Search

Background: Maize is an important crop that is cultivated in many parts of the world. The evaluation of genotypes in breeding programs often faces two important challenges, the genotype × environment interaction effect for the target trait and unfavorable relationships between the target traits. Even though many methods have been offered for stability analysis, especially graphical tools and their relatively good efficiency in interpreting the results, it seems that the best linear unbiased predictions (BLUP) method estimates the means with high accuracy, especially in mixed models, in multi-environmental trails (MET). Therefore, the stability index of weighted average absolute scores (WAASB), which is estimated from the integration of the two stability methods of additive main effect and multiplicative interaction (AMMI) and best linear unbiased predictions, can be used in METs to estimate more accurately the stability of genotypes. Maize breeding programs prioritize high grain yields and earliness as important traits. The multi-trait stability index (MTSI) is a valuable tool for the simultaneous selection of multiple traits. It is estimated based on the average performance and simultaneous stability of genotypes in different traits and environments. Therefore, the current research aimed to identify stable and high grain yield maize hybrids along with the optimal level of grain moisture percentage at harvest time and days to physiological maturity using the integration of AMMI and BLUP methods with WAASB, WAASBY, and MTSI indices. Methods: This study involved the evaluation of seven promising maize hybrids along with four commercial check varieties, including SC647, TWC647, SC704, and SC715, in maize METs based on a randomized complete block design with four replications across 10 regions (Karaj, Moghan, Shiraz, Kermanshah, Kerman, Mashhad, Dezful, Miyandoab, Jiroft, and Mazandaran) during two cropping seasons of 2019-2020. The recorded traits were grain yield adjusted at 14% moisture content, grain moisture percentage at harvest time, and days to physiological maturity. The WAASB was used to estimate genotypic stability for each genotype. It was computed from the singular value decomposition (SVD) of the matrix of best linear unbiased predictions of genotype vs. environment interaction effects generated by a linear mixed-effect model. The WAASBY index for simultaneous selection based on grain yield (Y) and stability (WAASB) was estimated by assigning different weights to grain yield and stability. The simultaneous selection for grain yield and stability based on several traits was conducted using the scores obtained from an exploratory factor analysis (MTSI). Results: Based on the grain yield across 10 environments over two years, promising hybrid NO. 3 had the highest grain yield with 12.80 tons per hectare. According to the likelihood ratio test (LRT), the genotype-by-environment interaction was significant for the traits of grain yield, grain moisture percent at harvest time, and the days to physiological maturity. Therefore, BLUP analysis can be performed on these data due to the significant genotype by environment interaction. The BLUPs performed for hybrids were followed by stability analysis using the AMMI method on these BLUPs. The results indicated that the first and second components justified 27.7% and 24.6% of the hybrid by environment interaction variances, respectively. The highest predicted grain yield by the BLUP method belonged to hybrids No. 3, 2, 4, and 1, with higher than average predicted grain yields. Based on the biplot for the first principal component of the environments against the nominal grain yield, hybrids 2, 6, 3, and 1, having the lowest scores of the first principal component (coefficient b or line slope), had a negligible contribution to the hybrid by environment interaction and were distinguished stable. To enable simultaneous selection based on both grain yield and stability, the WAASBY index was estimated by integrating grain yield (Y) and the WAASB stability index. Considering the 50% contribution of each of the two grain yield and yield stability components, five hybrids (1, 2, 3, 6, and 4) showed above-average WAASBY. Among these, hybrids 1, 2, and 3 had significantly higher WAASBY than the other hybrids. All four control cultivars SC647, TWC647, SC704, and SC715 had lower-than-average WAASBY. Based on the MTSI, hybrid 3 was selected as the best hybrid. In addition, the estimated variance components by restricted maximum likelihood (REML) for grain yield indicated that 75.72% and 7.57% of the phenotypic variance were explained by the environment and GEI variances, respectively, whereas the contribution of residual variance to the phenotypic variance was 16.77%. Conclusion: Based on the results, hybrid 3 (K47/2-2-1-4-2-1-1-1 × MO17) was identified as a high-yielding hybrid, which can be introduced to farmers as a new superior maize hybrid. It seems that the use of the ratio of the WAASB stability index to grain yield (WAASB/Y) and the selection of superior genotypes based on the MTSI could identify hybrids with high grain yields, stability, and desirable levels of important agronomic traits. [ABSTRACT FROM AUTHOR]

