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| Registros recuperados : 6 | |
| 2. |  | ZUBIETA, Á.S.; ; SAVIAN, J.V.; DE SOUZA FILHO, W.; WALLAU, M.O.; GÓMEZ, A.M.; BINDELLE, J.; ; BONNET, O.J.F.; DE FACCIO CARVALHO, P.C. Does grazing management provide opportunities to mitigate methane emissions by ruminants in pastoral ecosystems? (Review). Science of the Total Environment, 1 February 2021, Volume 754, Article number 142029. DOI: https://doi-org.proxy.timbo.org.uy/10.1016/j.scitotenv.2020.142029 Article history: Received 18 May 2020 // Received in revised form 26 August 2020 // Accepted 26 August 2020 // Available online 31 August 2020| Biblioteca(s): INIA Treinta y Tres. |
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| 3. | | DE AZEVEDO, E.B.; SAVIAN, J.V.; DO AMARAL, G. A.; DE DAVID, D. B.; GERE, J.I.; MOURA, M.; BREMM, C.; JOCHIMS, F.; ZUBIETA, A. S.; GONDA, H. L.; BAYER, C.; DE FACCIO CARVALHO, P.C. Feed intake, methane yield, and efciency of utilization of energy and nitrogen by sheep fed tropical grasses. Tropical Animal Health and Production, 2021, volume 53, Article number 452. doi: https://doi.org/10.1007/s11250-021-02928-4 Article history: Received: 10 July 2020; Accepted: 10 September 2021; Published online: 18 September 2021.| Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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| 4. | | PORTUGAL, T. B.; SZYMCZAK, L. S.; DE MORAES, A.; FONSECA, L.; MEZZALIRA, J.C.; SAVIAN, J.V.; ZUBIETA, A. S.; BREMM, C.; DE FACCIO CARVALHO, P. C.; MONTEIRO, A. L. G. Low-intensity, high-frequency grazing strategy increases herbage production and beef cattle performance on sorghum pastures. Animals 2022, volume 12, number 1, 13 pages. OPEN ACCESS. doi: https://doi.org/10.3390/ani12010013 Article history: Received: 17 October 2021 / Revised: 8 November 2021 / Accepted: 10 November 2021 / Published: 22 December 2021 .| Biblioteca(s): INIA Treinta y Tres. |
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| 5. | | CEZIMBRA, I.M.; DE ALBUQUERQUE NUNES, P.A.; DE SOUZA FILHO, W.; TISCHLER, M.R.; GENRO, T.C.M.; BAYER, C.; SAVIAN, J.V.; BONNET, O.J.F.; SOUSSANA, J.-F.; DE FACCIO CARVALHO, P.C. Potential of grazing management to improve beef cattle production and mitigate methane emissions in native grasslands of the Pampa biome. Science of the Total Environment, 2021, Volume 780, Article number 146582. Doi: https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.146582 Article history: Received 19 August 2020; Received in revised form 15 March 2021; Accepted 15 March 2021; Available online 19 March 2021.
Editor: Elena Paoletti.| Biblioteca(s): INIA Las Brujas. |
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| 6. | | PORTUGAL, T. B.; DE FACCIO CARVALHO, P. C.; DE CAMPOS, B.M.; SZYMCZAK, L.S.; SAVIAN, J.V.; ZUBIETA, A.S.; DE SOUZA FILHO, W.; ROSSETTO, J.; BREMM, C.; DE OLIVEIRA, L.B.; DE MORAES, A.; BAYER, C.; GOMES MONTEIRO, A.L. Methane emissions and growth performance of beef cattle grazing multi-species swards in different pesticide-free integrated crop-livestock systems in southern Brazil. Journal of Cleaner Production, 15 August 2023, Volume 414, Article 137536. https://doi.org/10.1016/j.jclepro.2023.137536 Article history: Received 28 December 2022; Received in revised form 16 May 2023; Accepted 19 May 2023; Available online 22 May 2023. -- Correspondence author: Portugal, T.B.; Department of Crop Production and Protection, Federal...| Biblioteca(s): INIA Las Brujas. |
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| Registros recuperados : 6 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
14/09/2023 |
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Actualizado : |
14/09/2023 |
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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 : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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Afiliación : |
MARÍA INÉS REBOLLO PANUNCIO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO MOLINA CASELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIÉRREZEPARTMENT OF STATISTICS, UNIVERSITY DE LA REPÚBLICA, COLLEGE OF AGRICULTURE, GARZÓN 780, MONTEVIDEO, MONTEVIDEO, URUGUAY DEPARTMENT OF AGRONOMY, UNIVERSITY OF WISCONSIN–MADISON, 1575 LINDEN DRIVE, MADISON, WI, UNITED STATES, Department of Statistics, University de la República, College of Agriculture, Montevideo, Uruguay; Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI, United States; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Statistics, University de la República, College of Agriculture, Garzón 780, Montevideo, Montevideo, Uruguay. |
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Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
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ISSN : |
0011-183X (print); 1435-0653 (electronic). |
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DOI : |
10.1002/csc2.21029 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ ) |
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Contenido : |
ABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America. MenosABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiati... Presentar Todo |
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Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
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Asunto categoría : |
-- |
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URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
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024 7 $a10.1002/csc2.21029$2DOI
100 1 $aREBOLLO, I.
245 $aGenotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 21 November 2022, Accepted 10 May 2023, Published online 16 June 2023. -- Correspondence: Rosas, J.E.; INIA, Estación Experimental Treinta y Tres, Road 8 km 281, Treinta y Tres, Uruguay; email:jrosas@inia.org.uy -- FUNDING: Funding for this project was provided by Instituto Nacional de Investigación Agropecuaria (Projects AZ35, AZ13, and fellowship to I. R.), Agencia Nacional de Investigación Agropecuaria (grant MOV_CA_2019_1_156241), Comisión Sectorial de Investigación Científica, Universidad de la República (grant Iniciación a la Investgación 2019 No. 8), Comité Académico de Posgrado (fellowship to I. R.), and the Agriculture and Food Research Initiative Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA National Institute of Food and Agriculture. -- LICENSE: This is an open access article under the terms of theCreative Commons Attribution-NonCommercial (http://creativecommons.org/licenses/by-nc/4.0/ )
520 $aABSTRACT.- Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype-specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega-environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single-step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank-change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME. © 2023 The Authors. Crop Science © 2023 Crop Science Society of America.
653 $aGenotype by environment interaction (GEI)
653 $aRandom regression models (RRMs)
653 $aRice (Oryza sativa L.)
700 1 $aAGUILAR, I.
700 1 $aPÉREZ DE VIDA, F.
700 1 $aMOLINA, F.
700 1 $aGUTIÉRREZ, L.
700 1 $aROSAS, J.E.
773 $tCrop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS.
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