Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in chickpea to fusarium wilt using GGE biplot

Srivastava, A. K.; Saxena, D. R.; Saabale, P. R.; Raghuvanshi, K.S.; Anandani, V.P.; Singh, Rakesh; Sharma, Om P.; Wasinikar, A.R.; Sahni, Sangita; Varshney, Rajeev K.; Singh, Narendra Pratap; Dixit, G. P.

doi:10.1016/j.cropro.2021.105571

Cited by 9 publications

(5 citation statements)

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“…AEC technique has been extensively utilized for identifying superior genotypes and visualized graphically. The evaluation of rice lines contributes to identifying trait relationships and for selecting lines for specific traits [46].…”

Section: Discussionmentioning

confidence: 99%

Participatory Varietal Selection for Promising Rice Lines

et al. 2021

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The purpose of rice breeding is to create varieties that are well adapted, highly productive, and acceptable to farmers. However, rice productivity is limited as a result of combined biotic stresses (pests/diseases). This study combines assessment by farmers with the evaluation by breeders with respect to promising rice lines within a range of environments. The aim is to investigate farmers’ preferences and to characterize the yield of promising rice lines, as well as their resistance to pests/diseases by consulting 120 farmers and breeders. This study used an oversite design replicated three times with thirteen promising lines and two varieties, which were all evaluated at farmers’ fields between December 2019 and May 2020. The Importance Performance Analysis was used to compare line performance and farmers’ expectations. Lines Gamapadi-2 and Gamapadi-4 had the highest acceptability scores based on the farmers’ preferences. The yield performances were evaluated using the Finlay–Wilkinson test and the genotypes were evaluated using environmental models (GGE biplot) to determine the most stable lines to be recommended for large-scale planting. The Finlay–Wilkinson and GGE biplot conclusion analyses also showed that the Gamapadi-2 and Gamapadi-4 lines exhibited high potential yield and stability, as well as indications of specific advantages. The results for both lines in all locations indicated no symptoms of damage caused by brown planthoppers or bacterial leaf blight. These lines in all age ranges at two sites showed no symptoms of leaf blast.

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Section: Discussionmentioning

confidence: 99%

Participatory Varietal Selection for Promising Rice Lines

et al. 2021

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“…Predominance of G × E interaction contribution to the total variation indicated the importance of understanding G × E interaction patterns in breeding and selection for soybean anthracnose resistance in India. This is in accordance with the previous reports (Sharma et al ., 2013; Das et al ., 2021; Srivastava et al ., 2021). Significant environmental effect is attributed to the variability in the pathogenic population prevailing in different environments.…”

Section: Discussionmentioning

confidence: 99%

“…Multivariate, graphic-based stability models such as GGE (genotype main effect (G) plus genotype by environment interaction (GE)) (Yan et al ., 2000) and additive main effect and multiplicative interaction (AMMI) (Zobel et al ., 1988) biplot analyses have been employed by researchers to understand the complex G × E interaction patterns and to identify stable genotypes for several economically important traits, especially grain yield. Further, these two models have been used extensively for disease resistance trait in different crops for different diseases to identify stable and durable-resistant sources and to understand G × E interactions (Sharma et al ., 2012, 2013; Persaud and Saravanakumar, 2018; Diatta et al ., 2019; Pandey et al ., 2021; Srivastava et al ., 2021)…”

Section: Introductionmentioning

confidence: 99%

“…Multivariate, graphic-based stability models such as GGE (genotype main effect (G) plus genotype by environment interaction (GE)) (Yan et al, 2000) and additive main effect and multiplicative interaction (AMMI) (Zobel et al, 1988) biplot analyses have been employed by researchers to understand the complex G × E interaction patterns and to identify stable genotypes for several economically important traits, especially grain yield. Further, these two models have been used extensively for disease resistance trait in different crops for different diseases to identify stable and durable-resistant sources and to understand G × E interactions (Sharma et al, 2012(Sharma et al, , 2013Persaud and Saravanakumar, 2018;Diatta et al, 2019;Pandey et al, 2021;Srivastava et al, 2021) Complex interaction will create disagreement between realized and expected genotypic responses, thereby affecting the selection efficiency (Pande et al, 2013) and accurate prediction models aids in enhancing selection efficiency in MET, thereby increasing the genetic gains (Gauch and Zobel, 1988). Through its shrinkage property, the best linear unbiased prediction (BLUP) model is reported to outperform AMMI family models in predicting genotypic means (Piepho, 1994;Olivoto et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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WAASB index revealed stable resistance sources for soybean anthracnose in India

et al. 2021

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Anthracnose caused by Colletotrichum truncatum is a major soybean disease in India. Genetic resistance is the viable option to combat yield losses due to this disease. In the current study, 19 soybean genotypes were evaluated for anthracnose disease resistance at five locations (Medziphema, Palampur, Dharwad, Jabalpur and Indore) for three consecutive years (2017–2019) to identify stable and superior genotypes as resistant sources and to elucidate genotype (G) × environment (E) interactions. Genotype effect, environment effect and G × E interactions were found significant (P < 0.001) where G × E interactions contributed highest (42.44) to the total variation followed by environment (29.71) and genotype (18.84). Through Weighted Average of Absolute Scores (WAASB) stability analysis, PS 1611 (WAASB score = 0.33) was found to be most stable and through WAASBY superiority analysis NRC 128 (WAASBY score = 94.31) and PS 1611 (WAASBY score = 89.43) were found to be superior for mean performance and stability. These two genotypes could be candidate parents for breeding for durable and stable anthracnose resistance. Through principal component analysis, disease score was found to be positively associated with relative humidity, wind speed at 2 m above ground level, effect of temperature on radiation use efficiency and global solar radiation based on latitude and Julian day. Among the five locations, Indore was found to be highly discriminative with the highest mean disease incidence and could differentiate anthracnose-resistant and susceptible genotypes effectively, therefore can be considered an ideal location for breeding for field resistance against anthracnose disease.

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“…Two widely utilized biplot models are the AMMI biplot (additive main effects and multiplicative interaction) and the GGE biplot (genotype-plus-genotype × environment) [25][26][27][28][29]. Researchers commonly employ genotype-plus-genotype by environment (GGE) biplots for various purposes [30][31][32], such as classifying mega environments, assessing genotype rankings, and selecting discriminative and representative environments. Best linear unbiased prediction (BLUP) had a higher predictive accuracy than any AMMI family member in analyzing multi-environment trials (METs) with a random analysis of the genotype-environment interaction (GEI) effect using a linear mixed-effect model (LMM).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Model Based Stability Analysis Employing Multi-Environmental Trials (METs) Data for Discerning Heat Tolerance in Chickpea (Cicer arietinum L.) Landraces

Danakumara,

Kumar,

Kumar

et al. 2023

Plants

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Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype–environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia–North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype–environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which–won–where pattern of genotype plus genotype–environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

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Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in chickpea to fusarium wilt using GGE biplot

Cited by 9 publications

References 34 publications

Participatory Varietal Selection for Promising Rice Lines

Participatory Varietal Selection for Promising Rice Lines

WAASB index revealed stable resistance sources for soybean anthracnose in India

A Multi-Model Based Stability Analysis Employing Multi-Environmental Trials (METs) Data for Discerning Heat Tolerance in Chickpea (Cicer arietinum L.) Landraces

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