Genetic variability, association and multivariate analysis for yield parameters in cold tolerant rice (Oryza sativa L.) genotypes

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Research Articles | Published:

Print ISSN : 0970-4078.
Online ISSN : 2229-4473.
Website:www.vegetosindia.org
Pub Email: contact@vegetosindia.org
Doi: 10.1007/s42535-022-00501-4
First Page: 1465
Last Page: 1474
Views: 1373


Keywords: Rice, Heritability, Grain yield, Cluster, PCA


Abstract


Genetic variability in germplasm lines imparts basic knowledge on the genetic properties of a population upon which principle breeding methods are constituted for further crop improvement. The relative contribution of variability pertaining to crop species also aids in handling selected donors with high yielding capacity. In this study, 38 cold tolerant rice genotypes were evaluated for four seasons (Kharif, mid-Kharif, late-Kharif and Rabi) during the year 2020–21 in a Randomized Block Design at College farm, PJTSAU, Rajendranagar, Hyderabad to study the genetic variability, association, path analysis and genetic diversity studies for yield and its related traits. ANOVA revealed significant differences for all the traits with high genotypic and phenotypic coefficient of variance for panicle exertion (27.79%, 42.31%) and grain yield per plant (23.11%, 33.87%). High heritability along with high genetic advance as per cent of mean was noticed for plant height (83%, 32.94%), along with high variability as depicted by box plots and heatmap suggesting a direct selection for this trait. The correlation and path analysis identified plant height (0.411, 0.113), days to 50% flowering (0.233, 0.038), panicle length (0.404, 0.173), tillers per plant (0.278, 0.230), spikelet fertility (0.346, 0.203), filled grains per panicle (0.505, 0.304) and test weight (0.032, 0.012) to be having a direct positive effect on grain yield, indicating an overall enhancement in grain yield with the selection of these traits. Among the four seasons evaluated, mid-Kharif was observed to be congenial for improving of most of the traits including grain yield. Since clustering pattern coupled with genetic diversity analysis gives a scope for developing diverse genetic base, D2 analysis along with Principal component analysis were performed which identified diverse genotypes like K116, K429, HPR-2336 and VL DHAN with high cluster means for grain yield per plant (19.37 g) that might be utilized as good combiners in future breeding programmes.


Rice, Heritability, Grain yield, Cluster, PCA


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References


Abebe T, Alamerew S, Tulu L (2017) Genetic variability, heritability and genetic advance for yield and its related traits in rainfed lowland rice (Oryza sativa L.) genotypes at Fogera and Pawe, Ethiopia. Adv Crop Sci Technol 5:272–274


Ahmad SQ, Khan S, Ghaffar M, Ahmad F (2011) Genetic diversity analysis for yield and other parameters in maize (Zea mays L.) genotypes. Asian J Agric Sci 3(5):385–388


Akinwale MG, Glenn N, Francis ABO, Ayoni AO (2011) Heritability and correlation coefficient analysis for yield and its components in rice (Oryza sativa L.). GC Space 5:207–212


Allard RW (1960) Principles of plant breeding. John Wiley and Sons Inc., New York, pp 22–23


Aswathi S, Lal JP (2014) Investigating phenotypic correlation and path analysis in rice (Oryza sativa L.) under irrigated and rain-fed conditions. Int J Sci Foot Prints 2(5):22–33


Basavaraja T, Asif M, Mallikarjun S, Gangaprasad S (2013) Correlation and path analysis of yield and yield attributes in local rice cultivars (Oryza sativa L.). Asian J Bio Sci 8(1):36–38


Burton GW (1952) Quantitative inheritance in grasses. In: Proceedings of International Council of Agricultural Research Publication, pp 87–89


Dewey DR, Lu HK (1959) A correlation and path coefficient analysis of components of creasted wheat grass and seed production. Agron J 51:515–518


Elgamal WH, Elsayed M, ElShamey EA, Anis G (2018) Genetic diversity for cold tolerance at seedling stage in rice (Oryza sativa L.) under Egyptian conditions. J Sustain Agric Sci 44(2):101–113


Fahad S, Hussain S, Bano A, Saud S, Hassan S, Shan D, Khan FA, Khan F, Chen Y, Wu C, Tabassum MA (2015) Potential role of phytohormones and plant growth-promoting rhizo-bacteria in abiotic stresses: consequences for changing environment. Environ Sci Pollut Res 22(7):4907–4921


Falconer DS (1981) Introduction to quantitative genetics. Longmans Green, London, pp 33–34


Fisher RA, Immer FR, Tedin O (1932) The genetical interpretation of statistics of the third degree in the study of quantitative inheritance. Genetics 17:107–124


Garcia-Oliveira AL, Tan L, Fu Y, Sun C (2009) Genetic identification of quantitative trait loci for contents of mineral nutrients in rice grain. J Integr Plant Biol 51(1):84–92


Hammer O, Harper DAT, Ryan PD (2001) Paleontological statistics software: package for education and data analysis. Palaeontol Electron 4(1):1–9


INDOSTAT version 8.1. (2022). https://indostat.software.informer.com/


Jamal K, Ifftikhar B, Abdul K, Sajid ZI (2009) Genetic variation for yield and yield components in rice. Agric Sci 4(6):60–64


