Stability, Adaptability Analysis of Wheat Genotypes by AMMI with BLUP for Restricted Irrigated Multi Location Trials in Peninsular Zone of India

Highly significant effects of the environment (E), GxE interaction, and genotypes (G) had been observed by AMMI analysis for wheat genotypes evaluated under restricted irrigated timely sown multi-location trials in Peninsular zone of the country during 2018-19 and 2019-20 cropping seasons. Ranking of genotypes had changed with the number of interaction Principal Component Axes (IPCA’s) included for the calculation of Additive Main and Multiplicative Interaction (AMMI) based as well as Weighted Average of Absolute Scores (WAASB) stability measures. The Superiority indexes while assigning more weights to yield as compared to stability measures pointed out wheat genotypes MACS6695, HI1605, NIAW3170 & MACS6696 would maintain high yield and stable performance for the first year. Adaptability measures as per various averages expressed deviation from other measures and maintained right angle with MASV1 and stability measures. Moreover, the Superiority indexes as per various averages clustered in the same qua-drant. Second-year of the study observed MP1358, NIAW3170, NIDW1149, MACS4087 wheat genotypes pointed by Superiority indexes. Adaptability measures as per arithmetic, geometric and harmonic means expressed strong bondage and grouped in a separate quadrant. This cluster maintained the right angle with stability measures and cluster of Superiority indexes as per various averages placed in a different quadrant. Superiority indexes would provide the reliable estimates of genotype performance in future studies in a biplot as considered all of significant IPCA’s.


Introduction
An efficient assessment of GxE interaction had been emphasized to determine the yield potential and stability of the genotypes under multi location trials [1].
Earlier the regression techniques employed for the analyses of GxE interaction adequately described the linear behavior of genotypes over different environments [2]. AMMI model had been proved as a useful analytic approach for linear and non-linear response of genotypes over the environmental conditions [3] [4]. Prediction of genotypes yield behaviour would be better explained by genotype's random interaction with a specific environment [5]. The prediction of random variables has been carried out by Best Linear Unbiased Prediction (BLUP) [6]. The need was felt to employ better models and techniques like, AMMI and BLUP for valid and meaningful predictions about genotypic performance [7]. Usually these two analytic approaches have been used separately in the field evaluation of genotypes under multi location trials [8]. The simultaneous consideration of yield and stability in a single measure had been advocated [9]. Simultaneous Selection Indices had been developed by adding the corresponding ranks as per stability measure and yield performance of genotypes [10], [11]. The benefits of these two important techniques incorporated into a Superiority Index measure for the stability and adaptability of genotypes [12]. The present study dealt with the analysis of GxE interaction with emphasis on yield stability by AMMI along with BLUP techniques for restricted irrigation evaluation of wheat genotypes in the Peninsular zone of India.

Materials and Methods
Maharashtra and Karnataka states jointly represent the Peninsular zone and three species of wheat viz T. aestivum, T. durum, and T. dicoccum are cultivated in this zone. Twelve promising wheat genotypes under advanced trials were evaluated at eight major locations and ten genotypes at eight locations during 2018-19 and 2019-20 respectively. Field trials were laid out in randomized complete block designs with four replications. Recommended agronomic practices were followed to harvest good yield. The location details and parentage of evaluated genotypes were reflected in Table 1 & Table 2 for ready reference.
Stability measure as Weighted Average of Absolute Scores has been calculated as where WAASB i was the weighted average of absolute scores of the ith genotype (or environment); IPCA ik the score of the ith genotype (or environment) in the kth IPCA, and EP k was the amount of the variance explained by the kth IPCA.
Superiority index allowed variable weights to yield and stability measure (WAASB) to select genotypes that combine high performance and stability as  where rG i and rW i were the rescaled values for yield and WAASB, respectively, for the ith genotype; G i and W i were the yield and the WAASB values for ith genotype. SI superiority index for the ith genotype that weighted between yield and stability, and θ Y and θ S were the weights for yield and stability assumed to be of order 65 and 35 respectively in this study, [8] Modified AMMI stability Value ( ) ( )

First-Year 2018-19
Highly significant effects of the environment (E), GxE interaction, and genotypes (G) had been observed by AMMI analysis. Analysis observed the greater contribution of environments, GxE interactions, and genotypes to the total sum of squares (SS) as compared to the residual effects. Further SS attributable to GxE interactions was partitioned as attributed to GxE interactions Signal and GxE interactions Noise. AMMI analysis is appropriate for data sets where-in SS due to were of magnitude at least of due to additive genotype main effects [4].
The SS for GxE interactions Signal was higher compared to genotype main effects, indicated appropriateness of AMMI analysis. Environment explained about significantly 21.6% of the total sum of squares due to treatments indicating that diverse environments caused most of the variations in genotypes yield (Table 3). Genotypes explained only 18% of the total sum of squares, whereas

Ranking of Genotypes Vis-à-Vis Number of IPCA's
The IPCA scores of genotypes in the AMMI analysis indicated the stability or adaptability over environments. The greater the IPCA scores, the more specific adapted is a genotype to certain locations. The more the IPCA scores approximate to zero, the more stable or adapted the genotypes is overall the locations.

Biplot Analysis of Measures
Approximately 74.2% of the total variation (Table 7)

Second-Year 2019-20
Highly significant effects of the environment (E), GxE interaction, and genotypes (G) had been observed by AMMI analysis. Environment explained significantly 33.7%, GxE interaction accounted for 20.5% and genotypes contributed only 10.7% of the total sum of squares due to treatments (Table 3). First six multiplicative terms explained 99.3% of GxE interaction and 0.7% was the residual or noise.

Conclusion
GxE interaction study in multi-environment trials had been carried out by well established AMMI model. The simultaneous consideration of stability measures and yield would be more appropriate to recommend high-yielding stable wheat genotypes. In the present study, the main advantages of AMMI and BLUP had been combined to increase the reliability of multi-locations trials analysis. An additional advantage provided by Superority Indexes to assign variable weights to the yield and stability performance. Depending upon the goal of crop breeding trials, the researchers may prioritize the productivity of a genotype rather than its stability (and vice-versa). The stability index of genotype performance has the potential to provide reliable estimates of stability in future studies along with a joint interpretation of performance and stability in a biplot while considering number of significant IPCA's.