DNA FINGERPRINTING OF RICE (Oryza sativa L.) GENOTYPES ALONG WITH ALLELIC VARIATIONS

Transcription

1 DNA FINGERPRINTING OF RICE (Oryza sativa L.) GENOTYPES ALONG WITH ALLELIC VARIATIONS BASED ON sd1 GENE M. Sc. (Ag.) Thesis by RICHA SAO DEPARTMENT OF GENETICS AND PLANT BREEDING COLLEGE OF AGRICULTURE INDIRA GANDHI KRISHI VISHWAVIDYALAYA RAIPUR (CHHATTISGARH) 2017

2 DNA FINGERPRINTING OF RICE (Oryza sativa L.) GENOTYPES ALONG WITH ALLELIC VARIATIONS BASED ON sd1 GENE Thesis Submitted to the Indira Gandhi Krishi Vishwavidyalaya, Raipur By RICHA SAO IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science in Agriculture (Genetics and Plant Breeding) Roll No ID No JUNE, 2017

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5 ACKNOWLEDGEMENT I would like to take this opportunity to first and foremost thank God for being my strength and guide in the writing of this thesis. Without whom, I would not have had the wisdom or the physical ability to do so. I take immense pleasure to express my sincere and deep sense of gratitude to my major advisor Dr. Ritu R. Saxena, Senior Scientist, Department of Genetics and Plant Breeding, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), for her sustained enthusiasm, creative suggestions, motivation and exemplary guidance throughout the course of my master research. She has gone beyond the call of a thesis advisor to assume the role of an academic mother, apart from the subject of my research, I learnt a lot from her, which I am sure will be useful in different stages of my life. I solemnly submit my honest and humble thanks to her for bringing my dreams into reality. I emphatically and gratefully acknowledge extend my loyal and venerable thanks to members of my Advisory Committee, Dr. S.B. Verulkar, Professor and Head, Department of Plant Molecular Biology and Biotechnology, Dr. A.K. Kotasthane, Professor and Head, Department of Plant Pathology, Dr. N. Mehta, Principal Scientist (Linseed), Department of Genetics and Plant Breeding, Dr. R.R. Saxena, Professor (ADR), Department of Agriculture Statistics and Social Science, College of Agriculture, IGKV, Raipur. They were always ready to provide valuable guidance, regular encouragement and timely advice whenever required for enriching with productive scientific discussion, during the most trying times in the tenure of this research work. I wish to record my grateful thanks to Dr. S. K. Patil, Hon ble Vice Chancellor, Shri S. R. Verma, Registrar, Dr. S. S. Rao, Director Research Services, Dr. S. S. Shaw, Director of Instructions and Dr. O. P. Kashyap, Dean, College of Agriculture, IGKV, Raipur for providing necessary facilities technical and administrative supports for conductance of this research work. I am immensely thankful to Dr. P. K. Chandrakar, Dr. N.K. Motiramani, Dr. R. N. Sharma, Dr. H. C. Nanda, Dr. Nandan Mehta, Dr. P. K. Joshi, Dr. N. K. Rastogi, Dr. Rajeev Shrivastava, Dr. Sandeep Bhandarkar, Dr. S. K. Nair, Shri P. L. Johnson, Dr. G. R. Sahu, Dr. Ravindra K. Verma, Dr.(Smt.) Alice Tirkey, Shri Sunil K. Nag, Smt. Mangla Parikh, Dr. iii

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7 TABLE OF CONTENTS Chapter Title Page ACKNOWLEDGEMENT iii-iv TABLE OF CONTENTS V LIST OF TABLES vi-vii LIST OF FIGURES Viii LIST OF NOTATIONS Ix LIST OF ABBREVIATIONS X ABSTRACT xi-xiv I INTRODUCTION 1-4 II REVIEW OF LITERATURE Agro-morphological characterization Genetic variability Association analysis Correlation coefficient analysis Principal component and cluster analysis Quality parameter Molecular characterization MATERIALS AND METHODS III 3.1 Experimental site Climate and weather Experimental materials and methods Observations recorded Molecular Study Statistical analysis RESULTS AND DISCUSSION IV 4.1 Agro-morphological and quality characterization Estimation of genetic variance Analysis of variance Mean performance and variability parameters of different characters Phenotypic and Genotypic coefficient of variation Heritability and genetic advance as percent of mean Association analysis Correlation coefficient Principal Component Analysis Cluster Analysis Molecular characterization Development of genotypic data based on SSR Markers SSR marker analysis a Similarity coefficient analysis and Clustering b Polymorphism Information Content of SSR Markers c Single Marker Analysis SUMMARY AND CONCLUSIONS REFERENCES V APPENDICES RESUME 175 v

8 LIST OF TABLES Table Title Page 3.1 List of materials used in the study Scale for Amylose test Alkali spreading value classification along with GT Numerical scale for scoring Alkali spreading value PCR mix for one reaction Temperature profile used for PCR amplification using micro 53 satellite Markers 3.7 Skeleton of analysis of variance Frequency distribution of agro-morphological traits based on DUS 4.2 Analysis of variance of 27 yield and quality traits of 47 rice germplasm accessions 4.3 Mean and Variability parameters for 27 yield and quality traits 4.3.a List of germplasm categorized into early, medium and late days to flowering b 4.3.c 4.3.d 4.3.e List of germplasm categorized into dwarf, semi-dwarf, semi-tall and tall stature of plant height List of germplasm categorized into very short, short, medium and long panicle length List of germplasm categorized into soft, medium and hard gel consistency List of germplasm categorized into low, low-intermediate, intermediate and high amylose content Genetic parameters of 27 yield and quality traits of 47 rice 95 germplasm accessions 4.5 Association analysis (phenotypic and genotypic) of 27 yield 102 and quality traits of 47 rice germplasm accessions 4.6 Summarized data representing the correlation of different traits 106 with grain yield and HRR at genotypic level 4.7 Eigen values of 27 yield and quality traits of 47 rice germplasm accessions Eigen vectors of 47 rice genotypes for yield and quality characters List of selected accessions in each principal component on the 114 basis of top 10 PC Scores 4.10 Principal component score of 47 rice germplasm accessions Clustering pattern of 47 rice genotypes Estimates of intra (Diagonal and bold) and inter cluster 119 distances among eight clusters vi

9 4.13 Percent contribution of each character towards divergence Cluster mean for quantitave characters in 47 rice accessions List of 69 microsatellite markers with their chromosome locations, number of alleles, Allele size and PIC value found among 47 rice accessions 4.16 Characterization of dwarf, semi-dwarf, semi-tall and tall accessions at sd1 locus with SD1E1, SD1E2 and SD1E3 exons sites along with SSR markers near sd1 gene The t-test table from single marker analysis for yield contributing traits. 134 vii

10 LIST OF FIGURES Figure Title Page 3.1 Meteorological data recorded during crop growth season (28 June to November, 2016) 4.1 Frequency distribution of 27 polymorphic DUS traits Coleoptile colour Basal of sheath Colour Leaf :intensity of green colour Leaf auricle Leaf: anthocyanin colouration of auricle Leaf: collar Leaf: ligule Colour of ligule Attitude of flag leaf Lemma : anthocyanin coloration of keel Lemma: anthocyanin colouration of below apex Colour of stigma Panicle: curvature of main axis Lemma and palea colour Panicle : awn Panicle: colour of awn Panicle : Secondary branching Panicle exertion Grain phenol reaction Alkali spreading value test Gel consistency Graph representing significant correlation between grain yield, 105 HRR with other traits 4.24 Screen plot showing eigen value and percentage of cumulative 112 Variability 4.25 Distribution of genotypes among two different principal component Dendogram of eight clusters based on Eucledian distance An UPGMA cluster dendogram showing the genetic relationships Graphical among 48 representation long and short of grain PIC accessions value of SSR of rice markers based on the alleles Gel detected picture by 59 of SSR PCR marker amplification of 48 rice accessions with SSR primers RM 431, RM 3825, RM 12091, RM 212, RM 315 and RM viii

11 LIST OF NOTATIONS/SYMBOLS % Per Cent C Degree Celsius μl Micro Litre bp Base Pairs cm Centimeter d.f. Degree of Freedom et al. and others g Gram H2O Water ha Hectare HCl Hydrochloric Acid i.e. that is KCl Potassium Chloride m Meter M Molar MgCl 2 Magnesium Chloride min Minutes ml Milliliter NaCl Sodium Chloride ng Nanogram rpm Rotations per Minute U Units ix

12 LIST OF ABBREVIATIONS BYPP DTF datp dctp dgtp DNA dntps dttp EDTA EtOH EtBr EI ET FLL FLW GB GL GYPP HI H % HRR % M % NOS PCV GCV PCA PC PCR PH PL SSR TBE Biological Yield Per Plant Days To 50 % Flowering deoxy adenosine 5 triphosphate deoxy cytidine 5 triphosphate deoxy guanosine 5 triphosphate Deoxyribo Nucleic Acid deoxynucleotide triphosphates deoxy thymidine 5 triphosphate Ethylene Diamine Tetra Acetic Acid Ethanol Ethidium Bromide Elongation Index Effective Tillers Per Plant Flag Leaf Length Flag Leaf Width Grain Breadth Grain Length Grain Yield Per Plant Harvest Index Hulling Per Cent Head Rice Recovery Per Cent Milling Per Cent Number of Spikelet Per Panicle Phenotypic Coefficient of Variation Genotypic Coefficient of Variation Principal Component Analysis Principal Component Polymerase Chain Reaction Plant Height Panicle Length Single Sequence Repeats Tris Boric Acid EDTA Buffer x

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14 Raipur (C.G.) in randomized block design (RBD) during Kharif The data was statistically analyzed to calculate various descriptive statistics and to perform Correlation analysis, principal component analysis (PCA) and the un- weighted variable pair group method of the average linkage cluster analysis (UPGMA) between 27 yield and other yield attributing traits. All considered morphological and quality descriptors showed remarkable differences in their distribution and amount of variations within them. The analysis of variance indicated existence of considerable amount of variability for all observed characters. The high amount of genotypic and phenotypic coefficient of variation with high heritability and genetic advance as percentage of mean was observed for 100 seed weight, number of filled grains per panicle, total spikelets per panicle, number of unfilled grains per panicle and grain weight per plant, length and width of decorticated and milled grain and gel consistency. The result of correlation revealed that the traits such as panicle length, number of panicle, number of filled grains per panicle, total spikelet per plant, spikelet fertility, shoot dry weight, harvest index, decorticated grain length, decorticated grain width, plant height, HRR, hulling% and milled grain length are positively correlated with grain yield. PCA showed the contribution of each character to the classification of the rice accessions. First 8 components explains the 80.01% of the total variation and eigen value >1 for 27 characters. Jaya is the best accession for both quality and yield attributing traits followed by MTU 1010, IGKVR 1, TN 1 and Safri 17. These identified accessions may be used as donor to improve the yield and quality traits in varietal development programme. Eight cluster groups of 47 rice genotypes were obtained from the 27 yield and quality characters using multivariate analysis. The pattern of constellation proved the existence of significant amount of variability. Cluster III and cluster IV constituted of 8 accessions each, forming the largest clusters. Since, the inter cluster distance between clusters is quite large therefore, genotypes under them can be use to obtain higher variability and heterotic effects. A total of 69 SSR markers (primers) of rice situated on chromosome one xii

15 and the number of alleles per locus generated by each marker ranged from 1 to 8 alleles with an average number of 2.56 alleles per locus. Forty-seven accessions were grouped into two major clusters having 19 and 28 genotypes in SSR analysis. Jaccard s similarity coefficient ranged from 0.04 to 0.75 with highest similarity coefficient between Dhaur and Badalphool (0.75) and lowest between Dhaur and Machhari Ankhi as revealed by UPGMA cluster analysis of SSR marker. Single marker analysis of all the 47 genotypes for nine markers (all present in chromosome one) was done for 27 traits including plant height revealed that the Marker RM 212 was observed to be linked with many traits such as Plant height, stem length (excluding panicle), number of filled grain per panicle, number of unfilled grain per panicle, total spikelet per panicle, spikelet fertility, Panicle weight per plant, Shoot dry weight per plant, Harvest index, Length of paddy and HRR. Marker RM was found to be linked with HRR, hulling%, milled grain length, milled grain width and length of paddy. Marker analysis of 9 SSR markers, including markers of sd1 gene, shows that none of the markers among these were linked with dwarfism stature of plant. The results show that there might be some other gene held responsible for dwarfness in three dwarf accessions (Dhaur, JS-5 and Badalphool) instead of the expression of sd1 gene. xiii

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17 DUS DNA ANOVA III IV xv

18 SSR SSR RM HRR JS-5 xvi

19 CHAPTER- I INTRODUCTION Rice (Oryza sativa L.) is the central to the lives of billions of people around the world. Rice (2n=24) belonging to the family, Poaceae and subfamily, Oryzoidea is the world s most important cereal crop and serves as the primary source of staple food for more than half of the global population. It is one of the very few crop species endowed with rich genetic diversity which account over 100,000 landraces and improved cultivars. Agro-morphological characterization of germplasm variety is fundamental in order to provide information for plant breeding programmes (Lin, 1991). Growing rice is largest single use of land producing food covering 9 percent of earth arable land. Rice provides 21% per capita energy and 15 percent per capita protein (Shrivastava, 2015). Rice occupies a pivotal place in Indian agriculture and it contributes to around 17 percent of annual GDP and provides 43 percent calorie requirement for more than70 percent of the Indians. India ranks 1 st in area (43.95 million hectare) and 2 nd in production (103.61milliontonnes) after China (2 nd advance estimate, , Department of Agriculture, Cooperation and Farmers Welfare, Ministry of Agriculture, GOI). Chhattisgarh state is eminent by the name Rice Bowl of India because maximum area is covered under rice cultivation. The rich biodiversity of rice in Chhattisgarh is the evidence of this fact. During Kharif, Chhattisgarh state covers maximum area under rice crop and contributes major share in national rice production. The total estimated area of Rice in C.G. is 3.76 million ha, production is 7.71 million tones and productivity is 2050 kg/ ha, in (Krishi Darshika, 2016). Rice, being the staple food for more than70 percent of the population and the source of livelihood for million rural households, is the backbone of the Indian agriculture. In the 1960s, the rapid expansion of the world population and dramatic decrease in cultivated lands raised concern that food production would not meet the global demand, leading to a global food crisis (Asano, 2007). However, the development and widespread adoption of high-yielding semi-dwarf varieties 1

20 2 of wheat and rice led to major increase in food and consequently large-scale famine was averted. This remarkable revolution achievement was referred as green revolution. A major factor for the success of the green revolution was the introduction of high-yielding semi-dwarf varieties in combination with application of large amounts of nitrogen fertilizer. Nitrogen fertilization is essential for the increase in grain yield, but it also promotes stem and leaf elongation, resulting in an overall increase in plant height. Under high nitrogen application most conventional varieties of wheat and rice grow excessively tall and are easily flattened by wind and rain resulting in significant yield loss. By contrast, the semi-dwarf varieties respond to fertilizer inputs to produce an increased yield because their short stature confers lodging resistance even under high nitrogen application (Asano, 2007). The rice production in India has augmented significantly in the last 20 years largely because of availability of improved varieties and better crop management. Tropical Asia accounting for over 90 percent of production and consumption of rice has been growing tall statured, lodging prone varieties of very low yield until the advent of the non-lodging high yielding semi-dwarf varieties in late sixties. The short statured varieties developed using Dee-Gee- Woo-Gen (DGWG), have enabled many countries to achieve self-sufficiency in rice in a short span of 15 years. Initial attempts to study the genetics of semidwarfism using crosses of traditional tall with semi-dwarf varieties indicated that it was controlled by a single recessive gene designated as sd1 (Cho et al., 1994). The rice semidwarf-1 (sd1) gene is well known as the green revolution gene. The success of DGWG gene based varieties such as IR8 and Taichung Native 1 has made breeders allover to depend excessively on these two rice cultivars for source of short stature. With over 90 percent of the high yielding varieties in cultivation today having DGWG gene (Ashikari and Matsuoka, 2002; Spielmeyer et al., 2002; Cho et al., 1994), the genetic base, as result is quite narrow. Apprehending, high genetic similarity might render the crop genetically vulnerable to biotic or abiotic stresses, many efforts have been made

21 3 for broadening the genetic base through identification and use of alternate sources of dwarfing genes (Reddy and Padma, 1979; Singh et al., 1979; Neeraja et al., 2008). In rice, as many as 61 dwarfing genes designated as d1 to d61 have been identified (Cho et al. 1994; Ashikari and Matsuoka, 2002). Even though many different dwarf accessions of mutant and spontaneous origin have been tried as alternate sources for developing semi-dwarf varieties, none other than sd1 locus of DGWG source proved to be of practical value. Different alleles of this locus have been used by scientists of Asia and America (Asano et al. 2007). Dwarfing genes are often associated with agronomically undesirable traits which restrict their use in rice breeding program. Also, genetic barriers between indica and japonica cultivars restrict transfer of genes. Hence, there is an urgent need to discover allelic variants of sd1 gene or finding non-allelic semidwarfing genes in indica background of rice. Utilization of molecular markers has greatly facilitated the investigation of the genetic basis of complex quantitative traits. Molecular marker technique has proved valuable in identification of loci involved in quantitative traits related to grain yield and has provided insight in to its complex relationship with associated factors and their underlying genes are now far more accessible. Characterization is the most basic and important step in the process of evaluation and cataloguing of germplasm. It is essential for its evaluation, judicious use and protection against illegal utilization (Shrivastava, 2015). Generally, germplasm accessions are evaluated for morphological, physiological and biochemical, plant pathological, entomological and other features. Characterization of several agro morphological traits is helpful to develop distinctiveness among the genotypes. Qualitative characters are considered as morphological markers in the identification of landraces of rice, because they are less influenced by environmental changes (Raut, 2003). The characters assessed must be related to the need of the breeders for its proper utilization in breeding programme. Agro-morphological characterization gives the mark of identification which distinguishes one genotype from other. Many studies on genetic diversity using agro-morphological characterization have been conducted and it led to

22 4 identification of phenotypic variability in rice (Ogunbayo et al., 2005; Bajracharya et al., 2006 and Barry et al., 2007). Traditionally, morphological traits are used to determine genetic diversity and classify germplasm. Characterization and evaluation of diversity among traditional varieties will provide plant breeders the information necessary to identify initial materials for hybridization to produce varieties with in proved productivity and quality (Thilang et al., 2014). So, collection and characterization of this germplasm is not only important for utilizing the appropriate attribute based donors inbreeding programmes, but is also essential in the present era for protecting the unique rice. Several researchers reported the use of agro-morphological markers in the study of characterization of rice germplasm diversity. DUS based morphological characterization with molecular marker based genotyping of existing dwarf germplasm lines of rice can be a good option to identify the new allelic variants of sd1 Molecular markers provide a powerful tool for locating and distinguishing key genetic regions on the whole genome and hence differentiating genotypes from each other. Realizing the importance of broadening the genetic base for plant height, the present study was initiated to identify alternative dwarfing gene source(s) among a collection of induced and spontaneous mutants using molecular markers linked to sd1 locus that can distinguish DGWG allele type from the other alleles. Keeping in view the above facts, the present investigation entitled DNA fingerprinting of rice (Oryza sativa L.) genotypes along with allelic variations based on sd1 Gene. Has been planned and was carried out at the Research cum Instructional farm, College of Agriculture, IGKV, Raipur (C.G), Department of Genetics and Plant Breeding and R. H. Richhariya research laboratory, College of Agriculture, IGKV, Raipur (C.G.) during Kharif, 2015 with the following objectives: 1. DUS (Distinctiveness, Uniformity and Stability) based characterization for yield and grain quality attributing traits. 2. SSR based DNA fingerprinting of rice genotypes. 3. Allelic variations for sd1 gene based on SSR markers of sd1 region.

