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Singh, G. P.

Characterization of Wheat Genotypes for Stay Green and Physiological Traits by Principal Component Analysis under Drought Condition

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Authors

R. Shivramakrishnan ¹, R. Vinoth ², Ajay Arora ¹, G. P. Singh ³, B. Kumar ⁴, V. P. Singh ¹

Affiliations
1 Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi, IN
2 Centre for Plant Breeding and Genetics and Plant Breeding, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
3 Division of Genetics, Indian Agricultural Research Institute, New Delhi, IN
4 Department of Plant Breeding and Genetics, Rajendra Agricultural University, Pusa, Samastipur (Bihar), IN

Source

International Journal of Agricultural Sciences, Vol 12, No 2 (2016), Pagination: 245-251

Abstract

An experiment was conducted to examine the magnitude of genetic diversity and characters contributing to genetic diversity among 35 core elite wheat germplasm from INDIA and CIMMYT under water deficit condition. Principal components (PC) analysis showed that three components explained 67.73 per cent of the total variation among traits. The first PC contribute 38.8 per cent, second PC contribute 17.17 per cent and third PC contribute 11.66 per cent of total variation between traits. The first PC was more related to LSR, DSI, SCMR, RWC, ear weight per plant, harvest index and grain yield. The second PC was more related to plant height, LSR, tillers per plant, biological yield, thousand kernel weight and RWC. Therefore, selection based on first component is helpful for a good hybridization breeding program. Genetic divergence was carried out and grouped genotypes into six genetically distinct clusters. Cluster II genotypes viz., CHIRYA7, HW2041 and PBW502 shows superiority for functional stay green trait by exhibiting low cluster mean for leaf and DSI, and high cluster mean for SCMR, photosynthetic rate, RWC, tillers per plant, ear weight, 1000 kernel weight, biological yield, harvest Index, grain yield per plant and in contrast Cluster IV genotypes are non-stay green and drought susceptible by exhibiting high cluster mean for LSR and DSI. A three dimensional (3D Plot) depicts maximum genetic divergence between HW2041 and CBW38 and CHIRYA7 and HW2033. Stay green trait and all yield attributing traits except plant height can be improved by intermating HW2041 with CBW38 and CHIRYA7 with HW2033 genotypes which result in a highly heterotic hybrid for these traits under water deficit stress in wheat.

Keywords

Principal Component Analysis, Genetic Diversity, Leaf Senescence Rate, Wheat, Drought.

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References

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Statistical Methods to Study Adaptability of Barley Genotypes Evaluated Under Multi Environment Trials

Abstract Views :194 | PDF Views:0

Authors

Ajay Verma ¹, V. Kumar ¹, A. S. Kharab ¹, G. P. Singh ¹

Affiliations
1 Statistics and Computer Center, ICAR- Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN

Source

International Journal of Agricultural Sciences, Vol 14, No 2 (2018), Pagination: 283-291

Abstract

Genotypes G5, G8, G3, G21 and G18 had achieved higher yields besides b_i > 1.0. G21 and G3 identified as appropriate one, because had higher yield value than the mean, bi values near 1.0 and low S²_di. Lower values (W²_i) resulted for G12, G5, G2, G21 while higher for G5, G3 and G14. Genotypes G12 followed by G2, G20, and G7 had the smallest environmental variance (S²_xi). Smaller values of (CV_i) considered G12, G2, G20, and G10 of stable performance. α² _i measure pointed out G12, G7 and G2 with smallest values. Desirable lower P_i values reflected by G18, G5, G21, and G4 while GAI values identified G18, G11, G4 G10 as desirable genotypes. S_i ⁽¹⁾ and S_i⁽²⁾ showed lower values of G12, G2 and G7 genotypes. Significant tests of S_i ⁽¹⁾ and S_i⁽²⁾ proved the highly significant difference in ranks among the 21 genotypes grown in 8 environments. Genotypes G12, G2, and G7 had the lower S_i⁽³⁾ and S_i⁽⁶⁾ values. Yield of genotypes had significant negative correlation with bi, S_i⁽²⁾, S_i⁽³⁾, S_i⁽⁶⁾, NP_i ⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾ and significant positive correlation with GAI, P_i and Rank Sum. Hierarchical cluster analysis classified genotypes into three clusters as largest cluster included genotypes with more than average yield along with high yielders G18, G11, G3, G5, G21 and unstable performance indicated by non parametric measures. Biplot analysis while considering first two significant principal components grouped the parametric and non parametric measures into four groups. The smaller group consisted of b_i and S² _di and adjacent to group of non parametric measures S_i⁽²⁾, S_i⁽⁶⁾, NP_i⁽²⁾, NP_i⁽³⁾ and NP_i⁽⁴⁾.

