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Analysis of genetic divergence, principal component, correlation and path coefficient for quantitative traits of Gerbera (Gerbera jamesonii) in the north eastern region, India

Published online by Cambridge University Press:  08 September 2023

Heiplanmi Rymbai Open the ORCID record for Heiplanmi Rymbai [Opens in a new window] ,
V.K. Verma Open the ORCID record for V.K. Verma [Opens in a new window] ,
J. Mawleiñ  and
S. Hazarika
Heiplanmi Rymbai*
Affiliation:
ICAR Research Complex for NEH Region, Umiam, Meghalaya, India – 793103
V.K. Verma
Affiliation:
ICAR Research Complex for NEH Region, Umiam, Meghalaya, India – 793103
J. Mawleiñ
Affiliation:
ICAR Research Complex for NEH Region, Umiam, Meghalaya, India – 793103
S. Hazarika
Affiliation:
ICAR Research Complex for NEH Region, Umiam, Meghalaya, India – 793103
*
Corresponding author: Heiplanmi Rymbai; Email: rymbaihort@gmail.com
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Abstract

Gerbera is an important commercial flower crop in the world. Augmenting the genetic diversity is desirable for further improvement in Gerbera breeding programme. Thirty-seven (37) Gerbera accessions were evaluated and analysed for genetic variability, heritability, correlation and path coefficient analysis using yield and quality traits. Results showed that phenotypic coefficients were higher than genotypic coefficients of variation for all the characters. All the traits, except stalk and flower diameter, had high heritability (>60%) and genetic advance (>20%). Based on cluster analysis, the 37 accessions were grouped into seven clusters. Cluster I consists of the maximum number of genotypes (22), while clusters IV–VII are monogenotypic with unique traits. Cluster analysis (D2) showed wider genetic diversity within and between the clusters. The first four principal components (Eigenvalue >1) contributed 72.3% of the total variation. The number of leaves, number of flowers and flower stalk length contributed the most to genetic divergence. Flower yield showed a significant positive genotypic correlation with the number of leaves and suckers. The path analysis (genotypic) revealed that number of suckers, flower stalk diameter and flower stalk length contribute major positive direct effects on number of flowers. Therefore, the wide genetic variability existing in the germplasm may facilitate improvement in gerbera crops. Superior lines (RCGH-12, -22, -114, -117) with higher yield (>23 flowers/plant) and vase life (>5 days) could be promoted for commercial production under open field conditions. While RCGH 28 has a dwarf flower stalk that can be promoted for pots and decoration purposes.

Keywords

genetic divergenceGerberaopen conditions cultivationsuperior genotypesgenetic variation
Type
Research Article
Information
Plant Genetic Resources , Volume 21 , Issue 3 , June 2023 , pp. 229 - 236
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of National Institute of Agricultural Botany

