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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
05/07/2023 |
Actualizado : |
12/07/2023 |
Tipo de producción científica : |
Libros |
Autor : |
BLUMETTO, O.; DE BARBIERI, I.; NAVAJAS, E.; BAETHGEN, W.; BECOÑA, G.; CIAPPESONI, G. |
Afiliación : |
OSCAR RICARDO BLUMETTO VELAZCO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUIS IGNACIO DE BARBIERI ETCHEBERRY, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; WALTER E. BAETHGEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GONZALO BECOÑA LÓPEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Ganadería regenerativa en Uruguay. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Montevideo (UY): INIA, 2023. |
Páginas : |
23 p. |
Serie : |
(INIA, Edición Especial). |
ISBN : |
978-9974-38-486-6; e-ISBN: 978-9974-38-487-3 |
Idioma : |
Español |
Notas : |
Revisión técnica: Ing. Agr. PhD Verónica Ciganda y DMV PhD Georgget Banchero. -- Esta publicación deriva del trabajo de investigación del Área de Recursos Naturales, Producción y Ambiente, en el marco del Sistema Ganadero Extensivo. |
Contenido : |
La agricultura regenerativa tiene como objetivo la conservación y/o rehabilitación de los sistemas alimentarios y agrícolas para una producción sostenible. ----- CONTENIDO: 1. ¿Qué es la ganadería regenerativa?. -- 2. Evidencia científica para la mejora de los indicadores ambientales. -- 3. Antecedentes de la propuesta. -- 4. La propuesta de INIA. -- 5. Escalado de la propuesta y el rol de INIA. -- 6. Reconocimientos. -- 7. Bibliografía. |
Palabras claves : |
ÁREA DE RECURSOS NATURALES, PRODUCCIÓN Y AMBIENTE - INIA; GANADERÍA REGENERATIVA; SISTEMA GANADERO EXTENSIVO - INIA. |
Thesagro : |
GANADERÍA. |
Asunto categoría : |
L01 Ganadería |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17283/1/Ganaderia-regenerativa-2023.pdf
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Marc : |
LEADER 01399nam a2200253 a 4500 001 1064247 005 2023-07-12 008 2023 bl uuuu u00u1 u #d 100 1 $aBLUMETTO, O. 245 $aGanadería regenerativa en Uruguay.$h[electronic resource] 260 $aMontevideo (UY): INIA$c2023 300 $a23 p. 490 $a(INIA, Edición Especial). 500 $aRevisión técnica: Ing. Agr. PhD Verónica Ciganda y DMV PhD Georgget Banchero. -- Esta publicación deriva del trabajo de investigación del Área de Recursos Naturales, Producción y Ambiente, en el marco del Sistema Ganadero Extensivo. 520 $aLa agricultura regenerativa tiene como objetivo la conservación y/o rehabilitación de los sistemas alimentarios y agrícolas para una producción sostenible. ----- CONTENIDO: 1. ¿Qué es la ganadería regenerativa?. -- 2. Evidencia científica para la mejora de los indicadores ambientales. -- 3. Antecedentes de la propuesta. -- 4. La propuesta de INIA. -- 5. Escalado de la propuesta y el rol de INIA. -- 6. Reconocimientos. -- 7. Bibliografía. 650 $aGANADERÍA 653 $aÁREA DE RECURSOS NATURALES, PRODUCCIÓN Y AMBIENTE - INIA 653 $aGANADERÍA REGENERATIVA 653 $aSISTEMA GANADERO EXTENSIVO - INIA 700 1 $aDE BARBIERI, I. 700 1 $aNAVAJAS, E. 700 1 $aBAETHGEN, W. 700 1 $aBECOÑA, G. 700 1 $aCIAPPESONI, G.
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
14/09/2023 |
Actualizado : |
14/09/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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