Keywords

Barley, Parametric, Non-Parametric Measures, Biplot Analysis, Hierarchical Clustering.

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References

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Wheat Genotypes Evaluated under Central Zone for Stability Analysis by Rank based Measures Considering BLUP and BLUE of Yield Values

Abstract Views :439 | PDF Views:0

Authors

Ajay Verma ¹, G. P. Singh ¹

Affiliations
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN

Source

International Journal of Agricultural Sciences, Vol 16, No 2 (2020), Pagination: 105-121

Abstract

Rank based measures of stability had been compared for wheat genotypes evualated in Central Zone of the country as per the BLUP and BLUE of yield values. Measures based on ranks of BLUP of original yield for 2016-17, S_i^s measures identified G3, G7, G4 as stable genotypes. Corrected yield measures CS_i^s selected G4, G7, G3 for stable performance. Values of NP_i^(s) identified G1, G6 as of undesirable types. Association analysis observed positive correlations of S_i^s, with others and themselves. Positive relationships also exhibited by CS_i^s and NP_i^(s) values to other measures. Biplot analysis exhibited cluster of S_i⁶ , S_i³ , CV, NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾ and CS_i⁷. Larger cluster comprised of NP_i⁽¹⁾ CCV, CSD S_i¹, S_i², S_i⁴, S_i ⁵, S_i⁷ , CS_i ¹, CS_i², CS_i ³, CS_i ⁴, CS_i ⁵, CS_i⁶ measures. Based on BLUE’s of genotypes yield, measures S_is found G3, G7, G4 as the stable genotypes, however G1, G2 would express unstable performance. CS_i^s identified G7, G3, G6 as opposed to G3, G5, G7 genotypes as by values NP_i^(s). Positive correlations exhibited by S_isexcept of negative with CMR, CMed, Z1 and Z2 values. Ranks of genotypes as per values of CS_i^s and NP_i^(s) measures expressed direct relationship with most of the measures. Biplot analysis observed large cluster comprised of CCV, CSD, NP_i⁽¹⁾, S_i¹, S_i², S_i⁴,CS_i¹, CS_i², CS_i ³, CS_i⁴, CS_i⁵, CS_i⁶, CS_i⁷ measures. Second year of study (2017-18) as per BLUP’s seen, S_i^s settled for G6, G5, G3 genotypes. While NP_i^(s) settled for G6, G3,G5 as genotypes of stable performance. Highly significant negative correlation of yield observed with most of the measures MR, CV, Med, S_i³, S_i⁶ ,CMR,NP_i⁽²⁾, NP_i⁽³⁾, NP_i⁽⁴⁾. Biplot analysis as per first two significant components (accounted for 88.7 %) marked larger cluster contains CS_i^s with NP_i⁽¹⁾, S_i¹, S_i2 S_i⁴, S_i5 S_i⁷ ,SD, CSD measures. S_is rank based measures as per BLUE’s of genotypes pointed towards G5,G4, G6, G1 whereas G6, G5, G1,G3 by CS_i^s values. Wheat genotypes G1,G2, G3,G5 settled by least values of NP_i^(s) . Direct relationships portraited by S_i^s, CS_i^s and NP_i^(s) with others. Larger cluster grouped NP_i^(s), CV, CCV, Z1, Z2, Yield, GAI, CS_i⁵, CS_i6 measures.

Keywords

Blup, Blue, S_i^(s), CS_i ^(s), NP_i^(s), Co-efficient Of Concordance, Biplot Analysis.

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