Volume 17, Issue 2 (7-2025)
J Crop Breed 2025, 17(2): 29-42 |
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Sheikh F. (2025). Assessment of the Interrelationship between Agronomic Traits of Faba Bean Genotypes using Double and Triple Biplots of the Genotype, Trait, and Yield. J Crop Breed. 17(2), 29-42. doi:10.61882/jcb.2024.1566
URL: http://jcb.sanru.ac.ir/article-1-1566-en.html
URL: http://jcb.sanru.ac.ir/article-1-1566-en.html
Crop and Horticultural Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran
Abstract: (798 Views)
Extended Abstract
Background: Faba bean (Vicia faba L.) is grown worldwide as a protein source for food and feed. It is a very valuable legume crop that contributes to the sustainability of cropping systems by its ability to fix biological N2. Genetic control of yield is directly affected by parameters correlated with yield. Recognizing the correlation of yield and its components and finding the type of relationships between them can increase yield. The selection of genotypes based on multiple traits is a critical challenge in plant breeding and is important for improving stable crop varieties. In this study, the genotype × trait biplot (GT biplot) and the genotype × yield-trait biplot (GYT biplot) methods were used in combination with cluster analysis to investigate the relationships between grain yield and simultaneous improvement of quantitative traits and select the best faba bean genotypes. Also, the strengths and weaknesses of each genotype were determined by combining yield and other target traits with the GYT biplot method.
Methods: To select suitable genotypes, genotypes were evaluated in the field, based on a simple lattice design (6 × 6) with two replications in Gorgan research stations in the 2021-2022 cropping season. Plant materials included 36 faba bean (Vicia faba L.) genotypes. The phonological traits, plant height (PH) and lowest pod height (HLP), were calculated before harvesting. In each plot, plants were harvested by hand at the harvest maturity stage, and seed number/pod (NS), pod number/plant (NP), and hundred-seed weight (100SW) were measured on ten plants selected randomly from all plots. Data were analyzed using SAS software, and the means were compared using the LSD test at 1% and 5% probability levels. Two graphical multipurpose selection procedures, GT biplot and GYT biplot, were conducted in combination with cluster analysis for the simultaneous improvement of quantitative traits. The genotypes and traits were clustered separately in each experiment using the Ward method and Square Euclidean Distance, and the corresponding heat map was plotted using MetaboAnalyst 3. 0 software.
Results: Based on the analysis of variance, there were significant differences (P ≤ 0.01) in all parameters. Clustering and heat map graphical mapping assigned the faba bean genotypes to five groups. The different groups obtained can be useful for deriving the genotypes with diverse features and diversifying the heterotic pools. According to the mean grain yield of the genotypes, the maximum grain yield was obtained for G25 (12TER-115-S2008, 058-4), G26 (12TER-124-S2009, 039-3), and G4 (ILB1814× WRB 1-5) genotypes, with mean grain yields of 6251.7, 5965.6, and 5815.6 kgha-1, respectively. Results of the GT biplot in the present study indicated that G26, G25, G23, G21, and G16 were identified as superior genotypes. The GT biplot vector view indicated a strong positive association between pod yield, the number of branches, plant height, and days to flowering with seed yield. The GT biplot vector view indicated that the number of seeds per pod, 100-seed weight, plant height, and early maturity traits could be used as a selection criterion for improving seed yield in faba bean. Findings suggested that the genotype by trait (GT) biplot explained less total variation (47%) than the GYT biplot (87.5%). The which-won-where view of the GYT biplot divided the GYT polygon into five parts, out of which only two parts had combinations of traits. The first part harbored the combination of the yield trait with phenological traits, plant height (PH), lowest pod height (HLP), pod number/plant (NP), biomass, harvest index, and 100-seed weight, and nine genotypes (G4, G6, G13, G16, G21, G23, G25, G26, and G27), for which G25 and G26 were the winner genotypes. In the second part of the GYT polygon, genotype G32 (Feyz cultivar) was placed with yield × number of grains per pod (NG) and yield × length of pod (LP). Based on the results of the GYT biplot, G25 (12TER-115-S2008, 058-4) and G26 (12TER-124-S2009, 039-3) were the best genotypes in combining grain yield with valuable traits. The average tester coordinate (ATC) view of the GYT biplot identified genotypes G25, G26, and G4 with all positive yield-trait combinations as the best genotypes and genotypes G35, G7, and G12 as the weakest genotypes.
