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چکیده
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Abstract Objective: Fenugreek (Trigonella foenum-graecum L.), as an economically and culturally significant legume, is valued for its diverse uses in food, medicine, and sustainable agriculture. This study aimed to assess the genetic diversity among 26 fenugreek landraces collected from diverse agroecological zones in Iran. Methods: The landraces were evaluated under field conditions using a randomized complete block design with three replicates. Morphological traits, including plant height, number of lateral branches, leaf dimensions, pods per plant, and shoot biomass, were recorded alongside physiological traits such as chlorophyll-a and chlorophyll-b content. Data analysis involved a genotype-by-trait biplot approach based on principal component analysis to visualize genotype × trait interactions and to identify traits contributing most significantly to yield and biomass. Results: The two components explained 66% of the total phenotypic variance, revealing additive and crossover associations among landraces and traits. Key positive trait associations included shoot biomass with plant height, chlorophyll-a with chlorophyll-b, lateral leaf width with lateral leaf area, and a cluster of branching and pod number traits. Genotype-specific patterns highlighted the superiority of genotype 6 (Sarab) and genotype 8 (Tabriz-II) for the shoot biomass and middle leaf area. In contrast, genotype 9 (Tehran-II) excelled in branching and pod production. These landraces also exhibited high stability and representativeness in multivariate trait space, positioning them as promising candidates for breeding programs and commercial release. Conclusion: This study confirms that middle leaf area, number of lateral branches, lateral leaf length, pods per plant, and plant height are critical discriminative and representative traits for fenugreek improvement. The integration of morphological and physiological traits in a multivariate framework provides a robust basis for indirect selection and ideotype development. Furthermore, the results highlight the importance of considering genotype × trait interactions to prevent misleading selection decisions.
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