^{*}

Stability for wheat genotypes had been compared in Central Zone of the country as per the BLUP and BLUE of yield values. Measures based on ranks of BLUP for 2015–16 i.e. S_{i}^{s} identified G1, G5, G7, G6. Corrected yield measures CS_{i}^{s} pointed towards G1, G2, G5, G6. Values of NP_{i}^{(s)} identified G1, G2, G7. Overall similarity among non-parametric measures tested by Kendall’s coefficient of concordance. Positive correlations of S_{i}^{s}, CS_{i}^{s} & NP_{i}^{(s)} observed with other measures. Biplot analysis exhibited cluster of CV with CCV, S_{i}^{1}, S^{2},’ S_{i}^{4}, S^{5}, S_{i}^{7} , S^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)} & NP_{i}^{(4)}. Based on BLUE’s of genotypes, S_{i}^{s} found G1, G7, G4, G5 while CS_{i}^{s} identified G5, G4, G2 as opposed to G7, G1, G4 genotypes as by values NP_{i}^{(s)}. Positive and negative correlations exhibited by S_{i}^{s}, CS_{i}^{s} & NP_{i}^{(s)} with the measures. Biplot analysis observed large cluster comprised of Yield with GAI, NP_{i}^{(2)}, NP^{(3)}, NP^{(4)}, S_{i}^{1}, S^{2}, S_{i}^{4}S_{i}^{3}, S_{i}^{5}, S_{i}^{6} measures. Second year of study (2017-18) as per BLUP’s seen, S_{i}^{s} settled for G8, G7, G2 genotypes’ While NP_{i}^{(s)} settled for G1, G2, G8, G5. Negative correlations of yield had been observed with MR, CV, Med, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} while positive with GAI, CMR, CS_{i}^{6}. Measure CV expressed affinity with NP_{i}^{(2)}, NP_{i}^{(3)} & NP^{(4)}, SD, S_{i}^{3}, S_{i}^{6}, S_{i}^{1}, S^{2}, S_{i}^{4}, S_{i}^{5}, S_{i}^{7} in Biplot analysis.- Measures S_{i}^{s} as per BLUE’s pointed towards G2, G5,G8, G7 whereas CS_{i}^{s} settled for G6, G8, G7. ^{.} Wheat genotypes G8, G2, G7, G5 favoured by least values of NP_{i}^{(s)}. Positive correlation S_{i}^{s}, CS_{i}^{s} & NP_{i}^{(s)} with others. Large cluster of CCV, CSD, NP^{(1)}, S_{i}^{1}, S_{i}^{2}, S_{i}^{4}, CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} and Z1measures.

Stability of wheat genotypes had been compared as per the BLUP and BLUE of yield values for Central Zone of the country.

Kendall’s coefficient of concordance resulted an overall similarity among rank-based measures based on BLUP for first year. Positive correlations of rank-based measures based on BLUP viz. S_{i}^{s}, CS_{i}^{s} & NP_{i}^{(s)} with other measures.

While both type of correlations observed among measures as per BLUE of genotypes.

First two PCA’s based on BLUP’s account for more of G*E sum of squares as compared to corresponding components by BLUE.

_{i}

^{(s)}

_{i}

^{(s)}

_{i}

^{(s)}

Significant genotype-by-environment (G*E) interaction had been reported in large number of multi environmental studies (Pour

The three defined objectives for current study was (1) analyse stability performance by nonparametric measures based on BLUP and BLUE of wheat genotypes yield (2) differentiate the performance pattern of wheat genotypes as per BLUP and BLUE values and (3) study the relationships, similarities and dissimilarities among non-parametric measures of stability.

Seven promising wheat genotypes were evaluated in research field trials at 14 centers of All India Coordinated Research Project on Wheat across zone during 2015–16 and a set of nine genotypes at twelve centers for 2017–18 cropping season. Randomized block designs had been laid out in field evaluation of genotypes with four replications. Recommended agronomical practices as per zone had followed in total to harvest the good wheat yield of genotypes. Parentage details and environmental conditions were reflected in _{ij}. denotes the phenotypic value of i^{th} genotype in ^{th} environment where _{ij} as the rank of the i^{th} genotype in the ^{th} environment, and ^{th} genotype. Sabaghnia ^{th} genotype in j^{th} environment as (^{*}_{ij} = _{ij} - ^{*}_{ij}, was the corrected phenotypic value; ^{th} genotype in all environments and

Non parametric measures for stability analysis proposed by Thennarasu (1995) as NR_{i}^{(1)}, NR_{i}^{(2)}, NR_{i}^{(3)} and NR_{i}^{(4)} based on ranks of corrected means of genotypes. In the formulas, ^{*}_{ij}. was the rank of ^{*}_{ij}, and _{di} were the mean and median ranks for original (unadjusted) grain yield, where ^{*} and ^{*}_{di} were the same parameters computed from the corrected (adjusted) data.

