Document Type : Review Paper
Authors
1
Post-Doctoral Researcher, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
2
Ph. D. Student, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
3
Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
4
Professor, Department of Plant Production and Genetics, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
5
Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
6
M. Sc. Graduate, Institut des Sciences du Cerveau de Toulouse, Toulouse, France
7
Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, University of Maragheh, Maragheh, Iran
8
Assistant Professor, Department of Plant Production and Genetics, Faculty of Agriculture, University of Saravan, Saravan, Iran
Abstract
Introduction
Genotype × environment interaction (GEI) significantly affects the performance of different genotypes under various environmental conditions, posing challenges for agricultural researchers focused on improving crop varieties. Selection and introduction of genotypes, as a key steps in breeding programs, is complex and time-consuming due to the impacts of biotic and abiotic stresses. An ideal genotype should not only have high yield, but also be able to maintain its stability across varying conditions and not have high yield fluctuations. This is a dynamic concept of stability and can help identify suitable genotypes, however, none of the existing methods alone can explain all dimensions of performance across different environments. Therefore, for the effective selection of superior genotypes and understanding the genotype × environment interaction, it is essential to analyze multiple datasets from multi-environment trials (METs) from various aspects of yield stability. In this regard, various methods with high accuracy have been proposed for analyzing the stability of genotypes, which can be divided into two main groups, including non-parametric and parametric (univariate and multivariate) methods. In this study, the efficiency of various non-parametric and univariate parametric stability methods are comprehensively reviewed and compared with an emphasis on cereals. Moreover, the fundamental concepts of GEI, its causes, its necessity and importance, as well as how to evaluate the stability and performance of genotypes in METs are explained.
Research findings
The results of this study indicated that stability analysis methods, including parametric methods based on regression analysis and analysis of variance as well as non-parametric methods, each have their specific advantages and disadvantages. It seems that parametric methods are more effective in analyzing genotype × environment interactions, while non-parametric methods are more suitable for analyzing non-crossing interactions. The sample size and the breeder’s objective are important and key factors in selecting the type of stability analysis method. In small sample conditions, parametric methods have an advantage, however, as the sample size increases, the effectiveness of both methods becomes nearly equal. It seems that the combination of these two types of indices can assist breeders in selecting superior and stable genotypes.
Conclusion
In the current study, the effectiveness of non-parametric and parametric methods in assessing and measuring the stability and performance in multi-environment trials (METs) was investigated and compared. The use of various stability analysis methods enables researchers and breeders to select promising genotypes based on performance and stability, ultimately contributing to increase the sustainability of crop production and food security.
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