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In this study, the curve identification was based on the variation of heading angle, and the curve radius was calculated with kinematic method, geometry method, and lateral acceleration method.
Advanced-backcross populations are promising materials for detecting a lot of QTLs involved in variation of heading date in Asian rice accessions.
In Arabidopsis, flowering time variation is controlled by allelic differences of a small number of genes with large genetic effects [ 36], whereas in maize natural variation of heading date is controlled by the additive effect of many QTLs with small effects [ 35].
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Figure 5 Variations of heading angles from 0° to 360°.
To help achieve higher yield, phenotype variations of heading date (HD), plant architecture and grain shape in a panel of 416 rice accessions were investigated in this study.
In rice, small-effect QTLs largely contributed to a wide range of natural variations of heading date, in addition to large-effect QTLs including 13 genes isolated previously.
The number of nodes was 550,000 for all the simulations as the variation of head with the increase in number of nodes were not significant.
Methods for identifying turbulent flow include correlating spring discharge with head variation, calculating Reynolds numbers from spring discharge and tracer velocity, and plotting the spatial variation of head differences between high flow and low flow.
To reveal the genetic architecture of natural variation for heading date in rice by detecting the hidden QTLs, we developed advanced-backcross populations (>14,000 plants) derived from crosses with the same F2 populations.
Variation within families ranged from 20.8% of the total variation for heading to blooming at PB in 2009 to essentially zero at PB in 2010.
By selecting parental cultivars with similar heading dates and using a method that minimizes the effect of heading date variation, we successfully detected a major QTL for BGR resistance on chromosome 1.
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