Significance of S_{i}^{(1)} and S_{i}^{(2)} non parametric measures had been explored by Nassar and Huehn (1987). Z1 and Z2 values were calculated for each genotype, based on the ranks of adjusted data and then sum of i.e. Z_{1} sum and Z_{2} sum are distributed as x^{2}. Degree of similarity among measures had assessed by estimating correlation coefficients while considering genotypes ranking. Spearman’s rank correlation values among pairs (Piephoand Lotito 1992) estimated as follows :

where _{i} denotes difference between ranks for ^{th} genotype and n is total number of pairs.

Mean wheat yield observed, G5 as the highest yielding with 58q/ha followed by G2 and G4, though remarkable yield differences were observed among the genotypes (

Seven nonparametric measures based on original grain yield of genotypes (S_{i}^{1}, S_{i}^{2}, Si^{3}, S_{i}^{4}, S_{i}^{5}, S_{i}^{6} and S_{i}^{7}) indicated that (G1, G7, G6), (G1, G6, G5), (G1, G7, G6), (G1, G6, G5), (G1, G6, G5), (G1, G7, G6), (G1, G6, G5) as sets of genotypes respectively. According to corrected yield (_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7}) identified stable genotypes (G5, G1, G6), ‘(G5, G1,G6), (G5, G1, G2), (G5, G1, G6), (G5, G2, G1), (G5, G2, G1), (G1, G5, G6) and G7, G3 were of unstable nature (Sabaghnia _{i}^{(1)} considered genotypes G5, G1, G6 of stable yield. G1, G2 and G7 had expressed the lower values of NP_{i}^{(2)} whereas as per NP_{i}^{(3)} & NP_{i}^{(4)} values genotypes G1, G7, G2 would be of stable performance. Z1 and Z2 pointed for G2, G4, G6 as of suitable performance.

Calculated value of Kendall’s coefficient of concordance (W=0.40) and for the significance of W value the magnitude of x^{2} = 72.3 statistics was less than table of x^{2} (0.01, 290) = 135.8, which resulted an overall similarity among non-parametric measures (Vaezi _{i}^{1} and CS_{i}^{2} measures, Z1 and Z2 values were calculated based on the ranks of adjusted data and then summed: Z_{1} sum = 10.3 and Z_{2} sum = 13.4 (^{2} and were less than the critical value of x^{2} (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per ranks of CS_{i}^{1} and CS_{i}^{2} measures. HD4728 was significantly unstable as compared to others due to Z values more than the critical value of x^{2} (0.05, 1) = 3.84.

Spearman’s rank correlation analysis as per BLUP’s of genotypes yield had been reflected in _{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, NP_{i}^{(1)} and negative correlation with MR, CV, Med, S_{i}^{1}, S_{i}^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}. GAI mentioned negative correlations with MR, CV, Med, S_{i}^{1}, Si^{3}, S_{i}^{6}. Value of MR expressed mostly significant positive correlations with CV, Med, S_{i}^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}. Significant positive correlation maintained by SD with S_{i}^{1}, S_{i}^{2}, S_{i}^{3}, Si^{4}, Si^{5}, S_{i}^{7}, CSD, CCV, CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS^{5}, CSj^{6}, CS_{i}^{7}, NP_{i}^{(1)} values. Similar behaviour portrayed by CV values. Median had expressed significant values for positive correlations. S_{i}^{1}, S_{i}^{2}, S_{i}^{3}, S_{i}^{4}, S_{i}^{5}, S_{i}^{6}, S_{i}^{7} exhibited direct relations of significant nature among themselves and with other measures. Measures CMR, CSD, CCV and CMed expressed positive correlations with most of the measures besides few weak negative correlations with NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}. Similar behaviour expressed by CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} and expressed only significant positive relationships. NP_{i}^{(1)}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} had also expressed only significant direct relationships. Z1 is related to Z2 in weak inverse manner.

