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Volume 8, Issue 4, August Issue - 2020, Pages:402-417

STABILITY ANALYSIS BY NONPARAMETRIC MEASURES OF WHEAT GENOTYPES EVALUATED UNDER NORTH EASTERN PLAINS ZONE OF INDIA

Authors: Ajay Verma, G.P. Singh

Abstract: Nonparametric measures of stability had been compared based on ranks of genotypes as per BLUP and BLUE of wheat genotypes evaluated in North Eastern Plains Zone of the country. S_i^smeasures, as per the BLUP of yield for the first year, identified G5, G1, G7 as the stable genotypes whereas corrected yield measures CS_i^s selected G4, G5, G7, G1. Values of measures NP_i^(s)settled for G7, G5, G4 wheat genotypes. The significance of Kendall’s coefficient of concordance resulted in an overall similarity of the ranking of genotypes as per nonparametric measures. Spearman coefficients had observed positive correlations by S_i^s, CS_i^s& NP_i^(s) with other measures. CV expressed close affinity with CCV, NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾& S_i⁶, CS_i³, CS_i⁶whereas SD, CSD, NP_i⁽¹⁾ associated with Z1, Z2, S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁷, CS_i¹, CS_i⁴,CS_i⁵ ,CS_i⁷in Biplot graphical analysis. BLUE’s of the yield shown G1, G2, G4, G5, G7 genotypes selected by S_i^s. Values of CS_i^s identified G2, G4, G5, G6 genotypes, as opposed to G4, G5, G7 by NP_i^(s). Positive correlation exhibited by S_i^s, CS_i^s&NP_i^(s) with other nonparametric measures. CV placed with NP_i⁽²⁾, NP_i⁽³⁾& NP_i⁽⁴⁾, S_i³, S_i⁶measures in biplot analysis. Largest cluster expressed by Z1, Z2, SD, CSD,CCV, S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CS_i¹, CS_i²,CS_i³,CS_i⁴, CS_i⁵, CS_i⁶,CS_i⁷measures. BLUP’s of yield values for the second year (2017-18) revealed that S_i^s selected G1, G12, G13 genotypes while CS_i^sfavouredG13, G1, G4 genotypes. Lower values of NPi^(s)identified G1, G4, G12, and G13 genotypes. Direct and positive relationships expressed by S_i^s, CS_i^s& NP_i^(s)measures. Maximum measures clustered together i.e. SD, CSD, CCV, NP_i⁽¹⁾, S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶,CS_i⁷. Based on BLUE’s values, S_i^smeasures pointed for G13, G1 G4 while G1, G13, G4 selected by CS_i^svalues. Lower values of NPi^(s)achieved by G1, G4, G11, G13 genotypes. S_i^s, CS_i^s& NP_i^(s)had achieved strong and weak relations with other measures. Measures Z1, Z2, SD, CSD, CCV, NP_i⁽¹⁾, S_i¹, S_i², S_i⁴, S_i⁵ , S_i⁷, CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵ CS_i⁶, CS_i⁷clubbed in bigger cluster. Nonparametric measures would be quite useful to researchers especially breeders to make the selection of genotypes in the presence of genotype X environment interactions.

