Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to dist...Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomicand industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from2007 to 2010 were conducted in completely randomized block design （CRBD） with split plots. Total nine oat cultivars with orwithout fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter andde-hulling index. Soft independent modeling of class analogy （SIMCA） was conducted as one-class and multi-classes models toidentify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index,de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can beused to easily discriminate some agronomic and quality parameters of oats.展开更多
文摘Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs fordevelopment of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomicand industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from2007 to 2010 were conducted in completely randomized block design （CRBD） with split plots. Total nine oat cultivars with orwithout fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter andde-hulling index. Soft independent modeling of class analogy （SIMCA） was conducted as one-class and multi-classes models toidentify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index,de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can beused to easily discriminate some agronomic and quality parameters of oats.