The loadings of measures based on first two significant principal components were reflected in _{i}^{(1)}, CSD, CS_{i}^{1}, CS^{2}, CS^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6} and CS_{i}^{7} was observed. CV expressed affinity with CCV, S_{i}^{1}, S_{i}^{2}, S_{i}^{4}, S_{i}^{5}, S_{i}^{7}, S_{i}^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)} & NP_{i}^{(4)} as separate cluster.

Average yield of genotypes showed G5 as of highest yield with 58 q/ha followed by G2 and G6, genotypes (

S_{i}^{1}, S_{i}^{2}, S_{i}^{3}, S_{i}^{4}, S_{i}^{5}, S_{i}^{6} and S_{i}^{7} indicated that (G7, G1, G5), (G7, G4, G1), (G7, G1, G4), (G7, G4, G1), (G7, G5, G4), (G7, G1, G4), (G7, G1, G4) as sets of desirable genotypes as per respective measure. G2, G5 & G1 by CMR values, G5, G4 & G2 by CSD and G5, G2 & G4 were the stable as per CCV. Median favoured G2, G6, G1 genotypes. CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} identified stable genotypes (G5, G4, G2) and G7, G3 were of unstable type as per these nonparametric measures. NP_{i}^{(1)} considered G5, G4 and G2 as desirable genotypes. Genotypes G7, G1 and G2 had expressed the lower values of NP_{i}^{(2)}, while as per lower values of NP_{i}^{(3)} & NP_{i}^{(4)}, G7, G1, G4 and lastly by Z1 and Z2 values selected G1, G2, G6 as suitable as well as G7 & G3 would be of unsuitable performance.

Calculated value of W (0.23) and for its significance x^{2} = 42.5 statistics was less than table of x^{2} (0.01, 290) = 135.8, which resulted an overall similarity among non-parametric measures. Values of Z_{1} sum = 18.9 and Z_{2} sum = 23.9 (^{2} and were less than the critical value of x^{2} (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per ranks of CS_{i}^{1} and CS_{i}^{2} measures.

Yield has expressed highly significant positive correlation with GAI, CSD, CCV, CS_{i}^{1}, CS_{i}^{2}, CSi^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7}, NPi^{(2)} along with negative correlation with other measures (_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7}, NP_{i}^{(2)}. Values of MR expressed positive correlation with CV, Med, S_{i}^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}. SD maintained only highly significant and significant positive correlations with almost all the measures. CV measure also showed significant positive correlation and negative correlation of moderate to weak nature. Same type of relations was depicted by Median. S_{i}^{1}, S_{i}^{2}, S_{i}^{3}, S_{i}^{4}, S_{i}^{5}, S_{i}^{6}, S_{i}^{7} exhibited indirect relations only with CS_{i}^{7}, Z1, Z2, NP_{i}^{(1)}. Negative correlations of weak nature by CMR, CSD, CCV, CMed with NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} only^{.}. CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} also expressed only weak indirect relations with NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} measures. Significant positive relationships of NP_{i}^{(1)}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} obtained with few negative values of lower magnitude.

_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}, S_{i}^{1}, S^{2}, S_{i}^{4}, S_{i}^{3}, S_{i}^{5}, S_{i}^{6} clustered in a group. Z1, Z2expressed affinity with, SD, CCV, CSD, NP_{i}^{(1)}, CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} inlarge cluster.

High yield achieved by G3 followed by G6, G7 wheat genotypes, whereas GAI selected G3, G6, G7 genotypes, large values of mean ranks selected G2, G5, G8 more over the consistent yield of G8, G7, G2 expressed by least values of standard deviation (_{i}^{1} and S_{i}^{2} measures selected G8, G7, G2 as opposed to G8, G2, G7 by S_{i}^{3} measure. Next two measures S_{i}^{4} & S_{i}^{5} settled for G8, G7, G2 and remaining two measures S_{i}^{6} and S_{i}^{7} favoured G8, G2, G7 wheat genotypes.

Average mean of ranks as per corrected yield values selected G7, G5, G6 and corrected standard deviation observed suitability of G8, G1, G7 genotypes. Coefficient of variation as per corrected yield values exhibited G7, G5, G8 while median values for G5, G6, G7 and G8, G1, G7 by CS_{i}^{1} & CS^{2}, CSi^{3} pointed for G8, G7, G5 & as per CS^{4} wheat genotypes G8 G1 G7, G1 G8 G7 by criterion of CS_{i}^{5} & CS_{i}^{6} settled for G7 G6 G5 and lastly by values of CS_{i}^{7} genotypes G8, G5, G6 (_{i}^{(1)} selected (G1, G6, G8); NP_{i}^{(2)} identified (G8, G2, G1) , NP_{i}^{(3)} & NP_{i} ^{(4)} settled for (G8, G2, G5), whereas G4 along with G6 would be of unsuitable type. Z1 and Z2 favoured G2, G5, G6 wheat genotypes.

