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Registro completo
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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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 | Acceso al texto completo restringido a Biblioteca INIA La Estanzuela. Por información adicional contacte bib_le@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela; INIA Tacuarembó. |
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Fecha actual : |
11/05/2020 |
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Actualizado : |
27/01/2021 |
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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 : |
LÓPEZ-GONZÁLEZ, F.A.; RODRIGO ALLENDE, R.; SOARES DE LIMA, J.M.; CANOZZI, M.E.A.; GIL, A.; BARCELLOS, J.O.J. |
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Afiliación : |
FREDY ANDREY LÓPEZ-GONZÁLEZ, Department of Animal Science, Federal University of Rio Grande do Sul, 7.712 Bento Gonçalves Ave., Porto Alegre, Rio Grande do Sul 91540-000, Brazil.; RODRIGO ALLENDE, Department of Animal Science, Faculty of Veterinary Sciences, University of Concepción, Chillán, Chile.; JUAN MANUEL SOARES DE LIMA LAPETINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARÍA EUGENIA ANDRIGHETTO CANOZZI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; AMIR GIL, Sessim Department of Animal Science, Federal University of Rio Grande do Sul, 7.712 Bento Gonçalves Ave., Porto Alegre, Rio Grande do Sul 91540-000, Brazil.; JÚLIO OTÁVIO JARDIM BARCELLOS, Department of Animal Science, Federal University of Rio Grande do Sul, 7.712 Bento Gonçalves Ave., Porto Alegre, Rio Grande do Sul 91540-000, Brazil. |
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Título : |
Intensification of cow-calf production: How does the system respond biologically to energy inputs in a long-term horizon?. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Livestock Science, Volume 237, July 2020, 104058. Doi: https://doi.org/10.1016/j.livsci.2020.104058. |
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DOI : |
10.1016/j.livsci.2020.104058 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 August 2019// Received in revised form 22 March 2020// Accepted 9 April 2020 //Available online 26 April 2020- Corresponding author. E-mail address: julio.barcellos@ufrgs.br (J.O.J. Barcellos).This study was ?nanced in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001. |
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Contenido : |
Abstract:
In southern Brazil, beef cattle production systems generally rely on grazing on natural pastures. However, their forage production, and consequently metabolizable energy (ME) production, is seasonal and influenced by climatic events. Thus, there is a scientific and commercial interest in evaluating and understanding the biological impacts of intensification using pasture irrigation and the effects of El Niño-Southern Oscillation (ENSO) phenomena on the long term on the productivity of cow-calf systems. Therefore, our objective was to develop a simulation model to evaluate the effects of intensification levels, using cultivated pastures and irrigation, on the productivity and on the efficiency metabolizable energy utilization of beef cow-calf systems in a 10-year horizon. This period allows capturing the effects of several production cycles as influenced by ENSO events. The model includes three submodels: herd structure, herd ME requirements, and forage ME production. The results of the present study demonstrate that the proposed model is able to evaluate the influence of intensification of grazing systems on metabolizable energy production, carrying capacity, productivity and biological efficiency of beef cow-calf systems over a long-term horizon. Productivity was increased in 15.9% when 20% of the grazing area was intensified and irrigated compared with the modeled non-intensified system, independently of climatic events. The main productive response was the increase in the number of dams in the herd, especially as a result of the use of irrigation. This study proposes different alternatives for increasing the productivity of beef cow-calf systems in southern Brazil. MenosAbstract:
In southern Brazil, beef cattle production systems generally rely on grazing on natural pastures. However, their forage production, and consequently metabolizable energy (ME) production, is seasonal and influenced by climatic events. Thus, there is a scientific and commercial interest in evaluating and understanding the biological impacts of intensification using pasture irrigation and the effects of El Niño-Southern Oscillation (ENSO) phenomena on the long term on the productivity of cow-calf systems. Therefore, our objective was to develop a simulation model to evaluate the effects of intensification levels, using cultivated pastures and irrigation, on the productivity and on the efficiency metabolizable energy utilization of beef cow-calf systems in a 10-year horizon. This period allows capturing the effects of several production cycles as influenced by ENSO events. The model includes three submodels: herd structure, herd ME requirements, and forage ME production. The results of the present study demonstrate that the proposed model is able to evaluate the influence of intensification of grazing systems on metabolizable energy production, carrying capacity, productivity and biological efficiency of beef cow-calf systems over a long-term horizon. Productivity was increased in 15.9% when 20% of the grazing area was intensified and irrigated compared with the modeled non-intensified system, independently of climatic events. The main productive response was the incre... Presentar Todo |
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Palabras claves : |
BIOLOGICAL EFFICENCY; EL NIÑO-SOUTHERN OSCILLATION(ENSO); HERD STRUCTURE; INTENSIFICACIÓN DE LA PRODUCCIÓN; IRRIGATION MODELING. |
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Asunto categoría : |
L01 Ganadería |
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Marc : |
LEADER 02980naa a2200265 a 4500
001 1061050
005 2021-01-27
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1016/j.livsci.2020.104058$2DOI
100 1 $aLÓPEZ-GONZÁLEZ, F.A.
245 $aIntensification of cow-calf production$bHow does the system respond biologically to energy inputs in a long-term horizon?.$h[electronic resource]
260 $c2020
500 $aArticle history: Received 21 August 2019// Received in revised form 22 March 2020// Accepted 9 April 2020 //Available online 26 April 2020- Corresponding author. E-mail address: julio.barcellos@ufrgs.br (J.O.J. Barcellos).This study was ?nanced in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001.
520 $aAbstract: In southern Brazil, beef cattle production systems generally rely on grazing on natural pastures. However, their forage production, and consequently metabolizable energy (ME) production, is seasonal and influenced by climatic events. Thus, there is a scientific and commercial interest in evaluating and understanding the biological impacts of intensification using pasture irrigation and the effects of El Niño-Southern Oscillation (ENSO) phenomena on the long term on the productivity of cow-calf systems. Therefore, our objective was to develop a simulation model to evaluate the effects of intensification levels, using cultivated pastures and irrigation, on the productivity and on the efficiency metabolizable energy utilization of beef cow-calf systems in a 10-year horizon. This period allows capturing the effects of several production cycles as influenced by ENSO events. The model includes three submodels: herd structure, herd ME requirements, and forage ME production. The results of the present study demonstrate that the proposed model is able to evaluate the influence of intensification of grazing systems on metabolizable energy production, carrying capacity, productivity and biological efficiency of beef cow-calf systems over a long-term horizon. Productivity was increased in 15.9% when 20% of the grazing area was intensified and irrigated compared with the modeled non-intensified system, independently of climatic events. The main productive response was the increase in the number of dams in the herd, especially as a result of the use of irrigation. This study proposes different alternatives for increasing the productivity of beef cow-calf systems in southern Brazil.
653 $aBIOLOGICAL EFFICENCY
653 $aEL NIÑO-SOUTHERN OSCILLATION(ENSO)
653 $aHERD STRUCTURE
653 $aINTENSIFICACIÓN DE LA PRODUCCIÓN
653 $aIRRIGATION MODELING
700 1 $aRODRIGO ALLENDE, R.
700 1 $aSOARES DE LIMA, J.M.
700 1 $aCANOZZI, M.E.A.
700 1 $aGIL, A.
700 1 $aBARCELLOS, J.O.J.
773 $tLivestock Science, Volume 237, July 2020, 104058. Doi: https://doi.org/10.1016/j.livsci.2020.104058.
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