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چکیده
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In the present research, 20 sugar beet genotypes were examined in a randomized block design for sugar content (SC), white sugar content (WSC), potassium amount (K), sodium content (Na), alpha-amino nitrogen (AN), alkalinity coefficient (ALC), extraction coefficient of sugar (ECS), molasses sugar (MS), plant number at harvesting (PNH) and storage root yield (RY) using standard protocols. The genotype × trait (GT) method was used to investigate the interaction patterns, revealing that the mentioned interaction accounted for 66% of the total observed variability. The biplot is categorized into four sections, and the traits RY, PNH, AN and Na were located in the section of genotype G3, meaning that G3 had the highest potential for developing these traits, while traits SC, WSC, ECS were located in the section of genotype G19. Genotype G16 was the best with respect to MS and K, while G9 was the best in terms of ALC. Thus, G3, followed by genotypes G4 and G5, was the most favorable one regarding root yield, while G19, followed by genotypes G11 and G13, was identified as the best for sugar-related traits. The other biplot demonstrated that SC, WSC, and ECS were positively correlated, while there was a negative correlation between these traits versus K, MS, AN and Na. Also, RY and PNH were positively correlated, while there was a negative correlation between these traits versus ALC. According to ideal genotype properties, G3 was the best, followed by G4, G1 and G12, and among the tested properties, RY and PNH scored the highest, followed by AN and Na. This study offers a new perspective on the varying performance of genotypes across attributes, and emphasizes the effectiveness of the GT biplot model in discovering superior genotypes. The selected genotypes – G3 and G4 for root yield, and G19 and G11 for sugar-related trait – present viable possibilities for commercial endorsement to farmers. These findings highlight the necessity of incorporating sophisticated multivariate techniques, such as the GT biplot, into breeding initiatives to improve sugar beet yield and quality.
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