Concordance coefficient W=0.46 and for its significance x^{2} = 110.8 statistic was less than table of x^{2} (0.05, 290) = 124.3 (135.8), which resulted an overall similarity among non-parametric measures. Values of Z_{1} sum = 14.4 and Z_{2} sum = 18.6 (^{2} (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per ranks of CS_{i}^{1} and CS_{i}^{2} measures. Unstable performance of HI1544 & UAS 465 judged by larger values as compared to the critical value of x^{2} (0.05, 1) = 3.84.

Spearman’s rank correlation analysis observed highly significant negative correlations of yield with MR, CV, Med, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} and positive with GAI, CMR, CS_{i}^{6} (_{i}^{6}. Significant direct relations maintained by MR along with few negative values of low magnitude. SD & CV maintained highly significant direct relations with almost all the measures. Median reflected both types of correlations. S.^{1}, S.^{2}, S:^{3}, S.^{4}, S.^{5}, S.^{6} , S.^{7} exhibited highly significant to significant positive correlations with exception of CMR &CMed. Negative relationships maintained CMR with most of the measures. CSD had maintained only significant and perfect positive relationships with CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{4}. Only direct relationships showed by CCV. While indirect relations of CMed had observed. CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6}, CS_{i}^{7} behaved in similar manner and exhibi_{i}^{(1)}, NR^{(2)}, NR^{(3)}, NP_{i}^{(4)} had expressed positive correlations. Z1 is related to Z2 in inverse manner.

Biplot analysis of nonparametric measures based on BLUP’s of genotypes (2015-16)

Biplot analysis of nonparametric measures based on BLUE’s of genotypes (2015-16)

Biplot analysis of nonparametric measures based on BLUP’s of genotypes (2017-18)

Biplot analysis of nonparametric measures based on BLUE’s of genotypes (2017-18)

Measure |
Component PC1 |
Component PC2 |
---|---|---|

Yield |
0.1316 |
0.2552 |

GAI |
0.1315 |
0.2553 |

MR |
-0.0953 |
-0.2753 |

SD |
-0.2150 |
0.1450 |

CV |
-0.0285 |
0.2921 |

Med |
-0.0550 |
-0.2895 |

S |
-0.1238 |
0.2345 |

S |
-0.2163 |
0.1461 |

S |
-0.1061 |
0.2738 |

S |
-0.2150 |
0.1450 |

S |
-0.1878 |
0.1624 |

S |
-0.0509 |
0.2926 |

S |
-0.2019 |
0.0885 |

CMR |
0.0954 |
-0.0615 |

CSD |
-0.2521 |
-0.0609 |

CCV |
-0.2411 |
0.0067 |

CMed |
0.0835 |
-0.0558 |

CS |
-0.2547 |
-0.0460 |

CS |
-0.2506 |
-0.0692 |

CS |
-0.2549 |
-0.0250 |

CS |
-0.2521 |
-0.0609 |

CS |
-0.2456 |
-0.0935 |

CS |
-0.2396 |
-0.0263 |

CS |
-0.2471 |
-0.0119 |

NP |
-0.2444 |
-0.0998 |

NP |
-0.0930 |
0.2580 |

NP |
-0.0498 |
0.2781 |

NP |
-0.0464 |
0.2799 |

Z1 |
-0.1700 |
-0.1536 |

Z2 |
-0.1827 |
-0.1560 |

% variance |
49.50 |
36.38 |

Five clusters among 30 rank-based measures had been expressed Biplot analysis and the loadings of measures were shown in _{i}^{(2)}, NP_{i}^{(3)} & NP_{i}^{(4)}, SD, S_{i}^{3}, S_{i}^{6}, S_{i}^{1}, S_{i}^{2}, S_{i}^{4}, S_{i}^{5}, S_{i}^{7} as grouped in cluster. Another large cluster comprised of Z1, Z2, CCV, CSD, Z1, Z2, NP_{i}^{(1)}, CS_{i}^{1}, CS^{2}, CS^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6} and CS_{i}^{7} measures.’

