Multivariate analyses of Ethiopian durum wheat revealed stable and high yielding genotypes.

Mulugeta B ，Tesfaye K ，Geleta M ，Johansson E ，Hailesilassie T ，Hammenhag C ，Hailu F ，Ortiz R ... -  《PLoS One》

Genotype-by-environment interaction and stability analysis of grain yield of bread wheat (Triticum aestivum L.) genotypes using AMMI and GGE biplot analyses.

Bread wheat is a vital staple crop worldwide; including in Ethiopia, but its production is prone to various environmental constraints and yield reduction associated with adaptation. To identify adaptable genotypes, a total of 12 bread wheat genotypes (G1 to G12) were evaluated for their genotype-environment interaction (GEI) and stability across three different environments for two years using Additive Main Effect and Multiplicative Interaction (AMMI) and genotype main effect plus genotype-by-environment interaction (GGE) biplots analysis. GEI is a common phenomenon in crop improvement and is of significant importance in genotype assessment and recommendation. According to combined analysis of variance, grain yield was considerably impacted by environments, genotypes, and GEI. AMMI and GGE biplots analysis also provided insights into the performance and stability of the genotypes across diverse environmental conditions. Among the 12 genotypes, G6 was selected by AMMI biplot analysis as adaptive and high-yielding genotype; G5 and G7 demonstrated high stability and minimal interaction with the environment, as evidenced by their IPCA1 values. G7 was identified as the most stable and high-yielding genotype. The GGE biplot's polygon view revealed that the highest grain yield was obtained from G6 in environment three (E3). E3 was selected as the ideal environment by the GGE biplot. The top three stable genotypes identified by AMMI stability value (ASV) were G5, G7, and G10, while the most stable genotype determined by Genotype Selection Index (GSI) was G7. Even though G6 was a high yielder, it was found to be unstable according to ASV and ranked third in stability according to GSI. Based on the study's findings, the GGE biplot genotype view for grain yield identified Tay genotype (G6) to be the most ideal genotype due to its high grain yield and stability in diverse environments. G7 showed similar characteristics and was also stable. These findings provide valuable insights to breeders and researchers for selecting high-yielding and stable, as well as high-yielding specifically adapted genotypes.

Mullualem D ，Tsega A ，Mengie T ，Fentie D ，Kassa Z ，Fassil A ，Wondaferew D ，Gelaw TA ，Astatkie T ... -  《Heliyon》

Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria.

Genotype × environment interaction (GEI) poses a critical challenge to plant breeders by complicating the identification of stable variety (ies) for performance across diverse environments. GGE biplot and AMMI analyses have been identified as the most effective and appropriate statistical techniques for identifying stable and high-performing genotypes across diverse environments. The objective of this study was to identify widely adapted and high-yielding soybean genotypes from Multi-Locational Trials (MLTs) using GGE and AMMI biplot analyses. Fifteen IITA-bred elite soybean lines and three standard checks were evaluated for stability of performance in a 3 × 6 alpha lattice design with three replications across seven locations in Nigeria. Significant (p < 0.001) differences were detected among genotypes, environments, and GEI for grain yield, which ranged between 979.8 kg ha-1 and 3645 kg ha-1 with a mean of 2324 kg ha-1. To assess the stability of genotypes, analyses were conducted using the general linear method, GGE, and the Additive Main Effect and Multiplicative Interaction (AMMI) approach, as well as WAAS and ASV rank indices. In the GGE biplot model, the first two principal components accounted for 67.4 % of the total variation, while in the AMMI model, the first two Interaction Principal Component Axes (IPCA1 and IPCA2) explained 73.20 % and 11.40 % of the variation attributed to genotype by environment interaction, respectively. GGE biplot identified G10 and G16 as the most stable and productive genotypes, while WAASB index revealed G16, G10, G9, G4 and G2 as the most adaptive, stable and productive genotypes across locations, and ASV identified G9, G13, G4, G14 and G10 as the most stable and productive. Consequently, genotypes G2, G4, G9, G10 and G16 displayed outstanding and stable grain yield performance across the test locations and are, therefore, recommended for release as new soybean varieties suitable for cultivation in the respective mega environment where they performed best. More importantly, the two genotypes are recommended for recycling as sources of high-yield and yield stability genes, and as parental lines for high-yield and stable performance for future breeding and genomic selection.