Conclusion: The study shows that the GYT biplot is a very good technique, by which the ideal and stable genotypes can be detected visually, and it can be used to define the best candidate based on combining yield and trait selection in breeding programs. Overall, these findings indicated that the genotypes G25 (12TER-115-S2008, 058-4), G26 (12TER-124-S2009, 039-3), and G4 (ILB1814 × WRB 1-5) had the right combination of traits of interest required to produce higher yield and hence are potentially valuable candidates to be tested in multi-location trials for stable performance prior to release as new commercial faba bean cultivars.
Background: Faba bean (Vicia faba L.) is grown worldwide as a protein source for food and feed. It is a very valuable legume crop that contributes to the sustainability of cropping systems by its ability to fix biological N2. Genetic control of yield is directly affected by parameters correlated with yield. Recognizing the correlation of yield and its components and finding the type of relationships between them can increase yield. The selection of genotypes based on multiple traits is a critical challenge in plant breeding and is important for improving stable crop varieties. In this study, the genotype × trait biplot (GT biplot) and the genotype × yield-trait biplot (GYT biplot) methods were used in combination with cluster analysis to investigate the relationships between grain yield and simultaneous improvement of quantitative traits and select the best faba bean genotypes
Methods: To select suitable genotypes, genotypes were evaluated in the field, based on a simple lattice design (6 × 6) with two replications in Gorgan research stations in the 2021-2022 cropping season. Plant materials included 36 faba bean (Vicia faba L.) genotypes. The phonological traits, plant height (PH) and lowest pod height (HLP), were calculated before harvesting. In each plot, plants were harvested by hand at the harvest maturity stage, and seed number/pod (NS), pod number/plant (NP), and hundred-seed weight (100SW) were measured on ten plants selected randomly from all plots. Data were analyzed using SAS software, and the means were compared using the LSD test at 1% and 5% probability levels. Two graphical multipurpose selection procedures, GT biplot and GYT biplot, were conducted in combination with cluster analysis for the simultaneous improvement of quantitative traits. The genotypes and traits were clustered separately in each experiment using the Ward method and Square Euclidean Distance, and the corresponding heat map was plotted using MetaboAnalyst 3. 0 software.
Results: Based on the analysis of variance, there were significant differences (P ≤ 0.01) in all parameters. Clustering and heat map graphical mapping assigned the faba bean genotypes to five groups. The different groups obtained can be useful for deriving the genotypes with diverse features and diversifying the heterotic pools. According to the mean grain yield of the genotypes, the maximum grain yield was obtained for G25 (12TER-115-S2008, 058-4), G26 (12TER-124-S2009, 039-3), and G4 (ILB1814× WRB 1-5) genotypes, with mean grain yields of 6251.7, 5965.6, and 5815.6 kgha-1, respectively. Results of the GT biplot in the present study indicated that G26, G25, G23, G21, and G16 were identified as superior genotypes. The GT biplot vector view indicated a strong positive association between pod yield, the number of branches, plant height, and days to flowering with seed yield. The GT biplot vector view indicated that the number of seeds per pod, 100-seed weight, plant height, and early maturity traits could be used as a selection criterion for improving seed yield in faba bean. Findings suggested that the genotype by trait (GT) biplot explained less total variation (47%) than the GYT biplot (87.5%). The which-won-where view of the GYT biplot divided the GYT polygon into five parts, out of which only two parts had combinations of traits. The first part harbored the combination of the yield trait with phenological traits, plant height (PH), lowest pod height (HLP), pod number/plant (NP), biomass, harvest index, and 100-seed weight, and nine genotypes (G4, G6, G13, G16, G21, G23, G25, G26, and G27), for which G25 and G26 were the winner genotypes. In the second part of the GYT polygon, genotype G32 (Feyz cultivar) was placed with yield × number of grains per pod (NG) and yield × length of pod (LP). Based on the results of the GYT biplot, G25 (12TER-115-S2008, 058-4) and G26 (12TER-124-S2009, 039-3) were the best genotypes in combining grain yield with valuable traits. The average tester coordinate (ATC) view of the GYT biplot identified genotypes G25, G26, and G4 with all positive yield-trait combinations as the best genotypes and genotypes G35, G7, and G12 as the weakest genotypes.
Conclusion: The study shows that the GYT biplot is a very good technique, by which the ideal and stable genotypes can be detected visually, and it can be used to define the best candidate based on combining yield and trait selection in breeding programs. Overall, these findings indicated that the genotypes G25 (12TER-115-S2008, 058-4), G26 (12TER-124-S2009, 039-3), and G4 (ILB1814 × WRB 1-5) had the right combination of traits of interest required to produce higher yield and hence are potentially valuable candidates to be tested in multi-location trials for stable performance prior to release as new commercial faba bean cultivars.
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