The effectiveness of selection by kernel size of winter rye

The kernel size of winter rye is a genetically determined characteristic as controlled by a series of multiple alleles and is available for artificial selection. As a result of long-term intensive selection average weight of 100 grains per plant increased from 2.94 to 6.94 g or 2.4 times. The implem...

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Veröffentlicht in:Plant varieties studying and protection 2014-03 (1(22)), p.34-44
1. Verfasser: Скорик, В. В.
Format: Artikel
Sprache:eng
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Zusammenfassung:The kernel size of winter rye is a genetically determined characteristic as controlled by a series of multiple alleles and is available for artificial selection. As a result of long-term intensive selection average weight of 100 grains per plant increased from 2.94 to 6.94 g or 2.4 times. The implemented effectiveness of selection by 100 grains weight per plant towards increase was systemic, but it was also uneven. Throughout 42 generations the spans of quick response to directed selection were observed, lasting slowdowns and even reverses of average weight of 100 grains per plant. Prompt increase of the directed selection efficiency corresponded to the changes that induced relatively big impact, and formed a plateau as large kernel size reached high frequency. Variability of rye population at high pressure of the selection by 100 grains weight pert plant occurred predominately at normal distribution and along the display of negative asymmetry following the effect of extreme grades emergence outbreak. The highest average kernel size achieved was followed by further slowdown of 100 grains increase observed, nonetheless a heavy pressure of the selection. 42 generations displayed the greatest impact upon the breeding efficiency by denominated breeding differential (36%), selection intensity (22%), broad sense inheritance ratio (8%), narrow sense inheritance ratio (10%). Predicted and implemented selection efficiency over 42 generations matched in 37% of cases certifying thus an actual forecast of the breeding prediction.
ISSN:2518-1017
2518-7457
DOI:10.21498/2518-1017.1(22).2014.56588