Abebe AT ，Adewumi AS ，Adebayo MA ，Shaahu A ，Mushoriwa H ，Alabi T ，Derera J ，Agbona A ，Chigeza G ... -  《Heliyon》

Graphical analysis of multi-environmental trials for wheat grain yield based on GGE-biplot analysis under diverse sowing dates.

Information on the nature and extent of genetic and genotype × environment (GE) interaction is extremely rare in wheat varieties under different sowing dates. In the present study, the GGE biplot method was conducted to investigate genotype × environment interaction effects and evaluate the adaptability and yield stability of 13 wheat varieties across eight sowing dates, in order to facilitate comparison among varieties and sowing dates and identify suitable varieties for the future breeding studies. Considerable genotypic variation was observed among genotypes for all of the evaluated traits, demonstrating that selection for these traits would be successful. Low broad sense heritability obtained for grain yield showed that, both genetic and non-genetic gene actions played a role in the control of this trait, and suggested that indirect selection based on its components which had high heritability and high correlation with yield, would be more effective to improve grain yield in this germplasm. Hence, selection based on an index may be more useful for improvement of this trait in recurrent selection programs. The results of the stability analysis showed that the environmental effect was a major source of variation, which captured 72.21% of total variation, whereas G and GE explained 6.94% and 18.33%, respectively. The partitioning of GGE through GGE biplot analysis showed that, the first two PCs accounted for 54.64% and 35.15% of the GGE sum of squares respectively, capturing a total of 89.79% variation. According to the GGE biplot, among the studied varieties, the performance of Gascogen was the least stable, whereas Sirvan, Roshan, and Pishtaz had superior performance under all sowing dates, suggesting that they have a broad adaptation to the diverse sowing dates. These varieties may be recommended for genetic improvement of wheat with a high degree of adaptation. The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting high yielding and stable varieties across sowing dates.

Saeidnia F ，Taherian M ，Nazeri SM 《BMC PLANT BIOLOGY》

AMMI and GGE biplot analysis of yield under terminal heat tolerance in wheat.

Wheat is an important cereal crop that helps to meet the food grain needs of people all over the world. Heat stress is one of the most significant abiotic stresses that wheat crops face during terminal growth stages in the wheat growing regions like India. It is very important to identify heat tolerant genotypes to be used as donors for breeding tolerant varieties. Thirty-six wheat genotypes were evaluated under different sowing dates viz., Timely sown (TS), Late sown (LS) and very late sown (VLS), and the fourth was sown in the Temperature controlled phenotyping facility (TCPF) across two years. Genotypes were planted following lattice square design with two replications. Data was recorded for yield and yield contributing traits and analysed using selection indices as well AMMI and GGE biplot stability models. Heat stress affected all the traits under different heat environments which ranged from 1.6% (Spikelet number) to 37.2% (grain yield). Regression analysis indicated that the thousand grains weight (R2 = 0.50) contributed significantly towards grain yield under heat stress. Stress susceptibility index (SSI) found genotypes GW322, RAJ3765, Raj4037and MACS6145 as heat tolerant whereas, Stress Tolerance Index (STI) identified C306, HD2967, WH1080, WH730, DBW90, HD2932, DBW17, RAJ3765 as heat tolerant and high yielding. AMMI biplot analysis indicated stable genotypes DBW90, WH730, RAJ4083, CBW38, HD2932, NI5439, WR544, whereas GGE biplot analysis revealed stable genotypes NIAW34, NI5439, RAJ4083, DBW90, PBW590, Raj3765, HUW 510, WH730, HD2967 and UP2382. Heat stress affects significantly all yield contributing traits. Thousand grain weight was the most important trait that can be used as a selection criterion for selecting tolerant lines. Based on selection indices and both AMMI and GGE analysis, genotype RAJ3765 was identified to be highly heat tolerant with good grain yield.

Gupta V ，Mehta G ，Kumar S ，Ramadas S ，Tiwari R ，Singh GP ，Sharma P ... -  《-》