1- Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO
2- Dryland Agricultural Research Institute, AREEO
3- Hamedan Agricultural and Natural Resources Research and Education Center, AREEO
4- , West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO
2- Dryland Agricultural Research Institute, AREEO
3- Hamedan Agricultural and Natural Resources Research and Education Center, AREEO
4- , West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO
Abstract: (25 Views)
Extended Abstract
Introduction and Objectives: Selection and breeding for high yield in chickpea have always been one of the main goals for chickpea breeders. This process becomes more complicated when, in addition to grain yield, other traits are considered in selection. On the other hand, achieving higher yield is often difficult due to the negative correlation between quantitative traits. At the same time, yield is a complex quantitative trait a that is often controlled by several genes and is affected by environmental conditions. Therefore, selecting superior genotypes by considering multiple traits is one of the main challenges for breeders. Two graphical methods of genotype-yield (GT) and genotype-yield-trait (GYT) analysis for selecting superior genotypes by considering multiple traits have been proposed to overcome the problem of trait/genotype × environment interaction and negative correlation between desirable traits. This study aimed to identify the relationship between traits and rank chickpea genotypes based on multiple traits
Materials and Methods: In this study, 16 kabuli chickpea genotypes along with two checks (‘Samin’ and ‘Jam’) were evaluated in a randomized complete block design with four replications at the research stations of Kurdistan, Maragheh, Hamedan, and Urmia under spring planting conditions over two cropping seasons (2019–2021). The genotypes were planted in in four lines four meters long and spaced 30 cm apart, with a plant density of 35 plants per square meter. Phosphorus fertilizer was applied in autumn, and nitrogen fertilizer was added at sowing in spring as a starter. Plant materials planting was carried out at the earliest possible time in early April, immediately after field conditions were suitable. The GT (genotype × trait) and GYT (genotype × yield × trait) biplots were used to identify relationships among traits and to select the best genotypes based on multiple traits. The measured traits included days to flowering, days to maturity, plant height, 100 seed weight, and seed yield.
Results: The results of the combined analysis of variance indicated that the effects of environment, genotype, and genotype × environment interaction were highly significant for most traits, indicating significant genetic variation among genotypes and their different responses to variable environmental conditions. Mean comparisons revealed a wide range of seed yield among genotypes (106.7 to 1176 kg ha⁻¹), while other traits such as plant height, days to flowering and maturity, and 100 seed weight also showed significant variation. Phenotypic correlations between traits indicated that grain yield was negatively and significantly correlated with 100-seed weight, indicating competition between grain size and grain number under dryland conditions. Strong positive correlations among days to flowering, days to maturity, and plant height confirmed that late-maturing genotypes generally had greater vegetative growth, although this growth does not necessarily lead to increased yield. The results of the genotype-trait (GT) biplot explained a total of 99.8% of the total data variation. Based on the polygon view, genotypes 6 and 7 were identified as superior for most traits. The GT biplot also revealed negative correlations between yield and all other measured traits, while days to flowering, days to maturity, and plant height were strongly and positively correlated. Seed yield was the closest trait to the ideal trait and showed the highest discriminating ability and representativeness. Comparison with the ideal genotype showed that genotypes 6 and 7 were the most desirable for most traits. the genotype-ranking plot for stability indicated that although genotypes 6 and 7 were strong in yield, they performed relatively weak for other traits such as 100-seed weight, plant height, and days to flowering and maturity, whereas genotype 2 was more stable in terms of most traits in across environments. Although for all traits, it was lower than the overall average. The superiority index based on standardized GYT data identified genotypes 6 and 7 as the best genotypes for yield–trait combinations. The GYT biplot explained 92.9% of the variation in yield–trait combinations. According to the polygon view of the GYT biplot, genotypes 6 and 7 were the most desirable genotypes in terms of yield-plant height and days to flowering and maturity, and genotype 15 was the most desirable genotype in terms of yield-trait combination. All yield–trait vectors were closely aligned, indicating positive correlations among them. The yield × plant height combination was identified as the ideal yield-trait index to identify superior genotypes, and genotypes 6 and 7 were the closest genotypes to the ideal genotype. The ATC view of the GYT biplot identified genotype 6, followed by genotype 7, as the best genotypes, and genotype 14 as the weakest in terms of yield–trait combinations. Cluster analysis based on standardized GYT data also proved effective for distinguishing above-average genotypes, similar to the two-dimensional GYT mean × stability plot.
Conclusion: Overall, the results of this study showed that genotypes 6 and 7, with high yield across most environments and relatively stable performance for multiple traits, are promising candidates for breeding programs and for recommendation to farmers in the cold and low-rainfall regions of Iran. This study highlights the importance of considering genotype × environment interaction and the central role of grain yield as the main selection indicator, providing essential and practical information for improving chickpea productivity under rainfed conditions. Furthermore, the findings demonstrate the effectiveness of both GT and GYT biplots as powerful graphical tools for identifying superior and stable genotypes in breeding programs.
