Fecha/hora: 31/05/2024 21:23:30

Biblioteca(s): 	INIA Treinta y Tres.	
Tipo de la producción científica: 	Artículos en Revistas Indexadas Internacionales	
Circulación/Nivel: 	Internacional - --	
Fecha corriente: 	11/05/2018	
Fecha de la última actualización: 	28/05/2019	
Autoria: 	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).	
Año 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.	
Palabras-Clave: 	GENOTYPE X ENVIRONMENT INTERACTION; INTERACCIONES GENOTIPO-AMBIENTE.	
Thesagro: 	ARROZ; GENOTIPOS; RICE.	
Categoría del tema: 	F30 Genética vegetal y fitomejoramiento	
Registro original: 	INIA Treinta y Tres (TT)	
Ejemplares: 	TT - (102369-1) - PXI - AP - DD - PP/CROP-SCIENCE/2018/58 -  - cs-2018-2 - DIS