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

Experiments and associated varieties implemented in Los Baños (LB) and Nueva Ecija (NE), Philippines, during the dry seasons of 2011 (2011-DS), 2012 (2012-DS), 2013 (2013-DS) and wet season of 2013 (2013-WS).

<p>RF indicates switching from continuously flooded to rainfed condition after panicle initiation while CF indicates continuously flooded throughout the season. The datasets were marked for calibration (C) and evaluation (E).</p

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

Adaptability ranking of the varieties to different levels of drought stress.

<p>Severity can be severe (S), moderate (M), and none to mild (L) which can occur at vegetative (V), reproductive (R), and combined vegetative and reproductive (V+R) timing.</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

The datasets used to determine TPE Classes and yield stability in the TPE.

<p>YP and YA are the irrigated and rainfed rice grain yields in the best rainfed season. The λ is the coefficient of variation of rainfed yield in YA, φ is the spatial variability of yields among environments, and the ψ is the temporal variability of yield among different growth seasons.</p

Results derived from field experiments in limited environments and simulations under a large number of environments.

<p>Results derived from field experiments in limited environments and simulations under a large number of environments.</p

Proportional change in TPE types with increase in number of genotypes used for TPE classification.

<p>The TPE types in this study were defined by the drought stress severity and types.</p

Statistical analysis for the calibration and validation datasets of all tested varieties in this study.

<p>AGB is above-ground biomass, PB is panicle biomass, and GY is grain yield.</p

The potential dissemination areas of GSR-IR1-1-Y4-Y1.

<p>The potential dissemination regions of the identified outstanding variety GSR-IR1-1-Y4-Y1 in Southern Asia.</p