Introduction and Objectives: Selection and breeding for high yield in chickpea have always been one of the main goals for chickpea breeders. This process becomes more complicated when, in addition to grain yield, other traits are considered in selection. On the other hand, achieving higher yield is often difficult due to the negative correlation between quantitative traits. At the same time, yield is a complex quantitative trait a that is often controlled by several genes and is affected by environmental conditions. Therefore, selecting superior genotypes by considering multiple traits is one of the main challenges for breeders. Two graphical methods of genotype-yield (GT) and genotype-yield-trait (GYT) analysis for selecting superior genotypes by considering multiple traits have been proposed to overcome the problem of trait/genotype × environment interaction and negative correlation between desirable traits. This study aimed to identify the relationship between traits and rank chickpea genotypes based on multiple traits
Materials and Methods: In this study, 16 kabuli chickpea genotypes along with two checks (‘Samin’ and ‘Jam’) were evaluated in a randomized complete block design with four replications at the research stations of Kurdistan, Maragheh, Hamedan, and Urmia under spring planting conditions over two cropping seasons (2019–2021). The genotypes were planted in in four lines four meters long and spaced 30 cm apart, with a plant density of 35 plants per square meter. Phosphorus fertilizer was applied in autumn, and nitrogen fertilizer was added at sowing in spring as a starter. Plant materials planting was carried out at the earliest possible time in early April, immediately after field conditions were suitable. The GT (genotype × trait) and GYT (genotype × yield × trait) biplots were used to identify relationships among traits and to select the best genotypes based on multiple traits. The measured traits included days to flowering, days to maturity, plant height, 100 seed weight, and seed yield.
Results: The results of the combined analysis of variance indicated that the effects of environment, genotype, and genotype × environment interaction were highly significant for most traits, indicating significant genetic variation among genotypes and their different responses to variable environmental conditions. Mean comparisons revealed a wide range of seed yield among genotypes (106.7 to 1176 kg ha⁻¹), while other traits such as plant height, days to flowering and maturity, and 100 seed weight also showed significant variation. Phenotypic correlations between traits indicated that grain yield was negatively and significantly correlated with 100-seed weight, indicating competition between grain size and grain number under dryland conditions. Strong positive correlations among days to flowering, days to maturity, and plant height confirmed that late-maturing genotypes generally had greater vegetative growth, although this growth does not necessarily lead to increased yield. The results of the genotype-trait (GT) biplot explained a total of 99.8% of the total data variation. Based on the polygon view, genotypes 6 and 7 were identified as superior for most traits. The GT biplot also revealed negative correlations between yield and all other measured traits, while days to flowering, days to maturity, and plant height were strongly and positively correlated. Seed yield was the closest trait to the ideal trait and showed the highest discriminating ability and representativeness. Comparison with the ideal genotype showed that genotypes 6 and 7 were the most desirable for most traits. the genotype-ranking plot for stability indicated that although genotypes 6 and 7 were strong in yield, they performed relatively weak for other traits such as 100-seed weight, plant height, and days to flowering and maturity, whereas genotype 2 was more stable in terms of most traits in across environments. Although for all traits, it was lower than the overall average. The superiority index based on standardized GYT data identified genotypes 6 and 7 as the best genotypes for yield–trait combinations. The GYT biplot explained 92.9% of the variation in yield–trait combinations. According to the polygon view of the GYT biplot, genotypes 6 and 7 were the most desirable genotypes in terms of yield-plant height and days to flowering and maturity, and genotype 15 was the most desirable genotype in terms of yield-trait combination. All yield–trait vectors were closely aligned, indicating positive correlations among them. The yield × plant height combination was identified as the ideal yield-trait index to identify superior genotypes, and genotypes 6 and 7 were the closest genotypes to the ideal genotype. The ATC view of the GYT biplot identified genotype 6, followed by genotype 7, as the best genotypes, and genotype 14 as the weakest in terms of yield–trait combinations. Cluster analysis based on standardized GYT data also proved effective for distinguishing above-average genotypes, similar to the two-dimensional GYT mean × stability plot.
Conclusion: Overall, the results of this study showed that genotypes 6 and 7, with high yield across most environments and relatively stable performance for multiple traits, are promising candidates for breeding programs and for recommendation to farmers in the cold and low-rainfall regions of Iran. This study highlights the importance of considering genotype × environment interaction and the central role of grain yield as the main selection indicator, providing essential and practical information for improving chickpea productivity under rainfed conditions. Furthermore, the findings demonstrate the effectiveness of both GT and GYT biplots as powerful graphical tools for identifying superior and stable genotypes in breeding programs.
Keywords: Genotype/trait × environment interaction, GT biplot, GYT biplot, Multi-trait ranking, Trait relationships, Chickpea.
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