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Singh, G. P.
- Observations on the Wetland Ecosystem of Kabar Lake in Begusarai, Bihar, with Special Reference to Vegetation
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Authors
Affiliations
1 Botanical Survey of India, Howrah, IN
1 Botanical Survey of India, Howrah, IN
Source
Nelumbo - The Bulletin of the Botanical Survey of India, Vol 30, No 1-4 (1988), Pagination: 134-139Abstract
The article presents the results of observations on vegetation, flora, fauna, human habitations and environment of the wetland ecosystem of Kabar Lake and surrounding areas in the district of Begusarai, Bihar.- Characterization of Wheat Genotypes for Stay Green and Physiological Traits by Principal Component Analysis under Drought Condition
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Authors
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
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: 245-251Abstract
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.References
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- Srivalli, S. and Khanna-Chopra, R. (2009). Delayed wheat flag leaf senescence due to removal of spikelets is associated with increased activities of leaf antioxidant enzymes, reduced glutathione/oxidized glutathione ratio and oxidative damage to mitochondrial proteins. Pl. Physiol. & Biochem., 47 : 663-670.
- Growth and Pigment Evaluation of Cyanobacterium Oscillatoria agardhii in Various Inorganic Media
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Authors
Affiliations
1 Department of Botany, University of Rajasthan, Jaipur- 302004, IN
2 Suresh Gyan Vihar University, Jaipur, IN
1 Department of Botany, University of Rajasthan, Jaipur- 302004, IN
2 Suresh Gyan Vihar University, Jaipur, IN
Source
Research Journal of Science and Technology, Vol 5, No 2 (2013), Pagination: 239-244Abstract
The establishment of suitable nutrient medium is prime and imperative step for achieving optimal growth of alga. Nutrient requirements of Oscillatoria agardhii have been worked out employing five inorganic media varying in their chemical composition and pH. O. agardhii choose CFTRI media for its best growth with high biopigment accumulation followed by Zarrouk's, Hughe's, Chu-10 and Allen-Arnon's medium respectively. More than any other factor, chemical composition of the medium has influenced the growth of cyanobacteria. The presence of sodium, nitrate, chloride, phosphate, sulphate, magnesium and carbonate has been found to be responsible for the rapid growth of the algae, on the other hand, absence of any of these ions may hamper the growth. These were very much present in CFTRI and Zarrouk's medium.Keywords
Oscillatoria agardhii, Optimal Growth, Medium, Biopigment.- Interpreting Genotype X Environment by Non-Parametric Methods for Malt Barley Evaluated under North Western Plains Zone
Abstract Views :164 |
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Authors
Affiliations
1 Statistics and Computer Center, ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
1 Statistics and Computer Center, ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 236-242Abstract
The present study was carried out to identify malt barley genotypes with high yield and stability across eight different environments, using non-parametric statistical measures. Descriptive statistics MR, SD and CV identified DWRB147, DWRB150 and RD2943 stable genotypes. BH902 and PL890 were identified as unstable genotypes by CMR CSD and CCV. Non-parametric measures selected DWRB147 and DWRB150 as the stable genotypes and BH902 and PL890 unstable genotypes. Significant tests for Si 1 and Si 2 were based on sum of Zi 1 and Zi 2 measures and sum of Zi 1 was greater than critical value confirmed significant differences among the twenty genotypes. Results of the NPi 2, NPi 3 and NPi 4were similar for unstable performance of BH902, DWRB150 and DWRB147. Biplot analysis of PCA1 and PCA2 accounting for 70.08 per cent showed three distinguish groups among non-parametric measures. Clustering by Ward’s hierarchical method expressed four clusters by using the squared Euclidean distance as dissimilarity measure.Keywords
Non-Parametric Measurements, Rank Correlation, Biplot Analysis, Hierarchical Clustering.References
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- Huehn, M. (1996). Non-parametric analysis of genotype x environment interactions by ranks. In: Kang, M.S. and Gauch, H.G. (Ed.) Genotype by Environment Interaction. CRC Press, Boca Raton, pp. 213-228.
- Hussein, M.A., Bjornstad, A. and Aastveit, A.H.(2000). SASG × ESTAB: A SAS program for computing genotype 3 environment stability statistics. Agron. J., 92 : 454-459.
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- Karimizadeh, R., Mohammadi, M., Sabaghnia, N. and Shefazadeh, M.K. (2012). Using Huehn’s non-parametric stability statistics to investigate genotype × environment interaction. Not. Bot. Hort. Agrobo., 40 : 195-200.
- Kaya, Y. and Taner, S. (2003). Estimating genotypic ranks by nonparametric stability analysis in bread wheat (Triticuma estivum L.). J. Central Eur. Agric., 4 : 47-54.
- Kilic, H., Akcura, M. and Aktas, H. (2010). Assessment of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in multienvironments. Not. Bot. Hort. Agrobo., 38 : 271-279.
- Kumar, V., Khippal, A., Singh, J., Selvakumar, R., Malik, R., Kumar, D., Kharub, A.S., Verma, R.P.S. and Sharma, I. (2014). Barley research in India: Retrospect and prospects. J. Wheat Res., 6 (1) : 1-20.