23 CHAPTER- II REVIEW OF LITERATURE Genetic variability, nature and magnitude of genetic diversity, present in the available breeding materials are the key resource of a breeding program. Those criteria create the opportunities for a successful breeding program by the association of different traits both at physio - morphological and molecular levels. People in different areas of the world prefer different types of rice for general consumption. Grain quality characteristics of rice are related to a complexity of physicochemical properties viz., dimension, shape and weight, fragmentation, hardness, milling properties, chemical composition of the endosperm, aroma and nutritional factors. Grain length and shape determine appearance in rice, and affect milling, cooking and eating quality and are therefore, important traits in rice breeding. On that point of view, this study was conducted for characterization of forty eight landraces of rice of Chhattisgarh using agro-morphological and molecular parameters to provide useful information to facilitate the choice of breeders for rice plant breeding programme. In this chapter, an attempt has been taken to review the relevant literatures, which focuses the basic features of rice plant and associated genetic variability, nature and magnitude of genetic divergence, association among different traits, evaluation of field performance and diversity analysis through SSR markers in rice under the following subheads: 2.1 Agro-morphological characterization 2.2 Genetic variability 2.3 Correlation coefficient analysis 2.4 Principal component and cluster analysis 2.5 Quality parameter 2.6 Molecular characterization 5

24 6 2.1 Agro-morphological characterization Molecular characterization of the genotypes gives precise information about the extent of genetic diversity which helps in the development of an appropriate breeding program. It is also very important for germplasm management, varietal identification and DNA fingerprinting. A brief reviews has been summarized below: Caldo et al. (1996) worked on parental diversity analysis of 81 ancestral lines of Phillipine modern rice varieties and their contribution to the variation of cultivated descendants. Subba Rao et al. (2001) reported that genetic diversity probably serves as an insurance against crop failure. Hien et al. (2007) studied Genetic diversity of morphological responses and the relationships among Asia aromatic rice (Oryza sativa L.) cultivars. Characterization for 22 morphological characters with 101 morpho-metric descriptors was carried out. Most traits were polymorphic except to ligule color. Grain size, grain shape, culm strength, plant height and secondary branching contributed the highest mean diversity indices. Rajna et al. (2011) characterized two Indian bred public hybrids such as K KMR-3R, IR-68897A, IR-68897B, and DR R based on the seed, seedling, and plant morphological traits play an important role to distinguish between crop varieties, Out of 38 qualitative and quantitative morphological traits (as per the National DUS Test Guidelines) observed, flag leaf attitude, flag leaf length and width, days to 50% flowering and maturity, degree of panicle exertion, presence of awns, panicle secondary branching, days to maturity, leaf senescence and seed traits such as seed weight, grain length and width and shape of grain were found to be more useful for grouping of genotypes. The characters such as leaf length (varied from cm (KRH-2) to cm (IR-68897A), days to 50% flowering (from 73 days (IR-68897B) to 102 days (KMR-3R), panicle secondary branching (from weak to clustered), days to maturity from 112 days (IR-68897B) to 134 days (IR-58025B)), seed weight from (IR58024A) to g (IR-68897B)), and decorticated grain shape seed

25 7 weight from (IR58024A) to g (IR-68897B) and decorticated grain shape (from semi-spherical to elongated) exhibited more variation among the parents and hybrids. Sarawgi et al. (2011) characterized seven hundred eighty-two rice germplasm accessions on the basis of twenty-nine morphological and eight agronomical traits. Most of the morphological characters showed variation in different accessions except leaf: collar, leaf: ligule and leaf: shape of ligule. A significant amount of variation was displayed for most of the agronomical traits examined. Dixit et al. (2012) characterized one hundred five accessions of traditional rice landraces collected from tribal areas of Santhal Parganas, Jharkhand, India for 11 qualitative and 13 quantitative traits. He observed high degree of variation in agro morphological traits of rice accessions with a diversity index ranging from.236 to.951. the coefficient f variation was more than 10% for most of the characters, highest being total number of spikelet per panicle (39.75 %). Parikh et al. (2012) evaluated physio-chemical characters and cooking quality of 36 rice genotypes and reported that the fine grain genotypes like Rajim-12, Kalimuchh, and Munibhog were good for moderate kernel length and L:B ratio; Rajabhog, Jhulari, and Baghmuchha for kernel length after cooking and L:B ratio of cooked rice Kalajira and Bikoni for head rice recovery (%); Barang, Bantaphool, Gangabalu, and Bikoni for elongation ratio; Barang, Rajabhog, Gangabalu, Bikoni, and Chirainikhi for elongation index; Sonth, Rajim-12, Jhulari, Gangabalu, Jhilli Safri, and Bikoni for intermediate alkali values. These genotypes may be utilized as donors for improvement of quality traits. Sarawgi et al. (2012) characterized 46 aromatic rice accessions of Dubraj group from Chhattisgarh and Madhya Pradesh for twenty morphological, six agronomical and eight quality characters. The specific accessions D: 1137, D: 812, D: 950, D: 959, D: 925, D: 1008, D: 939, D: 666I and D: 1090 were identified for quality and agronomical characteristics. These may be used in hybridization programme to achieve desired

26 8 segregants for good grain quality with higher yield. Subba Rao et al. (2013) characterized 65 landraces of rice using 43 agro-morphological traits following Distinctiveness, Uniformity and Stability test (DUS). Out of 65 varieties studied, 32 were found to be distinctive on the basis of 22 essential and 24 additional characters. This study will be useful for breeders, researchers and farmers to identify and choose the restoration and conservation of beneficial genes for crop improvement and also to seek protection under Protection of Plant Varieties and Farmer s Rights Act. Gupta et al. (2014) worked on characterization of fifty three accession of rice germplasm from IGKV, Raipur, Chhattisgarh germplasm. These germplasm accessions were evaluated for fourteen morphological and seventeen agronomical characters. The specific genotypes S: 663, K: 1514, J: 311 were identified for agronomic characteristics. These may be used in hybridization programme to achieve desired segregants for higher yield. Sarawgi et al. (2014) on the basis of frequency distribution for eighteen qualitative traits of 408 rice germplasm accessions reported that majority of genotypes possessed green basal leaf sheath colour (87.25 %), green leaf blade colour (89.70 %), pubescent leaf (48.03 %), well panicle exsertion (57.10 %), white stigma colour (65.93 %), straw apiculus colour (78.18 %), compact panicle type (55.63 %), awnless (88.48 %), white seed coat (82.84 %), straw hull colour (70.34 %), intermediate threshability (47.30 %), erect flag leaf angle (57.59 %), medium leaf senescence (67.15 %) and straw sterile lemma (97.05 %). Sajid et al. (2015) has characterized thirty indigenous rice germplasm on the basis of 32 different agro-morphological traits (15 qualitative and 17 quantitative). Highly significant differences (p<0.01) were observed for the traits of flag leaf length, flag leaf breadth, culm length, days to 50% flowering, panicle length, length of primary branches panicle-1, secondary branches panicle-1, grain length, grain width, awn length and percent leaf lesion while significant differences (p<0.05) were observed for peduncle length and primary branches. The rice germplasm exhibited

27 9 sufficient genetic variation for most of the qualitative and quantitative traits. Sinha et al. (2015) has studied fifty five traditional rice varieties of West Bengal, and investigated for grain morphological characters. A wide variation of grain characters, like grain size and shape, anthocyanin colouration of lemma-palea and kernels, presence or absence of aroma, awning characteristics, were found among the studied varieties. Wide variation among the grain morphological characters indicated wide genetic variation present among these varieties, which may be utilized for the selection of the parents for the plant breeding and production of new improved variety. Shrivastava et al. (2015) Characterized thirty lines of rice derived from Indica into Japonica derived crosses using DUS test guidelines for rice (2006). A total of thirty seven morphological and agronomical characters were observed. Out of this, for the trait leaf-pubescence on blade surface, culm attitude, time of heading, spikelet-density of pubescence of lemma, spikelet: colour of stigma, stem length, panicle: length of main axis, flag leaf attitude of blade (late observation), panicle: curvature of main axis, paniclenumber per plant, spikelet-colour of tip of lemma, lemma and palea colour, panicle awns, panicle: colour of awns, panicle: distribution of awns, paniclesecondary branching, panicle: attitude of branches, panicle exertion, timematurity, grain-weight of 1000 fully developed grains, grain length and grain width genotypes showed distinctiveness. 2.2 Genetic Variability Choudhary et al. (2004) studied genetic variability and genetic advance for plant traits viz., kernel length, panicle length, effective tiller per plant, fertile spikelets per panicle, spikelet density, biological yield per plant, harvest index and grain yield per plant. All these traits exhibited high heritability coupled with high genetic advance and genetic variability. Veni and Rani (2006) studied variability and heritability for seven important physico-chemical traits viz., kernel length, kernel breadth, length/breadth ratio, kernel length after cooking, elongation ratio, alkali spreading value and amylose content. Low to moderate estimates of

28 10 variability (both at genotypic and phenotypic level), moderate to high heritability and low expected genetic advance for all the characters indicated the preponderance of both additive and non-additive gene effects in conditioning these traits. Yadav et al, (2010) reported high heritability coupled with high to moderate genetic advance as % of mean was observed on plant height seed yield per plant, biological yield, harvest index, test weight and number of spikelets per panicle suggesting preponderance of additive gene action in the expression of these characters. Das and Ghosh (2011) characterize thirty one qualitative traits of four hundred thirty one traditional rice cultivars from germplasm collection of Rice Research Station, Chinsurch. Among the qualitative traits considerable variability was recorded for basal leaf sheath color, awning and auricle color. Maximum variability was observed for grains per panicle followed by spikelet per panicle. Parikh et al. (2011) evaluated seventy one rice accession and studied diversity pattern among genotype. The genotypes were grouped into eight clusters. The genotypes in these clusters i.e. Tulsi Mala (cluster II), Kali Kamod (cluster VI), Shankar Jeera and Bhata Dubraj (cluster VII) and Lohandi and TilKasturi (cluster VIII) can be used as potential donors for future hybridization programs to develop genotype with high grain yield. Ravindra et al, (2012) reported that the characters like number of filled grains per panicle, number of chaffy grains per panicle and iron content exhibited high Genotypic Coefficient of Variation (GCV) and Phenotypic Coefficient of Variation (PCV). Small differences between GCV and PCV were recorded for all the characters studied which indicated less influence of environment on these characters. The characters viz., number of filled grains per panicle and water uptake exhibited high heritability coupled with high genetic advance indicating that simple selection could be effective for improving these characters. Chakravorty et al. (2013) studied fifty-one landraces of rice to characterize, evaluate and work out the interrelationship among the 18 agro-

29 11 morphological traits with a view to exploiting them directly in the field and forming a base for using these landraces in breeding program. The analysis of variance found significant variability in eighteen quantitative traits. Leaf length had mean value of cm with a wide variation from 34.0 cm to 61.0 cm. Most of the lines (58.8 %) were in the range of cm. The highest leaf breadth value (2.20 cm) was observed in Rupsal and Sitasal. Maximum plant height (43.0 cm) was observed in variety Sarkele aman, while minimum (24.0 cm) in Tolsibhog. Kumari et al. (2013) evaluated twelve accessions of rice for physical and biochemical traits and observed highest kernel length in NDR 6265 (7.07 mm) and kernel breadth in NDR 625 (1.81 mm). Maximum elongation ratio was observed in Kankjeer and Banta Phool A (1.88 mm) and kernel length after cooking was maximum in NDR 6265 (11.4 mm). Maximum amylose content was found in variety Kalanamak Berdpur (19.8 %). On the basis of above parameters variety Kalanamak Berdpur, Badshah pasonda, NDR 6265 and NDR 625 were rated superior among the all varieties/accessions tested in the present investigation. Phenotyping of the 41 rice genotypes was done by Pachauri et al. (2013) for grain quality characters viz., grain length, grain breadth, length breadth ratio, elongation ratio, alkali spreading value and aroma. The longest grain length (unmilled and milled) was recorded as 11.67±0.4 mm and 8.2±0.38 mm respectively for SS20, while Sulendas had shortest grain length of 6.93±0.37 mm and 5.07±0.15 mm respectively. Diverse L: B ratio (unmilled grain) was observed, ranging from 2.29±0.24 mm (Suranit) to 5.66±0.22 mm (SS20). Highest kernel elongation ratio was observed in Kakeria-2 (1.608±0.19), while SHPP-20 showed the lowest elongation ratio of 1.078±0.06. Most of the rice varieties had an ASV of 2 and 1. Sensory analysis of grain aroma revealed the range of sensory scores between 0 and 3. Highly aromatic varieties such as Tilakchandan and Basmati-334 having a sensory aroma score of 3 as well as moderately aromatic varieties with a sensory score of 2 had been identified along with some non-aromatic and less aromatic varieties.

