The nutritious rich green fodder for the livestock and acceptable quality grain for human consumption have been provided by the dual purpose barley.In the recent past, dual purpose barley has been widely utilized as an alternative source of green forage in the arid and semi-arid regions especially owing to the scarcity of green forage availability for the animals. In terms of the world’s most essential crops by production quantity, barley is ranked fourth amongst the cereals after maize, rice and wheat, although eleventh overall, and is widely grown across the world. Barley’s ability to adapt to multiple biotic and abiotic stresses would be desirable for future popularity particularly in the scenario of environmental change. Reliable information about cultivar performance in different environments has been provided by genotype × environment interaction effects with the execution of multi-environment trials [2],[1]. The good number of precise analytic approaches t has been advocated in literature [8]. Visibilities of AMMI based measures (AMMI stability value (ASV), ASV1, Modified AMMI stability value (MASV) & MASV1) have been observed [11].  Best linear unbiased prediction (BLUP) based measures harmonic mean of genotypic values (HMGV), the relative performance of genotypic values (RPGV), and harmonic mean of relative performance of genotypic values (HMRPGV), were also exploited for the stability and adaptability of genotypes [6],[7]. Nonparametric measures have been also utilized S_i¹,S_i²,S_i³,S_i⁴ ,S_i⁵ ,S_i⁶ ,S_i⁷ , NP_i ⁽¹⁾, NP_i ⁽²⁾, NP ⁽³⁾, NP_i ⁽⁴⁾ to interpret the response of genotypes to environmental conditions [8]. Recent analytic measures have been compared to decipher the GxE interactions effects for dual purpose barley genotypes evaluated in northern hills zone of the country.

Materials and Methods

Twenty three promising dual-purpose barley genotypes were evaluated in research field trials at 10centers of the All India Coordinated Research Project across northern hills zone of the country during 2020-21 cropping season. More emphasis had been placed to increase the dual purpose barley production of this zone to augment the fodder and grain production of the country. Field trials were laid out in Randomized block designs with four replications. Recommended practices of packages had followed in total to harvest the good yield. Parentage details and environmental conditions were reflected in table 1 for ready reference.  Various non parametric and parametric measures had been recommended for assessing GxE interaction and stability analysis [8]. For a two-way dataset with k genotypes and n environments X_ij de­notes the phenotypic value of ith genotype in jth environ­ment  where i=1,2, …k, ,j =, 1,2 ,…,n and r_ij  as the rank of the ith genotype in the jth environment, and  as the mean rank across all environments for the ith geno­type. The correction for yield of ith genotype in jth environment as (X*_ij=  X_ij– .+  ) as X*_ij, was the corrected phenotypic value; .was the mean of ith  genotype in all environments and was the grand mean.

Non parametric composite measures NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾ and NP_i⁽⁴⁾ based on the ranks of genotypes as per yield and corrected yield of genotypes. In the formulas, r^*_ij was the rank of X^*_ij, and  and M_di was the mean and median ranks for original (unadjusted) grain yield, where ^* and M^*_di were the same parameters computed from the corrected (adjusted) data.

AMMISOFT version 1.0 software utilized for AMMI analysis of data sets and SAS software version 9.3 for further analysis.

Results and Discussion

AMMI analysis

Highly significant variations due to environments, GxE interactions, and genotypes were observed by AMMI analysis (Table 2). This analysis also revealed about 71.8% of the total sum square of variation for yield was due to environments followed by 21.5%  of GxE interactions,  whereas genotypes accounted for marginally 3.3% [3]. Interaction effects are further portioned into  seven Interaction principal components totalled more than 98.6% of interactions sum of square variations. AMMI1 explained a total variation of 34.3%, followed by 28.1% for AMMI2, 11.5% for AMMI3, AMMI4 accounted for 8.3% and followed by 6.6%, 4.5% & 3.3% respectively. The first two AMMI components in total showed 62.8% of the total variation indicating the two AMMI components well fit and confirm the use of AMMI model [7]. Estimated sums of squares for G×E signal and noise were 88.5%  and 11.4% of total G×E respectively.  Early IPCs selectively capture signal, and late ones noise[4]. Note that the sum of squares for GxE-signal is 5.83 times that for genotypes main effects. Hence, narrow adaptations are important for this dataset [12]. Even just IPC1 alone is 2.26 times the genotype‘s main effects. Also, note that GxE-noise is 0.76 times the genotypes effects. Discarding noise improves accuracy, increases repeatability, simplifies conclusions, and accelerates progress.

