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Smart planners push to set density limits as high as local residents will tolerate, which is basically always below the optimal level.
The various distance measures are inversely related to the data set density around the future object (short distances reflect high data density).
The SparseBoost method integrates an improved sparse kernel density estimator (RSDE-WL1) into the Boost algorithm and the RSDE-WL1 estimator is achieved by introducing weighted l1 penalty term into the reduced set density estimator.
Fourteen QTLs on eight chromosomes influenced spikelet setting density (SSD).
Five novel QTLs stable over two or more locations explaining 8-258-25%phenotypic variation were identified for spikelet setting density.
As a result, assumptions based on a set density parameter will considerably affect a mass estimate [ 54, 56].
For spikelet setting density, two novel QTLs qSSD4-1, qSSD4-2 expressing across three locations were identified with a LOD value of 15 explaining up to 23% of phenotypic variation.
Significant effect of location was observed for all traits and block effects were significant for all traits except panicle length and spikelet setting density.
QTLs affecting flag leaf length were distributed on nine chromosomes followed by spikelet setting density on eight chromosomes, whereas, per cent sterility had QTLs on two chromosomes.
In the present study, 6 epistatic QTLs were identified for five traits with phenotypic variation ranging from 6.1% for panicle length to 21.6% for spikelet setting density.
Nine novel QTLs expressing across two environments were identified for five traits namely plant height (1), panicle length (2), flag leaf length (2), 1000-grain weight (1) and spikelet setting density (3).
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