30 12 Vanisree et al. (2013) investigated fifty genotypes comprising both basmati and aromatic short grain types and revealed significant differences among genotypes for yield, its components and grain quality traits. The high variability was observed for productive tillers per plant and filled grain per panicle whereas, the estimates for panicle length, days to 50 % flowering, kernel breadth and kernel elongation ratio were low. Kumar et al. (2014) reported high variation in genetic variability for kernel length breadth ratio followed by the number of filled spikelets per panicle, kernel length after cooking, number of spikelets per panicle, kernel length, effective tillers per plant and grain length, indicating sufficient variation for most of the traits studied. Correlation studies indicated significant positive association of grain yield per plant with days to 50 % flowering, number of spikelets per panicle, number of filled spikelets per panicle, spikelet fertility per cent, hulling %, milling %, head rice recovery %, kernel breadth and kernel breadth after cooking. The result of path analysis revealed that the kernel length had maximum direct effect on grain yield per plant followed by number of filled spikelets per panicle, kernel breadth, 1000 grain weight, kernel elongation ratio, head rice recovery %, kernel length breadth ratio and plant height. Tuhina-Khatun et al. (2015) evaluated forty-three genotypes all genotypes exhibited a wide and significant variation for 22 traits. The highest phenotypic and genotypic coefficient of variation was recorded for the number of filled grains/panicle and yields/plant (g). The highest heritability was found for photosynthetic rate, transpiration rate, stomatal conductance, intercellular CO2, and number of filled grains/panicle and yields/plant (g). Cluster analysis based on 22 traits grouped the 43 rice genotypes into five clusters. Cluster II was the largest and consisted of 20 genotypes mostly originating from the Philippines. The first four principle components of 22 traits accounted for about 72% of the total variation and indicated a wide variation among the genotypes. Lingaiah et al. (2015) conducted experiment to estimate the genetic variability parameters for the quantitative characters in mid early group

31 13 genotypes of rice cultivars. The analysis of variance revealed significant difference among the genotypes for the traits studied indicating that a large amount of variability was present in the material. The magnitude of phenotypic co-efficient of variation was higher to genotypic co-efficient of variation for all the traits. 2.3 Correlation coefficient analysis Grain yield of plant is influenced by a number of components, either directly or indirectly. Contribution of each character towards increase in grain yield varies from crop to crop. Correlation coefficient is therefore used to measure the mutual relationship between various plant characters and to determine the component characters on which selection can be based for genetic improvement in the yield. Madhavilatha et al. (2005) reported positive and significant association of grain yield per plant with days to 50 % flowering, plant height, number of effective tillers per plant, panicle length, number of grains per panicle, harvest index and1000 grain weight. Satyanarayana et al. (2005) observed positive association of grain yield per plant with spikelet fertility, panicle length, number of grains per panicle and number of effective tillers per plant. Muthuswamy and Ananda Kumar (2006) reported significant positive correlation of grain yield per plant with the characters viz., plant height, number of effective tillers per plant, panicle length, and number of grains per panicle, spikelet fertility and 1000 grain weight. Girish et al. (2006) reported positive and significant association of grain yield per plant with plant height, panicle length, number of spikelets per panicle, number of tillers per plant, biological yield, harvest index and grain breadth. Ahmed et al, (2007) concluded that the number of filled grains panicle-1 had direct positive contribution to the grain yield ha-1 and positive indirect effect on grain yield ha -1 through days to 50% maturity and number of grains panicle-1 while number of filled grains per panicle had negative and indirect effect on grain yield ha-1 through number of tillers plant-1 and

32 14 number of panicles m -2, respectively. Agahi et al. (2007) estimated correlations among the traits to find out association and showed that the grain yield was significantly correlated with days to heading, total tillers, number of productive tillers, days to maturity, number of grains per panicle and plant height. Gnanasekaran et al. (2008) reported positive correlation between grain yield and length-breadth ratio. Kernel length and Kernel breadth, respectively had positive and negative correlations with length breadth ratio was reported by Mahala et al. (2008). Khan et al. (2009) reported significant and positive correlation of grain yield per plant with plant height, panicle length, flag leaf width, number of grains per panicle. Chakraborty et al. (2010) revealed significant positive correlation of grain yield per plant with plant height, number of panicles per plant, panicle length, number of filled grains per panicle and harvest index. Nandan et al. (2010) revealed strong positive association of yield with days to 50 % flowering, plant height, number of grains per panicle, number of spikelets per panicle and spikelet fertility. Jayasudha et al, (2010) studied on genetic variability, character association and path-coefficient analysis were conducted on forty seven (47) rice including thirty three hybrids and fourteen parents for grain. Analysis of variance revealed considerable variability among the genotypes for all the characters. A high genotypic and phenotypic coefficient of variation was observed for grain yield per plant, harvest index, pollen fertility (%) and spikelet fertility (%). Characters like pollen fertility (%), spikelet fertility (%), days to 50% flowering and grain yield per plant showed high value of heritability coupled with high genetic advance. Spikelet fertility (%) and harvest index showed positive and significant correlation with seed yield per plant both at genotypic and phenotypic levels. Mia et al. (2010) observed highly significant negative correlation between grain aroma and gelatinization temperature. However, positive correlation was observed between grain aroma and kernel elongation by

33 15 Golam et al. (2010). Yadav et al, (2010) reported the correlation coefficient between seed yield per plant and other quantitative attributing to yield showed that grain yield was significantly and positively associated with harvest index, number of tillers per hill, number of panicle per plant, panicle length, number of spikelet's per panicle and test weight at both genotypic and phenotypic levels. Path coefficient at genotypic level revealed that harvest index, biological yield, number of tillers per hill, panicle length, number of spikelets per panicle, plant height and test weight had direct positive effect on seed yield per hill. Ekka et al. (2011) on the basis of association analysis reported that grain yield per plant had positive significant correlation with leaf width, days to 50 % flowering, plant height, panicle length, number of filled grains per panicle, 100 seed weight and paddy (grain) length. A positive and significant correlation of head rice recovery percentage was also observed with leaf length, leaf width, days to 50 % flowering, number of filled grains per panicle, spikelet sterility % and milling %. Ambili and Radhakrishnan (2011) reported significant and positive correlation of grain yield per plant with plant height, total number of tillers per plant, number of productive tillers per plant, panicle length, straw yield and harvest index. At genotypic level yield was positively and significantly correlated with days to flowering and number of spikelets per panicle Chakravorty and Ghosh (2012) reported positive and significant association of plant height with panicle length and grain weight. At genotypic level, kernel weight was correlated positively and significantly with maturity, grain weight, grain length, grain breadth and flag leaf angle. Chakravorty et al. (2013) studied fifty-one landraces of rice to work out the interrelationship among the 18 agro-morphological traits and found all the traits except ligule length, culm length, number of grains per panicle and number of primary branches per panicle exhibited positive and significant correlation coefficients with kernel weight. Leaf length was positively and significantly correlated with leaf breadth, plant height and

34 16 culm length. Seraj et al. (2013) revealed significant and positive association of grain aroma with grain length width ratio; significant and negative association with grain width, gelatinization temperature, and with grain length. Gelatinization temperature had significant and negative correlation with grain length, grain length width ratio, significant and positive association with grain width. Grain length had significant and negative correlation with grain width; significant and positive correlation with length width ratio. Sinha and Mishra (2013) reported that days to 50 % flowering was highly correlated with maturity time and also correlated with stem length. Panicle length was negatively correlated with 100 grain weight. Panicle number shows maximum correlation with grain length. 100 grain weight shows maximum of correlation with grain length, grain width, kernel width and kernel length. Grain length and grain weight possesses maximum correlation with kernel length and kernel weight respectively. Stem length was highly correlated with length of blade, showing the morphogenetic compatibility in the architectural configuration of rice plant. Vanisree et al. (2013) studied association analysis of fifty genotypes comprising both basmati and aromatic short grain types and revealed that grain yield was significantly associated with harvest index, plant height, days to 50 % flowering, panicle length, number of grains per panicle and filled grain per panicle. Rashid et al. (2014) reported highly significant and positive association of the traits days to heading, days to maturity, number of productive tillers, grain weight with grain yield per plant whereas flag leaf area, plant height and panicle length showed highly significant negative correlation with grain yield per plant. Number of grains per panicle was non - significant positively correlated with grain yield per plant. Sohgaura et al. (2014) reported positive and significant association of grain yield per plant with number of spikelets per panicle, panicle weight per plant, kernel elongation ratio, head rice recovery % and number of leaves

35 17 per plant, indicated that these are primary yield contributing traits and selection for above traits might be utilized as inbred for production of hybrids in rice. Islam et al. (2015) evaluated twenty three rice genotypes including three check varieties Grain yield was found to be positively and significantly correlated with filled grain per panicle, plant height, days to 50% flowering and days to maturity both at genotypic and phenotypic levels, indicating the importance of these traits for yield improvement in rice. Naseer et al. (2015) studied twenty four Asian accessions of rice Plant yield was positively and significantly correlated with filled grains weight per panicle, number of grains per panicle, 1000-grain weight and spikelet fertility percentage at genotypic and phenotypic levels. Thus, these traits could play pivotal role in the development of high yielding rice genotypes. Al-Salim et al. (2016) evaluate the performance of different ten genotypes of bread rice under irrigated field conditions. The results indicated the existence of genetic variability, in a significant manner (at the level 5%). The study showed the importance of the Panicle Length due to its positive and high significant correlation with the grain yield, so it can be used as indicator of suitable selection for the development of high-yielding genotypes. Results also showed that correlation between grain yield and plant height was negative and significant. 2.4 Principal component and cluster analysis: Multivariate statistical tools have found extensive use in summarizing and describing the inherent variation among crop genotypes. One of the tools includes Principal Component Analysis (PCA). This technique identifies plant traits that characterize the distinctness among selected genotypes. These are often extended to the classification of a population into groups of distinct orders based on similarities in one or more characters, and thus guide in the choice of parents for hybridization. Cluster analysis is also a multivariate method which aims to classify a sample of subjects (or objects) on the basis of a set of measured variables into a number

36 18 of different groups such that similar subjects are placed in the same group. Zhang et al. (2004) studied principal component and correlation analyses to test the quality characteristics of 89 japonica rice varieties. Principal component analysis showed that brown rice rate, milled rice rate, length: width, chalkiness, gelatinization temperature and gel consistency should be taken as the principal properties for estimating rice quality. Rashid et al. (2008) in order to identify the major characters which account for variation among Basmati rice mutants used Single Linkage Cluster Analysis (SLCA) and Principal Component Analysis (PCA). The first three PCs with eigen values > 1 contributed 78.7 % of the variability among the genotypes. Four characters were positive to PC3 than PC2 and PC1. Productive tillers per plant and panicle fertility contributed maximum in PC3. Yang et al, (2009) classified ten agronomic traits of 98 accessions of upland rice using PCA and showed that there was remarkable variance among traits of the accessions. Ten agronomic traits of the accessions could be classified into four principal components with cumulative proportion of %. The first principal component was determined by spikelets per panicle, total grains per panicle. The second was determined by effective tillers per plant, 1000-grain weight and panicle length. The third mainly represented yield per plant, and the fourth reflected grain and growth period of the accessions. Li et al. (2010) carried out principal component analysis and clustering of 46 introduced black pericarp rice cultivars based on 8 agronomic traits. On the basis of principal components, these 46 black rice varieties were divided into three groups for 4.19 Euclidean distances. The characters of the first group were late maturity, high stalk, moderate spikes and many grains; and the second group had the characteristics of early maturity, medium stalk, long spike, and weighty grains; the third group was type of late maturity, high stalk, many spikes, many and light grains. Anandan et al, (2011) assessed diversity of 44 rice genotypes from different geographic regions using Mahalanobis D 2 and Principal Component

37 19 Analysis (PCA). The PCA revealed that axes 1 and 2 accounted for % and % of the variance, respectively. The highest contributing variable was the number of grains per panicle in PC1 and the plant height in PC2. Both D2 and PCA revealed that the morpho-metric diversity was based on the pedigree and independent of geographical origin. Ashfaq et al. (2012) performed PCA for twelve morphological traits and reported four principal components out of twelve which exhibited more than one Eigen value and showed about 67.7 % variability. The PC1 was more related to plant height, panicle length, primary branches per panicle, number of spikelets per panicle, number of seed per panicle, seed weight per panicle, plant yield, heading days and maturity days so, it must be considered. In PC2 the primary branches, seeds per panicle, seed weight per panicle, 1000 grain weight and plant yield were more related traits. The PC3 exhibited positive effect for plant height, panicle length, flag leaf area, primary branches per panicle and 1000 grain weight. The PC4 was more related to number of spikelets per panicle, 1000 grain weight, heading days and maturity days. Based on first our PCs it was cleared that the 1000 grain weight, number of spikelets per panicle, primary branches per panicle, number of seeds per panicle and seed weight per panicle had high weightage value and number of tillers had lowest value. Chanbeni et al. (2012) reported nine clusters using by considering 13 quantitative characters in 70 rice genotypes. Cluster I and cluster III constituted maximum number of genotypes (12 each). The genotypes falling in cluster VII had the maximum divergence, which was closely followed by cluster V and cluster I. The inter cluster distance was maximum between cluster VI and VII followed by cluster III and IX, suggesting that the genotypes constituted in these clusters may be used as parents for future hybridization programme. Traits like spikelets per panicle; plant height and biological yield were the major contributors to genetic divergence. Chakravorty et al. (2013) studied 51 landraces of rice to determine the nature and magnitude of the variability among the genetic materials, and the intensity of relationships among the traits using multivariate tools. They

38 20 identified six principal components with Eigen value greater than 1.0 and that explained 75.9% of the total cumulative variance within the axes could effectively be used for selection among them. In PC1, the traits that accounted for most of the % observed variability among 51 genotypes included leaf length, plant height, culm diameter, culm number and panicle length. PC2 is related to leaf width, ligule length, number of primary branches per panicle and number of grains per panicle. PC3 was more related to grain breadth and grain length: breadth ratio. PC4 was related to flag leaf angle, maturity and sterile lemma length. PC5 included grain length while PC6 was related to culm length. Thus, principal component analysis revealed that six quantitative characters viz., leaf length, culm number, culm diameter, number of grains per panicle, grain length: breadth ratio and grain length significantly influenced the variation in these cultivars. Clustering pattern using the first two principal components permitted the separation 51 landraces of rice into ten major clusters from diverse geographical location, suggesting environmental adaptation of the landraces. Kumar et al. (2013) reported five Principal Components (PCs) exhibited more than 1.8 Eigen value and showed about % variability on the basis of principal component analysis. The PC1 showed %, while PC2, PC3, PC4 and PC5 exhibited %, 9.56 %, 8.58 % and 7.16 % variability respectively, among the RILs for the traits under study. Rotated component matrix revealed that each principal component separately loaded with various yield and quality attributing traits. The PC1, PC2, PC3 and PC5 mostly related to yield attributing traits whereas PC4 related to quality traits. As PC1 was constituted by most of the yield attributing traits, an intensive selection procedures can be designed to bring about rapid improvement of dependent traits i.e., grain yield by selecting the lines from PC1. Similarly, for quality aspect a good breeding programme can be initiated by selecting the lines from PC4. PC scores of RILs in these five PCs suggested that RIL 2-36 is the best for yield attributing traits whereas RIL 2-52 for quality traits. These selected RILs may be used as inbred in production of hybrid in rice. However, RIL 2-50 is the best for both yield and quality traits, which can be

39 21 used directly for cultivation. Sinha and Mishra (2013) characterized 34 landraces of rice based on 12 quantitative agro-morphological characters using Multivariate statistical analysis and enabled pattern of variation of the germplasm of landraces of rice and identification of the major traits contributing to the diversity of landraces. Five cluster groups were obtained from the 12 agro-morphological characters. PCA showed the contribution of each character to the classification of the rice landraces into different cluster groups. The first three principal components explained about 86.9 % of the total variation among the 12 characters. The results of PCA suggested that characters such as leaf length, leaf width, panicle length and grain size (100 grain weight, length and width of grain and kernel were the principal discriminatory characteristics of landraces of rice. Shiva Prasad et al. (2013) reported significant differences among the 470 genotypes for all the nineteen characters studied. The quantum of genetic divergence was assessed by cluster analysis using Mahalanobis Euclidean squared distances which grouped the entire material into eight clusters and estimates the average distance between them. It was interesting to observe that most of the genotypes of one cluster were adapted to only one region. The clustering pattern reflects the closeness between the clusters and the geographical adaptation of the genotypes. Also, traits contributing maximum to genetic divergence are fertile grains/ panicle and panicle length may be utilized in selecting genetically diverse parents. It was also reported that genotypes within the cluster with high degree of divergence would produce more desirable breeding materials for achieving maximum genetic advance. Nachimuthu et al, (2014) used principal component analysis to examine the variation and to estimate the relative contribution of various traits in a population panel of 192 rice genotypes for 12 agro-morphological traits. Component 1 had the contribution from the traits such as days to 50 % flowering, leaf length, plant height, panicle length, days to maturity and number of filled grains which accounted % of the total variability.

40 22 Grain width and grain length width ratio has contributed 16.8 % of total variability in component 2. The remaining variability of 14.4 %, 11.7 % and 9.3 % was consolidated in component 3, component 4 and component 5 by various traits such as spikelet fertility, single plant yield, grain length and number of productive tillers. The cumulative variance of % of total variation among 12 characters was explained by the first five axes. Kumar et al. (2014) reported five clusters based on D 2 analysis for 23 genotypes of rice. Among the five clusters, cluster III consists of 7 genotypes forming the largest cluster followed by cluster I and IV with 5 genotypes each. Cluster II with 4 genotypes and cluster V with 2 genotypes. Inter cluster distances were found to be higher than intra cluster distances which depicted wide genetic diversity among the rice genotypes. The contribution of various characters towards the expression of total genetic diversity indicated that 1000 grain weight contributed maximum (54.55 %) followed by plant height (13.44 %) and kernel breadth (11.86 %). Clustering of the cultivars did not show any pattern of association between the morphological characters and the origin of the cultivars. Cluster analysis performed by Rashid et al. (2014) on twenty diverse cultivars of rice revealed that maximum genetic diversity was present between Cluster I and Cluster VI. Minimum genetic diversity was found between Cluster III and Cluster IV. It was concluded that traits like number of productive tillers, number of grains per panicle and 1000-grain weight was useful for direct selection criteria for higher grain yield. Apsath et al., (2015) grouped 60 rice genotypes from different ecogeographical regions of India into six clusters. Cluster I was found to be the largest comprising of 50 genotypes followed by cluster II had four genotypes. The clusters IV and V had two genotypes each while cluster III and VI are mono-genotypic in nature. The pattern of distribution of genotypes from different eco-geographical regions into various clusters was at random indicating that geographical diversity and genetic diversity were not related. The characters grain yield plant-1, number of grains panicle-1 and plant height contributed maximum towards genetic divergence among

41 23 the genotypes. Cluster III recorded highest mean value for grain yield plant - 1 and lowest mean value for days to first flower. The highest inter-cluster distance was recorded between clusters III and VI. Ayesha et al. (2015) genetic variability among the Oryza sativa germplasm using agro-morphological characters. The data were analyzed by cluster analysis and principal component analyses. A significant level of variability was noticed for a number of agro-morphological traits. The largest variation was observed in seed yield per plant, (588.32), sterile culms per plant (341.25) and flag leaf length (291.09). The 116 accessions in this study were grouped into seven clusters based on hierarchical clustering method. PCI which is most important explained 28.41%, PC II contributed 13.38%, and PC III accounted for 11.69% of total morphological variability. Rathore et al. (2016) studied the functional traits of 76 weedy rice populations and commonly grown rice cultivars from different agro-climatic zones for nine morphological and five physiological parameters in a field experiment. The results of principal component analysis revealed the first three principal components to represent 47.3% of the total variation, which indicates an important role of transpiration, conductance, leaf-air temperature difference, days to panicle emergence, days to heading, flag leaf length, grain weight, plant height, and panicle length to the diversity in weedy rice populations. 2.5 Quality parameter: Babu et al. (2006) studied genetic divergence for different grain quality traits in 70 rice genotypes from different eco-geographical regions of India. The genotypes were grouped into nine clusters. Water uptake, gel consistency and head rice recovery contributed the maximum towards genetic divergence. Geographical diversity was not related with genetic diversity. Roy et al. (2007) evaluated genetic divergence in twenty eight rice genotypes using D 2 statistics. These genotypes were grouped into four clusters. Seed yield per plant contributed the maximum towards genetic divergence followed by amylose content cooked kernel length and thousand

