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Registros recuperados : 6 | |
1. | | TITTONELL, P.; KLERKX, L.; BAUDRON, F.; FÉLIX, G.F.; RUGGIA, A.; VAN APELDOORN, D.; DOGLIOTTI, S.; MAPFUMO, P.; ROSSING, W.A.H. Ecological intensification: local innovation to address global challenges. In: Lichtfouse, Eric (Ed.). Sustainable Agriculture Reviews: volume 19. Springer, 2016, Chap.1, pp.1-34.Biblioteca(s): INIA Las Brujas. |
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2. | | GERMINARA, D.; DE GRAZIA, J.; TITTONELL, P.; CHIESA, A. Fertilización fosfatada y nitrogenada en el cultivo de maíz dulce (Zea mays L. saccharata). ln: Congreso Nacional de Horticultura, 8.; Seminario Regional de Frutilla, 2001, Salto, Uruguay. Resúmenes. Montevideo (Uruguay): SUH; INIA, 2001. p. 12 "Sociedad Uruguaya de Horticultura; Instituto Nacional de Investigación Agropecuaria, Uruguay"Biblioteca(s): INIA Las Brujas; INIA Salto Grande; INIA Tacuarembó; INIA Treinta y Tres. |
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3. | | RUGGIA, A.; ALBICETTE, M.M.; ALBÍN, A.; BLUMETTO, O.; CARDOZO, G.; DOGLIOTTI, S.; SCARLATO, S.; TITTONELL, P.; ROSSING, W. A. H.; AGUERRE, V. Improving sustainability in livestock family farm systems based on natural grasslands in Uruguay implementing a co-innovation process. [Conference paper]. In: Proceedings of the 6th International Symposium for Farming Systems Design (FSD6). "Agricultural systems designs sustained by nature". Montevideo, Uruguay, 24 to 29 March, 2019. 3 p.Biblioteca(s): INIA Las Brujas. |
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5. | | RUGGIA, A.; DOGLIOTTI, S.; AGUERRE, V.; ALBICETTE, M.M.; ALBÍN, A.; BLUMETTO, O.; CARDOZO, G.; LEONI, C.; QUINTANS, G.; SCARLATO, S.; TITTONELL, P.; ROSSING, W. A.H. The application of ecologically intensive principles to the systemic redesign of livestock farms on native grasslands: A case of co-innovation in Rocha, Uruguay. Agricultural Systems, June 2021, Volume 191, Article 103148. OPEN ACCESS. Doi: https://doi.org/10.1016/j.agsy.2021.103148 Article history: Received 30 November 2020; Received in revised form 5 April 2021; Accepted 6 April 2021.
Editor: Guillaume Martin.
The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license...Biblioteca(s): INIA Las Brujas; INIA Treinta y Tres. |
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6. | | ARISTIDE, P.; CITTADINI, E.; BLUMETTO, O.; GIOBELLINA, B.; LEDESMA, S.; OVALLE, C.; MARCHAO, R.; CABALLERO, P.J.; OSMAN, A.; TITTONELL, P. Variables claves para la evaluación de la sustentabilidad de los sistemas agropecuarios. Hacia un sistema de indicadores de intensificación sostenible en el Cono Sur. Montevideo (UY) : PROCISUR, 2020. 22 p. (Procisur, Documentos) Grupo de Trabajo: Instituto Nacional de Tecnología Agropecuaria (INTA) de Argentina; Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA); Instituto de Investigaciones Agropecuarias (INIA) de Chile; Instituto Paraguayo de Tecnología...Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 6 | |
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| Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
11/05/2018 |
Actualizado : |
28/05/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
MONTEVERDE, E.; ROSAS, J.E.; BLANCO, P.H.; PÉREZ DE VIDA, F.; BONNECARRERE, V.; QUERO, G.; GUTIERREZ, L.; MCCOUCH, S. |
Afiliación : |
ELIANA MONTEVERDE, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA.; JUAN EDUARDO ROSAS CAISSIOLS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; PEDRO HORACIO BLANCO BARRAL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GASTÓN QUERO CORRALLO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; LUCÍA GUTIERREZ, Department of Agronomy, University of Wisconsin, WI, USA.; SUSAN MCCOUCH, Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, USA. |
Título : |
Multienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa). |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
Crop Science, 2018, 58:1519-1530. |
DOI : |
10.2135/cropsci2017.09.0564 |
Idioma : |
Inglés |
Notas : |
Article history: Accepted on May 09, 2018. Published online June 21, 2018. |
Contenido : |
ABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. MenosABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to
accommodate correlation among environments.
We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when pr... Presentar Todo |
Palabras claves : |
GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE. |
Thesagro : |
ARROZ; GENOTIPOS; RICE. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
Marc : |
LEADER 02635naa a2200289 a 4500 001 1058574 005 2019-05-28 008 2018 bl uuuu u00u1 u #d 024 7 $a10.2135/cropsci2017.09.0564$2DOI 100 1 $aMONTEVERDE, E. 245 $aMultienvironment models increase prediction accuracy of complex traits in advanced breeding lines of rice (O. sativa).$h[electronic resource] 260 $c2018 500 $aArticle history: Accepted on May 09, 2018. Published online June 21, 2018. 520 $aABSTRACT: Genotype x environment interaction (G x E) is the differential response of genotypes in different environments and represents a major challenge for breeders. Genotype x year-interaction (G x Y) is a relevant component of G x E, and accounting for it is an important strategy for identifying lines with stable and superior performance across years. In this study, we compared the prediction accuracy of modeling G x Y using covariance structures that differ in their ability to accommodate correlation among environments. We present the use of these approaches in two different rice (Oryza sativa L.) breeding populations (indica and tropical japonica) for predicting grain yield, plant height, and three milling quality traits—milling yield, head rice percentage, and grain chalkiness—under different cross-validation (CV) scenarios. We also compared model performance in the context of global predictions (i.e., predictions across years). Most of the benefits of multienvironment models come from modeling genetic correlations between environments when predicting performance of lines that have been tested in some environments but not others (CV2). For predicting the performance of newly developed lines (CV1), modeling between environment correlations has no effect compared with considering environments independently. Response to selection of multienvironment models when modeling covariance structures that accommodate covariances between environments was always beneficial when predicting the performance of lines across years. We also show that, for some traits, high prediction accuracies can be obtained in untested years, which is important for resource allocation in small breeding programs. 650 $aARROZ 650 $aGENOTIPOS 650 $aRICE 653 $aGENOTYPE X ENVIRONMENT INTERACTION 653 $aINTERACCIONES GENOTIPO-AMBIENTE 700 1 $aROSAS, J.E. 700 1 $aBLANCO, P.H. 700 1 $aPÉREZ DE VIDA, F. 700 1 $aBONNECARRERE, V. 700 1 $aQUERO, G. 700 1 $aGUTIERREZ, L. 700 1 $aMCCOUCH, S. 773 $tCrop Science, 2018, 58:1519-1530.
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