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Full Text: 1 Introduction High yield and stable genotypes have been identified by breeders for diverse environmental conditions to sustain a good harvest of the crop (Pour-Aboughadareh et al., 2019). Multi-environment trials (MET) have been established as inevitable to recommend promising genotypes for different locations of the country (Vaezi et al., 2018). Genotype x Environment (GxE) interaction effects decrease the association between genotypic and phenotypic values and also masks the selection of the desirable genotypes (Mohammadi et al., 2016). Stability analysis methods are categorized into two parametric and non-parametric groups (Huhn & Leon, 1995; Farshadfar et al., 2014; Golkar et al., 2020). The parametric approach is based on statistical assumptions about the distribution of genotype, environment, and GEI effects (Khalili & Pour-Aboughadareh, 2016). Moreover, nonparametric showed robust behavior without specific assumptions (Rasoli et al., 2015). Mostly breeding experiments utilized parametric methods for assessing the stability of genotypes. Recent studies showed the use of nonparametric approaches as the performance of genotypes had been judged by ranks. Additionally, these methods reduce the bias factor due to outliers and simple to use and interpret (Zali et al., 2011; Ahmadi et al., 2015; Khalili & Pour-Aboughadareh, 2016; Vaezi et al., 2018; Pour-Aboughadareh et al., 2019). A large number of nonparametric procedures has been compared in the literature to interpret the stability and genotype x environment interactions analysis (Deli? et al., 2009; Balali? et al., 2011; Karimizadeh et al., 2012; Mahtabi et al., 2013; Mortazavian & Azizinia, 2014; Ahmadi et al., 2015; Khalili & Pour-Aboughadareh, 2016; Vaezi et al., 2018; Pour-Aboughadareh et al., 2019 ). With this in mind, the objectives of the present study were (1) to analyze stability based on BLUP and BLUE values of genotypes by nonparametric methods (2) to differentiate genotypes performance possessing high yield along with adaptations as per BLUP and BLUE across environments, (3) to find out the similarities or dissimilarities among the nonparametric measures. 2 Materials and Methods Seven promising wheat genotypes were evaluated under field trials at sixteen major locations of the zone for 2016-17 whereas fifteen genotypes were tested at thirteen locations during 2017-18 cropping seasons of the country. Recommended agronomic practices had followed to have a good harvest of wheat genotypes. Parentage details and environmental conditions were reflected in tables 1 & 2 for ready reference. Huehn (1990 a & b) proposed seven nonparametric methods for assessing GxE interaction and stability analysis. X_ij denotes the yield of ith genotype in jth environment where i=1,2, ...k, , j =, 1,2 ,..., n and rank of the ith genotype in the jth environment by r_ij, and ri as the mean of ith genotype. Sabaghnia et al. (2012) proposed the correction for the yield of ith genotype in jth environment as (X*_ij= X_ij–xi .+ x.. ) as X*_ij, was the corrected phenotypic value; Xi was the mean of ith genotype in all environments and X.. was the grand mean. Generally used seven statistics based on ranks of genotypes yield and corrected yield was expressed as follows: Si1=2Sjn-1Sj'=j+1n½rij-rij'½[n(n-1)] Si7=Sj=1n(_r_ij-_r_i)2Sj=1n|_r_ij-_r_i| Si3=Sj=1n(_r_ij-_r_i)2_r_i. Si4=Sj=1n(_r_ij-_r_i)2_n Si5=Sj=1n|_r_ij-_r_i|_n Si6=j=1n|_r_ij-_r_i|_r_i. Si2=Sj=1n (_r_ij-_r_i')2(n-1) _r_i ₌₁_n_j=1_n_r_ij. Ziv=Siv-E {Siv}2Var{Siv}, v=1,2

Nonparametric measures for stability analysis proposed by Thennarasu (1995) as NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾ and NP_i⁽⁴⁾ based on ranks of corrected means of genotypes. Ranks of genotypes as per corrected yield X^*_ijdenoted by r^*_ijwith average of ranks and median byri ^* and M^*_di. NPi1=1nj=1n½rij*-Mdi* ½ NPi3=S(rij*-ri.*)2/nri. NPi2=1nSj=1n½rij*-Mdi*½Mdi NPi4=2n(n-1)Sj=1n-1Sj'=j+1mrij*-rij'*ri.