Introduction and Objectives: The evaluation of the genotype × environment interaction effect provides valuable information regarding the yield of plant cultivars in different environments and plays an important role in evaluating the stability of the yield of breeding cultivars. Genotype × environment interaction effect, especially in stressful environments, are important limiting factors in the introduction of new cultivars; therefore, it is very important to know the type and nature of the interaction effect and reach the verities that have the least role in creating interaction effects. Various methods have been introduced to evaluate the interaction effect, each of which examines the nature of the interaction effect from a specific point of view. The results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. The purpose of this research was to evaluate the genotype × environment interaction effect in experiments conducted in different environments, to determine the relationships between genotypes and environments and to introduce the most stable red bean genotypes. Material and Methods: In this research, 14 red bean lines along with Yakut, Ofog and Dadfar control cultivars were cultivated in the form of a randomized complete block design with three replications in Khomein, Borujerd, Shahrekord and Zanjan research stations for 2 crop years under the same conditions. After combined variance analysis, according to the significance of genotype × environment interaction, AMMI and GGE-Biplot analysis methods were used to determine the compatibility and stability of genotypes. After AMMI Analysis, the stability parameters of AMMI were calculated. In addition to the AMMI stability parameters, the simultaneous selection index was also calculated for each of the indices, which was the sum of the rank of the genotypes based on each of the AMMI stability indices and the average seed yield rank of the genotypes in all environments. Results: The significance of the double and triple interaction effects of genotype with year and place (environment) in this study showed that genotypes showed different responses in different environments, and in other words, the difference between genotypes is not the same from one environment to another, and in these conditions, the stability of grain yield can be evaluated. The contribution of about 2.5 times the interaction effect of genotype × environment from the total sum of squares, compared to the effect of genotype, indicated the possibility of the existence of megaenvironmental groups in which some genotypes show their maximum performance potential in those environmental groups. Genotypes G12, G5 and G17 had the highest seed yield among the genotypes with yields of 3288, 3136 and 3111 kg per hectare, respectively. AMMI analysis showed that the first to seventh main components were significant at the 1% probability level, and despite the significance of all model components, the first and second main components had the largest contribution to the expression of genotype × environment interaction (66.5%). Based on AMMI1 biplot Genotypes G4, G5, G16, G17 and G12 had the highest values (positive and negative) of IPCA1. In contrast, genotypes G8, G3, G2, G7 and G11 had IPCA1 values close to zero. However, only the genotype G11 showed a performance higher than the average total yield and therefore it can be introduced as a stable genotype with high general compatibility. Based on AMMI2 biplot, genotypes G2, G7, G3 and to some extent G8 and G13 were introduced as stable genotypes, but only G13 genotype had a higher yield in all environments, so this genotype can be introduced as a stable genotype with good yield.; also, every two years, the same place under investigation had a high correlation with each other, so that Bro1 and Bro2 environments on the one hand and Kho1 and Kho2 environments and finally Zan1 and Zan2 showed a high positive correlation (the same effect) to create a mutual effect. In total of the simultaneous selection indices calculated based on AMMI analysis, genotypes G11, G17, G7, G13 and G12 were introduced as stable genotypes with high yield. GGE-Biplot analysis based on average yield and stability showed that genotypes G1, G2, G3, G8 and G7 had the highest general stability compared to other genotypes despite having the lowest yield. On the other hand, G12, G5 and G17 genotypes had the highest yield with less stability. No ideal environment was observed. But Kho1, Kho2 and Sha1 environments are closer to the ideal environment than other environments and they can be used to distinguish the studied genotypes to some extent. On the other hand, G12 genotype can be considered as a desirable genotype that has high average yield and high yield stability. In the same way, G17, G5 and G11 genotypes were in the next stage compared to the ideal genotype and to some extent they can also be considered as desirable genotypes. Conclusion: According to all the results, G12 genotype can be considered as a desirable genotype that has a high average yield and also has yield stability, and G17, G5 and G11 genotypes were in the next stage. [ABSTRACT FROM AUTHOR]