Comparative Analysis of Statistical Models for Evaluating Genotype × Environment Interaction in Rainfed Safflower

Alizadeh, Khalil; Mohammadi, Reza; Shariati, Abdollah; Eskandari, Masoud

doi:10.1007/s40003-017-0279-1

Cited by 20 publications

(17 citation statements)

References 26 publications

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“…Because of the increasing interest, several statistical methods have been developed for multi-environment trials (MET) to study GEI effects [14,15]. The two most common methods used for MET are additive main effects and multiplicative interaction (AMMI), and genotype plus genotype environment interaction (GGE) biplot [16]. Both methods are used for straightforward graphical representation of a complex genotype by two-way environment tables using principal component analysis [17].…”

Section: Introductionmentioning

confidence: 99%

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GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

et al. 2021

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In plant breeding and agricultural research, biplot analysis has become an important statistical technique. The goal of this study was to find the winning genotype(s) for the test settings in a part of the Southwest region of Nigeria, as well as to investigate the nature and extent of genotype X environment interaction (GEI) effects on Bambara groundnut (BGN) production. The experiment was carried out in four environments (two separate sites, Ibadan and Ikenne, for two consecutive years, 2018 and 2019) with ninety-five BGN accessions. According to the combined analysis of variance over environments, genotypes and GEI both had a substantial (p < 0.001) impact on BGN yield. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. The results revealed that BGN accessions performed differently in different test conditions, indicating that the interaction was crossover in nature. To examine and show the pattern of the interaction components, biplots with the genotype main effect and genotype X environment interaction (GEI) were used. The first two PCs explained 80% of the total variation of the GGE model (i.e., G + GE) (PC1 = 48.59%, PC2 = 31.41%). The accessions that performed best in each environment based on the “which-won-where” polygon were TVSu-2031, TVSu-1724, TVSu-1742, TVSu-2022, TVSu-1943, TVSu-1892, TVSu-1557, TVSu-2060, and TVSu-2017. Among these accessions, TVSu-2017, TVSu-1557, TVSu-2060, TVSu-1892, and TVSu-1943 were among the highest-yielding accessions on the field. The adaptable accessions were TVSu-1763, TVSu-1899, TVSu-2019, TVSu-1898, TVSu-1957, TVSu-2021, and TVSu-1850, and the stable accessions were TVSu-1589, TVSu-1905, and TVSu-2048. In terms of discriminating and representativeness for the environments, Ibadan 2019 is deemed to be a superior environment. The selected accessions are recommended as parental lines in breeding programs for grain yield improvement in Ibadan or Ikenne or similar agro-ecological zones.

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Section: Introductionmentioning

confidence: 99%

“…With AMMI, SVD is applied to the data excluding genotype and environment means, while GGE biplot excludes from the data the environment means only [18]. According to Alizadeh et al [16], both methods are highly correlated, and therefore can be used interchangeably.…”

Section: Introductionmentioning

confidence: 99%

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

et al. 2021

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“…The significance of the G × E interaction can affect the selection of safflower genotypes, making it difficult to recommend new cultivars. Several researchers also found significant interactions when studying safflower genotypes in other environments around the world: Iran [ 8 , 20 , 21 , 22 ], Turkey [ 23 ], and Brazil [ 5 ].…”

Section: Resultsmentioning

confidence: 99%

Adaptability and Stability of Safflower Genotypes for Oil Production

Neto

Zeffa

Freiria

et al. 2022

Plants

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The study aimed to analyze the agronomic performance of 11 safflower genotypes using adaptability and stability methods, while identifying safflower genotypes with stable behavior and a high grain yield in different environments of the Brazilian Cerrado. Ten lines and a cultivar of safflower were evaluated in four environments in the Brazilian conditions. Our results revealed the genotypes P30, P35, P9, P11, and P31 to be superior for grain yield and P43, P7, P11, and P31 to be superior for oil content. The lowest Wricke index, an indication of genotype stability, was observed for P9 (0.41%), which is considered the most stable genotype, followed by P35 (1.29%) and P31 (1.98%). For the predictability of the behavior of genotypes in the environments, P7 (80.85%), P35 (86.10%), P31 (85.90%), and P9 (97.42%) were considered predictable genotypes. The genotypes P11 (1045.6 kg ha−1 and 19.7%) and P21 (952.7 kg ha−1 and 20.6%) are recommended for cultivation in this region, considering both their grain yield and oil content. Safflower is viable to use out of season in the Brazilian Cerrado. The crop can generate profits for farmers and be used for oil production in periods of uncertain corn production.

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“…The determination of GEI factors helps geneticists in their breeding programs to shift the selection toward varieties suited for wide environments. For analysis of MEYT, additive main effects and multiplicative interactions (AMMI), as well as genotype plus genotype environment interaction (GGE) biplots, have been developed to study GEI effects [12]. However, the GGE biplot model provided an excellent graphical presentation for breeders, visualizing various aspects of genotype and genotype × environment in different biplots, not only showing the stability of genotype but also discrimination strength of recognition in given environments [13].…”

Section: Introductionmentioning

confidence: 99%

Performance of Different Varieties of Spring Field Pea (Pisum sativum L.) under Irrigated and Rainfed Environments in North China

Yang

et al. 2022

Agronomy

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Field pea (Pisum sativum L.) is one of the most important pulse crops, and is widely grown in North China. To exploit potential pea varieties suitable for growing in spring planting areas, a set National Regional Trials of Spring Pea Genotype was carried out under irrigated and rainfed environments across seven locations. Grain yield and agronomic traits of fourteen pea cultivars were evaluated. Analysis of variance indicated that the effect of genotype, environments and genotypes × environments interaction was significant (p < 0.01). Results of GGE biplot analysis showed that the first and second principal components accounted for 77.5% and 14.5%, respectively. Genotypes accounted for just 14.7% of the variation in seed yield, while environments accounted for 39.9%, and genotypes × environments interaction accounted for 45.5% of the variation. Investigating the polygon view led to the identification of five superior genotypes and five productivity environments. Superior genotypes and key environments for grain yield were determined using a general linear model. The significant genotypes × environments interaction effects indicated the inconsistent performance of genotypes across the tested environments. Among the tested genotypes, significant differences were observed for grain yield and yield-related traits. The semi-leafless pea variety Longwan 10 was observed the highest yield (3308 kg/ha) over seven locations. Dingwan 12, Dingwan 13, Tongwan 5, Kewan 7 showed higher, stable grain yield than the overall mean of genotypes and check variety Zhongwan 6. The most representative and productivity regions for grain yield of field pea were Yondeng, Qitai, Liaoyang, Dingxi and Tangshan. These findings represent a comprehensive analysis of yield and stability of spring pea varieties and growing locations, which may be useful for national and international pea improvement programs.

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Comparative Analysis of Statistical Models for Evaluating Genotype × Environment Interaction in Rainfed Safflower

Cited by 20 publications

References 26 publications

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

GGE Biplot Analysis of Genotype × Environment Interaction and Yield Stability in Bambara Groundnut

Adaptability and Stability of Safflower Genotypes for Oil Production

Performance of Different Varieties of Spring Field Pea (Pisum sativum L.) under Irrigated and Rainfed Environments in North China

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