Document Type : Original Article
Authors
1 Zanjan Agricultural and Natural Resources Research Center, AREEO, Zanjan, Iran
2 Dry Land Agricultural Research Institute (DARI), Agricultural Research, Education and Extension(AREEO), Iran
3 Crop and Horticultural Science Research Department, Southern Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Jiroft, Iran
Abstract
Introduction
Drought is recognized as one of the most common and challenging environmental stresses in agriculture worldwide (Hu et al., 2020) and the production of this plant Reduces by 21%. Annual production of 13.7 million tons with a cultivated area of 5.8 million hectares in Iran shows the importance of this plant. Of this level, 67% is related to rainfed cultivation and the rest to irrigated cultivation. Numerous studies have been conducted to investigate the traits and determine their relationship with wheat grain yield using multivariate methods (Alavi-Siney and Saba, 2015). In most of these studies, the relationship between traits and grain yield has been discussed, but there has been no discussion about the selection of superior genotypes. Therefore, a method is needed to be able to select the desired genotypes according to all of studied traits. The Selection Index for Ideal Genotype (SIIG) is one of the methods that in addition to selecting the ideal genotype that can determine the distance between genotypes. In this method, it is possible to identify genotypes with the desired characteristics. The aim of this study was to investigate different wheat genotypes under rainfed conditions and determine the best genotypes in terms of yield and early maturity through SIIG selective method.
Materials and methods
In this study, 24 bread wheat genotypes (21 lines and Baran, Sadra, Hashtrood cultivars) were carried out in a randomized complete blocks design with four replications at the rainfed research station of Zanjan Agricultural and Natural Resources Research Center located in Khodabandeh for three years. In this experiment, 5 morpho-phenological traits were measured as follows. Plant height (PLH), days to heading (DHE), days to maturity (DMA), 1000-seed weight (TGW) and grain yield (YLD) were measured after physiological maturation. Analysis of variance, comparison of mean traits by LSD method and matrix of phenotypic correlation coefficients were performed using SAS software (9.4). SPSS software (21) was used to group the genotypes by cluster analysis through Ward method. Multivariate analysis of variance was performed to investigate the differences between clusters and compare the mean between clusters using SPSS software (21). In order to study the genetic diversity and integration of morpho-phenological traits, SIIG method was used (Zali et al., 2015).
Results and discussion
The results of analysis of variance showed a significant difference between the genotypes, indicating sufficient diversity in terms of the studied traits. The matrix of correlation coefficients showed a positive and significant relationship between1000 grain weight, grain yield and SIIG index. In addition, a negative and significant relationship was observed between days to heading with 1000 grain weight, grain yield and SIIG index. Grouping of genotypes using cluster analysis and multivariate analysis of variance showed that genotypes 4, 12, 16, 17, 18, 22 and 23 have the highest value in terms of most traits, especially grain yield. Grouping of genotypes based on SIIG index also placed 7 genotypes (6, 10, 12, 16, 17, 18 and 23) in the top group. Comparison of the two methods showed that 5 genotypes are common in the last two methods, which indicates the high efficiency of these methods in selecting the best. The advantage of SIIG method over other methods is the grouping of genotypes based on the desirability of traits; for example, the low average of genotypes in days to heading and days to maturity traits are desirable, which in this method is considered. This is not considered in multivariate analyzes such as cluster analysis. Therefore selected genotypes through the SIIG index as superior genotypes during three years of experiment are recommended for use in rainfed breeding programs.
Conclusion
According to the ranking based on SIIG index and comparison with control cultivars, genotypes No. 6, 10, 12, 16, 17, 18 and 23 can be recommended as superior genotypes for use in breeding programs under rainfed conditions.
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