Measure |
Component PC1 |
Component PC2 |
---|---|---|

Yield |
-0.2322 |
-0.0864 |

GAI |
-0.2307 |
-0.0944 |

MR |
0.2263 |
0.0825 |

SD |
-0.1891 |
-0.1642 |

CV |
-0.2231 |
-0.1055 |

Med |
0.2000 |
0.1107 |

S |
-0.1874 |
-0.1704 |

S |
-0.1810 |
-0.1687 |

S |
-0.2191 |
-0.1378 |

S |
-0.1891 |
-0.1642 |

S |
-0.1611 |
-0.1768 |

S |
-0.2189 |
-0.1358 |

S |
-0.2076 |
-0.1255 |

CMR |
-0.0884 |
0.2577 |

CSD |
0.1900 |
-0.2045 |

CCV |
0.1622 |
-0.2522 |

CMed |
-0.0898 |
0.1423 |

CS |
0.1948 |
-0.1928 |

CS |
0.1924 |
-0.2033 |

CS |
0.1741 |
-0.2377 |

CS |
0.1900 |
-0.2045 |

CS |
0.1639 |
-0.2426 |

CS |
0.1430 |
-0.2744 |

CS |
0.2131 |
-0.1479 |

NP |
0.1791 |
-0.2124 |

NP |
-0.1178 |
-0.2542 |

NP |
-0.1375 |
-0.2429 |

NP |
-0.1432 |
-0.2323 |

Z1 |
0.1649 |
-0.0487 |

Z2 |
0.1498 |
-0.0866 |

% variance |
56.08 |
28.66 |

Higher average yield had expressed by G3, G6, G7 wheat genotypes, whereas G3, G6, G7 for possessed higher adaptability index values, mean of ranks selected G2, G9, G8 more over the consistent yield of G2, G8, G5 expressed by least values of standard deviation (_{i}^{1} & S_{i}^{2} measures selected G2, G8, G7 opposed to G2, G8, G5 by S_{i}^{3} values. Genotypes G2, G8, G7 considered by S_{i}^{4} as well as by S_{i}^{5}, genotypes G2, G8, G5 favoured by S_{i}^{6} whereas S_{i}^{7} settled for G2, G8, G7 genotypes.

CMR selected G7, G6, G5 and CSD observed suitability of G7, G8, G6 genotypes. CCV exhibited genotypes G7, G6, G8 while median values for G9, G7, G6 and genotypes G7, G8, G6 by CS_{i}^{1}, CS^{2} , CS_{i}^{3} CS_{i}^{4} measures whereas as per least values of CS_{i}^{5} genotypes were G8, G7, G6 & for CS_{i}^{6} values G7, G8, G6 while G7,G6, G8 by CS_{i}^{7} (_{i}^{(1)}favoured (G8, G7, G6); while (G8, G2, G5) by NP_{i}^{(2}, NP_{i}^{(3)} & NP_{i} ^{(4)}. Measure Z1 settled for G5, G2, G6 and while genotypes G5, G2, G1 by Z2 values.

W=0.42 and x^{2} =101.2 statistic was less than table of x^{2} (0.05, 290) = 124.3 (135.8), which resulted an overall similarity among non-parametric measures. Values of Z_{1} sum = 8.08 and Z_{2} sum = 9.9 (^{2} (0.05, 29) = 42.6. This indicated the non-significant differences among genotypes as per ranks of CS_{i}^{1} and CS_{i}^{2} measures.

Majority of highly significant negative correlations of yield had evident from table. At the same time yield expressed positive values observed with GAI, CMR, CCV, CMed and CS^{7} (_{i}^{3}, S_{i}^{6}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}. SD and CV depicted same type of correlations with measures. Median had maintained highly significant and significant direct relations with the measures. S_{i}^{1}, S_{i}^{2}, S_{i}^{3}, S_{i}^{4}, S_{i}^{5}, S_{i}^{6}, S_{i}^{7} exhibited significant positive correlation with other measures. CMR measure maintained both type of relationships. CSD, CCV had expressed positive correlations with measures & CMed maintained only negative values of correlation. CS_{i}^{1}, CS_{i}^{2}, CS_{i}^{3}, CS_{i}^{4}, CS_{i}^{5}, CS_{i}^{6} and CS_{i}^{7} expressed only significant positive relationships with others and themselves. NP_{i}^{(1)}, NP_{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)} had expressed only direct relationships. More over Z1 was related Z2 values by an inverse relationship.

