Efeito Adverso Do Vento Em Arroz Irrigado

Marchezan, Enio; Aude, Maria Isabel da Silva

doi:10.1590/s0103-84781993000300025

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the tropical japonica population, the two EC that showed the highest (negative) coefficients for GY were wind speed during flowering, and maximum temperature during grain filling. Both wind speed and high temperatures during reproduction have been proven to negatively affect GY due to pollen dehydration and consequent spikelet sterility (Marchezan and Aude 1993; Raju et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativaL.) Grown in Subtropical Areas

Monteverde

Gutiérrez

Blanco

et al. 2019

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

Understanding the genetic and environmental basis of genotype × environment interaction (G×E) is of fundamental importance in plant breeding. If we consider G×E in the context of genotype × year interactions (G×Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice ( Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations ( indica and tropical japonica ). We also sought to explain G×E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in subtropical and temperate rice growing areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativaL.) Grown in Subtropical Areas

Monteverde

Gutiérrez

Blanco

et al. 2019

G3 Genes|Genomes|Genetics

View full text Add to dashboard Cite

show abstract

“…Climatic variables related to temperature, wind, radiation, and precipitation from different time windows were found significant for the different analyzed response variables related to GY. Temperature and wind during reproduction negatively affect GY by causing spikelet sterility (Alvarado, 2002; Marchezan & da Silva Aude, 1993; Raju et al., 2013; Yoshida, 1981). Moreover, radiation is directly involved in grain filling capacity.…”

Section: Discussionmentioning

confidence: 99%

Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in temperate areas

Monteverde

Gutiérrez²,

Blanco

et al. 2018

Preprint

View full text Add to dashboard Cite

Understanding the genetic and environmental basis of genotype × environment interaction (G×E) is of fundamental importance in plant breeding. If we consider G×E in the context of genotype × year interactions (G×Y), predicting which lines will have stable and superior performance across years is an important challenge for breeders. A better understanding of the factors that contribute to the overall grain yield and quality of rice (Oryza sativa L.) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. In this study, we used molecular marker data and environmental covariates (EC) simultaneously to predict rice yield, milling quality traits and plant height in untested environments (years), using both reaction norm models and partial least squares (PLS), in two rice breeding populations (indica and tropical japonica). We also sought to explain G×E by differential quantitative trait loci (QTL) expression in relation to EC. Our results showed that PLS models trained with both molecular markers and EC gave better prediction accuracies than reaction norm models when predicting future years. We also detected several milling quality QTL that showed a differential expression conditional on humidity and solar radiation, providing insight for the main environmental factors affecting milling quality in temperate rice growing areas.

show abstract

Efeito Adverso Do Vento Em Arroz Irrigado

Cited by 3 publications

References 0 publications

Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativaL.) Grown in Subtropical Areas

Integrating Molecular Markers and Environmental Covariates To Interpret Genotype by Environment Interaction in Rice (Oryza sativaL.) Grown in Subtropical Areas

Genotype by environment interaction characterization and its modeling with random regression to climatic variables in two rice breeding populations

Integrating molecular markers and environmental covariates to interpret genotype by environment interaction in rice (Oryza sativa L.) grown in temperate areas

Contact Info

Product

Resources

About