Using double and triple biplots for identification of superior winter wheat genotypes in term of root traits, yield and yield components under rainfed conditions

Document Type : Research Paper

Authors

1 Graduate Ph.D., Department of Plant Genetics and Production, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

2 Professor, Department of Plant Genetics and Production, Faculty of Agriculture, University of Zanjan, Zanjan, Iran

3 Research Associate Professor, Dryland Agricultural Research Institute (DARI), Agricultural Research Education and Extension Organization (AREEO), Maragheh, Iran

Abstract

Introduction
Selection and breeding for yield enhancement has always been one of the main goals of wheat breeders. Since the yield of rainfed wheat depends on the moisture stored in the soil during the rainy season, therefore, different crop varieties should be evaluated in terms of their potential to use the water stored in the soil during long periods of drought in each region. Roots are the main organs to respond, understand and maintain yield in drought conditions, which are known as the hidden half of the plant and their function plays an important role in the growth and yield of plants. On the other hand, selection based on multiple traits increases the suitability of crop varieties for farmers. Genotype × trait (GT) and genotype × yield × trait (GYT) biplots are graphical methods that have been proposed to evaluate and identify favorable genotypes in terms of multiple traits. The objective of the current study was to use the GYT method to investigate the relationships between root traits and yield and yield components, to rank wheat varieties and advanced lines, and to select superior genotypes based on a set of root traits affecting grain yield under rainfed conditions.
Materials and methods
In plant materials of this study were 24 bread wheat genotypes including 11 rainfed winter wheat varieties along with 13 advanced lines. The studied genotypes were evaluated in a randomized complete block design with three replications in the research field of Faculty of Agriculture, Zanjan University, Zanjan, Iran, during two cropping years, 2016-2017 and 2017-2018, under rainfed conditions. The measured traits included root length, root diameter, root volume, root area, root biomass, number of grains per spike, 1000-kernel weight, number of spikes per m2, and grain yield (g.m-2). For statistical analysis, the data were first standardized and then principal component analysis was performed. GT and GYT biplots were used to identify the relationship between root traits and grain yield and yield components and select the best wheat genotypes based on multiple traits.
Research findings
The results showed that the use of GYT biplot is more efficient than GT biplot. Based on GYT biplot and superiority index, genotype No. 2 was identified as the superior genotype in term of all yield-trait combinations except yield- root length and area combination at a depth of more than 25 cm. Genotypes No. 10 and 11 were the most favorable genotypes for combination of yield with root length and area at a depth of more than 25 cm. In the GYT method, root penetration depth, number of spikes per m2, root diameter up to 25 cm depth, followed by 1000-kernel weight, root volume, root length, root dry weight and root area up to 25 cm depth and root diameter at a depth of more than 25 cm were identified as the most important traits for determining the genetic progress in breeding programs. In total, considering these traits, genotype No. 2 was the closest genotype to the ideal genotype and can be introduced as the best genotype in this study.
Conclusion
The results of this research showed that there is a potential for simultaneous genetic improvement of grain yield and root traits especially in the surface part of the soil in the studied wheat genotypes. Based on the obtained results of this experiment, it can be said that the GYT graphic method is a new efficient and practical method to identify superior genotypes based on multiple traits in breeding programs.

Keywords

Main Subjects

References

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Cereal Research
Volume 14, Issue 3 - Serial Number 52
January 2024
Pages 267-290

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