Further, Nassar & Huehn (1987) proposed a test to judge the significance of Si⁽¹⁾ and Si⁽²⁾ measures. The degree of had been assessed by correlation among genotypes ranking. Spearman’s rank correlation values used to test the degree of similarity among measures (Piepho & Lotito, 1992) as: _r_s_=1-₆_i=1_n_d_i₂_n(_n₂_-1) where d_i denotes the difference between ranks for ith genotype and sum over the number of pairs. 3 Results and Discussion 3.1 First year 2016-17 3.1.1 Analytic analysis based on BLUP’s High yielder genotypes were G6, G5, G4, whereas Geometric Adaptability Index (GAI) selected G6, G5, G4 genotypes. Average of ranks (MR), standard deviation of ranks (SD), and coefficient of variation of ranks (CV) were calculated as per ranks based on yield. MR identified for G7, G5, G4 while the consistent yield of G5, G1, G7 expressed by values of SD (Table 3). Genotypes G7, G5, G4 pointed out by values of CV. Sabaghnia et al. (2012) proposed two ranking methods according to mean and standard deviation of ranks while Mortazavian & Azizinia (2014) reported the advantages of these nonparametric procedures in stability studies. Median of ranks (Med) pointed towards G7, G5, G4 wheat genotypes. Measure S_i¹ selected G5, G7, G1 as opposed to G5, G1, G7 by S_i²values. G7, G5, G1 genotypes considered by Si³& S_i⁴measures, next two S_i⁵& S_i⁶ pointed towards G5, G7, G1 genotypes while S_i⁷favored G1, G7, G5 genotypes. Average ranks as per corrected yield values (CMR) selected G6, G5, G4, and corrected standard deviation (CSD) observed suitability of G1, G6, G9 genotypes. Coefficient of variation as per corrected yield (CCV) values found G5, G7, G1 while median values (CMed) G2, G1, G3 genotypes. Measures based on ranks as per corrected yield CS_i¹, CS_i², CS_i³, CS_i⁴& CS_i⁵pointed towards G5, G7, G1 while G5, G7, G4 by CS_i⁶ and CS_i⁷identified G1, G7, G4 wheat genotypes (Table 4). The mentioned strategy determines the stability of genotype over the environment if its rank is similar over other environments (biological concept). Many authors that have used the corrected Huehn’s (1990b) nonparametric measures of stability and demonstrated the association of these measures with the biological concept of stability (Sabaghnia et al., 2012; Mahtabi et al 2013). Measures NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾, consider the ranks of genotypes as per yield and corrected yield simultaneously, found G7, G5, G4 as suitable wheat genotypes while Z1 & Z2 settled for G2, G4, G1 genotypes. Kendall ’s coefficient of concordance, W was used as an additional tool to evaluate the degree of agreement among non-parametric measures and mean yield. Perfect agreement among rankings of the measures across the environments denoted as W = 1, while zero value indicated total disagreement (Vaezi et al., 2018). Calculated values of W = 0.56 and for its significance values of c² statistic = 100.8 (Table 4). The calculated value was less than the table value of c² (0.05, 290) = 124.3 (135.8), which resulted in an overall similarity among nonparametric measures. Z1 and Z2 values were calculated for each genotype, as per the ranks of adjusted yield, and then summed: Z₁ sum = 10.20 and Z₂ sum = 11.99 (Table 4). Both these statistics were distributed as c² and were less than the critical value of c² (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per ranks of CS_i¹ and CS_i² measures (Kilic et al 2010). Moreover the no individual Z values more than the critical value of c² (0.05, 1) = 3.84as observed by Hameed et al., 2020. 