GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Yüksel Kaya

doi:10.47115/bsagriculture.1069462

Abstract

Durum wheat (Triticum durum L.) is a vital crop in the rain-fed areas of Turkey. In this study, the grain yield of twenty durum wheat genotypes (sixteen advanced lines with four checks) tested across 8 rain-fed environments during the 2008-2009 cropping season was evaluated using GGE (Genotype (G) Main Effect + Genotype by Environment Interaction (GEI)) Biplot Approach. Environment (E) captured most (83 %) of the grain yield (GY) variation, while the portions attributed to G and GEI were only 6 and 11 %, respectively. In addition, most of the testing Es were highly correlated. According to GGE-Biplot analysis, ‘Which won where’ pattern partitioned the testing Es into three mega-environments (ME): the first ME with six Es with G13 (the highest yielder) as the winning genotype; the second ME encompassed one environment (E1, Konya) with G4 (the lowest yielder) as the winning genotype, and the last ME represented by one location (E6, Altintas) with G10 (the higher yielder) as the winning genotype. GGE-Biplot analysis showed that although the Durum Wheat Yield Trials were conducted in many environments, outcomes alike can be obtained from one or two representatives of each ME. On the other hand, no correlation of these MEs with their geographic location was observed. In conclusion, the presence of cross-over GEI underscores that efforts should be given to identify specifically adapted genotypes rather than broadly adapted ones tested on multi-environment trials (METs).

Highlights

Multi-environment trial (MET) plays an essential role in plant breeding
To show the usefulness of the G + GEI (GGE)-Biplot method in dissecting the complex GEI in MET data, we analyzed the grain yield (GY) of 16 improved lines with four checks tested in eight rainfed environments
As is typical of most METs, GY was significantly affected by E, accounting for 83% of the total variation (G+E+GEI)

Summary

Introduction

Multi-environment trial (MET) plays an essential role in plant breeding. The main goal in plant breeding is to select new cultivars agronomically superior (i.e., high grain yielder) over commonly grown cultivars (Rakshit et al, 2012; Li and He, 2021). Two types of Biplot, AMMI (Additive Main-effect and Multiplicative Interaction) Biplot (Crossa, 1990; Gauch, 1992) and GGE (Genotype + Genotype by Environment Interaction) Biplot (Yan et al, 2000; Yan and Kang, 2003), are the most commonly used to understand GEI comprehensively. Both G and GEI should simultaneously be included in a model to evaluate genotypes (Yan and Tinker, 2006; Sabaghnia et al, 2008). Despite reports on GGE-biplot analysis in selecting superior genotypes or test environments in such crops, its application to durum wheat METs in Turkey is insufficient (Tekdal et al, 2017; Kendal, 2019; Mohammadi et al, 2021). To show the usefulness of the GGE-Biplot method in dissecting the complex GEI in MET data, we analyzed the GYs of 16 improved lines with four checks tested in eight rainfed environments

Methods

Results

Discussion

Conclusion