DENNIS POPPI, School of Agriculture and Food Sciences, The University of Queensland, Gatton, Australia; FABIO MARCELO MONTOSSI PORCHILE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; XIMENA MARIA LAGOMARSINO LARRIERA, FCA-UDE, Facultad de Ciencias Agrarias, Universidad de la Empresa, Montevideo, Uruguay; SANTIAGO FELIPE LUZARDO VILLAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO WALTER BRITO DIAZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ IGNACIO VELAZCO DE LOS REYES, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA BREMM, Grazing Ecology Research Group, Federal University of Rio Grande do Sul, Porto Alegre, Brazil; FERNANDO A. LATTANZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Supplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter: a collated analysis. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Translational Animal Science. 2023, Volume 7, Issue 1, txad028. https://doi.org/10.1093/tas/txad028 -- OPEN ACCESS |
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ISSN : |
2573-2102 (online) |
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DOI : |
10.1093/tas/txad028 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 03 October 2022; Accepted 09 March 2023; Published 10 March 2023; Corrected and typeset 01 April 2023. -- Corresponding author: fcazzuli@inia.org.uy -- Issue Section: Forage Based Livestock Systems. -- License: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), -- Supplementary data: txad028_suppl_Supplementary_Data - docx file |
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Contenido : |
Supplementing growing cattle grazing native subtropical Campos grasslands during winter improves the low, even negative, average daily weight gain (ADG) typical of extensive animal production systems in Uruguay. Nonetheless, to render the practice profitable, it is crucial to control supplement feed efficiency (SFE), that is, the difference in ADG between supplemented and control animals (ADGchng) per unit of supplement dry matter (DM) intake. Little has been studied specifically on how SFE varies in these systems. The objective of this study was to quantify the magnitude and variation in SFE of growing beef cattle grazing stockpiled native Campos grasslands during winter and assess putative associations with herbage, animals, supplements, and climatic variables. We compiled data from supplementation trials carried out in Uruguay between 1993 and 2018, each evaluating between one and six supplementation treatments. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. |
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Palabras claves : |
Concentrate supplementation; Growing cattle; Native grasslands; Nutritive value; Protein; Supplement feed efficiency. |
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Asunto categoría : |
L02 Alimentación animal |
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URL : |
https://academic.oup.com/tas/article-pdf/7/1/txad028/49725363/txad028.pdf
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Marc : |
LEADER 02746naa a2200421 a 4500
001 1064170
005 2023-05-30
008 2023 bl uuuu u00u1 u #d
022 $a2573-2102 (online)
024 7 $a10.1093/tas/txad028$2DOI
100 1 $aCAZZULI, F.
245 $aSupplement feed efficiency of growing beef cattle grazing native Campos grasslands during winter$ba collated analysis.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 03 October 2022; Accepted 09 March 2023; Published 10 March 2023; Corrected and typeset 01 April 2023. -- Corresponding author: fcazzuli@inia.org.uy -- Issue Section: Forage Based Livestock Systems. -- License: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), -- Supplementary data: txad028_suppl_Supplementary_Data - docx file
520 $aSupplementing growing cattle grazing native subtropical Campos grasslands during winter improves the low, even negative, average daily weight gain (ADG) typical of extensive animal production systems in Uruguay. Nonetheless, to render the practice profitable, it is crucial to control supplement feed efficiency (SFE), that is, the difference in ADG between supplemented and control animals (ADGchng) per unit of supplement dry matter (DM) intake. Little has been studied specifically on how SFE varies in these systems. The objective of this study was to quantify the magnitude and variation in SFE of growing beef cattle grazing stockpiled native Campos grasslands during winter and assess putative associations with herbage, animals, supplements, and climatic variables. We compiled data from supplementation trials carried out in Uruguay between 1993 and 2018, each evaluating between one and six supplementation treatments. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.
653 $aConcentrate supplementation
653 $aGrowing cattle
653 $aNative grasslands
653 $aNutritive value
653 $aProtein
653 $aSupplement feed efficiency
700 1 $aSÁNCHEZ, J.
700 1 $aHIRIGOYEN, A.
700 1 $aROVIRA, P.J.
700 1 $aBERETTA, V.
700 1 $aSIMEONE, A.
700 1 $aJAURENA, M.
700 1 $aDURANTE, M.
700 1 $aSAVIAN, J.V.
700 1 $aPOPPI, D.
700 1 $aMONTOSSI, F.
700 1 $aLAGOMARSINO, X.
700 1 $aLUZARDO, S.
700 1 $aBRITO, G.
700 1 $aVELAZCO, J.I.
700 1 $aBREMM, C.
700 1 $aLATTANZI, F.
773 $tTranslational Animal Science. 2023, Volume 7, Issue 1, txad028. https://doi.org/10.1093/tas/txad028 -- OPEN ACCESS
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