3.1.2 Association analysis Mean yield had expressed both positive and negative correlations with measures (Table 11). Highly significant positive correlation with GAI, MR, CMR measures and very weak relationships expressed with S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CCV, CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷measures. GAI had a highly significant positive with MR & CMR only. Positive association of MR observed with CV, Med, CCV, CS_i⁶, NP_i^(3), and NP_i⁽⁴⁾measures. SD & CVexpressed highly significant positive correlations with measures. Med expressed only positive correlations with measures. Values of S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁶, S_i⁷maintained direct relationships with exception of CMed. Measure CMR maintained highly significant negative with CMed and weak correlation with Z1 & Z2 values. CSD & CCV measures showed highly significant direct relationships with CS_i¹,CS_i²,CS_i³,CS_i⁴,CS_i⁵,CS_i⁶, CS_i⁷,NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾,NP_i⁽⁴⁾. Weak and very weak relationships were observed for CMed measure. CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷had displayed highly significant to significant correlation with other measures (Hameed et al., 2020). Similar behavior is managed by measures NP_i⁽¹⁾, NP_i⁽²⁾, NP_i^(3), and NP_i⁽⁴⁾as reported by Rasoli et al. (2015). Z1 & Z2,normalized forms of CS_i¹& CS_i²measures,were related in negative manner. 3.1.3 Biplot graphical analysis Biplot analysis of nonparametric measures had been carried out to explore any type of association among studied measures. Loadings of the first two principal components axes (PCA) of ranks of nonparametric measures were shown in figure 1 (Mohammadi et al., 2016). Two separate groups of Yield with GAI and MR with Med were displayed in the graphical analysis. Measure CV expressed close affinity with NP_i⁽²⁾, NP_i⁽³⁾& NP_i⁽⁴⁾, CCV, S_i⁶, CS_i³,CS_i⁶. Bigger cluster consisted of Z1, Z2, SD, CSD, NP_i⁽¹⁾, S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁷, CS_i¹, CS_i⁴,CS_i⁵,CS_i⁷measures. 3.1.4 Analytic analysis based on BLUE’s Maximum yield and GAI selected G6, G1, G4 genotypes while MR identified G5, G7, G4, and consistent performance of G4, G5, G2 judged by SD. Lower values of CV identified G4, G5, G7, and median settled for G5, G4, G7 genotypes (Table 5). S_i¹favored G4, G5, G1 and S_i² for G4, G5, G1 whereas as per S_i³ values G4, G5, G7 & S_i⁴found G4, G5, G1 as suitable genotypes and G4, G5, G2 by S_i⁵while as per S_i⁶values G5, G4, G7 were genotypes of choice. Lastly by S_i⁷,G4, G2, G1 would be of stable performance genotypes. CMR exhibited G6, G5, G7 as desirable ones and consistent yield would be of G4, G2, G5 by CSD values while CCV pointed for G5, G4, G6, and CMed for G2, G3, G7 wheat genotypes (Table 6). Genotypes G4, G2, G5 mentioned by CS_i¹and CS_i²found G4, G5, G2 as suitable genotypes; CS_i³& CS_i⁴ settled for G4, G2, G5 genotypes while G4, G5, G2 selected by CS_i⁵values & CS_i⁶settled for G6, G5, G4. Lastly, CSi⁷ pointed towards G5, G2, G7 as genotypes of choice. Values of NP_i^(s)identified G5, G4, G7 wheat genotypes for considered locations of the zone. Measure Z1 selected G1, G7, G6 while for Z2 genotypes G1, G7, G5 would be of choice. Calculated values of Kendall’s coefficient of concordance W = 0.45 and values of c² statistics = 80.5 was less than the critical value. Values of Z₁ sum = 5.08 and Z₂ sum = 5.35 were less than the critical value of c² (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per the ranks of CS_i⁽¹⁾ and CS_i⁽²⁾ measures. However, individual Z values showed HD3043 &MACS6677were significantly unstable (Table 6). 3.1.5 Association analysis Spearman’s rank correlation analysis among all possible pairs of considered measures was tabulated. The yield had expressed significant positive relation with GAI and negative correlations were seen with most measures (Table 12). Significant indirect relations of GAI were observed with MR, Med. Significant positive correlations were expressed by SD and CV measures, while weak negative with CMed, Z1, Z2. Med had a highly significant positive with NP_i⁽²⁾. Measures S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁶, S_i⁷maintained direct relationships with measures exception of CMed and Z1 only. CMR maintained significant positive to moderate positive with measures. Highly significant direct relationships shown by CSD & CCV with CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷, NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾& NP_i⁽⁴⁾measures. Weak and highly significant positive relationships were observed for values of CMed measure. CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷measures had displayed highly significant to significant direct correlations (Table 12). The same nature of relations was also expressed by NP_i⁽¹⁾, NP_i⁽²⁾, NP_i^(3), and NP_i⁽⁴⁾ measure. Z1 was negatively related to Z2. 