Multivariate analysis methods improve the selection of strawberry genotypes with low cold requirement

Area of Science:

• Agricultural Science
• Genetics
• Plant Breeding

Background:

• Multivariate analysis is crucial for simultaneous evaluation of multiple traits in crop genetic improvement.
• Selecting heat-tolerant genotypes requires understanding phenotypic diversity and trait interrelationships.

Purpose of the Study:

• To select heat-tolerant genotypes by analyzing phenotypic diversity.
• To identify relationships among traits and compare the effectiveness of different selection indices.

Main Methods:

• K-means clustering and Elbow method for diversity analysis.
• Path analysis for quantifying trait relationships.
• Application of parametric and non-parametric selection indices (Smith and Hazel, Mulamba and Mock).

Main Results:

• Two distinct clusters of genotypes were identified (40 and 154 individuals).
• Significant correlations were observed between traits, with fruit number directly impacting fruit mass.
• The Smith and Hazel index yielded the highest overall gains, while the Mulamba and Mock index excelled for biochemical traits.

Conclusions:

• K-means clustering, correlation, and path analysis effectively complement selection indices.
• This integrated approach facilitates the selection of genotypes with a superior balance of assessed traits for crop improvement.