42 24 seed weight. Shrivastava et al. (2007) studied genetic diversity using Mahalanobis D 2 statistics in 20 genotypes of rice. These genotypes were grouped into six clusters. There was lack of relationship between genetic and geographical diversity. Singh and Singh (2007) analyzed various cooking and physical qualities of rice. The hulling varied from 68.9 to 82.9%, milling from 56.1 to 74.2%, head rice recovery from 19.7 to 49.4%, kernel length (KL, uncooked) from 5.1 to 7.1 mm, kernel breadth (KB, uncooked) from 1.7 to 2.4 mm, kernel length breadth ratio from 2.31 to 3.94, KL (cooked) from 9.5 to 12.7 mm, KB (cooked) from 2.5 to 3.6 mm, kernel elongation ratio from 1.39 to 1.98, alkali score from 2.6 to 6.6, volume expansion from 2.78 to 3.12, water uptake number from 390 to 500, amylase content from to 41.62, gel consistency from 30 to 100, and aroma absent to strong. Sharma et al. (2008) studied under irrigated situation using D 2 statistics in a set of 100 aromatic rice genotypes. The genotypes were grouped into nine clusters and it was observed that there was no association between the geographical distribution and genetic diversity. Lang et al. (2009) studied a collection of 200 salt tolerance rice landraces was assessed for genetic diversity using quantitative agromorphological characters. The diversity indices for quantitative descriptors were high ranging from 0.68 to Overall the mean diversity index for all traits was 0.88). Cluster analysis generated by UPGMA grouped the 200 rice landraces into six clusters with similarity coefficient of The six clusters were distinct in terms of culm length, number of filled grains, panicle length, and panicles per plant, grain length, grain width, yield and biomass. Shahidullah et al. (2009) studied 40 genotypes composed of 32 local aromatic, five exotic aromatic and three non-aromatic rice varieties. Univariate and multivariate analyses were done. Enormous variations were observed in majority of characters viz. grain length, grain breadth, kernel weight, milling yield, kernel length, Length/Breadth ratio of kernel, volume expansion ratio (VER), protein content, amylose content, elongation ratio

43 25 (ER) and cooking time. Pandey and Anurag (2010) observed among 22 genotypes of indigenous rice for yield and quality contributing traits viz., volume expansion ratio, head rice recovery, kernel length and length breadth ratio, indicating that there is presence of sufficient amount of variability in the study material. On the basis of mean performance of yield and yield contributing traits they found that Indrani was the best performer for both yield and quality over Jhumeri. For quality parameters Narendra-359 and Indrani were good, milling percentage of Lohandi was best followed by Bayalu and Dudagi general types. Garg et al. (2011) studied forty eight genotypes of rice to study the nature and magnitude of genetic divergence using D 2 statistics. Seventeen yield and quality traits were recorded on the genotypes. The forty eight genotypes were grouped into five clusters based on Euclidean cluster analysis. Days to maturity, gel consistency and days to 50 per cent flowering contributed per cent of total divergence. Danbaba et al. (2011) studied on the cooking and eating quality of Ofada rice. The result showed that Ofada rice had high cooked rice volume with length and breadth increase of % and 87.85% respectively. Grain elongation ratio ranged from and highest length/breadth ratio of cooked rice (3.68) and lowest (2.49) was recorded. Water uptake ratio, cooking time, solids in cooking gruel and amylase content of Ofada rice samples ranged from , min, %, and % respectively. Satheeshkumar and Saravanan (2012) studied genetic diversity among fifty three genotypes of rice genotypes from various states of south Eastern region of India was evaluated using Mahalanobis D 2 statistic. Based on 15 morphological and quality characters, these genotypes were grouped into six Clusters. Geographical origin was not found to be a good parameter of genetic divergence. Grain yield per plant (38.52%) followed by filled grains per panicle (13.46%) and total number of grain per panicle (12.65%) contributed maximum to total divergence.

44 26 Subudhi et al, (2012) evaluated forty-one rice varieties of different ecologies at CRRI, Cuttack and found that hulling percentage in all the genotypes ranged from 71.0 to 81.0, milling recovery varied from 62.0 to 76.0 and head rice recovery % varied from 43.5 to The kernel length was highest (7.54) and lowest (3.88), kernel length after cooking varied from 7.9 to 12.5, elongation ratio was highest (2.07) and (2.0) and lowest (1.44). Volume expansion ratio was highest (5.25), and lowest (3.25). Amylose content was intermediate in all the tested genotypes and it ranged from 22.1 to Gnanamalar and Vivekanandan (2013) analysis of generation mean was carried out in six crosses of rice for hulling percentage, milling percentage, head rice recovery, kernel length, kernel breadth, kernel Length/Breadth ratio, kernel length after cooking, linear elongation ratio and alkali spreading value. The scaling test showed the presence of epistatic interactions for all the nine grain quality traits studied. Milling percentage was governed by additive, dominance and epistatic interactions of additive x additive, dominance x dominance and duplicate epistasis. Hulling percentage was governed by additive, dominance and duplicate type. Head rice recovery was under the control of additive, dominance, dominance x dominance and duplicate epistasis. Gangadharaiah et al. (2015) studied the physicochemical and cooking quality of traditioal rice cultivars grown in the farmer s field. Among the cultivars, Kichadi Sona and Salem Sanna recorded higher milling yield (66.0% and 65.0%) and head rice recovery (58.5% and 58.30%), respectively. Therefore, these cultivars could be utilized in the breeding programme is the need of the today towards the hidden hunger free world. Hosen et al. (2016) studied 17 Aus rice cultivars including 10 local cultivars. The highest milling outturn 72.22% was found in the traditional variety Chakilla and lowest in Kasalath (65.43%). The highest milled rice length (6.5 mm) was BRRI dhan42 and the highest length-breadth ratio (3.8) was found in both BR24 and BR26. The lowest grain length was found in BR20 (5.0 mm) and lowest length-breadth ratio was found in Phul Dumra

45 27 (2.0). In addition, Surjamukhi has aroma. These local Aus variety could be a useful germplasm in breeding program to get improve HYV especially for Aus season. 2.6 Molecular characterization: Monna et al, (2001) isolated sd1 gene by positional cloning and revealed to encode gibberellin 20-oxidase, the key enzyme in the gibberellin biosynthesis pathway. Analysis of 3477 segregants using several PCR-based marker technologies, including cleaved amplified polymorphic sequence, derived-caps, and single nucleotide polymorphisms revealed 1 ORF in a 6- kb candidate interval. Normal-type rice cultivars have an identical sequence in this region, consisting of 3 exons (558, 318, and 291 bp) and 2 introns (105 and 1471 bp). Dee-Geo-Woo-Gen-type sd-1 mutants have a 383-bp deletion from the genome from the middle of exon 1 to upstream of exon 2, including a 105-bp intron, resulting in a frame-shift that produces a termination codon after the deletion site Ashikari et al, (2002) described the physiological, molecular, genetic and biochemical characterization of the sd1 gene. The sd1 mutant contained lower gibberlin level than wild type plants but responded sensitively to exogenous GA. Cloning and sequence analysis revealed that the sd1 gene encoded a GA biosynthetic enzyme, GA 20 oxidase. In all of the sd1 mutants tested nucleotide deletions or substitution were observed in the GA20 oxidase gene which induced an internal stop codon or single amino acid substitution respectively. Spielmeyer et al, (2002) proposed that the semidwarf (sd-1) phenotype is the result of a deficiency of active GAs in the elongating stem arising from a defective 20-oxidase biosynthetic enzyme. Sequence data from the rice genome was combined with mapping studies to locate 20- oxidase gene (Os20ox2) at the predicted map location of sd-1 on chromosome 1. Two independent sd-1 alleles contained alterations within Os20ox2: a deletion of 280 bp within the coding region of Os20ox2 was predicted to encode a nonfunctional protein in an indica type semidwarf (Doongara), whereas a substitution in an amino acid residue (Leu-266) that is

46 28 highly conserved among dioxygenases could explain loss of function of Os20ox2 in a japonica semidwarf (Calrose76). Jain et al, (2004) worked on genetic relationships among Indian aromatic and quality rice (Oryza sativa) germplasm using 30 fluorescently labeled rice microsatellite markers. The 69 rice genotypes used in this study included 52 Basmati and detected at the 30 simple sequence repeat (SSR) loci (26.4%) of which were present only in Basmati and other scented/quality rice germplasm. Negano et al, (2005) worked on presense and absence of the two mutant alleles (DGWG type in Dee-geo-woo-gen and JKK type in Jikoku) were surveyed by PCR using 256 accessions of eight wild and two cultivated rice species. The DGWG allele was detected in landrace (Oryza sativa) and two accessions of wild rice (O. rufipogon), all of which are from china, showing their limited distribution. Genealogical studies of the OsGA20ox2 gene shows that the 62 sequences of O sativa and O. rufipogon included 20 distinct haplotypes, indicating that the species complex contained OsGA20ox2 gene from two different lineages. The silent site nucleotide diversities were extremely low in japonica rice, suggesting a genetic bottleneck. The haplotype network showed that the DGWG and JKK alleles were derived in different lineages. The DGWG carrier had unique polymorphism in the surrounding region of the locus, suggesting that it has been preserved in the wild progenitors. Asano et al, (2007) performed genetic and molecular analysis and revealed that the sd1 allele of IR8 has been used in the production of japonica varieties. The short stature of IR8 is due to loss of function of the SD1 gene that encodes GA20 oxidase -2 which catalyses late step of gibberlin biosynthesis. Sequence analysis of the SD1 locus of 57 semidwarf varieties showed that at least 7 sd1 alleles have been used in the breeding of semi dwarf varieties of China, USA and Japan. Lapitan et al, (2007) evaluated genetic diversity by using 164 SSR markers. A total of 890 alleles were detected by 151 polymorphic markers with an average of 5.89 per locus. Out of these markers, 89 generated a total

47 29 of 147 rare alleles. Based on Shannon s diversity index an overall genetic diversity of 0.71 was revealed indicating a high level of genetic variation these cultivars. Polymorphism information content (PIC) values of the markers ranged from 0.18 to 0.91 with an average of 0.68 per marker. SSR markers facilitated the classification of these cultivars according to their subspecies. Genetic diversity of indica was high on chromosome 11, while for japonica was high on chromosome 2. Thomson et al, (2007) characterized cultivars, using 30 fluorescently-labeled microsatellite markers. The landraces were selected across 21 provinces and include representatives of the classical subpopulation cere, bulu, and gundil rices. A total of 394 alleles were detected at the 30 simple sequence repeat loci, with an average number of 13 alleles per locus across all accessions, and an average polymorphism Information content value of Genetic diversity analysis characterized the Indonesian landraces as 68% indica and 32% tropicaljaponica, with Indica gene diversity 0.53 and a tropical japonica gene diversity of All of the improved varieties sampled were indica, and had an average diversity of Rahman et al, (2009) reported the utilization of a small set of three previously developed rice microsatellite markers for the identification and discrimination of 17 HYVs and 17 local rice cultivars including two wild rice cultivars. All analyzed microsatellite markers were found to be polymorphic with an average number of 6.33 alleles per locus. These three markers were able to identify 15 local rice cultivars and 11 HYVs. A total of three variety specific alleles, RM-11/147, RM-151/289 and RM-153/178 were identified for BR-11, Badshabhog and BR-19 cultivars, respectively. DNA fingerprints of rice cultivars by means of microsatellites provided meaningful data, which can be extended by additional microsatellite markers. Neeraja et al, (2009) made an attempt to identify the alternate sources of DGWG allele of sd1 gene by characterizing 29 induced and 3 spontaneous dwarf accessions employing marker for DGWG allele of sd1

48 30 gene and exogenous application of gibberellic acid (GA). When occurrence of DGWG allele of sd1 gene and GA3 response were analyzed together, existence of two kinds of dwarfs was noticed viz., dwarf accessions with DGWG allele and dwarf accessions without DGWG allele of sd1 allele exhibiting varying responses to GA3. As many as 22 of 32 dwarf accessions showed absence of DGWG allele of sd1 gene with varying response to GA3 could be used as excellent alternate sources for DGWG allele of sd1 gene. These dwarf accessions could be used for broadening the genetic base for the plant height and thereby minimize the risk of genetic vulnerability. Negrao et al, (2010) performed marker assisted backcrossing (MAB) - to track sd1 introgression in two traditional rice varieties. For selection of sd1 plants he first confirmed the efficiency of specific markers based on Os200 x 2 gene sequence. Background selection was also performed with the help of microsatellites markers (SSR) and a total of 7 breeding lines were recovered containing a higher percentage of recurrent parent genome (RPG). Analysis of Covariance (ANCOVA) using mean progenitor plant height as covariate was performed to compare several agronomic and quality-related parameters in two different environments. The results suggest that plant height differs significantly between the two environments. From the total variability of plant height he concluded that 73% is due to the genotype, while 10.4% depends on the environment. MAB and background selection thus revealed as useful tools to assist breeding for semi-dwarfism in traditional rice varieties. Reagon et al, (2011) determined polymorphism patterns in the growth candidate gene, SD1, to assess its possible role in the evolution of divergent phenotypes. Introgression of a chromosomal block containing the SD1 allele from tropical japonica, the predominant U.S. rice cultivar, was detected in one weedy rice population and is associated with a change in growth patterns in this group. This study demonstrates the role of introgressive hybridization in evolutionary divergence of an important weed. Das et al. (2012) reported the diversity among 26 indigenous nonbasmati aromatic rice genotypes, six basmati and 9 HYV; both

49 31 morphologically using 12 grain and kernel traits and genetically using 23 previously mapped SSR markers. High genetic diversity was observed for the grain and kernel dimension and quality traits, in the indigenous nonbasmati aromatic rice genotypes through D2 analysis. The polymerase chain reaction (PCR) profile obtained from 23 SSR markers generated 172 alleles including 28 rare alleles and 9 null alleles. The ensuing dendogram obtained from the SSR profiles clustered the basmati rice and the indigenous nonbasmati aromatic rice genotypes separately. Rahman et al. (2012) studied thirty-four microsatellite markers across 21 types of rice to characterize and discriminate among different varieties. The number of alleles per locus ranged from 2 to 11, with an average of 4.18 alleles across 34 loci. A total of 57 rare alleles were detected at 24 loci, whereas 42 unique alleles were detected at 20 loci. The results revealed that 14 rice varieties produced unique alleles that could be used for identification, molecular characterization, and DNA fingerprinting of these varieties. Polymorphic Information Content (PIC) values ranged from to 0.838, with an average of 0.488, which revealed that much variation was present among the studied varieties. The PIC values revealed that RM401 might be the best marker for identification and diversity estimation of rice varieties, followed by RM566, RM3428, RM463, other scented/quality rice varieties from different parts of India and 17 indica and japonica varieties that served as controls. A total of 235 alleles were and RM8094 markers. In this study, eight SSR markers (RM10713, RM279, RM424, RM6266, RM1155, RM289, RM20224, and RM5371) were identified that produced specific alleles only in the aromatic rice varieties and were useful for varietal identification and DNA fingerprinting of these varieties. The findings of this study should be useful for varietal identification and could help in background selection in backcross breeding programs. Sajib et al. (2012) used a total of 24 SSR markers across 12 elite aromatic rice genotypes for their characterization and discrimination. Among these 24 markers 9 microsatellite markers were showed polymorphism. The number of alleles per locus ranged from 2 alleles (RM510, RM244, and

50 32 RM277) to 6 alleles (RM163), with an average of 3.33 alleles across 9 loci obtained in the study. The polymorphic information content values ranged from 0.14 (RM510) to 0.71 (RM163) in all 9 loci with an average of RM163 was found the best marker for the identification of 12 genotypes as revealed by PIC values. The frequency of most common allele at each locus ranged from 41 % (RM163, RM590, and RM413) to 91 % (RM510). The microsatellite marker based molecular fingerprinting could serve as a sound basis in the identification of genetically distant accessions as well as in the duplicate sorting of the morphologically close accessions. Zhang et al, (2012) conducted two series of experiments were to characterize the pleiotropic effects of SD1 and its relationships with large numbers of QTLs affecting rice growth, development and productivity. The pleiotropic effects of SD1 in the IR64 genetic background for increased height root length/mass and grain weight, and for reduced spikelet fertility and delayed heading were first demonstrated using large populations derived from near isogenic IR64 lines of SD1. In the second set of experiments, QTLs controlling nine growth and yield traits were characterized using a new molecular quantitative genetics model and the phenotypic data of the wellknown IR64/ Azucena DH population evaluated across 11 environments, which revealed three genetic systems: the SD1-mediated, SD1-repressed and SD1-independent pathways that control rice growth development and productivity. Meti et al. (2013) reported the allelic diversity and relationship among 48 traditional indigenous aromatic rice germplasm grown under Eastern part of India using SSR markers. Out of 30 primers, 12 primers showed DNA amplification and polymorphism among 48 aromatic rice genotypes. The number of alleles per locus ranged from 1 to 5 with an average Out of 28 bands, 25 bands were polymorphic and three were monomorphic bands. The results reveal that all the tested primers showed distinct polymorphism among the landraces/varieties indicating the robust nature of SSR markers. The cluster analysis indicates that the 48 traditional indigenous aromatic rice genotypes were grouped into two major clusters.