3.1.6 Biplot graphical analysis Loadings of the first two principal components axes (PCA) of ranks of nonparametric measures were shown in figure 2. Both significant PAC’s accounting for 80.2% of the variance of the original variables. Two separate groups of Yield with GAI and MR with Med were displayed. Measure CV expressed close affinity with NP_i⁽²⁾,NP_i⁽³⁾, NP_i⁽⁴⁾, S_i³,S_i⁶measures. Largest group consisted of Z1, Z2, SD, CSD, CCV, S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷measures. 3.2 Second year of study 3.2.1 Analytic measures based on BLUP’s (2017-18) The mean yield of genotypes showed G12 was the highest yielding with 46.7q/ha followed by G2 and G8, with remarkable differences among the studied genotypes (Table 7). GAI selected G12, G2, and G9 as desirable genotypes. MR pointed towards G4, G7, G11 and SD settled for G13, G1, G12, whereas CV for G1, G13, G4 as stable genotypes, while G12, G2 based on MR, G8, G5 based on SD and G2, G8 based on CV, were most unstable wheat genotypes for studied locations of this zone. G13, G1, G12 were the stable genotypes as per values of S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i^6, and S_i⁷, however G8, G5 would be genotypes of unstable yield. CMR selected G9, G8 & G11; CSD observed suitability of G13, G1 & G4 and G13, G1 & G9 were the stable genotypes by CCV. Genotypes G8, G9, G11favoured by CMed criterion. CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i^6, and CS_i⁷values pointed for G13, G1, G4 as stable genotypes, and G10, G6 would be undesirable ones (Table 8). NP_i⁽¹⁾ selected G13, G1, G12; while measures NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾pointed towards G1, G13, G4 as desirable wheat genotypes at the same time reflected G2 & G12 would not be able to express stable yield performance for the locations of this zone. Values of Z1 found G7, G15, G3 whereas G15, G7, G3 would be genotypes of choice as per Z2. Values of W = 0.51 and c² statistic = 215.7 showed an overall dissimilarity among ranking of genotypes as per considered non parametric measures (Table 8).Values of Z₁ sum = 19.6 and Z₂sum = 20.9 were less than the critical value of c² (0.05, 29) = 42.6. This indicated the non-significant differences among ranks as per CS_i⁽¹⁾ and CS_i⁽²⁾ measures. Genotypes DBW233, WH1218, DBW39expressedvalues more than the critical value of c² (0.05, 1) = 3.84to showthe unstable performance. 3.2.2 Association analysis Significant positive Spearman’s rank correlation of Yield observed with GAI, CMR while negative with MR, CV, NP_i^(3), and NP_i⁽⁴⁾measures (Table 13).GAI showed mostly negative correlations with measures. MR expressed weak, moderate and significant positive relationships. SD and CV had maintained highly significant positive and weak negative correlations. More degree of positive as compared to negative correlation expressed by Med. Measures S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁶, S_i⁷maintained direct relationships as evident with positive values. CMR, CSD, CCV & CMed had developed only direct relationships with one or two exceptions. Highly significant, significant, and moderate positive correlations had seen for CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷measures. Highly significant and significant direct relationships were achieved by NP_i⁽¹⁾, NP_i⁽²⁾, NP_i^(3), and NP_i⁽⁴⁾. A weak negative correlation was observed between Z1 and Z2 values. 3.2.3 Biplot graphical analysis The first two principal components axes (PCA) contributed more than 80% of the variance of the original variables. Biplot analysis of nonparametric measures had displayed five clusters in figure 3. Three separate clusters each of two measures were observed as Yield with GAI, MR with Med, and CMR with Cmed. NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾, S_i⁶& S_i³expressed close affinity with CV measure. Largest cluster comprised of SD, CSD, CCV, NP_i⁽¹⁾, S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CS_i¹,CS_i²,CS_i³ ,CS_i⁴, CS_i⁵,CS_i⁶,CS_i⁷measures. 