AMMI with BLUP analysis for stability assessment of wheat genotypes under multi locations timely sown trials in Central Zone of India

AMMI analysis explained the highly signifi cant effects of the environment, GxE interaction, and genotypes for wheat genotypes evaluated under restricted irriation timely sown multi-location trials in the Central zone of the country during 2018-19 and 2019-20. About 77.1%, 12.2% & 2.3% had been contributed by environments, interactions and genotypes of the total sum of squares due to treatments respectively in the fi rst year. The utilization of more number of IPCA’s in AMMI and WAASB stability measures had altered the ranking of genotypes. Analytic measures of adaptability and Superiority indexes as per BLUP of genotypes identifi ed DBW110, MP3288. Adaptability measures as per arithmetic, geometric and harmonic means and their corresponding values expressed deviation as observed in a seperate quadrant of Biplot graphical analysis. However, this group maintained the right angle with MASV, MASV1, and stability measures. The cluster of Superiority indexes as per averages yield of wheat genotypes placed in the adjacent quadrant. Superiority indexes favored HI8823, MP3288, DBW110 wheat genotypes for high yield and stable performance for the second year. Adaptability measures as per arithmetic, geometric and harmonic means along with the corresponding values of RPGV & MHRPGV expressed bondage and placed in a different quadrant. Cluster of Superiority indexes as per averages of the yield of wheat genotypes seen in the same quadrant as more than 73.5% variation accounted for by the fi rst two principal components. Research Article AMMI with BLUP analysis for stability assessment of wheat genotypes under multi locations timely sown trials in Central Zone of India Ajay Verma* and GP Singh Indian Institute of Wheat & Barley Research, Research institute in Karnal, Haryana, India Received: 22 January, 2021 Accepted: 12 March, 2021 Published: 15 March, 2021 *Corresponding author: Ajay Verma, Indian Institute of Wheat & Barley Research, Research institute in Karnal, Haryana, India, Tel: 0184-2209149, 01812267390; E-mail:


Introduction
Citation: Verma   and harmonic mean based measure of the relative performance of the genotypic values (MHPRVG) for the simultaneous analysis of stability, adaptability, and yield [11]. complete block designs with three replications. Recommended agronomic practices were followed to harvest good yield. Details of genotype parentage along with environmental conditions were refl ected in Tables 1,2

First-year 2018-19
Environment (E), Genotypes (G), and GxE interaction effects were highly signifi cant as mentioned by the AMMI analysis (Table 3). 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 was appropriate for data sets where-in SS due to were of magnitude at least of due to additive genotype main effects [12]. The SS for GxE interactions Signal was higher compared to genotype main effects, indicated appropriateness of AMMI analysis. The environment signifi cantly explained about 77.1% of the total sum of squares due to treatments indicating that diverse environments caused most of the variations in genotypes yield [13]. Genotypes explained only 2.3% of the total sum of squares, whereas GxE interaction accounted for 12.2% of treatment variations in yield. First four signifi cant multiplicative terms explained 94.7 % of interactions sum of squares and the remaining 5.3% was the discarded residual [14].

Ranking of genotypes vis-à-vis number of IPCA's
The IPCA scores of genotypes in the AMMI analysis were an indication of stability or adaptability over environments. The greater the IPCA scores, the more specifi c adapteded genotype to certain locations. The more the IPCA scores approximate to zero, the more stable or adapted the genotypes is overall the locations. The ranking of genotype as per absolute IPCA-1 scores were DDW47, MP3288 (Table 4). While for IPCA-2, genotypes DBW277, DDW47 would be of choice. Values of IPCA-3 favored DBW277, DBW110 wheat genotypes. As per IPCA-4, DDW47, DBW110 genotypes would be of stable performance. Analytic measures of adaptability MASV and MASV1 consider all signifi cant IPCAs of the analysis. Genotypes DDW47, DBW277 had been identifi ed by MASV & MASV1 measures (Ajay et al., 2019). To identify how the ranks of evaluated wheat genotype altered with utilizing numbers of IPCA in the WAASB estimation, the genotype's ranks were obtained while considering 1, 2,..., p IPCA's in the WAASB calculations. WAASB = |IPCA1| for using only fi rst IPCA. The genotype with the smallest WAASB value had been ranked with the fi rst-order. Preferences of wheat genotypes as per W1, W2 & W3 measures were the same as DDW47, MP3288 identifi ed as two promising genotypes through the higher-order varied from these measures. Stability measure WAASB based on all signifi cant IPCA's settled for DDW47, MP3288 genotypes for considered locations of the zone for stable high yield. The genotypes ranking was altered by the extent to which IPCAs were included in the WAASB estimation. This reinforces the benefi ts of using the WAASB index since it captures the variations of all IPCAs to compute the stability [5].

Second-year 2019-20
Environment (E), genotypes (G) and GxE interaction effects were highly signifi cant as mentioned by the AMMI analysis ( Table 3). The environment signifi cantly explained about 70.1%, GxE interaction accounted for 14.2% and genotypes accounted for only 1.3% of the total sum of squares. Signifi cant six multiplicative terms explained 98.7 % and the remaining 1.3% residual was discarded.

Ranking of genotypes vis-à-vis number of IPCA's
The preference order of genotypes as per IPCA-1 scores was DDW47, UAS466, HI8823 (

Biplot analysis of measures
The fi rst two signifi cant PC's jointly has explained 73.5%

Conclusions
GxE interaction in multi-loation trials had been studied by AMMI model. Recent analytic measures advocated simultaneous use of stability & yield to recommend high-       yielding stable wheat genotypes. Infact both BLUP and AMMI have their effi cacy increased depending on factors intrinsic to analysis. In the present study, the main advantages of AMMI and BLUP had been combined to increase the reliability of multi-locations trials analysis. The more interesting advantage provided by Superority Indexes that different weights may be assigned to the yield performance and stability. As per the goal of a breeding program or a cultivar recommendation trial, the researcher 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 biplots while considering more of IPCA's.