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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
14/09/2023 |
Actualizado : |
14/09/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
REBOLLO, I.; AGUILAR, I.; PÉREZ DE VIDA, F.; MOLINA, F.; GUTIÉRREZ, L.; ROSAS, J.E. |
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. |
Título : |
Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations. |
Complemento del título : |
Original article. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Crop Science. 2023, Volume 63, Issue 4, Pages 2220-2240. https://doi.org/10.1002/csc2.21029 -- OPEN ACCESS. |
ISSN : |
0011-183X (print); 1435-0653 (electronic). |
DOI : |
10.1002/csc2.21029 |
Idioma : |
Inglés |
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/ ) |
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 |
Palabras claves : |
Genotype by environment interaction (GEI); Random regression models (RRMs); Rice (Oryza sativa L.). |
Asunto categoría : |
-- |
URL : |
https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/csc2.21029
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Marc : |
LEADER 03749naa a2200253 a 4500 001 1064311 005 2023-09-14 008 2023 bl uuuu u00u1 u #d 022 $a0011-183X (print); 1435-0653 (electronic). 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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INIA Las Brujas (LB) |
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| Acceso al texto completo restringido a Biblioteca INIA Tacuarembó. Por información adicional contacte bibliotb@tb.inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
Fecha actual : |
24/09/2014 |
Actualizado : |
27/04/2020 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
REALINI, C.E.; FONT I FURNOLS, M.; SAÑUDO, C.; MONTOSSI, F.; OLIVER, M.A.; GUERRERO, L. |
Afiliación : |
C.E.REALINI, IRTA Monells, Girona, Spain; MARIA FONT I FURNOLS, IRTA Monells, Girona, Spain; C. SAÑUDO, University of Zaragoza, Zaragoza, Spain; FABIO MARCELO MONTOSSI PORCHILE, Instituto Nacional de Investigación Agropecuaria (INIA), Uruguay; M.A., IRTA, Girona, Spain; L. GUERRERO, IRTA Monells, Girona, Spain. |
Título : |
Spanish, French and British consumers' acceptability of Uruguayan beef, and consumers' beef choice associated with country of origin, finishing diet and meat price. |
Fecha de publicación : |
2013 |
Fuente / Imprenta : |
Meat Science, 2013, v. 95, no. 1, p. 14-21. http://dx.doi.org/10.1016/j.meatsci.2013.04.004 |
DOI : |
10.1016/j.meatsci.2013.04.004 |
Idioma : |
Inglés |
Notas : |
Article history: Received 5 February 2012; Received in revised form 25 February 2013; Accepted 4 April 2013. Acknowledgments: The authors wish to acknowledge the financial support of the Agencia Española de Cooperación Internacional para el Desarrollo (AECID), the INIA Uruguay, and the INIA España Corresponding author : carolina.realini@irta.es |
Contenido : |
The effect of country of origin (local, Switzerland, Argentina, Uruguay), finishing diet (grass plus concentrate, concentrate), and price (low, medium, high) on consumer's beef choice and segmentation was evaluated in Spain, France and United Kingdom. Sensory acceptability of Uruguayan beef from different production systems was also evaluated and contrasted with consumers' beef choices. Origin was the most important characteristic for the choice of beef with preference for meat produced locally. The second most important factor was animal feed followed by price with preference for beef from grass-fed animals and lowest price. The least preferred product was beef from Uruguay, concentrate-fed animals and highest price. Sensory data showed higher acceptability scores for Uruguayan beef from grass-fed animals with or without concentrate supplementation than animals fed concentrate only. Consumer segments with distinct preferences were identified. Foreign country promotion seems to be fundamental for marketing beef in Europe, as well as the development of different marketing strategies to satisfy each consumer segment. |
Palabras claves : |
BEEF; CONJOINT; CONSUMER; FEED; ORIGIN; PRICE. |
Thesagro : |
CARNE; URUGUAY. |
Asunto categoría : |
A50 Investigación agraria |
Marc : |
LEADER 02362naa a2200301 a 4500 001 1050506 005 2020-04-27 008 2013 bl uuuu u00u1 u #d 024 7 $a10.1016/j.meatsci.2013.04.004$2DOI 100 1 $aREALINI, C.E. 245 $aSpanish, French and British consumers' acceptability of Uruguayan beef, and consumers' beef choice associated with country of origin, finishing diet and meat price. 260 $c2013 500 $aArticle history: Received 5 February 2012; Received in revised form 25 February 2013; Accepted 4 April 2013. Acknowledgments: The authors wish to acknowledge the financial support of the Agencia Española de Cooperación Internacional para el Desarrollo (AECID), the INIA Uruguay, and the INIA España Corresponding author : carolina.realini@irta.es 520 $aThe effect of country of origin (local, Switzerland, Argentina, Uruguay), finishing diet (grass plus concentrate, concentrate), and price (low, medium, high) on consumer's beef choice and segmentation was evaluated in Spain, France and United Kingdom. Sensory acceptability of Uruguayan beef from different production systems was also evaluated and contrasted with consumers' beef choices. Origin was the most important characteristic for the choice of beef with preference for meat produced locally. The second most important factor was animal feed followed by price with preference for beef from grass-fed animals and lowest price. The least preferred product was beef from Uruguay, concentrate-fed animals and highest price. Sensory data showed higher acceptability scores for Uruguayan beef from grass-fed animals with or without concentrate supplementation than animals fed concentrate only. Consumer segments with distinct preferences were identified. Foreign country promotion seems to be fundamental for marketing beef in Europe, as well as the development of different marketing strategies to satisfy each consumer segment. 650 $aCARNE 650 $aURUGUAY 653 $aBEEF 653 $aCONJOINT 653 $aCONSUMER 653 $aFEED 653 $aORIGIN 653 $aPRICE 700 1 $aFONT I FURNOLS, M. 700 1 $aSAÑUDO, C. 700 1 $aMONTOSSI, F. 700 1 $aOLIVER, M.A. 700 1 $aGUERRERO, L. 773 $tMeat Science, 2013$gv. 95, no. 1, p. 14-21. http://dx.doi.org/10.1016/j.meatsci.2013.04.004
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