Measure |
Component PC1 |
Component PC2 |
---|---|---|

Yield |
0.0266 |
0.3596 |

GAI |
0.0179 |
0.3601 |

MR |
-0.0285 |
-0.3472 |

SD |
0.2233 |
0.0219 |

CV |
0.1705 |
0.2377 |

Med |
-0.0282 |
-0.3441 |

S |
0.2237 |
0.0035 |

S |
0.2259 |
0.0100 |

S |
0.2036 |
0.1525 |

S |
0.2233 |
0.0219 |

S |
0.2221 |
0.0109 |

S |
0.1802 |
0.2147 |

S |
0.2194 |
0.0379 |

CMR |
0.0327 |
0.1105 |

CSD |
0.2171 |
-0.1007 |

CCV |
0.1958 |
-0.1435 |

CMed |
0.0366 |
0.1042 |

CS |
0.2170 |
-0.0945 |

CS |
0.2160 |
-0.1098 |

CS |
0.2108 |
-0.1317 |

CS |
0.2171 |
-0.1007 |

CS |
0.2115 |
-0.0916 |

CS |
0.2068 |
-0.1391 |

CS |
0.2002 |
-0.1009 |

NP |
0.2068 |
-0.1078 |

NP |
0.1794 |
0.2126 |

NP |
0.1682 |
0.2408 |

NP |
0.1674 |
0.2431 |

Z1 |
0.1804 |
-0.1675 |

Z2 |
0.1773 |
-0.1776 |

% variance |
63.64 |
24.60 |

Yield |
-0.0767 |
-0.3088 |
---|---|---|

GAI |
-0.0763 |
-0.3077 |

MR |
0.0621 |
0.3231 |

SD |
-0.2290 |
-0.0348 |

CV |
-0.1739 |
-0.2297 |

Med |
0.0749 |
0.2930 |

Si1 |
-0.2287 |
-0.0239 |

S |
-0.2305 |
-0.0318 |

S |
-0.2084 |
-0.1527 |

S |
-0.2290 |
-0.0348 |

S |
-0.2304 |
-0.0176 |

S |
-0.1874 |
-0.2038 |

S |
-0.2163 |
-0.0575 |

CMR |
0.0423 |
-0.2524 |

CSD |
-0.2084 |
0.1627 |

CCV |
-0.1747 |
0.2142 |

CMed |
-0.0391 |
-0.0712 |

CS |
-0.2073 |
0.1611 |

CS |
-0.2096 |
0.1594 |

CS |
-0.1947 |
0.1901 |

CS |
-0.2084 |
0.1627 |

CS |
-0.2159 |
0.1282 |

CS |
-0.1943 |
0.1805 |

CS |
-0.1797 |
0.2128 |

NP |
-0.2201 |
0.1038 |

NP |
-0.1855 |
-0.1984 |

NP |
-0.1834 |
-0.2070 |

NP |
-0.1781 |
-0.2151 |

Z1 |
-0.1707 |
0.0508 |

Z2 |
-0.1425 |
0.0561 |

% variance |
58.08 |
29.15 |

Values of the loadings for measures as per first two significant principal components axes (PCA) were shown in _{i}^{(2)}, NP_{i}^{(3)}, NP_{i}^{(4)}, S_{i}^{3}, S_{i}^{5}, S_{i}^{6}. Large cluster comprises of CCV, CSD, NP_{i}^{(1)}, S_{i}^{1}, S_{i}^{2}, S_{i}^{4}, CS_{i}^{1}, CS_{i}^{2}, CS^{3}, CS_{i}^{4}, CS^{5}, CS_{i}^{6}, CS_{i}^{7} and Z1measures.

BLUP’s of wheat genotypes provide more valid estimates of yield in multi environment trials and more variations accounted by first two significant principal components of nonparametric measures. More affinity among measures had reflected by a smaller number of clusters in biplot analysis based on BLUP's. Association of S_{i}^{s}, CS_{i}^{s}, NP_{i}^{(s)} with other measures is independent of ranks as per BLUP or BLUE of genotypes. Positive and direct relationships exhibited by these measures with other nonparametric measures.

Authors sincerely acknowledge the training by Dr. J. Crossa and financial support by Dr. A.K. Joshi & Dr. R.P. Singh CIMMYT, Mexico along with hard work of the staff at coordinating centers of AICW & BIP project to carry out the field evaluation and data recording.