Ranking of genotypes as per on AMMI based measures

Since the genotypes yield, the mean yield was considered an important measure to assess the genotypes. Mean yield of genotypes expressed highly significant variations and yield potential selected G5, G9, G14 and the lowest yield for G23, G17 (Table 3). Stability or adaptability of genotypes judged by values of IPCA’s in the AMMI analysis. The, greater the IPCA scores reflect the specific adaptation of genotype to certain locations. While, the values approximate to zero were recommended for in general adaptations of the genotype [9].  Absolute IPCA-1 scores pointed for G17, G16, G11 as per IPCA-2, G2, G8, G18  genotypes would be of choice. Values of IPCA-3 favored G23,G19, G20 genotypes. As per IPCA-4, G20, G17 G9 genotypes would be of stable performance. As per IPCA5, G9, G15, G21 while IPCA6 favoured G9, G21, G22 whereas IPCA7 settled for G19,G6, G20 First two IPCAs in ASV & ASV1 measures utilized 68.2% of G×E interaction sum of squares. The two IPCAs have different values and meanings and the ASV and ASV1 parameters using the Pythagoras theorem and to get estimated values between IPCA1 and IPCA2 scores to produce a balanced measure between the two IPCA scores. Also, ASV parameter of this investigation used advantages of cross validation due to computation from first two IPCAs [10]. Using first two IPCAs in stability analysis could benefits dynamic concept of stability in the identification of the stable high yielder genotypes. ASV1 measures recommended (G20, G5, G19) and ASV pointed towards (G20 G5 G19) as of stable performance. Adaptability measures MASV and MASV1considered all seven significant IPCAs of the AMMI analysis using 98.6% of GxE interactions sum of squares [5]. Values of MASV1 identified G19, G22, G20 genotypes would express stable yield whereas genotypes G19, G22, G20 be of stable yield performance by MASV measure respectively.

Ranking of genotypes as per BLUP based & Non parametric measures

Major advantages of BLUP based measures are to account for the random nature of the genotype behavior in changes climatic conditions. At the same time allow ranking genotypes in relation to their performance based on the genetic effects [11]. An average yield of genotypes pointed towards G5, G9,G14 as high yielders. The consistent yield of G18, G11,G17  as per least values of standard deviation more over the values of CV identified G18, G11, G20 genotypes for the consistent yield performance for northern hills zone of the country. More over the values of GM favoured G18,G5,and G9. The BLUP-based simultaneous selections, such as HMGV identified G18, G9,G10 whilevalues of RPGV favored G18, G5, G9 and HMRPGV settled G18, G9, G5 genotypes. The evaluation of adaptability and stability of wheat genotypes through these BLUP-based indices was reported by Pour-Aboughadareh [8]. The estimates of HMGV, RPGV, and HMRPGV had the same genotype ranking that was reported Anuradha [2].

