IRRI MET 2011 2012 phenotype data all files

This archive contains the raw multi-environment (MET) phenotype data used for the MET GS experiments reported in this publication. Each .csv file in the archive contains the data for a particular year, season, and site. See ReadMe file for description of columns

binned_subsets_2_14

contains the evenly distributed genotype subsets used in Spindel et al., 201

Mean accuracies of cross-validation for prediction of grain yield (Kg/ha) (top row), flowering time (days to 50% flowering) (middle row), and plant height (cm) (bottom row) in the 2012 dry season, using 10 selections of SNP subsets either distributed evenly throughout the genome (right column) or chosen at random (left column) and five different statistical methods, error bars constructed using 1 standard error from the mean.

<p>The training population consisted of data from years 2009–2011, both seasons per year.</p

MET_crfilt_.90_outliers_removed_for_RRBlup

Post-imputation GBS dataset used specifically for GS cross-validation in Spindel et al., 2015. Dataset contains all markers with call rates >= .9 and lines that were included in the GS analysis (i.e., sub-population outliers are removed from this dataset). The data are formatted for use with the R rrBLUP package

CV experiments.

<p>*AS = all seasons, DS = dry season only, WS = wet season only</p><p>CV experiments.</p

Summary of best performing GS experiments for predicting grain yield (YLD), flowering time (FL), and plant height (PH) in the 2012 dry season (2012 DS) and the 2012 WS (2012 WS)

<p>TP = Training population, all = both dry and wet seasons for each year, DS only = dry seasons only for each year. VP = validation population. Accuracy = correlation of the predicted GEBV and the phenotype in the validation population, where the training population included the validation season/year for individuals not in the validation fold. Statistical methods not connected by the same letter performed significantly different from each other across experiments by pairwise students t (α = .05).</p><p>Summary of best performing GS experiments for predicting grain yield (YLD), flowering time (FL), and plant height (PH) in the 2012 dry season (2012 DS) and the 2012 WS (2012 WS)</p

RYT_plotdata_by_GHID_corrected_PUBLIC_ACCESS

contains the raw phenotype data for genotyped individuals for 2009-2012, dry and wet seasons, used in Spindel et al., 2015

Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties

<div><p>Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. With available local weather and soil information, this approach can also be adopted to other rice producing domains or other crops using appropriate crop models.</p></div

Parentage, breeding methodology and duration of 8 green super rice (GSR) lines and check varieties used in this study.

<p>Parentage, breeding methodology and duration of 8 green super rice (GSR) lines and check varieties used in this study.</p

The TPE classes of the tested environments in South Asia.

<p>The TPE classes of drought stress to rainfed rice at possible areas and the best rainfed season.</p