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- Sabaghnia, N., Karimizadeh, R. and Mohammadi, M. (2012). The use of corrected and uncorrected non-parametric stability measurements in durum wheat multi-environmental trials. Span. J. Agric. Res., 10 : 722-730. Doi: 10.5424/sjar/2012103-384-11.
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- Statistical Methods to Study Adaptability of Barley Genotypes Evaluated Under Multi Environment Trials
Abstract Views :196 |
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Authors
Affiliations
1 Statistics and Computer Center, ICAR- Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
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: 283-291Abstract
Genotypes G5, G8, G3, G21 and G18 had achieved higher yields besides bi > 1.0. G21 and G3 identified as appropriate one, because had higher yield value than the mean, bi values near 1.0 and low S2di. Lower values (W2i) 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 (S2xi). Smaller values of (CVi) considered G12, G2, G20, and G10 of stable performance. α2 i measure pointed out G12, G7 and G2 with smallest values. Desirable lower Pi values reflected by G18, G5, G21, and G4 while GAI values identified G18, G11, G4 G10 as desirable genotypes. Si (1) and Si(2) showed lower values of G12, G2 and G7 genotypes. Significant tests of Si (1) and Si(2) proved the highly significant difference in ranks among the 21 genotypes grown in 8 environments. Genotypes G12, G2, and G7 had the lower Si(3) and Si(6) values. Yield of genotypes had significant negative correlation with bi, Si(2), Si(3), Si(6), NPi (2), NPi(3), NPi(4) and significant positive correlation with GAI, Pi 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 bi and S2 di and adjacent to group of non parametric measures Si(2), Si(6), NPi(2), NPi(3) and NPi(4).Keywords
Barley, Parametric, Non-Parametric Measures, Biplot Analysis, Hierarchical Clustering.References
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- Tackling Wheat Rusts Through Resistance - Success, Challenges and Preparedness
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Authors
Affiliations
1 ICAR-Indian Institute of Wheat and Barley Research, Regional Station, Flowerdale, Shimla - 171 002, IN
2 ICAR-Indian Institute of Wheat and Barley Research, Karnal - 132 001, IN
1 ICAR-Indian Institute of Wheat and Barley Research, Regional Station, Flowerdale, Shimla - 171 002, IN
2 ICAR-Indian Institute of Wheat and Barley Research, Karnal - 132 001, IN
Source
Current Science, Vol 116, No 12 (2019), Pagination: 1953-1954Abstract
No Abstract.Keywords
No Keywords.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 :445 |
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Authors
Ajay Verma
1,
G. P. Singh
1
Affiliations
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
Source
International Journal of Agricultural Sciences, Vol 16, No 2 (2020), Pagination: 105-121Abstract
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, Sis measures identified G3, G7, G4 as stable genotypes. Corrected yield measures CSis selected G4, G7, G3 for stable performance. Values of NPi(s) identified G1, G6 as of undesirable types. Association analysis observed positive correlations of Sis, with others and themselves. Positive relationships also exhibited by CSis and NPi(s) values to other measures. Biplot analysis exhibited cluster of Si6 , Si3 , CV, NPi(2), NPi(3), NPi(4) and CSi7. Larger cluster comprised of NPi(1) CCV, CSD Si1, Si2, Si4, Si 5, Si7 , CSi 1, CSi2, CSi 3, CSi 4, CSi 5, CSi6 measures. Based on BLUE’s of genotypes yield, measures Sis found G3, G7, G4 as the stable genotypes, however G1, G2 would express unstable performance. CSis identified G7, G3, G6 as opposed to G3, G5, G7 genotypes as by values NPi(s). Positive correlations exhibited by Sisexcept of negative with CMR, CMed, Z1 and Z2 values. Ranks of genotypes as per values of CSis and NPi(s) measures expressed direct relationship with most of the measures. Biplot analysis observed large cluster comprised of CCV, CSD, NPi(1), Si1, Si2, Si4,CSi1, CSi2, CSi 3, CSi4, CSi5, CSi6, CSi7 measures. Second year of study (2017-18) as per BLUP’s seen, Sis settled for G6, G5, G3 genotypes. While NPi(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, Si3, Si6 ,CMR,NPi(2), NPi(3), NPi(4). Biplot analysis as per first two significant components (accounted for 88.7 %) marked larger cluster contains CSis with NPi(1), Si1, Si2 Si4, Si5 Si7 ,SD, CSD measures. Sis rank based measures as per BLUE’s of genotypes pointed towards G5,G4, G6, G1 whereas G6, G5, G1,G3 by CSis values. Wheat genotypes G1,G2, G3,G5 settled by least values of NPi(s) . Direct relationships portraited by Sis, CSis and NPi(s) with others. Larger cluster grouped NPi(s), CV, CCV, Z1, Z2, Yield, GAI, CSi5, CSi6 measures.Keywords
Blup, Blue, Si(s), CSi (s), NPi(s), Co-efficient Of Concordance, Biplot Analysis.References
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Affiliations
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
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Current Science, Vol 120, No 2 (2021), Pagination: 262-263Abstract
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- The Re-Emerging Karnal Bunt Disease of Wheat and Preparedness of The Global Wheat Sector
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1 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal 132 001, IN
Source
Current Science, Vol 120, No 12 (2021), Pagination: 1814-1817Abstract
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