51 33 The information obtained from the SSR profile helps to identify the variety diagnostic markers in 48 traditional indigenous aromatic rice genotypes. Vohra et al. (2013) reported the genetic diversity among twenty aromatic and non-aromatic rice genotypes using twenty five microsatellite markers (SSR). They used fifteen markers for analysis of aromatic and non-aromatic rice genotypes. These markers generated higher level of polymorphism because they generated 356 polymorphic reproducible bands with 164 loci. The remaining ten markers were used for the study of quality traits which shown 222 polymorphic bands with 101 alleles. The cluster analysis using SSR markers could distinguish the different genotypes. The dendogram generated on the principle of Unweighted Pair Wise Method using Arithmetic Average (UPGMA) was constructed by Jaccard s Coefficient and the genotypes were grouped in to clusters. The dendogram developed for aroma and quality traits showed that the genotypes with common phylogeny and geographical orientation tend to cluster together. Kumar et al. (2014) characterized a set of 72 rice genotypes collected from different villages of Chhattisgarh state, using molecular (SSR) marker. SSR analysis with 15 polymorphic SSR primers produced 44 different alleles with an average of 2.93, ranging from 1 to 4 alleles per locus. Aslam and Arif (2014) studied 48 rice accessions and there are a lot of gene/qtls were identified by different groups on chromosome 3 and 7 controlling grain length. Clustering based on grain length divided the 48 accessions into two major clusters with some contradiction. Genetic relationships among the 48 rice accessions were determined based on allelic diversity using Power Marker tree, structure analyses and PCA using 51 SSR markers located on chromosome 3 and chromosome 7. Two-dimensional PCA scaling and power marker tree analysis showed high-level of differentiation between Basmati and indica rice accessions and divide these rice accessions in two distinct clusters. Wu et al, (2015) reported that the plant height quantitative trait locus (QTL) qph3 with moderate effect and the yield per plant (QTL) qyd3

52 34 were identified on chromosome 3 in a population derived from two semidwarf cultivars. The alleles of both QTLs contributed to increasing phenotypic values. In the near-isogenic background, both QTLs exhibited the characteristics of a single Mendelian gene. qph3 and qyd3 were tightly linked, no more than 130 kb apart, with additive effects of 5.2 cm and 4.4 g, respectively. They were further fine-mapped to a 19-kb region and a 65-kb region, respectively, where the recombination hotspot occurred. The gene encoding gibberellin 20-oxidase associated with plant height was regarded as the candidate of qph3. Both linked QTLs are in coupling phase, which is convenient for simultaneously selecting both genes for breeding high yield varieties maintaining strong lodging resistance. Kunusoth et al. (2015) has reported the genetic diversity assessment of 24 elite Indian rice varieties was based on 24 agro-morphological traits and 86 SSR markers. The morphological and grain traits exhibiting significant variation are useful for discrimination of the rice varieties and were confirmed by Principal Component Analysis. Genetic diversity assessment based on SSR markers displayed genetic similarity coefficients and grouped the varieties into five major clusters. The genetic population structure obtained was predominantly associated with UPGMA clustering and the structure bar plot. Cluster analysis based on both phenotype and SSR marker data did not show perfect congruence between the two measures of genetic diversity. Chaudhary et al, (2013) studied genetic diversity in representative sets of high yielding varieties of rice released in India between 1970 and 2010 was studied at molecular level employing hyper-variable microsatellite markers. Of 64 rice SSR primer pairs studied, 52 showed polymorphism, when screened in 100 rice genotypes. A total of 184 alleles were identified averaging 3.63 alleles per locus. The trend of diversity over the decadal periods estimated based on the number of alleles (Na), allelic richness (Rs), Nei s genetic diversity index (He), observed heterozygosity (Ho) and polymorphism information content (PIC) revealed increase of diversity over the periods in year of release wise and longevity wise classification of rice varieties.

53 CHAPTER- III MATERIALS AND METHODS The present investigation entitled, DNA Fingerprinting of rice (Oryza sativa L.) genotypes along with allelic variations based on sd1 Gene was carried out during Kharif, The techniques followed and materials used during the course of investigation are presented below: 3.1 Experimental site: The present research work was conducted at Research cum Instructional farm, Department of Genetics and Plant Breeding, College of Agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, during the Kharif season of Climate and weather: Chhattisgarh is located between N and N latitudes and E and E longitudes. Raipur is the capital of the Chhattisgarh state and lies at N latitude and E longitude with an altitude of meters above mean sea level. The maximum temperature was 34.8 C and minimum 14.4 C during the crop growth period. The total rainfall received during crop growth stage was mm. The maximum rainfall received during month of September was mm. The data pertaining to weekly rainfall, minimum and maximum temperatures, relative humidity, evaporation, wind velocity, vapour pressure and bright sunshine hours of entire crop growing period have been presented in Appendix A. 35

54 Fig 3.1: Meteorological data recorded during crop growth season (28 June to 17 November, 2016) 36

55 Experimental Materials and Methods: Forty-seven genotypes including land races and released varieties of rice belonging to Chhattisgarh, Andhra Pradesh and IRRI, Philippines were selected for this study (Table 3.1). Nurseries were raised in area of 3 X 0.03 m in 47 lines. Twenty-one days old seedlings were subsequently transplanted in the field, in Randomized Block Design (RBD) with two replications. Net plot size was 9.2 m x 2 m with both row to row & plant to plant distance of 20 cm X 20 cm on The crop was maintained under irrigated condition. Recommended dose of fertilizer was applied. The entire dose of phosphorus and potassium along with half the dose of nitrogen was applied as basal dose before transplanting. The remaining dose of nitrogen was applied in two splits, first at the time of beginning of tillering and second one week after it. Agronomical practices adopted were similar for all the treatments. Five random plants from each of the plot were taken for recording data on agro-morphological and yield characters. To assess distinctness, uniformity and stability (DUS), the characteristics and their status were done as given by PPV & FR Authority, GOI, Observations recorded: In research work, observations on various agro-morphological and quality traits were recorded to fulfill the objectives of the study. Five random plants from each of the rows were taken for recording data of various characters at optimum plant growth stage. Averages of the data from the sampled plants with respect to different characters were used for various statistical analyses Agro-morphological Characters: The observations on various morphological traits including qualitative and quantitative characters as diagnostic descriptors were recorded. The classification of DUS test guidelines issued by PPV&FRA (2007).

56 Seedling Characters Coleoptile color: The coleoptiles color was recorded at first leaf stage by visual observation of individual plants. The categories observed were colorless, green and purple color of the coleoptile. Table 3.1: List of 47 rice genotypes with 33 landraces and 14 released varieties S. CGR No. Name S. CGR No. Name No. No. 1 CGR NO Dhaur 24 CGR NO- 569 Basmati 2 CGR NO JS-5 25 CGR NO- 741 Dhamna panda 3 CGR NO Badal phool 26 CGR NO No :21 (A) 4 CGR NO-1 Amakoyali 27 CGR NO Badi barik 5 CGR NO- 14 Rambhoj 28 CGR NO Basmati 6 CGR NO- 72 Churhala dhan 29 CGR NO Kondi ajan 7 CGR NO- 218 Lal batra 30 CGR NO Basmati (I) 8 CGR NO- 242 Lallo CGR NO Beo (I) 9 CGR NO- 255 Machhri kata 32 CGR NO Bodi 10 CGR NO- 300 Parra dhan 33 CGR NO Bhata ajan 11 CGR NO- 320 Pinna basengi 34 Swarna sub 1 12 CGR NO- 369 Sakra 35 IGKVR 1 13 CGR NO- 389 Satka 36 IGKVR CGR NO- 447 Shyam jir 37 Karma mahsuri 15 CGR NO- 529 Banko II 38 Badshabhog Selection 1 16 CGR NO- 659 Changadi 39 Dubraj Selection 1 17 CGR NO- 746 Dhaura 40 Tarunbhog Selection 1 18 CGR NO- 930 Kadam phool 41 Mahamaya 19 CGR NO- 953 Kakdo 42 Safri CGR NO- 993 Kanji 43 IGKVR 2 21 CGR NO Machhari 44 Indira Aerobic Dhan 1 ankhi 22 CGR NO Nalla wadlu 45 TN 1 23 CGR NO Parra 46 Jaya 47 MTU Leaf characters: Basal leaf sheath color: The color of the leaf sheath, which is wrapped around the culms

57 39 above the basal node, was visually recorded at early boot stage on individual plants. The categories observed were green, light purple, purple lines and purple color at basal leaf sheath. Intensity of green color in leaf: The intensity of green color of leaves was visually recorded at early boot stage by observation of a group of plants. The major categories recorded were light, medium and dark green color. Anthocyanin coloration on leaf: The presence or absence of anthocyanin coloration on leaf was recorded at early boot stage by visual assessment in a group of plants. Distribution of anthocyanin coloration on leaf blade: The distribution of anthocyanin coloration on leaf was recorded at early boot stage by visual assessment of group of plants. The major categories are on leaf tips only, on leaf margins only, in blotches only and uniform presence of anthocyanin color on leaf lemma. Anthocyanin coloration on leaf sheath: The presence or absence of leaf sheath anthocyanin coloration was recorded at early boot stage by visual assessment of group of plants. Intensity of anthocyanin coloration on leaf sheath: The intensity of anthocyanin coloration on leaf sheath was visually recorded at early boot stage of a group of plants. The major groups recorded were very weak, weak, medium and strong based on anthocyanin coloration. Presence of pubescence on leaf blade surface: The intensity of leaf pubescence was recorded at early boot stage by visual assessment of individual plants of every land race. The categories

58 40 observed under this character were absence, weak, medium, strong and very strong presence of pubescence on blade surface. Presence of auricles on leaf: Most of the leaves possess small paired hairy appendages on either side of the base of the blade. These appendages are called auricles. The presence or absence of auricles was visually assessed at early boot stage by observation of individual plant. Anthocyanin coloration of auricles: The anthocyanin coloration of auricles i.e. colorless, light purple and purple color in auricles was recorded at early boot stage with visual assessment by observation of individual plants. Presence of collar on leaves: The presence or absence of leaf collar that is the juncture between leaf blade and leaf sheath was recorded at early boot stage by visual assessment of individual plants. Anthocyanin coloration of collar: The presence or absence of anthocyanin coloration at collar was recorded at early boot stage by visual assessment of individual plants. Presence of ligule on leaf: Presence or absence of papery membrane at the inside juncture between the leaf sheath and blade called ligule was recorded at early boot stage by observation of individual plants or parts of plants. Shape of ligule: The shapes of ligule i.e. truncate, acute and split shapes was recorded at early boot stage by visual assessment of individual plants.

59 41 Color of ligule: The colors of ligule i.e. green, light purple or purple was recorded at early boot stage by visual assessment of individual plants or parts of plants. Length of leaf blade: The length of the leaf blade was measured in centimeter and categorized in to short, medium and long leaves. Width of leaf blade: The width of the leaf blade was measured in centimeter and categorized in to narrow, medium and broad leaves. Attitude of flag leaf (early observation): The flag leaf attitude was recorded at beginning of anthesis through visual assessment and categorized in to erect, semi-erect, open and spreading types by observation of group of plants. Attitude of flag leaf (Late observation): The attitude of flag leaf was recorded at ripening stage through visual observation and grouped in to erect, semi-erect, horizontal and deflexed classes according to the features of majority of plants of the landraces. Leaf senescence: The leaf senescence was visually recorded at stage when caryopsis became hard on a group of plants. Senescence is categorized in to early, medium and late classes Characters of culm: Culm: attitude: The Culm attitude was recorded at early boot stage by visual assessment and grouped in to erect, semi-erect, open or spreading culm

60 42 attitude by observation of individual plants. Stem thickness: The stem thickness was recorded at milk development stage. Thickness was measured in centimeter and categorized into thin, medium and thick stem classes. Anthocyanin coloration of nodes: The presence or absence of anthocyanin coloration of nodes was recorded at milk filling stage through visual assessment of individual plants nodes. Intensity of anthocyanin coloration of nodes: The intensity of anthocyanin coloration on nodes was recorded at milk filling stage of each landrace and through visual assessment the plants are categorized in to weak, medium and strong intensity of anthocyanin coloration at node. Anthocyanin coloration of internodes: The presence or absence of anthocyanin coloration on internodes was recorded at milk development stage through visual assessment of each landrace Flower characters: Days to 50 percent flowering: Number of days was recorded from date of sowing to the days when primary panicles in 50 percent plants were emerged. Color of stigma: The color of stigma was recorded at stage of half-way anthesis and grouped in to white and purple stigma through visual assessment by

61 43 observation of individual plants Characters of panicle: Panicle length (cm): Panicle length was measured at the time of maturity from the base of panicle to the tip of last spikelet prior to harvesting. The categories under this class are very short (<16 cm), short (16-20 cm), medium (21-25 cm), long (26-30 cm) and very long (>30 cm). Curvature of main axis of panicle: The curvature of main axis of panicle was recorded at ripening stage and grouped into straight, semi-straight, drooping and deflexed classes through visual assessment by observation of a group of plants. Number of effective tillers per plant: The numbers of panicle bearing tillers of the plants were counted in five random plants. Density of pubescence of lemma: The density of pubescence of lemma was recorded at beginning of anthesis to dough development stage through visual assessment and grouped in to absent, medium and strong categories by visual observation of individual plants. Anthocyanin coloration on apex of lemma: The anthocyanin coloration on apex of lemma was recorded at half way of anthesis by visual observation and grouped into absent, very weak, weak, strong and very strong.

62 44 Anthocyanin coloration below apex of lemma: The anthocyanin coloration below apex of lemma was recorded at half way of anthesis by visual observation and grouped into absent, weak, medium and very strong. Presence of secondary branching on panicles: The presence or absence of secondary branching was recorded at ripening stage through visual observation of a group of plants. Density of secondary branching on panicles: The panicles which possess secondary branching were classified in to weak, strong and clustered branching categories. Observations were visual observed on a group of plants. Exertion of panicle: The panicle exertion was recorded at ripening stage and classified into partly exerted, exerted and well exerted classes. The classes were recorded through visual assessment of a group of plants Spikelet characters: Color of lemma and palea: The lemma and palea color was recorded at dough development to ripening stage through visual assessment of group of plants of landraces and classified into straw, gold and gold furrows on straw background, brown spots on straw, brown furrows on straw, brown, reddish to light purple, purple spots on straw, purple furrows on straw and purple black. Presence of awns on panicles: The individual landraces were classified on the basis of presence or absence of awns at ripening stage and assessed through visual observation of

63 45 a group of plants. Color of awns: The color of awns was recorded at ripening stage through visual assessment of individual plants and grouped into classes yellowish white, yellowish brown, brown, reddish brown, light red, red, light purple, purple and black on the basis of awn color. Length of longest awn: Length of longest awn was recorded at ripening stage through centimeter measurement of individual panicle and grouped into very short, short, medium and long awn length. Distribution of awns: The distribution of awns was recorded at ripening stage through visual assessment by observation of individual plants and grouped in to presence of awns at tip only, upper half only and whole length. Days to maturity: This was recorded in days from sowing to maturity. This character is categorized into very early, early, medium, late and very late duration. Color of sterile lemma: The color of sterile lemma was recorded at maturity stage when caryopsis get hard by visual assessment by observation of individual panicle and grouped into straw color, purple and gold sterile lemma color Grain characters Grain length (mm): The average length of randomly selected ten hulled spikelets was measured in terms of millimeters. This is grouped into very short, short,

64 46 medium, and broad grain. 100 grain weight (g): Thousand seeds of each of the entry were taken randomly and weighed in gram. Grain yield per plant (g): The grain (filled) yield of each of the five plants was recorded in grams after sun drying for 5-8 days after harvesting and averaged. Biological yields per plant (g): Weight of each of the five plant excluding root was recorded in grams after sun drying for 5-8 days after harvesting and averaged. Harvest index (%): The ratio of grain yield to the biological yield was calculated and expressed as percentage. Harvest index was calculated as follows: Grain yield Harvest index (%) = Biological yield Grain quality characters: Following grain quality characters were recorded: Hulling (%): 100 g of paddy sample was used; it was properly cleaned, before starting the dehulling. The dehusking of rice was done by dehusker and

65 47 hulled rice weight was recorded. Weight of the dehusked kernel Hulling percentage = X 100 Weight of paddy Milling (%): Brown rice was put into standard miller or polisher and later milled rice weight was recorded. Weight of polished kernel Milling percentage = X 100 Weight of paddy Head rice recovery (%): From milled rice the ¾ kernel was taken as whole grain. The sorting out of full and broken rice was done and its weight was recorded. Weight of whole polished kernel Head Rice Recovery = X 100 Weight of paddy Amylose content: Procedure for determining Amylose content: (Juliano, 1979) Weighing of 100 mg of fine powdered rice grain in 100ml volumetric flask. Add 1 ml 95% ethanol and 9 ml of 1N NaOH. Then it was heated for 10 minutes in pre-heated water-bath. Cool it and make up 100 ml volume with distilled water. From this, 5 ml sample in volumetric flask was taken into another 100 ml volumetric flask. 1ml of acetic acid and 2ml of potassium iodide (KI) reagent was added. Again the volume was made up with 100 ml distilled water and kept for 20 minutes. Finally the reading of the sample at 620 nm on spectrophotometer was recorded. Amylose (percent) = R X 76.92SS R = Reading at 620nm spectrophotometer