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References

Burton, GW and Devane, EH (1953) Estimating heritability in tall fescue (Festuca arundinaceae) from replicated clonal material. Agronomy Journal 45, 478–481.10.2134/agronj1953.00021962004500100005xCrossRefGoogle Scholar
Dewey, DR and Lu, KH (1959) A correlation and path coefficient analysis of components of crested wheat grass and seed production. Agronomy Journal 51, 515–517.CrossRefGoogle Scholar
Ghimiray, TS and Sarkar, I (2015) Studies on genetic variability in gerbera (Gerbera jamesonii). International Journal of Bioresource Science 2, 81–83.CrossRefGoogle Scholar
Hanson, CH, Robinson, HF and Comstock, RE (1956) Biometrical studies on yield in segregating population of Korean lespedesa. Agronomy Journal 48, 268–272.10.2134/agronj1956.00021962004800060008xCrossRefGoogle Scholar
Johnson, HW, Robinson, HF and Comstock, RE (1955) Estimation of genotype and environmental variability in Soybean. Agronomy Journal 47, 314–318.10.2134/agronj1955.00021962004700070009xCrossRefGoogle Scholar
Kannan, M and Ramdas, S (1990) Variability and heritability studies in Gerbera (Gerbera jamesonii L.). Progressive Horticulture 22, 72–76.Google Scholar
Khalaj, MA, Kumar, SP and Roosta, HR (2014) Evaluation of nutrient uptake and flowering of Gerbera in response of various growing media. World Journal of Environmental Biosciences 8, 12–18.Google Scholar
Kolte, SL (2008) Studies on genetic diversity in gerbera (Gerbera jamesonii). M.Sc. Thesis submitted to the Mahatma Phule Krishi Vidyapeeth, Rahuri, Ahmednagar (M.S.).Google Scholar
Kumari, A, Patel, KS and Choudhary, M (2011) Genetic variability studies in Gerbera. Research in Plant Biology 1, 01–04.Google Scholar
Magar, SD, Warade, SD, Nalge, NA and Nimbalkar, CA (2010) Correlation and path analysis studies in Gerbera (Gerbera jamesonii). International Journal of Plant Sciences 5, 553–555.Google Scholar
Mahalanobis, PC (1928) A statistical study at Chinese head measurement. Journal of the Asiatic Society of Bengal 25, 301–377.Google Scholar
Nair, SA and Shiva, KN (2003) Genetic variability, correlation and path coefficient analysis in Gerbera. Journal of Ornamental Horticulture 6, 180–187.Google Scholar
NHB (2016) Horticultural Statistics at a Glance 2016. Available at www.agricoop.nic.in.Google Scholar
Panse, VG and Sukhatme, PV (1967) Statistical Methods for Agricultural Workers. New Delhi: Indian Council of Agricultural Research, pp. 152–154.Google Scholar
Rao, CR (1952) Advanced Statistical Methods in Biometrical Research. New York: John Wiley and Sons, p. 121.Google Scholar
Rymbai, H, Jha, AK, Ngachan, SV, Shimray, W and Kumar, R. (2015) Blooms of Gerbera for year-round prosperity. New Age Protected Cultivation 1, 18–20. doi: 10.13140/RG.2.1.1723.7601Google Scholar
Rymbai, H, Jha, AK, Talang, HD, Assumi, SR, Deshmukh, NA and Roy, AR (2017) Evaluation of new Gerbera (Gerbera jamesonii Bolus) genotypes under fan and pad polyhouse. Journal of Ornamental Horticulture 20, 126–131. doi: 10.5958/2249-880X.2017.00006.8CrossRefGoogle Scholar
Rymbai, H, Jha, AK and Talang, HD (2021) Variability among genotypes of Gerbera in the north eastern region of India. Indian Journal of Horticulture 78, 179–186. doi: 10.5958/0974-0112.2021.00026.8CrossRefGoogle Scholar
Singh, S, Prakash, A, Chakraborty, NR, Wheeler, C, Agarwal, PK and Ghosh, A (2016) Trait selection by path and principal component analysis in _Jatropha curcas for enhanced oil yield. Industrial Crops and Products 86, 173–179.10.1016/j.indcrop.2016.03.047CrossRefGoogle Scholar
Sravan, T, Rangare, N, Suresh, B and Kumar, S (2012) Genetic variability and character association in rainfed upland rice (Oryza sativa L.). Journal of Rice Research 5, 24–29.Google Scholar
Titumeer, SM, Rahim, MA and Zeba, N (2014) Genetic variability, character association and genetic divergence in mungbean (Vigna Radiate L. WILCZEK). Agronomski glasnik: Glasilo Hrvatskog agronomskog društva 76, 305–326.Google Scholar
Tiwari, DN, Tripathi, SR, Tripathi, MP, Khatri, N and Bastola, BR (2019) Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Advances in Agriculture, 2019, 1–9. doi: 10.1155/2019/5975901CrossRefGoogle Scholar
van der Kooi, CJ, Elzenga, JTM, Staal, M and Stavenga, DG (2016) How to colour a flower: on the optical principles of flower coloration. Proceedings of the Royal Society B: Biological Sciences 283, 9.Google ScholarPubMed
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