Evaluation of grain yield and some important agronomic traits in spring wheat international nurseries in moderate climate regions of Iran. | ||
| پژوهش های کاربردی زراعی | ||
| Volume 33, Issue 4 - Serial Number 129, March 2021, Pages 40-61 PDF (926.79 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/aj.2021.125628.1387 | ||
| Authors | ||
| Saeed Bagherikia* 1; Farshad Bakhtiar2; Navid Sharsoda3 | ||
| 1Plant Breeding and Biotechnology Department, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 2Assistant Professor, Seed and plant Improvement Institute, Agriculture Research Education and Extension Organization (AREEO), Karaj, Iran | ||
| 3Researcher, Seed and plant Improvement Institute, Agriculture Research Education and Extension Organization (AREEO), Karaj, Iran | ||
| Abstract | ||
| Introduction: Variation and selection play a key role in breeding programs. The proper selection is related to the desired variation in the desired trait. In order to take advantage of the existing variation; the evaluation of germplasm resources is necessary. Genotype–environment interactions are particularly important for breeders and one of the complex issues in breeding programs is the selection of high yielding and stable crop genotypes. Therefore, knowledge of the genotype–environment interactions is a necessity to evaluate new cultivars in different environments. GGE biplot model is one of the most used multivariate methods in the study of genotype–environment interactions that is performed based on principal component analysis (Yan et al., 2010). Materials and Methods: In order to select the superior lines from the elite spring bread wheat yield trial (17ESBWYT), 50 spring bread wheat lines (considering Parsi culivar as a local check) were studied at three stations (Karaj, Kermanshah and Zarghan), which represent moderate climate regions of Iran. The experiments were conducted in the randomized complete block design (RCBD) with three replications in the 2016- 2017 growing season. The plant materials had been received from the international center for agricultural research in the dry areas (ICARDA). The measured traits included days to heading (DHE), days to maturity (DMA), plant height (PLH), thousand grain weight (TKW), seed filling period (SFP), seed filling rate (SFR), grain yield (GY), relative yield to mean (RYM), relative yield to local check (RYLC) and plant response to yellow rust (YR) and leaf rust (LR). Homogeneity of the variances in different environments was tested following Bartlett's test. Then, the combined analysis of variance and stability analysis were performed using GGE biplot method. Data was analyzed using GGE biplot4, heatmapper, SAS and Excel. Results and Discussion: The combined analysis of variance confirmed that the effects of environment, genotype and interaction between them were statistically significant. Mean comparison of grain yield showed that the highest grain yield (6.489-ton ha-1 ) was achieved with the genotype no 32 and the lowest grain yield (4.728-ton ha-1) was related to the genotype no 25. Total mean of grain yield at stations of Karaj, Kermanshah and Zarghan was 6.589, 5.317 and 4.753-ton ha-1 respectively. The correlation biplot of the environments revealed that the location of Kermanshah had a positive correlation with Karaj and Zarghan, because the angles of the vectors were less than 90◦, exhibiting a positive correlation among the environments. Also, there was a weak correlation between Karaj and Zarghan, indicating that two environments have been almost independent of each other because the angle of the vectors was 90◦ (Yan & Rajcan, 2002). The yield and stability of the genotypes were evaluated using the average-environment coordination (AEC) view. Presence of genotypes on this axis is approximation of grain yield (Yan et al., 2000). Hence, the genotypes no 32, 18, 24, 28, 31, 14, 33, 19, 29 and 2 had the highest grain yield. The vertical axis of AEC biplot also showed the interaction between genotype and environment and determined the stability of the genotypes. Therefore, among the genotypes that gave a high grain yield, the genotypes no 32, 18, 24, 14, 29 and 2 were the most stable for grain yield. The final selection of the genotypes was carried out considering the response of genotypes to yellow rust and leaf rust and other desirable traits. Conclusion: In the current study, 50 genotypes of bread wheat were evaluated in terms of grain yield, response to yellow rust and leaf rust and some desirable agronomic traits at three stations (Karaj, Kermanshah and Zarghan) representing moderate climate regions of Iran. Considering the results of GGE biplot method and response to yellow rust, leaf rust and other desirable agronomic traits, eight genotypes: no 32, 24, 28, 14, 33, 19, 29, and 2 were selected as superior lines and were conducted to preliminary regional wheat yield trial (PRYWT) in the moderate climate regions of Iran. It is hoped that in the coming years a new cultivar will be released among the selected genotypes in wheat breeding programs in moderate climate regions of Iran, after preliminary and adaptation experiments. | ||
| Keywords | ||
| Biplot; bread wheat; compatibility; selection; variation | ||
| References | ||
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