66 48 Table 3.2: Scale for Amylose test Very low <10% Low 11-19% Medium 20-25% High 26-30% Very High >30% Alkali spreading value and Gelatinization temperature: Alkali spreading values were determined as per procedure described by Jennings et al. (1979) and is as follows (i) (ii) (iii) (iv) (v) Six milled rice kernels without cracks were selected of each variety from each replication and placed in Petri dishes. 10 ml of 1.7 % KOH was added to each Petri dish. These were evenly placed in Petri dishes to allow enough space for spreading. Petri dishes were covered and placed for 23 hours at constant temperature of 30 C. Disintegration of endosperm was visually rated as per following scale (Little et al. 1958). Table 3.3: Alkali spreading value classification along with gelatinization temperature Classification Alkali spreading Gelatinization temperature (GT) value (ASV) 1-2 Low High >74 0 C 3 Low, intermediate High, intermediate 4-5 Intermediate Inter-mediate (70 0 C 74 0 C) 6-7 High Low (55 0 C 69 0 C)

67 49 Table 3.4: Numerical scale for scoring Alkali spreading value Score Spreading Clearing 1 Kernel not affected Kernel chalky 2 Kernel swollen Kernel chalky collar powdery 3 Kernel swollen, collar complete and Kernel chalky collar narrow cottony or cloudy 4 Kernel swollen, collar complete and Center cottony, collar wide cloudy 5 Kernel split or segregated, collar Center cottony, collar complete and wide clearing 6 Kernel dispersed merging with collar Centre cloudy collar clear 7 Kernel completely dispersed and Center and collar clear intermingled Aroma: Aroma was determined at post-harvest stage using the technique developed at International Rice Research Institute, Philippines (Jennings et al., 1979). According to this 20 to 30 freshly harvested milled grains were taken in a test tube with 20 ml of distilled water. Stoppers were put on the mouth of test tubes and placed in boiling water bath for minutes. Test tubes were removed and cooled. Aroma was then detected by smelling and categorized into presence and absence of aroma. Gel consistency: Gel consistency determines the cohesiveness, tenderness and consistency gloss of cooked rice. Harder gel consistency is associated with harder cooked rice and this feature is particularly evident in high amylase rice. Hard cooked rice also tends to be less sticky. Method of determining gel consistency was given as follow: Take 100 mg of rice flour in culture tubes. Add 0.2 ml of ethanol containing 0.25% thymol blue. Shake the tube and add 2 ml of 0.2 N Potassium Hydroxide (KOH). Mix the solution on a cyclone mixer. Keep the test tube in water bath at C for 8 minutes after putting one glass tube marble on each test tube.

68 50 After removing the culture tubes from water bath cool them for 5 minutes. Mix the solution on cyclone mixer. Keep the culture tube in low temperature bath at 0-2o C for 20 minutes. The culture tubes are removed from ice bath and laid horizontally for one hour over graph paper. Length of blue colored gel from the inside bottom of the test tube to the gel front was then measured as gel consistency of the sample and categorized as: mm Hard gel consistency mm Medium gel consistency mm Soft gel consistency Chalkiness: The degree of chalkiness describes the milled sample rices with respect to (a) White belly (b) White center (c) White back. Notation Notation Kernel Area (Extent) A Absent None VOC Very occasionally Small (less than 10%) kernel OC present Occasionally present Medium (11% to 20 %) P Present Long (more than 20%) Grain Shape: Based on length and L/B ratio the grain type is classified as per the guidelines of DUS, PPV & FR State Kernel length (mm) Length/breadth ratio Short Slender < 6.0 > 3.0 Short Bold < 6.0 < 2.5 Medium Slender < Long Slender > 6.0 > 3.0 Long Bold > 6.0 < 3.0 Basmati type > 6.61 > 3.0 Extra Long Slender > 7.5 > 3.0

69 Molecular Study Forty seven lines were used for molecular characterization which included dwarf, semi-dwarf, semi-tall and tall rice genotypes. For assessing the genetic diversity of rice germplasm molecular study was performed, which included DNA isolation, quantification, dilution of DNA, PCR amplification using SSR primers, electrophoresis using polyacrylamide gel, scoring and analysis of data Genomic DNA isolation Whole genomic DNA was extracted out from rice seedlings of each of the landraces of rice. The protocol CTAB method of DNA isolation from rice seedling leaves was as follows. Young plant leaves were collected at seedling stage, about one gram of leaves bits were cut by scissors and put in 2 ml of Eppendrof tube. a. Add 100µ l of CTAB extraction buffer and three metal beads into Eppendrof tube and keep them into blocks. b. These blocks are then fixed into tissuelyser and grind the leaves for five minutes. c. Add remaining 600 µl of CTAB extraction buffer into it. d. Spin the tubes for one minute and vertex it. e. Add 700 µ l of chloroform isoamylalcohol (24:1). f. Vertex the sample. g. Centrifuge it for 10 min at rpm in centrifuge machine. h. Transfer the supernatant in 1.5 ml of fresh Eppendorf tube, (Repeat the protocol from step e to h) i. Add 70 µ l of Sodium acetate and about 400 µ l of pre-chilled isopropanol (equal volume of the supernatant transferred) in this and kept it for incubation at 4 0 C for 2 hr. or C for overnight. j. Centrifuge it for rpm pellets of DNA is seen at the bottom of the tube. k. Decant the solution and add 50 µ l of 70 % ethanol for

70 52 washing and centrifuged at rpm for 3 minutes. l. Decant the solution and dry the pellet for 2 hours or overnight until the smell of ethanol is evaporated. m. Finally dissolved the pellets in 50 μl of TE buffer and dissolve the pellet. n. Stored at C until use Nanodrop spectrophotometer based quantification of DNA For quantification, DNA samples isolated from each line were quantified on Nano Drop Spectroscopy (NANODROP, 2000c). After quantification, the DNA was diluted with TE buffer such that the final concentration of DNA was 50 ηg / μl for PCR analysis PCR amplification using SSR and ISSR primers: About, 2 μl of diluted template DNA (50 ηg/μl) of each line was dispensed in the bottom of 96 wells of PCR plates (AXYGEN-MAKE). Separately cocktail was prepared in an eppendorf tube as described in Table 3.5. About 18 μl of the cocktail was added to each tube to make final volume 20 μl. Then, the PCR was set as per the temperature profile given is Table 3.6 and 3.7. Table 3.5: PCR mix for one reaction (Volume 10 μl) Reagent Stock Concentration Volume (μl) Nanopure H2O PCR buffer A 10 X 1.0 dntps (Mix) 1.0 mm 1.0 Primer (forward) 5 ρmol 0.5 Primer (reverse) 5 ρmol 0.5 Taq polymerase 1 U/ μl 0.05 DNA template 50 ηg/ 1.0 Total 20

71 53 Table 3.6: Temperature profile used for PCR amplification using micro- satellite Markers Steps Temperature ( C) Duration Cycles Activity (min.) Denaturation Denaturation Annealing Extension Final Extension 6 4 Storage Visualization of amplified products in Polyacrylamide gel electrophoresis Five percent polyacrylamide gels (vertical) were used for better separation and visualization of PCR amplified microsatellite products, since polyacrylamide gels have better resolution for amplified products. Gels were casted in electrophoresis unit. Glass plates were prepared before making the gel solution. Both glass plates (outer and inner notched glass plates) were cleaned thoroughly with warm water, detergent and then with deionized water Assembling and pouring the gel Gasket was fixed to the three sides of the outer plate (without notches). Spacers of 1.5mm thickness were placed along the sides by just attaching the gasket of outer plate. Later, notch plate was kept on the outer plate so that spacers were between the two plates. Clamps were put on the three sides of plates leaving notch side of unit. It was checked with water to found any leakages. For casting each gel, 65 ml of acrylamide gel (5%) solution was prepared just prior to pouring. For each 65 ml of solution, 70 μl of TEMED (N-N-N-N- Tetramethylethylene diamine) and 700 μl of (freshly prepared)

72 54 ammonium per sulphate (APS, 10%) were added to initiate the polymerization process. The contents were mixed gently by swirling, but bubbles were avoided. Before pouring, assembly was kept on the bench top so that it made 45 degree angle with bench top. Then gel solution was poured from notch side with maximum care to avoid air bubbles. Comb of 1.5 mm thickness (63 wells) was inserted with tooth side in the gel. Later, the assembly was kept for polymerization for min Electrophoresis After polymerization process, gasket was removed and assembly was kept in the electrophoresis unit with electrophoresis unit clamps so that notch side facing inner side of the unit and facing other plate without notch to outer side. TBE (1x) was poured in upper tank in the unit and the rest was poured in bottom chamber. Comb was removed with care so that it does not disturb the wells formed in the gel. Atlast, 4 μl loading dye (10 x) was added to PCR products. Finally, 5 μl of each sample were loaded into the wells for facilitating the sizing of the various alleles. Ladder (50bp, Bangalore GeNei, Mereck Bio Science) was loaded in the first well. Gel was run at 180 volts till the dye reached bottom of the gel. After electrophoresis, gels were stained with Ethidium bromide (10μl/ 100ml) and visualized in BIORAD Gel Doc XR Visualization of bands After electrophoresis, clamps were removed and glass plates were separated without damaging the gel. a) Gel was taken out from plate into staining box with care by flipping the

73 55 gel with help of spatula and by pouring little amount of water for easy removal. b) Ethidium bromide solution (prepared by adding 10 μl to 100 ml double distilled water) was poured into the staining box to stain the gel. c) It was agitated for about five minutes to stain the gel. d) Gel stained with Ethidium Bromide was washed two times with double distilled water to have clear images. e) The gels were scanned with the help of BIO-RAD gel doc XR+. f) Care was taken while using TEMED and staining with Ethidium bromide solution as they are carcinogenic and mutagenic agents, respectively Detection of varietal polymorphism using simple sequence repeats (SSR) Primers The varietal polymorphism was detected by using 69 SSR primers. Stock solutions: a) Stock preparation for dntps 10μl of each dntps (i.e. datp/dctp/dgtp/dttps) was taken in 1.5 ml of Eppendorf tube, mix well by vortexing, final volume is made to 40μl having 100 mm dntps stock concentration. For dilution 10 μl dntps of stock solution was taken in 1.5 ml Eppendorf tube and add 990 μl SIGMA water to the tube, so the total volume became 1000 μl. This makes 1mM dntps is ready to use for PCR. b) DNA extraction buffer: Tris HCl (1M; ph-8) (0.5M; ph-8) NaCl (4M) SDS (20% W/V) 5 ml EDTA 5 ml 7.5 ml 5 ml

74 Scoring and analysis of data: The banding pattern of population developed by each set of primer was scored separately. The size of amplified fragments was determined by comparing the migration distance of amplified fragments relative to the molecular weight of known size markers, 50 base pairs (bp) DNA ladder. Particular base pair position was scored as 1 and absence of band for that particular base pair position was scored as 0 (zero). For analysis NTSYSpc software was used to construct a UPGMA (unweighted pair group method with arithmetic averages) dendrogram showing the distance-based interrelationship among the genotypes Reagents and solutions a) Primers: Highly variable rice microsatellite markers from Imperial life sciences (ILS), USA or Sigma Aldrich were used in the study. b) dntps (datp/dctp/dgtp/dttp):10 mm stock of dntps (Bangalore GeNei, Mereck Bio Science) was used. c) PCR buffer (10X): 10X GeNei buffer was used. d) Taq polymerase: 1 unit/μl, Taq polymerase (GeNei) was used for PCR. Final volume was adjusted to 100 ml with distilled water and the ph was maintained to 8.0. c) TE buffer: 1M Tris-HCl (ph-8) 10 ml 0.5M EDTA (ph-8) 2 ml Final volume was adjusted to 100 ml and autoclaved and the ph was maintained to 8.0. d) EDTA (0.5M; ph-8): g of EDTA was dissolved in 700 ml of distilled water. The ph was set to 8 using NaOH. Final volume was adjusted to 1000 ml with

75 57 distilled water and sterilized by autoclaving. e) 4M NaCl: g of NaCl was dissolved in 80 ml of distilled water. Final volume was adjusted to 100 ml and sterilized by autoclaving. f) 1M Tris HCl (ph 8.0 at 25 C): g of Trizma base was dissolved in 200 ml of distilled water. The ph was set to 8.0 using concentrated HCl. The final volume was adjusted to 250 ml with distilled water and sterilized by autoclaving Solutions for electrophoresis a) 10X TBE buffer: Tris base EDTA (0.5M) Boric Acid 104 g 40 ml 55 g Distilled water ml Final volume was adjusted to 1 liter with distilled water. b) 1X TBE buffer: 100 ml of 10 X TBE ml of distilled water were taken to make 1 liter of 1X TBE. c) 10X loading dye Sucrose 667 mg Bromophenol Blue 4.2 mg Water 1.0 ml d) 50 bp DNA ladder: GeNei Mereck Biosciences, Bangalore Company was used as known marker. This is prepared by taking 0.1ml of 50bp with 0.2 ml of 6X loading buffer and making the volume with 0.4 ml sigma water Stocks and solutions for PAGE a) Five percent PAGE solution (1000ml) Acrylamide when dissolved in water, slow spontaneous auto polymerization takes place joining molecules together by head and tail

76 58 fashion to form long single chain polymers. A solution of these polymer chains become viscous but simple slide over one another. Acrylamide 47.5g Bis-Acrylamide 2.5g 10X TBE 100 ml Acrylamide and bis-acrylamide were weighed and dissolved in (to make up volume to 1000 ml) 500 ml distilled water and then added to the beaker containing 100 ml of 10X TBE and the volume was made upto 1000 ml by adding autoclaved double distilled water. The solution was sterilized by passing through 0.22 micron filter and stored in amber colour bottle at 4 0 C. b) 10% Ammonium persulphate (APS) solution was prepared by mixing following components: Most frequent used linking agent for polyacrylamide gel. Ammonium persulphate 1.0g Distilled water 10ml c) TEMED Stabilizers free radicals and improve polymerization Instruments used in the laboratory Veriti 96 well thermal cycler (Applied Biosystems) Refrigerated centrifuge Microwave oven C.B.S. PAGE unit with power pack Transilluminator and Gel documentation system( BIORAD Gel Doc XR+) Micropipettes Eppendorf tubes Electronic balance

77 Statistical analysis The data recorded in respect to different morphological and quantitative characters on the forty eight short and long grain accessions were subjected to the statistical analysis: Analysis of variance Firstly, mean values were worked out for all traits for each genotype. These mean data were utilized to calculate variability parameters viz. range, standard deviation, and coefficient of variation. ANOVA is calculated by using O.P.STAT software. Table 3.8 Skeleton of analysis of variance Source of Degree of Sum of square Mean sum of Variation Freedom square Replication (r-1) SSR MSR MSR/MSE Genotypes (g-1) SSG MSG MSG/MSE Error (r-1)(g-1) SSE MSE Total (rg-1) SS total Assessment of variability: Range: The lower and higher value of a character determines its range, which is expressed as follows: Range = Highest value Lowest value Mean: The mean is calculated by the following formula ΣXi/ N Where, ΣXi = summation of all observations N = Total number of observations Standard deviation (SD): Standard deviation is the root of sum of squares of deviation

78 60 divided by their number, calculated by the formula: Where, Standard deviation = Σd 2 / n d 2 = Sum of squares of deviations n = Total number of observations Standard error (SE): Standard error = S/ n Where, S = Standard deviation n = Total number of observation Estimation of coefficients of variation: The coefficient of variation for different characters was estimated by formula as suggested by Burton and De Vane (1953). a) Phenotypic coefficient of variation (PCV %) PCV (%) = 2 x p x 100 b) Genotypic coefficient of variation (GCV %) GCV (%) = 2 g x x Heritability (broad sense): It is the ratio of genotypic variance to the phenotypic variance (total variance). Heritability for the present study was calculated in a broad sense by adopting the formula as suggested by Hanson et al., (1956): x 100 Where, h 2 (bs) = heritability in broad sense σ 2 g = Genotypic variance

79 61 σ 2 P = Phenotypic variance The estimates of heritability broad sense were classified as low, moderate and high according to Robinson (1966): < 50 % Low % Moderate > 70 % High Genetic advance Improvement in the mean genotypic value of selected plants over the parental population is known as genetic advance. Expected genetic advance (GA) was calculated by the method suggested by Johnson et al., (1955) Where, G A = K.h 2. p GA= Genetic advance K = Constant (Standardized selection differential) having the value of 2.06 at 5 per cent level of selection intensity Genetic advance as percentage of mean It was calculated by the following formula n n n n n n n n Where, GA = genetic advance = mean of the character The range of genetic advance as percent of mean is classified as suggested by Johnson et al., (1955) GA > 20 per cent High GA = per cent Moderate GA < 10 per cent Low

80 Association analysis: Correlation coefficients analysis measures the mutual relationship between various characters at genotypic (g), phenotypic (p) and environmental levels with the help of following formula suggested by Miller et al. (1958) Principal Components Analysis It is a multivariate statistical analysis to reduce the data with large number of correlated variables into a substantially smaller set of new variables through linear combination of the variables that accounts most of the variation present in the original variables. Principal components are generally estimated either from correlation matrix or covariance matrix. When the variables are measured in different units, scale effects can influence the composition of derived components. In such situations it becomes desirable to standardize the variables. In the present investigation correlation matrix was used to extract the principal components. PCA is a well-known method of dimension reduction (Massy, 1965; Jolliffe, 1986), which seeks linear combinations of the columns of X with maximal variance, or equivalently, high information. The analysis was performed using XLSTAT 2014 software Cluster analysis Cluster analysis is a multivariate method which aims to classify a sample of subjects (or objects) on the basis of a set of measured variables into a number of different groups such that similar subjects are placed in the same group. Cluster analysis has no mechanism for differentiating between relevant and irrelevant variables. Therefore, the choice of variables included in a cluster analysis must be underpinned by conceptual considerations. This is very important because the clusters formed can be very dependent on the variables included.

