Volume 8, Issue 3, June Issue - 2020, Pages:287-295 |
Authors: Ajay Verma, GP Singh |
Abstract: Highly significant effects of genotypes, environments and their interactions were observed by AMMI analysis of wheat genotypes. . Smaller values of EV1 observed for G4, G6, G8, corresponding to D1 were (G4, G6, G8) while as per ASTAB1 (G4, G6, G8) and SIPC1 values for (G2, G3, G10). Genotypes selected by Least values EV2 settled for G7, G4 and G3 genotypes whereas as D2 , G7, G4, G3 would be desirable & SIPC2 suitable wheat genotypes were (G6, G2, G3). Same set of genotypes (G4, G6, G7) identified by ASV & ASV1 measures. Composite measures MASV and MASV1 identified G2, G5, G6 & G9. GAI found G5, G10 and G2 as of stable adaptations whereas HM and PRVG identified G5, G10, G3 and G7. MHPRVG found G7, G4, G6 genotypes for broad adaptations. Biplot analysis exhibited that largest cluster consisted of D1, D2, EV1, EV2, ASV, ASV1, ASTAB1, ASTAB2 and SIPC3 measures.. Second year of study as per smaller values of EV1 observed for (G3, G5, G6) & D1 for (G3, G5, G6), absolute values of SIPC1 for (G4, G8, G10) and ASTAB1 were (G3, G5, G6). D7 expressed minimum values for G7, G10, G1 genotypes; SIPC7 observed G8, G5, G4 for stable & EV7 pointed towards G7, G1, G6 & ASTAB7 identified G7, G10, G1 as desirable. Wheat genotypes G7, G10, G1 marked by MASV & MASV1 measures for stable behavior. Analytic measure MHPRVG found G1, G3, G8 as suitable genotypes. Largely four clusters were seen by biplot analysis of AMMI and other measures. Large cluster consisted of EV7, D7, Ev3, ASTAB7, ASTAB3, ASTAB5, MASV, EV5, D5, EV2 & ASTAB2 measures. |
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Full Text: 1 Introduction Multi Environment trials (MET) of cereal crops consider efficient estimation of main and interaction effects (Agahi et al., 2020). Yield potential of genotypes and their adaptation demands an efficient estimation of GXE interactions in crop improvement program (Bocianowsk et al., 2019). Numbers of statistical methods are available for analysis of MET, aimed to partition the information contained in a complex GXE interactions into simpler and more meaningful interpretations (Haider et al., 2017). Methods varied from univariate parametric models, such as regression slope; environmental variance, to multivariate models such as the additive main effect and multiplicative interaction (AMMI) analysis (Shahriari et al., 2018). Mostly practiced linear regression analysis failed to explain confounding of main effects (Tekdal & Kendal, 2018) as well as unable to highlight non linear response genotypic effects (Yau, 1995). AMMI model is a hybrid model involving both additive and multiplicative components of two-way data structure (Kamila et al., 2016). The model separates the additive variance from the multiplicative variance and then applies principal component analysis (PCA) to the interaction portion (Hongyu et al., 2014). The AMMI analysis has been shown to be effective because it captures a large portion of the GXE sum of squares, clearly separating main and interaction effects to present different kinds of opportunities for agricultural researchers (Gauch, 2013).The study was carried out with prime objectives as (i) assess the various AMMI based measures (ii) explore the association analysis among different AMMI and adaptability measures. 2 Materials and Methods North Western Plain Zone of India comprises of the parts of sub-humid Sutlej-Ganga Alluvial Plains and arid western plains, which comprises Punjab, Haryana, Delhi, Rajasthan (except Kota and Udaipur divisions), Western Uttar Pradesh (except Jhansi division and hilly areas), parts of Jammu and Kashmir (Jammu and Kathua districts) and parts of Himachal Pradesh (Paonta Valley and Una districts). Ten advanced wheat genotypes were evaluated at thirteen locations and ten genotypes at fourteen major locations of the mega zone of country to interpret complex genotype by environment (GE) interactions by AMMI model during 2017-18 and 2018-19 cropping seasons. Genotypes were evaluated at research centers with recommended agronomic practices to harvest good yield. Tables 1 & 2 reflected the details of parentage and environmental conditions. Hybrid mechanism of AMMI calculates the additive effects by ANOVA afterwards use PCA for residual analysis. Purchase, 1997 proposed AMMI stability value (ASV) by utilizing relative weights of IPCA1 and IPCA2 scores. In certain cases where more than two IPCAs were significant, ASV failed to encompass all the variability explained by GEI. In order to overcome this difficulty, Zali et al. (2012) attempted to present a modified version ASV i.e., Modified ASV which would cover all available IPCAs. But in doing so, Zali et al. (2012) interpreted the formula of ASV incorrectly compared to the original formula of Purchase (1997). In the present study the original MASV formula of Zali et al. (2012) and a revised version of MASV (Ajay et al 2019) were compared with other AMMI based measures of interaction effects. AMMI based measures were mentioned as follows. |
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