Application of multivariable of Additive Main Effects and Multiplicative Interaction and Biplot Graphical Analysis Multivariate Methods on the Study of Genotype-Environment Interaction on Safflower Genotypes Grain Yield

Food Science & Nutrition

Ahmadi

Bazrafshan

et al. 2022

Plant diseases are considered one of the main factors reducing yield and quality of crops, which are constantly developing and creating more virulent races and cause the resistance of more genes to break. Identifying resistance sources and including them in breeding programs will improve resistant genotypes. Rhizomania is the most common, widespread, and devastating disease of sugar beet in Iran and worldwide.Breeding genotypes with disease resistance genes is one of the most important ways to deal with this destructive disease. Twenty sugar beet genotypes along with five controls were evaluated in a randomized complete block design with four replications in rhizomania-infected conditions in four regions of Mashhad, Shiraz, Miandoab, and Hamedan for 2 years. The results of genotypic reaction to rhizomania showed that the genotypes with resistance reaction were much more frequent than those with susceptibility reaction. The analysis of multiplicative effects of the AMMI model showed that the first six components were significant and explained 98.80% of the interaction variations. The biplot obtained from the mean white sugar yield and the first interaction principal component confirmed the superiority of the RM5 genotype due to its high white sugar yield and stability in infected conditions. The results obtained from the first three principal components biplot showed that the RM9 genotype with a mean white sugar yield of 11.91 t. ha −1 was a genotype with vast general stability in all disease-infected environments. Based on the results of the MTSI index, RM3, RM17, RM9, RM13, and RM15 are introduced as stable genotypes under rhizomaniainfected conditions. In conclusion, it seems that the studied genotypes have valuable and useful genes inherited from their parents to deal with rhizomania disease.Applying these genotypes in sugar beet breeding programs can effectively prevent the threat of rhizomania.

Section: Resultsmentioning

confidence: 99%

Evaluation of resistance and determination of stability of different sugar beet (Beta vulgaris L.) genotypes in rhizomania‐infected conditions

Food Science & Nutrition

Ahmadi

Bazrafshan

et al. 2022

“…The residual sum of squares from the AMMI with the lowest mean square was non-significant, which indicates the considerable accuracy of this model (36). In their study, Omrani, Omrani (17) showed that the first four IPCs together explained 83% of the variation of GEI. Fathi, Ranjbar (37) estimated the contribution of the IPC1 and IPC2 to be 49.10% and 22.50%, respectively, and these two IPCs could account for 71.60% of GEI variations.…”

Section: Additive Main Effects and Multiplicative Interaction (Ammi)mentioning

confidence: 96%

“…Methods for the analysis of phenotypic stability are constantly evolving to provide more precision in the assessment of genotypes and the study of environmental interaction components. In phenotypic stability analysis, a combination of several stability analysis methods is often used instead of relying on a single analysis (16)(17)(18)(19).…”

Section: | Backgroundmentioning

confidence: 99%

“…The BLUP of i th genotype was predicted as the sum of the general mean overall environments and genotypic effect. Harmonic mean of genotypic values (HMGV), relative performance of genotypic values (RPGV) and harmonic mean of relative performance of genotypic values (HMRPGV) were calculated as the most important statistics of BLUP based on equations 17 to 19 (77):(17)…”

mentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of resistance and stability of new sugar beet hybrids in field conditions infected with rhizoctonia root and crown rot disease

Saremirad

Rezaei

et al. 2023

Preprint

Background The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this study, 13 different sugar beet genotypes were grown in four naturally infected locations over two years. They were also assessed for resistance to Rhizoctonia disease in microplots artificially inoculated with the R133 isolate of Rhizoctonia solani. Results The additive main effect and multiplicative interaction (AMMI) analysis showed that both environment and genotype had significant additive effects, while GEI had a significant multiplicative effect. Further analysis of the interaction effects using IPCs revealed that the first three IPCs were significant. Based on the AMMI1 biplot, Rc3, Rc6, and Rc11 were recognized as the most stable genotypes. The TOPSIS calculated from AMMI statistics identified Rc3 as the most stable genotype. The LMM showed that the genotype and GEI were significant. Based on the best linear unbiased prediction (BLUP), Rc6 had the highest predicted mean white sugar yield (WSY). The obtained TOPSIS from BLUP statistics introduced Rc3 and Rc9 as the most stable genotypes. The WSY and WAASB biplot showed that Rc3, Rc5, and Rc11 had higher WSY in addition to stability. In terms of WAASBY/WSY ratio, BTS233, Rc5, and Rc7 were found to be stable genotypes. The simultaneous ranking and selection of genotypes based on the 50/50 ratio for WAASB and WSY yielded somewhat different results, with Rc3, Rc6, and Rc11 having relatively higher WAASBY values. Conclusion Based on the results, the AMMI alone cannot be successful in analyzing the structure of the LMM. In such a situation, using BLUP can bring better and more reliable results. However, the combination of AMMI power and BLUP prediction accuracy made it possible to investigate the genotypic stability and GEI derived from the LMM, and to reveal a complete view of the GEI of sugar beet product while eliminating the limitations of AMMI. In total, Rc3 followed by Rc6 and Rc11 were recognized as stable genotypes with high WSY. In addition to stability and high WSY, these genotypes also had genetic resistance against R. solani.

“…The residual sum of squares from AMMI with the lowest mean of squares for all studied traits was non-significant, which indicates the considerable accuracy of this model 37 . In a study by Omrani, et al 38 the first four components explained 83% of the GEI. Fathi, et al 39 reported the contribution of the first and second PCs of the GEI as 49.49% and 22.50%, respectively, and both accounted for 71.60% of the variations in the GEI.…”

Section: 1| Additive Main Effects and Multiplicative Interaction (Ammi)mentioning

confidence: 96%

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

Taleghani

Saremirad

et al. 2023

Preprint

The aim of this study was to evaluate the effect of genotype× environment interaction (GEI) and selection of the best sugar beet genotypes. Eighteen sugar beet genotypes were evaluated in randomized complete block design in five research stations for two years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effects analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.