Non parametric measures ranked the genotypes as per their corrected yield across environments S_i¹ values pointed for G19,G15,G17 while S_i² selected G19,G15,G20  and values of S_i³favoured G19,G20,G15  as desirable genotypes (Table 4). G19,G20,G15 selected by values of S_i⁴, S_i⁵ for G1,9 G20,G15, S_i⁶ forG19,G20,G9  and lastly S_i⁷ for G19, G20,G15 (Table 4). The mentioned strategy determines the stability of genotype over environment if its rank is similar over other environments (biological concept). Nonparametric measures of phenotypic stability were associated with the biological concept of stability [12]. Non parametric composite measures NP_i⁽¹⁾  to NP_i⁽⁴⁾, consider the ranks of genotypes as per their yield and corrected yield across environments simultaneously [9]. NP_i ⁽¹⁾ measure the observed suitability ofG19,G20,G15 whereas as per NP_i⁽²⁾,   genotypes G15,G19,G20 would be of choice while NP_i⁽³⁾  identified G15,G19,G17. Last composite measure NP_i⁽⁴⁾  found G15,G19,G20 as genotypes of choice for this zone.

Biplot analysis

The first two significant PC’s has explained about 60.1% of the total variation in the AMMI, BLUP and non parametric measures considered for this study in biplot analysis (Table 5) with respective contributions of 37.8% &22.2% by first and second principal components respectively [1]. Measures S_i¹, S_i², S_i³, S_i⁴, S_i⁵,S_i⁶ ,S_i⁷,NP_i ⁽¹⁾, NP_i ^(2), NP_i ⁽³⁾, NP_i ^(4), MASV, MASV1 accounted more of share in first principal component whereas Mean, Average, HMPRVG, PRVG, IPC5, GM, HM were major contributors  in PC2. The association analysis among measures had been explored with the biplot analysis.  In the biplot vectors of measures expressed acute angles would be positively correlated whereas those achieved obtuse or straight line angles would be negatively correlated. Independent types of relationships had expressed by right angles between vectors. Very tight positive relationships observed for NP_i⁽²⁾ , NP_i⁽³⁾ , NP_i⁽⁴⁾with Stdev, IPC7, IPC4.BLUP based measures GM, HM, PRVG, HMPRVG, Averagealso expressed tight relationships among themselves in the same quadrant.AMMI based measures MASV, MASV1 closely associated with Stdev, S_i² , S_i³ S_i⁵ , S_i^{6 ,} ASV  ASV1 values ^. BLCV observed withS_i¹ , S_i⁴ S_i⁷. This group of measures expressed no relationship with group consisted of BLCV ,S_i¹ , S_i⁴ S_i⁷ .BLCV alsoexpressed no relationships IPC3, NP_i⁽³⁾, NP_i⁽⁴⁾. Group  MASV, MASV1 also expressed right angles with BLUP based measures (Fig. 1).  Seven clusters of small and moderate sizes observed in biplot analysis. AMMI analysis based measures IPC1 & IPC5 formed first cluster as placed in first quadrant. Three clusters were observed in second quadrant. BLUP based measures GM, HM, PRVG, HMPRVG grouped with average and adjacent cluster comprised of NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾ whileStdevgrouped with IPC4, IPC7 in smaller cluster. Next quadrant occupied by two AMMI based measures i.e. one of ASV, ASV1 with MASV1 whereas other consisted of NP_i⁽¹⁾, S_i² ,S_i⁵S_i² ,S_i⁵ ,S_i⁶ with MASV Last cluster of CV with IPC2 & IPC6 placed in last quadrant (Fig. 2).

Acknowledgements

The training by Dr. J Crossa and financial support by Dr. A.K Joshi & Dr. RP Singh CIMMYT, Mexico sincerely acknowledged along with the hard work of the staff to carry out the field evaluation of genotypes at coordinating centers.

Conflict of Interests

No conflict of interests reported by the authors

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Table 1: Parentage vis-a-vis location details of dual purpose barley genotypes

Table 2: AMMI analysis of dualpurpose barley genotypes evaluated under coordinated trials

Table 3: AMMI based measures of dual purpose barley genotypes 

Table 4: BLUP based and Non parametric measures of dual purpose barley genotypes

Table 5:  Loadings of AMMI, BLUP and Non parametric measures

Figure 1: Biplot analysis of AMMI, BLUP and Non parametric measures

Figure 2: Clustering pattern of AMMI, BLUP and Non parametric measures