Multivariate Identification of Key Yield Determinants in Bread Wheat Using Path Analysis and PCa Techniques

Abstract

Understanding the multivariate structure of yield components is fundamental for improving grain yield in bread wheat (Triticum aestivum L.). This study evaluated twelve commercial wheat varieties using a comprehensive set of agronomic traits to identify the key determinants of seed yield and to characterize varietal differences through integrated statistical approaches. Descriptive statistics revealed substantial phenotypic variability among genotypes, indicating strong potential for selection and genetic improvement. Path analysis demonstrated that seed weight per spike and seed number per spike were the most influential traits, exhibiting the highest direct and indirect effects on yield, respectively. Thousand-seed weight, harvest index, and plant height contributed mainly through indirect pathways, highlighting their secondary yet supportive roles in the yield formation process. Principal Component Analysis (PCA) further confirmed that the first two components explained over half of the total variation, with seed weight, seed number, spikelet number, thousand-seed weight, and plant height clustering along the primary productivity axis. Overall, the integrated analytical framework emphasizes that grain yield in bread wheat is governed primarily by the balance between grain number and grain weight, with morphological and physiological traits providing additional support. These findings offer valuable guidance for breeding programs aiming to enhance yield potential through targeted trait selection and ideotype development.