81 63 In the present study, Euclidian distance between genotypes was calculated from the standardized data matrix by Unweighted Pair Group Method using Arithmetic Averages (UPGMA) method and clustering was done by Agglomerative Hierarchical method using XLSTAT 2014 software.

82 CHAPTER- IV RESULTS AND DISCUSSION Rice is the principal cereal food crop grown most extensively in the tropical and sub-tropical regions of the world. Though, cultivated on large area, rice crop is characterized by low productivity due to lack of high yielding varieties adapted to different seasons and agronomic conditions. Now most of the plant breeders recognize the importance of utilizing genetic diversity in breeding programmes to meet the continuously expanding needs of varietal improvement. The experimental results obtained from present investigation have been described in following heads: 4.1 Agro-morphological and quality characterization 4.2 Estimation of genetic variance Analysis of variance Mean performance of genotypes and variability parameters of different characters Genotypic and phenotypic component of variation Heritability and genetic advance as percent of mean 4.3 Association analysis Correlation Coefficient 4.4 Principal component analysis 4.5 Cluster analysis 4.6 Molecular characterization Development of genotypic data based on SSR markers SSR marker analysis a Similarity coefficient analysis and Clustering b Polymorphism Information Content c Single marker analysis 4.1 Agro-morphological and quality characterization These observations were recorded on 47 rice germplasm accessions all descriptors showed makeable differences in their distribution 64

83 65 and percentage value of agro-morphological and quality characters of forty seven accessions of rice are presented in Table 4.1., Table 4.2 and Fig 4.1) Coleoptile colour: All forty seven landraces under study were classified into two different classes of coleoptiles colour (DRR, DUS descriptors), colourless and purple, out of which coleoptiles colour was observed under two categories; white (36) and purple (11) (Fig: 4.1a and 4.2) Basal leaf sheath colour: Basal leaf sheath colour was observed under two categories, green (45) and purple line (2) (Fig: 4.1b and 4.3) Leaf: intensity of green colour: This trait was observed under three categories; light (30) medium (4) and dark green (13) (Fig: 4.1c and 4.4) Leaf: Pubescence of blade surface: This trait was observed under four categories; weak (20), strong (4), medium (21) and very strong (2) (Fig: 4.1d) Leaf: Auricles: Leaf auricle was present in all forty seven germplasm accessions (Fig: 4.5) Leaf: Anthocyanin colouration of auricles: This trait was observed under three categories; colourless (43), light purple (2) and purple (2). (Fig: 4.1e and 4.6) Leaf: Collar: Leaf collar was present in all forty seven rice genotypes (Fig: 4.7) Leaf: Ligule: This trait was found in all the forty seven rice genotypes (Fig: 4.8) Leaf: Shape of ligule: Shape of ligule was split in all forty seven rice genotypes.

84 Colour of ligules: Colour of ligule was found white in all the forty seven germplasm accessions. (Fig: 4.9) Culm: Attitude: This trait was observed under three categories; erect (30), semi-erect (15) and spreading (3) (Fig: 4.1f) Altitude of flag leaf (Early): This trait was observed under three categories; erect (21) and semi-erect (15) and horizontal (11) (Fig: 4.1g and 4.10) Spikelet: Density of pubescence of lemma: This trait was observed under four categories; weak (8), medium (13), strong (18) and very strong (8) (Fig: 4.1h) Male Sterility: Male sterility was found absent in all the forty seven germplasm accession Lemma: Anthocyanin colouration of keel: Anthocyanine colouration of keel of lemma was found under three categories; Medium (1), Strong (2), and absent (44) (Fig: 4.1i and 4.11) Lemma: Anthocyanin colouration of area below apex: This trait was found under five categories; absent (38), weak (2), medium (2), strong (5) (Fig: 4.1j and 4.12) Lemma: Anthocyanin colouration of apex: This trait was found under five categories; absent (29), weak (2), medium (6), strong (8) and very strong (2) (Fig: 4.1k) Spikelet: Colour of Stigma: The colour of stigma of different forty seven accessions were found under two categories; white (39) and purple (8) (Fig: 4.1l and 4.13) Stem: Anthocyanine colouration of nodes: This trait was found under two categories; present (1) and absent 46) (Fig: 4.1m).

85 Stem: Intensity of anthocyanin colouration of nodes: This trait was found under four categories; absent (46) and strong (1). (Fig: 4.1n) Stem: Anthocyanin colouration of internode: This trait was found in two categories; present (1) and absent (46). (Fig: 4.1o) Flag leaf: Attitude of blade (late observation): This trait was found in three categories; erect (15), semi-erect (17) and horizontal (15) Panicle: Curvature of main axis: Curvature of panicle was found under four categories; deflexed (21), drooping (11), semi-straight (12) and straight (3) (Fig: 4.1p and 4.14) Spikelet: Colour of tip of lemma: This trait was found under four categories; black (3), brown (18), red (1) and yellow (25) (Fig: 4.1q and 4.15) Lemma and palea colour: Lemma and palea colour was observed under six categories; brown (3), brown furrows on straw (5), brown spot on straw (2), gold and gold furrows on straw (7), red (8) and straw (26) (Fig: 4.1r) Panicle: Awns: This trait was observed under two categories; present (15) and absent (32) (Fig: 4.1s and 4.16) Panicle: Colour of awns (late observation): This trait was found under four categories; absent (32) and yellowish (15) (Fig: 4.1t and 4.17) Panicle: Distribution of awns: This trait was found in three categories; tip only (6), whole length (9) and absent (32) (Fig: 4.1u) Panicle: Presence of secondary branching: Secondary branching in panicle was present in all forty seven germplasm accessions.

86 Panicle: Secondary branching: Secondary branching of panicle was found in two categories strong (18) and weak (29). (Fig: 4.1v and 4.18) Panicle: Attitude of branches: This trait was found under five categories; straight (1), erect to semi-erect (11), semi-erect (13), semi-erect to spreading (4) and spreading (15) (Fig: 4.1w) Panicle: Exertion: The exertion of panicle was found under three categories; mostly exerted (16), partly exerted (11) and well exerted (20) (Fig: 4.1y and 4.20) Leaf: Senescence: This trait was observed under four categories; early (19) and medium (13) late (7) and very late (8) (Fig: 4.1y) Sterile lemma: Colour: The colour of sterile lemma of all forty seven accessions were found straw Grain: Phenol reaction of lemma: Out of 47 rice accessions phenol reaction of lemma exhibited in 29 genotypes and in 18 accessions phenol reaction is absent (Fig 4.21) Decorticated grain shape: This trait was found under 3 category slender (17), medium (25) and Bold (5) Decorticated grain: colour: Out of 47 rice accessions white colour was observed in 30, red in 12 and light red in 5 genotypes Aroma: Out of forty seven genotypes, 5 showed mild aroma and rest 42 genotypes were non-scented Expression of white core: Expression of white core was observed in all genotypes and was categorized into four categories i. e, large (18), medium (14), small (11), very small (3) and fully chalky (1).

87 69 Table 4.1: Frequency distribution of agro-morphological and quality traits based on DUS S. No. Traits Category Number Frequency (%) 1 Coleoptile colour White Purple Basal leaf: Sheath colour Green Purple line Leaf: Intesity of green colour Light Medium Dark Leaf: Anthocyanin Absent colouration Present Leaf: Distribution of Margins only anthocyanin colouration 6 Leaf sheath anthocyanin Absent colouration Present Leaf sheath: intensity of Medium anthocyanin colouration Very strong Leaf: Pubescence of Weak blade Surface Medium Strong Very strong Leaf: Auricles Present Leaf: Anthocyanin colouration of auricles Colourless Light Purple Purple Leaf: Collar Present Leaf: Anthocyanin Absent colouration of collor Present Leaf: Ligule Present Leaf: Shape of Ligule Split Leaf: Colour of ligule White Purple Leaf: Length of blade Short Medium Long Leaf: Width of blade Medium Broad Culm: Attitude Erect Semi erect Spreading Time of heading (50% plants with panicles) 20 Flag leaf: Attitude of blade(early observation) Medium Late Very late Erect Semierect Horizontal

88 70 21 Spikelet: Density of Weak pubescence of lemma Medium Strong Very strong Male sterility Absent Lemma: Anthocyanin colouration of keel 24 Lemma: Anthocyanin colouration of area below apex 25 Lemma: Anthocyanin colouration of apex Strong medium Absent Strong medium Weak Absent Absent Weak medium Strong Very strong Spikelet: colour of stigma Purple White Stem: Thickness Thin Thick Medium Stem: Length(excluding panicle) 29 Stem: Anthocyanin colouration of nodes 30 Stem: Intensity of anthocyanin colouration of nodes 31 Stem: Anthocyanin colouration of internode 32 Panicle: length of main axis 33 Flag leaf: Attitude of blade(late observation) 34 Panicle: Curvature of main Axis Very Short Short Medium Long Very long Present Absent Strong Absent Present Absent Very short Short Medium Long Erect Semi erect Horizontal Straight Semi straight Deflexed Drooping Panicle: number per plant Few Medium Many Spikelet: Colour of tip of Lemma Yellow Brown Red Black

89 71 37 Lemma and Palea colour Brown Black Brown furrows on straw Brown spot on straw Gold and gold furrows on straw background Straw Panicle: Awns Present Absent Panicle: Colour of Absent awns(late observation) Yellowish white 40 Panicle: Length of Very short longest Awn Short Medium Long Very Long Panicle: Distribution of awns Absent Tip only Whole length Absent Panicle: Presence of Present secondary branching 43 Panicle: Secondary Strong Branching Weak Panicle: Attitude of Straight branches Erect to semi erect Semi erect Semi erect to spreading Spreading Panicle: Exertion Mostly exerted Partly exerted Well exerted Time Maturity(Days) Medium Late Very late Leaf: senescence Early Medium Late Very late Sterile lemma: Colour Straw Grain: Phenol reaction of Lemma Present Absent

90 72 50 Decorticated grain shape 51 Decorticated grain: Colour slender Medium Bold Red Light red White Expression of White core Small Very small Medium Large Full chalky Decorticate grain: Aroma Mild Scented Non Scented

91 73 Coleoptile Colour Leaf: Pubescence of blade surface Leaf: Intesity of Green Colour 8% 4% 39% 61% White purple 45% 43% weak Medium Strong 70% 9% 21% light Medium dark Very strong Fig. 4.1(a): Frequency distribution pattern of Coleoptile colour Fig. 4.1(b): Frequency distribution pattern of leaf pubescence of blade surface Fig. 4.1(c): Frequency distribution pattern of intensity of green colour of leaf Basal leaf: Sheath colour Leaf: Anthocyanin colouration of auricles 4% 4% 4% 96% Green Purple 92% Colourless Light purple Purple Fig. 4.1(d): Frequency distribution pattern of basal leaf sheath colour Fig. 4.1(e): Frequency distribution pattern of anthocyanin colouration of auricle

92 74 Culm: Attitude 2% Flag leaf: Attitude of blade(early observation) Spikelet: Density of pubescence of lemma 75% 23% Erect Semierect Open 23% 32% 45% Erect Semierect Horizntal 38% 17% 17% 28% weak medium strong very strong Fig. 4.1(f): Frequency distribution pattern ofattitude of culm Fig. 4.1(g): Frequency distribution pattern of altitude of blade (Early observation Fig. 4.1(h): Frequency distribution pattern of density of pubescence on lemma Lemma: Anthocyanin colouration of keel 4% 2% 94% Fig. 4.1(i): Frequency distribution pattern of anthocyanin colouration of keel on lemma absent strong medium Lemma: Anthocyanin colouration of area below apex 81% 4% 4% 11% weak medium strong absent Fig. 4.1(j): Frequency distribution pattern of anthocyanin colouration of area below apex on lemma

93 75 Lemma: Anthocyanin colouration of apex Spikelet: colour of stigma Stem: Thickness 15% 13% 4% 4% 17% 62% absent weak medium strong very strong 17% 83% white purple 40% 45% Thin Medium Thick Fig. 4.1(k): Frequency distribution pattern of anthocyanin colouration of apex on lemma Fig. 4.1(l): Frequency distribution pattern of colour of stigma Fig. 4.1(m): Frequency distribution pattern of stem thickness Stem: Intensity of anthocyanin colouration of nodes 2% Stem: intensity of Anthocyanin colouration of internode 2% 98% absent strong 98% Absent Present Fig. 4.1(n): Frequency distribution pattern of intensity of anthocyanin coloration of node Fig. 4.1(o): Frequency distribution pattern of intensity of anthocyanin coloration of internode

94 76 Panicle: Curvature of main axis Spikelet: Colour of tip of lemma Lemma and Palea colour 26% 6% 23% 45% Drooping Deflexed Semistraight Straight 38% 7% 2% 53% Yellow Brown Black 4% 11% 6% 15% 9% 55% Straw Red Fig. 4.1(p): Frequency distribution pattern of curvature of main axis Fig. 4.1(q): Frequency distribution pattern of colour of tip of lemma Fig. 4.1(r): Frequency distribution pattern of colour of lemma and palea Panicle: Awns Panicle: Colour of awns (late observation) 32% 32% 68% present Absent 68% yellowish white Absent Fig. 4.1(s): Frequency distribution pattern of panicle awn Fig. 4.1(t): Frequency distribution pattern of colour of awn

95 77 Panicle: Distribution of awns Panicle: Secondary branching Panicle: Attitude of branches 68% 13% 19% Tips only whole length Absent Fig. 4.1(u): Frequency distribution pattern of distribution of awn 62% 38% weak strong Fig. 4.1(v): Frequency distribution pattern of secondary branch on panicle 34% 17% 2% 4% 43% straight erect to semierect semierct to spreading Fig. 4.1(w): Frequency distribution pattern of altitude of branches Panicle: Exertion Leaf: senescence 43% 23% 34% mostly partially well 15% 17% 28% 40% early medum late very late Fig. 4.1(x): Frequency distribution pattern of panicle exertion Fig. 4.1(y): Frequency distribution pattern of leaf senescence

96 78 Fig 4.2 Coleoptile colour Fig 4.3 Basal leaf sheath colour Fig 4.4 Leaf: intensity of green colour Fig 4.5 Leaf :auricle Fig 4.6 Antocyanin colouration of auricle Fig 4.7 Leaf: collar

97 79 Fig 4.8 Leaf: ligule Fig 4.9 Colour of ligules Fig 4.10 Altitude of flag leaf Fig 4.11 Anthocyanin colour of keelof lemma Fig 4.12 Lemma: Anthocyanin colour of keel Fig 4.13 Colour of stigma

98 80 Fig 4.14 Panicle:curvature of axis Fig 4.15 Colour of tip of lemma Fig 4.16 Panicle: Awns Fig 4.17 Colour of awn Fig 4.18 : Panicle :Secondary branching Fig 4.19 Panicle exertion

99 81 Fig 4.20Grain: Phenol reaction of lemma Fig 4.21a Alkali spreading test Fig 4.21b Alkali spreading test Fig 4.22 Gel consistency

100 Estimation of genetic variance Analysis of variance The analysis of variance of 27 yield and quality traits in 47 rice germplasm accessions are presented in Table 4.2. The statistical procedure which separates or splits the total variation into different components is known as analysis of variation. It is useful in estimating the different components of variance. Such analysis divides the total variation into two main viz, variation between varieties and variation within varieties i.e, environmental variation into genotypic and environmental components. The results of the analysis of variance indicated that the mean sum of squares due to accession for replication were significant for characters like leaf: width of blade, panicle: length of main axis, Plant height, Stem length excluding panicle, panicle length, number if unfilled grains per panicle, total spikelet per panicle, spikelet fertility, paddy length, milled grain length and HRR. The mean sum of squares due to genotypes/ treatments was found to be highly significant for all the traits. This clearly indicates that variability does exist in all the genotypes for all the traits. The significant and relatively large percentage of the total variation attributable to G x E interaction suggests that genotypes responded differently to environment of rice. Under study, presence of high variability for plant height is in agreement with Hein et al. (2007), Sarawgi et al. (2012), Chakravorty et al. (2013). Significant variability for days to 50% flowering was estimated in present study supports the findings of Subba Rao et al. (2013), Sarawgi (2014), Sajid et al. (2015). Significant variability for grain yield observed in this study is supported by the findings of Ogunbayo et al. (2005), Vanisree et al. (2011), Sarawgi et al. (2012) and Tuhia-Khatun et al. (2015).