Jeffers JN (1967) Two case studies in the application of principal component analysis. J R Stat Soc 16(3):225–236


Kampe AK, Tassew AA, Gezmu AT (2018) Estimation of phenotypic and genotypic correlation and path coefficients in rainfed upland rice (Oryza sativa L.) genotypes at Guraferda, Southwest Ethiopia. J Rice Res 6:195


Kavya ME, Dushyanthakumar BM, Madhuri R, Gangaprasad S, Mallikarjuna HB, Sudharani N, Dhananjaya BC (2020) An appraisal of genetic variability and diversity among traditional red rice landraces. Int J Ecol Environ Sci 2(4):177–181


Khatun M, Hanafi M, Yusop M, Wong Y, Faezah M, Jannatul F (2015) Genetic variation, heritability, and diversity analysis of upland rice (Oryza sativa L.) genotypes based on quantitative traits. Biomed Res Int 15:7–14


Lakshmi VGI, Sreedhar M, Vanisri S, Anantha MS, Subba Rao LV, Gireesh C (2019) Multivariate analysis and selection criteria for identification of African rice (Oryza glaberrima) for genetic improvement of indica rice cultivars. Plant Genet Resour 17:1–7


Lilly SM, Sassikumar D, Suresh R (2018) Variability and heritability analysis for yield and grain quality attributes in F2 intervarietal populations of rice. Int J Curr Microbiol App Sci 7(7):4329–4338


Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci India 2:49–55


Mamata K, Rajanna MP, Savitha SK (2018) Assessment of genetic parameters for yield and related traits in F2 populations involving traditional varieties of rice (Oryza sativa L.). Int J Curr Microbiol Appl Sci 7(1):2210–2217


Panse VG, Sukhatme PV (1985) Statistical methods for agriculture workers. Indian council of Agricultural research Publication, New Delhi, pp 87–89


Pooniya V, Choudhary AK, Bana RS, Pankaj, S (2019) Zinc bio-fortification and kernel quality enhancement in elite basmati rice cultivars of South-Asia through legume residue-recycling and zinc fertilization. Indian J Agric Sci 89(2):279–287


Raghavendra P, Hittalmani S (2015) Genetic parameters of two BC2F1 populations for development of superior male sterile lines pertaining to morpho-floral traits for aerobic rice (Oryza sativa L.). SAARC J Agric 13(2):198–213


Rao N, Roja V, Satyanarayana RV, Srinivasa Rao V (2020) Genetic diversity and principal component analysis for yield and nutritional traits in rice (Oryza sativa L.). Int J Curr Microbiol Appl Sci 9(11):1916–1928


Robson DS (1956) Applications of the k 4 statistic to genetic variance component analyses. Biometrics 12(4):433–444


Sarangi D, Pradhan B, Sial P, Mishra CHP (2009) Genetic variability, correlation and path-coefficient analysis in early rice genotypes. Environ Ecol 27(1A):307–312


Shaikh S, Umate S, Syed AJ, Deosarkar DB (2017) Study on genetic variability, heritability and genetic advance in rice (Oryza sativa L.) genotypes. Int J Pure Appl Biosci 5(4):511–515


Sheela SKRV, Robin S, Manonmani S (2020) Principal component analysis for grain quality characters in rice germplasm. Electron J Plant Breed 11(1):127–131


Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft I, Zhao J, Meng J (2009) Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics 182(3):851–861


Shiva Prasad G, Sujatha M, Subba RLV, Chaithanya U (2013) Studies on variability, heritability and genetic advance for quantitative characters in rice (Oryza sativa L.). Ann Biol Res 4(6):372–375


Singh RK, Chaudhary BD (1985) Biometrical methods in quantitative genetic analysis. Kalyani Publishers, New Delhi, p 318


Tejaswini KLY, Ravi Kumar R, Mohammad AH, Raju SK (2018) Character association studies of F5 families in rice (Oryza sativa L.). Int J Agric Sci 10(4):5165–5169


Vanisri S, Laxmi VI, Wesly KC, Priyanka B, Sreedhar M, Rahul S (2020) Genetic variability and divergence studies on yield under delayed sowing conditions in rice (Oryza sativa L.). Curr J Appl Sci Technol 39(26):47–60


Zhang Q, Chen Q, Wang S, Hong Y, Wang Z (2014) Rice and cold stress: methods for its evaluation and summary of cold tolerance-related quantitative trait loci. Rice 7(1):24

 


Acknowledgements


The authors greatly acknowledge the support provided by the Institute of Biotechnology and PJTSAU, Hyderabad for providing the resources for conducting the experiment.


Author Information


Satturu Vanisri
Department of Molecular Biology and Biotechnology, Institute of Biotechnology, Jayashankar Telangana State Agricultural University, Hyderabad, India
submissionsvanisri@gmail.com
Lakshmi V. G. Ishwarya
Genetics and Plant Breeding, Jayashankar Telangana State Agricultural University, Hyderabad, India


Sreedhar M.
Genetics and Plant Breeding, Jayashankar Telangana State Agricultural University, Hyderabad, India