3.2.4 Analytic measures based on BLUE’s (2017-18) Genotype G12 was the highest yielding (48.9q/ha) followed by G2 and G9 (Table 9). GAI values pointed for G12, G3, and G9 genotypes. MR identified G11, G4, G15 and G13, G1, G4 selected as per SD whereas CV found G4, G13, G1 as stable genotypes. Genotypes G10, G5 asper MR, G1, G10 based on SD and G2, G5 by values of CV, would be of unstable yield. S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i^6, and S_i⁷observed maximum yield achievable by G13, G1, G4 genotypes, however G5, G10 would show inconsistence yield. Genotypes G8, G9 & G11 settled by CMR values, CSD found G1, G13 & G4 as genotypes of consistent yield and G13, G1 & G4 were of stable performance as per CCV. Genotypes G8, G11, G12 favored by CMed values. CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷measures identified G1, G13, G4 as stable genotypes (table 10). NP_i⁽¹⁾selected G1, G13, G11; while measure NP_i⁽²⁾settled for G13, G1, G15, and last two NP_i⁽³⁾, NP_i⁽⁴⁾ pointed towards G1, G4, G13 as desirable wheat genotypes. Z1 observed suitability of G9, G11, G12 whereas G13, G12, G9 would be genotypes of choice as per Z2. Calculated W = 0.58 and values of c² statistic = 244.1 observed overall similarity among the ranking of genotypes. Values of Z₁ sum = 15.1 and Z₂ sum = 17.3 were less than the critical value of c² (0.05, 29) = 42.6. The stable yield performance of genotypes reflected by lower Z values of genotypes as compared to the critical value. 3.2.5 Association analysis A significant positive correlation of yield had been observed with GAI & CMR and negative correlations of moderate to the weak degree with others (Table 14). GAI expressed only positive relations of moderate nature. Moderate to weak direct relationships maintained by MR values. SD expressed significant direct relations with CV, S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁶, S_i⁷, CSD, CCV, CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷, NP_i⁽¹⁾, NP_i⁽³⁾, NP_i⁽⁴⁾measures. Significant positive correlations of CV were observed with measures while weak with CMR, CMed, Z1, Z2 values. Med had achieved moderate to weak positive correlations. S_i¹, S_i², S_i³, S_i⁴, S_i⁵, S_i⁶, S_i⁷maintained direct relationships of moderate to a higher degree with measures. CMR maintained moderate to weak direct relations with measures. CSD & CCV expressed highly significant and significant positive values with measures, while CMed maintained a weak positive correlation. Measures CS_i¹, CS_i², CS_i³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷had displayed only direct highly significant to significant correlation with measures. Strong and weak relations were expressed by NP_i⁽¹⁾, NP_i⁽²⁾, NP_i⁽³⁾, and NP_i⁽⁴⁾. Weak negative correlation was seen between Z1 & Z2. 3.2.6 Biplot graphical analysis The loadings of the first two principal components axes (PCA) of nonparametric measures were shown in figure 4. More than 82% of the variance of the variables accounted for two significant PAC’s. Biplot analysis of nonparametric measures had displayed five clusters. Three clusters each of two measures were observed as Yield with GAI, MR with Med, and CMR with CMed. Measures NP_i⁽²⁾,NP_i⁽³⁾,NP_i⁽⁴⁾, S_i⁶& S_i³clubbed with CV. Large cluster comprised of Z1, Z2, SD, CSD, CCV, NP_i⁽¹⁾, S_i¹, S_i², S_i⁴, S_i⁵, S_i⁷, CS_i¹,CS_i²,CS_i³,CS_i⁴, CS_i⁵,CS_i⁶,CS_i⁷measures. Acknowledgments Authors sincerely acknowledge the training by Dr J Crossa and financial support by Dr. A.K Joshi & Dr RP Singh CIMMYT, Mexico along with the hard work of the staff at coordinating centers of AICW&BIP project to carry out the field evaluation and data recording. Conflict of Interest No conflict of interest among authors for this study.

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