Relationship of Rice Grain Amylose, Gelatinization Temperature and Pasting Properties for Breeding Better Eating and Cooking Quality of Rice Varieties - Fig 1

<p><b>Histogram distributions of AAC and GT in inbred (A and B) and hybrid groups (C and D)</b>. H, I, L, and VL were high, intermediate, low, and very low, respectively.</p

Phenotypic variance within clusters of inbred and hybrid groups.

<p>Phenotypic variance within clusters of inbred and hybrid groups.</p

Combining limited multiple environment trials data with crop modeling to identify widely adaptable rice varieties

DATA for the study. 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 (GEI) 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. This approach can also be adopted to other crops or other rice producing domains in various locations with available weather information

The observed and expected values of combinations of different classifications of AAC and GT in inbred and hybrid groups.

<p>The observed and expected values of combinations of different classifications of AAC and GT in inbred and hybrid groups.</p

Correlation relationships of AAC and RVA parameters in inbred and hybrid groups.

<p>The values were correlation coefficients (r) multiplied by 100. The areas and colors of ellipses showed the absolute value of corresponding r. Right and left oblique ellipses indicated positive and negative correlations, respectively. The values without glyphs indicated insignificant at 0.05.</p

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

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

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

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