Mohammadreza Shiri 
, Sajjad Moharramnejad , Afshar Estakhr , Sharareh Fareghi , Hamid Najafinezhad , Saeed Khavari Khorasani , Aziz Afarinesh , Kamran Anvari , Morteza Eshraghi-Nejad , Masoud Mohseni
, Sajjad Moharramnejad , Afshar Estakhr , Sharareh Fareghi , Hamid Najafinezhad , Saeed Khavari Khorasani , Aziz Afarinesh , Kamran Anvari , Morteza Eshraghi-Nejad , Masoud Mohseni
Department of Maize and Forage Crops Research, Seed and Plant Improvement Institute, Agricultural Research Education and Extension Organization (AREEO): Karj, Iran,
Abstract: (32 Views)
Extended Abstract
Introduction and Objective: 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. Despite the fact that 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 which 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 estimated more accurately the stability of genotypes. Maize breeding programs prioritize high grain yield 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 was carried out to identify stable and high grain yield maize hybrid along with the optimal level of grain moisture percent at harvest time and days to physiological maturity using the integration of AMMI and BLUP methods with WAASB, WAASBY and MTSI indices.
Material and 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 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 traits, namely grain yield adjusted at 14% moisture content, grain moisture percent at harvest time and days to physiological maturity were recorded. The weighted average absolute scores (WAASB) were 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. Also, 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, due to the significant interaction of genotype by environment, BLUP analysis can be performed on this data. So, the BLUPs for hybrids was done and 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 BLUP method belonged to hybrids No. 3, 2, 4, and 1, which had higher than average predicted grain yield. Based on the biplot for the first principal component of the environments against the nominal grain yield, hybrids No. 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 components of grain yield and yield stability, five hybrids including hybrids 1, 2, 3, 6, and 4 showed above average WAASBY. Among them, hybrids No. 1, 2, and 3 had significantly higher WAASBY than other hybrids. All four-control cultivars SC647, TWC647, SC704, and SC715 had lower than average WAASBY. In addition, based on multi-trait stability index (MTSI), hybrid No. 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 No. 3 (K47/2-2-1-4-2-1-1-1× MO17) was identified as a high-yielding hybrid, which can be introduced to the farmer 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 multi-trait stability index (MTSI) was able to identify hybrids with high grain yield, stable, and desirable levels of important agronomic traits.
Introduction and Objective: 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. Despite the fact that 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 which 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 estimated more accurately the stability of genotypes. Maize breeding programs prioritize high grain yield 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 was carried out to identify stable and high grain yield maize hybrid along with the optimal level of grain moisture percent at harvest time and days to physiological maturity using the integration of AMMI and BLUP methods with WAASB, WAASBY and MTSI indices.
Material and 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 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 traits, namely grain yield adjusted at 14% moisture content, grain moisture percent at harvest time and days to physiological maturity were recorded. The weighted average absolute scores (WAASB) were 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. Also, 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, due to the significant interaction of genotype by environment, BLUP analysis can be performed on this data. So, the BLUPs for hybrids was done and 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 BLUP method belonged to hybrids No. 3, 2, 4, and 1, which had higher than average predicted grain yield. Based on the biplot for the first principal component of the environments against the nominal grain yield, hybrids No. 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 components of grain yield and yield stability, five hybrids including hybrids 1, 2, 3, 6, and 4 showed above average WAASBY. Among them, hybrids No. 1, 2, and 3 had significantly higher WAASBY than other hybrids. All four-control cultivars SC647, TWC647, SC704, and SC715 had lower than average WAASBY. In addition, based on multi-trait stability index (MTSI), hybrid No. 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 No. 3 (K47/2-2-1-4-2-1-1-1× MO17) was identified as a high-yielding hybrid, which can be introduced to the farmer 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 multi-trait stability index (MTSI) was able to identify hybrids with high grain yield, stable, and desirable levels of important agronomic traits.
Type of Study: Applicable |
Subject:
اصلاح نباتات، بیومتری
Received: 2023/04/17 | Revised: 2024/05/6 | Accepted: 2024/01/7
Received: 2023/04/17 | Revised: 2024/05/6 | Accepted: 2024/01/7
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