Multivariate analysis of spike traits to identify optimal phenotypic profiles of productive tiller number in novel hybrid soft winter wheat breeding lines
Аннотация
Background. The ability to comprehend how spike traits contribute to total phenotypic variation and genetic divergence among genotypes is essential for breeding programs to be effective. Specifically, in wheat, genetic improvement of yield centers on manipulating component traits like productive tiller number and spike architecture. Therefore, identifying the optimal multivariate phenotypic profile of spikes for maximizing productive tiller number in hybrid backgrounds is crucial.
Purpose. Therefore, this study aimed to identify superior genotypes with optimal productive tiller numbers from novel soft winter wheat hybrids by analyzing multivariate spike trait profiles, in order to provide actionable selection criteria for enhancing breeding programs.
Materials and methods. The research was conducted at the Kaluga Agricultural Experimental Station during 2022-2023 period. Data were collected from 1,056 individual sets of spikes (plant stand) representing productive tiller groups across multiple lines and analyzed key spike yield-related traits such as number of productive tillers spikes per plant, spikes weight per plant, and grain weight per plant stand, along with productivity efficiency ratios and analyzed by principal component and cluster analyses using Python 3.11.7 in the Jupiter notebook environment.
Results. Principal component analysis identified two major components that together explained 84.78% of the observed phenotypic variation in the soft winter wheat hybrids. The first principal component (PC1) was primarily associated with grain weight, exhibiting a loading of 0.386, indicating its dominant contribution to variation along this dimension. The second principal component (PC2) was strongly influenced by number of spikes, with a loading of 0.749, and also reflected yield efficiency traits, including grain weight (loading 0.591) and grain weight per spike number (loading 0.604), capturing variation related to spike productivity and grain filling efficiency. Cluster analysis distinguished three genotype groups with contrasting yield strategies: Cluster 1 had low number of spikes and grain weight with high variability, Cluster 2 combined high number of spikes and grain weight with moderate variability, and Cluster 3 exhibited fewer spikes but high grain weight per spike with low variability, indicating efficient grain filling. The top 5% of genotypes showed a high average number of spikes(productive tillers) (12.75) and grain weight (30.25 g), with excellent grain filling ratio (80.15%) and low variability, confirming the importance of balancing spike quantity and quality in breeding for enhanced yield.
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