101 83 Table 4.2: Analysis of variance of 27 yield and quality traits of 47 rice germplasm accessions SV DF Mean Sum of Squares Rep * * * * **s Treat ** 0.064** ** ** 12.45** 83.42** 16.52** 0.55** ** ** Error SV DF Mean Sum of Squares Rep ** 43.53** ** Treat ** ** 61.99** ** ** 1.93** 0.19** 2.78** ** 0.064** Error SV DF Mean Sum of Squares Rep * 38.15* Treat ** ** 12.45** 83.42** 16.52** 0.55** 19.66** Error ** Significant at 1% level of significance; * Significant at 5% level of significance 1= Leaf: Length of blade ; 2 = Leaf: Width of blade ; 3= Plant height ;4= Stem: Length(excluding panicle ; 5= Panicle length ; 6= Panicle weight (g) ; 7= Number of panicles per plant ; 8= 100 seed weight (g) ; 9= Number of filled grains/panicle ;10= Number of unfilled grains/panicle ;11= Total spikelets/ panicle; 12= Spikelet Fertlity % ; 13= Grain weight/plant (g) ; 14= Shoot dry weight/plant (g) ; 15=Harvest Index (%) ; 16= Paddy length ; 17= Paddy width ; 18= Decorticated grain length (mm) ; 19=Decorticated grain width(mm) ; 20= Milled grain length ; 21 ; Milled grain width ; 22= Hulling% ; 23= Milling% ; 24=HRR% ; 25= Gel consistency ; 26=Alkali spreading value ; 27= Content of amylose %

102 Mean and variability parameters for 27 yield and quality traits of 47 rice germplasm accessions Execution of the breeding programmes depends largely on the presence of significant genetic variability to permit effective selection. Relative magnitude of variability present in a crop species helps the breeder to handle the breeding population created by hybridizing the selected donors with high yielding base varieties. Results revealed that high degree of variability was present in the breeding lines for all the characters under study. The mean and variability parameters for 27 yield and quality traits of 47 rice germplasm accessions are presented in table 4.3 and mean performance for different quantitative and quality characters under present study is presented in Appendix D Leaf: Length of blade: The mean of leaf length of blade was found cm with minimum length of blade of cm (Krishna Bhog) and maximum of cm (Basmati (I)). The maximum leaf length of blade was recorded is cm in Basmati (I) followed by cm (Badi bariki) and cm (Basmati). The coefficient of variation observed for this trait was 17.7% Leaf: Width of blade: The mean of width of leaf blade was found 1.62 cm with minimum leaf width of blade of 1.23 cm (sakra) and maximum of 2.15 cm (Dhamna Panda). The maximum leaf: width of blade was recorded 2.15 cm in Dhamna Panda followed by 2.04 cm (Parra Dhan), Lal batra, Badshabhog selection 1, IGKVR1 and Jaya. The coefficient of variation recorded for this trait was 11.10% Time of heading (50% plant with panicle) days: The mean of time of heading (50% plant with panicle) was 113 days with minimum of 96 heading days in Beo (I) and maximum of 135 days in Dubraj Selection 1.

103 85 Table 4.3: Mean and Variability parameters for 27 yield and quality traits of 47 rice germplasm accessions S Characters Mean Standard Standard CV No. Error Deviation (%) 1 Leaf: Length of blade (cm) leaf: width of blade (cm) Plant height (cm) Stem: Length(excluding panicle) (cm) 5 Panicle length (cm) Panicle weight (g) Number of panicles per plant seed weight (g) Number of filled grains/panicle 10 Number of unfilled grains/panicle 11 Total spikelets/ panicle Spikelet Fertlity % Grain weight/plant (g) Shoot dry weight/plant (g) Harvest Index (%) Paddy length (mm) Paddy width (mm) Decorticated grain length (mm) 19 Decorticated grain width (mm) 20 Milled grain length (mm) Milled grain width (mm) Hulling (%) Milling (%) HRR (%) Gel consistency Alkali spreading value Amylose content (%)

104 86 Table 4.3a: List of germplasm categorized into early, medium and late days to flowering Category Early Medium Late Name of Accessions Rambhoj, Churhala dhan, Lal batra, Lal batra, Lallo-14, Machhri kata, Parra dhan, Pinna basengi, Banko II, Changadi, Dhaura, Kadam phool, Kakdo, Kanji, Basmati (I) Dhaur, JS-5, Badal phool, Amakoyali, Sakra, Satka, Shyam jir, Machhari ankhi, Nalla wadlu, Parra, Basmati, Dhamna panda, No :21 (A), Badi barik, Basmati, Kondi ajan, Basmati (I), Jaya, TN1, IGKVR2, MTU1010, Indira Aerobic dhan1, Bodi, Bhta ajan, IGKVR 1, IGKVR1244, Karma Mahsuri, Mahamya. Safri 17, Badshabhog selection 1, Dubraj selection 1, Tarunbhog selection 1, Swarna sub Plant height (cm): The range of plant height varied from cm (Basmati) to cm (Badal phool) with the grand mean of cm. The maximum pant height recorded was cm in Basmati followed by cm (Dhamna Pinda) and cm (Badshah bhog selection 1) and the C.V. was 14.39%. Table 4.3b: List of germplasm categorized into very short, short, medium, long and very long categories for plant height Category Rice germplasm Very short < 91.0 Badalphool Short Dhaur, JS-5, Swarna Sub 1 Medium MTU1010, Jaya, TN1, Skra, Karma Mahsuri, IGKVR 2 Long Churhala Dhan, lallo-14, Machhari Kata, Parra dhan, Pinna Basengi, Satka, Shyam Jir, Banko-II, Changadi, Dhaura, Kadam Phool, Kakdo, Kanji, Machhari Ankhi, NallaWadlu, Parra, IGKVR 1,igkvr 1244, Mahamya,Indira Barani Dhan, Beo (1) Very long >150.0 Amakoyali, Lal Batra, Basmati, Dhamna Pinda, No: 21 (A), Badi Barik, Basmati, Kondi Ajan, Basmati (I),Bodi, Bhata Ajan, Badshah Bhog Selection 1, Dubraj Selection 1, Safri 17, Tarun Bhog Selection 1

105 Stem Length (cm): The mean of stem length was found cm with minimum stem length of cm (Badal Phool) and maximum of cm (Basmati). The maximum stem length was recorded is cm in Basmati followed by cm in Dhamna pinda and cm in Dubraj selection 1. The C.V. was recorded for this trait was 17.54% Panicle: Length of main axis (cm): Four categories of panicle length was found i.e. Very short (< 16 cm), Short (16-20 cm), Medium (21-25 cm) and Long (26-30 cm). The maximum and minimum panicle length were found cm (Amakoyali) and cm (Sakra), respectively, with the overall mean of cm. Highest panicle length was found cm in Amakoyali followed by cm in Basmati and cm in Lallo-14. The coefficient of variation was recorded for this trait was 10.37%. Table 4.3c: List of germplasm categorized into very short, short, medium and long panicle length Category Rice germplasm accessions Short Sakra, Banko II, Nalla Wadlu, Safri 17 Medium Indira Aerobic Dhan 1, Mahamaya, Tarunbhog Selection 1, Dubraj Selection 1, Karma Mahsuri, Swarna Sub 1, Bodi, Kondi ajan, Badi barik, Dhamna Panda, Machhari Ankhi, Changadi, Parra Dhan, Lal Batra, Churhala Dhan, Badal Phool, JS-5 Long Very long Dhaur, Rambhoj, Lallo-14, Pinna Basengi, Satka, Shyam jir, Dhaura, Kadam Phool, Kakdo, Kanji, Parra, No :21 (A), Basmati, Basmati (I), Beo (I), Bhata ajan, IGKVR 1, IGKVR 1244, Badshabhog Selection 1, IGKVR 2, TN1, Jaya, MTU1010 Amakoyali, Machhri Kata, Basmati Number of panicle per plant: The range of number of panicle per plant varied from to 6.00

106 88 with an overall mean of The highest number of panicles per plant was recorded in Safri 17 (19) followed by Badal Phool, No 21(A) (16) and IGKVR 1, IGKV1244 (15). The minimum number of panicle per plant was recorded in Satka (6). The CV was recorded 26.80% for this trait Panicle weight (g): The highest panicle weight was recorded as g (MTU 1010) and minimum as 7.60 g (Sakra). The maximum panicle weight in MTU 1010 was followed by Safri 17 (29.3 g) and then in JS 5 (29 g). The CV was recorded as 41.46% seed weight (g): 100-seed weight ranged from gm to 2.94 gm with an average weight of 2.11g. The maximum 100-seed weight recorded in Beo I (2.94g) followed by Churhala Dhan (2.85 g) and Badi Barik (2.84 g). The minimum 100-seed weight recorded in Satka (0.710 g). The CV was 24.90% for this trait Number of filled grains per panicle: Number of filled grains per panicle ranged from 7.00 to The minimum value of filled grains per panicle was recorded in Satka (7.00) and maximum in Jaya (232.5) followed by Indira Aerobic Dhan (177) and TN 1 (175). The CV was found to be 63.63% Number of unfilled grains per panicle: Number of filled grains per panicle ranged from 8.00 to 67. The minimum value of filled grains per panicle was recorded in Parra dhan (8.00) and maximum in TN 1 (67.0) followed by Jaya (58). The mean was found to be 26.63% Total spikelets per panicle: Total spikelets per panicle ranged from to with an average of The maximum value was recorded in Jaya (304.00)

107 89 followed by Tarun bhog Selection 1 (274) and Churhala Dhan (166.50). The minimum value was recorded in Satka (17.5). The CV was % for this trait Spikelet fertility (%): The highest spikelet fertility % was recorded as 87.60% (Basmati) and minimum as 39.87% (Satka). The average spikelet fertility recorded was as 74.42%. The maximum panicle weight in Basmati (87.6%) was followed by IGKV1244 (83.68 %) and then in Karma Mahsuri (83.68%). The CV was recorded as 9.95% Grain weight per plant (g): Grain weight per plant ranged from 6.32 g to g with an average weight of g. The maximum grain weight per plant recorded in Safri 17 (26.02 g) followed by Js 5 (23.64 g) and TN1 (23.46 g). The minimum grain weight per plant was recorded in Sakra (632g). The CV was % for this trait Shoot dry weight per plant (g): The highest shoot dry weight per plant was recorded as (Safri 17) and minimum as (Machhari ankhi). The average shoot dry weight per plant recorded was as The maximum panicle weight per plant was recorded in Safri 17 (62.00) was followed by TN1 (59.25) and then in MTU 1010 (56.5). The CV was recorded as 20.33% Harvest index (%): The maximum harvest index was recorded as 51.25% in Machhari Ankhi followed by IGKVR1244 (45.69%) and IGKVR1 (45.34%) and minimum 19.62% in Dhura. The average harvest index recorded was as 31.66%. The CV was recorded as 27.62% Paddy length (mm): Paddy length ranged from 5.4 mm to 11.0 mm with an average length of 9.29 mm. The maximum grain weight per plant recorded in No:21

108 90 (A) (47.80 mm) followed by IGKVR 1244 (10.5 mm) and Safri 17 (9.35 mm). The minimum grain weight per plant was recorded in Machhari kata (5.4 mm). The C.V. was % for this trait Paddy width (mm): Paddy width ranged from 2.30 mm to 3.5 mm with an average weight of 2.97 mm. The maximum grain weight per plant recorded in Lal Batra, Bamko-II, Kakdo and Machhari ankhi (3.5 mm). The minimum grain weight per plant was recorded in Changadi (2.30 mm). The C.V. was % for this trait Decorticated grain length (mm): The maximum length of decorticated grain was recorded as 7.13 mm and minimum as 2.85 mm. The average decorticated grain length recorded was as The decorticated grain length was recorded in IGKVR 1244 (7.13 mm) was followed by IGKVR 2 (7.07 mm) and then in IGKVR1 (6.87 mm). The CV was recorded as 21.44% Decorticated grain width (mm): The maximum decorticated grain width was recorded as 2.55 mm and minimum as 1.1 mm. The average value recorded was as 1.97 mm. The maximum decorticated grain width was recorded in Lallo-14 (2.55 mm) was followed by Amakoyali (2.47 mm) and then in Churhala dhan and Mahamaya (1.1 mm). The minimum value was recorded in Dhaura (1.1). The CV was recorded as 20.66% Milled grain length (mm): The mean performance of milled grain length was 4.1 mm. It showed variation from 5.63 mm to 1.68 mm. The maximum milled grain length was recorded in IGKVR 1244 (5.63 mm) followed by IGKVR 1 (5.37 mm) and Bodi (5.27 mm). The minimum was recorded in Parra (1.68 mm). The CV recorded for this trait was 25.47%.

109 Milled grain width (mm): The range for milled grain width (mm) varied from 1.08 mm to 2.5 mm with a mean value of 1.94 mm. The maximum milled grain width was recorded in Lallo-14 (2.5 mm) followed by Amakoyali (2.45 mm) and Sakra (2.42 mm). The minimum milled grain width (mm) was recorded in Dhaura (1.08 mm). The CV was 20.90% recorded for this trait Hulling (%): Hulling % was found to vary from 76.11% to 61.47% with an overall mean of %. The maximum hulling (%) was recorded in MTU 1010 (76.11%) followed by Mahamaya (75.06%) and Swarna Sub 1 (75%). The minimum hulling (%) was recorded in Amaoyali (61.47%). The CV recorded for this trait was 5.83 % Milling percent (%): Milling percentage ranged from 70.8% to 34.25% with an overall mean performance of 57.6%. The maximum milling percent was recorded in MTU 1010 (70.8%) followed by Mahamaya (68.6%) and Safri 17 (68.5%). The minimum milling percent was recorded in Basmati I (34.25%). The CV observed for this trait was 11.71% Head Rice Recovery (%): The head rice recovery was found in the ranged from 22.1% to 63.43% with an overall average of 45.75%. The highest head rice recovery was recorded in Mahamaya (63.43%) followed by Tarunbhog Selection 1 (63.19%) and Dubraj selection 1 (61.27%). The minimum head rice recovery was recorded in Dhaura (22.1%). The CV recorded for this trait was 20.35% Gel consistency (%): The gel consistency in genotypes ranged from 33.0% to 97.0% with an overall mean of 69.34%. The highest gel consistency was observed in many genotypes like Lal Batra and Jaya (97%), followed by Machhari Kata (96.5%) and the minimum was recorded in Bhania (33%). The CV recorded for this trait was 22.72%.

110 92 Table 4.3.e: List of germplasm categorized into soft, medium and hard gel consistency Category Rice germplasm Soft Safri 17, IGKVR 2, MTU-1010, TN 1, Indira aerobic dhan 1, Tarunbhog selection 1, Dubraj selection 1, Badshabhog selection 1, Karma mahsuri, Swarna sub 1, Bhata ajan, Bodi, Kondi ajan, Basmati (I), Beo (I), No :21 (A), Badi barik, Basmati, Parra, Nalla wadlu, Rambhoj, Lal batra, Churhala dhan, Lallo-14, Machhri kata, Medium JS-5, Badal Phool, Amakoyali, Parra dhan, Pinna basengi, Banko II, Changadi, Dhaura, Kakdo, Kanji, Parra, Dhamna panda, Mahamaya Hard Dhaur Amylose content: According to percent of content of amylose recorded in all forty seven rice genotypes, three categories were recorded low (17), intermediate (28) and high (2) amylose. Table 4.3.f: List of germplasm categorized into low, low-intermediate, intermediate and high amylose content Category Low Low - intermediate Intermediate High Rice germplasm Dhaur, Badal phool, Amakoyali, Churhala Dhan, Lal Batra, Lallo- 14, Machhri Kata, Pinna Basengi, Satka, Satka, Banko II, Changadi, Dhaura, Kakdo, Kanji, Machhari ankhi, Parra, Kondi Ajan, Beo (I), Bhata Ajan, Swarna sub 1, Dubraj Selection 1, Safri 17, IGKVR 2, MTU Indira Aerobic Dhan 1, Karma Mahsuri, Bodi, Basmati (I), Badi barik, Machhari Ankhi, Shyam Jir, Sakra, Parra Dhan, Rambhoj Basmati, Dhamna panda, No :21 (A), IGKVR 1244, Badshabhog Selection 1, Tarunbhog selection 1, Mahamaya Jaya, TN 1, IGKVR 1, JS-5

111 93 The genetic variability in any breeding material is a prerequisite as it does not only provide a basis for selection but also provide some valuable information regarding selection of diverse parents for use in hybridization programme. Coefficient of variation was evolved by Karl Pearson. It is very useful for the study of variation. It indicates that when the coefficient of variation is high the sample is less consistent or more variable. Coefficient of variation truly provides a relative measure of variability among different traits. In the present investigation wide range of variability was observed for most of the quantitative traits. High magnitude of coefficient of variation (more than 20%) in the entire accessions was observed for panicle weight (41.46%), number of panicle per plant (26.80%), 100 seed weight (24.90%), number of filled grains per panicle (63.63%), total spikelet per panicle (62.67%), grain weight per plant (41.97%) harvest index (27.62%), decorticated grain (21.44%), decorticated grain width (20.66%),milled grain length (25.47%), milled grain width (20.90%), HRR (20.35%) and gel consistency (22.72%). High magnitude of coefficient of variation for grain yield was observed by Nachimuthu et al. (2014) and Sarawgi et al. (2014). Rest of the traits recoded moderate to low magnitude of coefficient of variation. Similar findings were also reported by the earlier workers (Chakravorty et al.,2013; Nachimuthu et al., 2014 and Sarawgi et al., 2014 and Lingaiah et al. 2015) Phenotypic and Genotypic coefficient of variation: Coefficient of variation was calculated at genotypic and phenotypic levels as analysis of variance permits estimation of phenotypic, genotypic and environmental coefficient of variation (Burton, 1952). As usual, phenotypic coefficient of variation was higher in magnitude than genotypic coefficient of variation. The P and G are classified as follows as suggested by Siva Subramanian and Madhavamenon (1973) (low <10%; moderate 10-20% and high>20%). The estimates of phenotypic and genotypic coefficient of variation for different quantitative characters and quality characters are present in Table 4.4. The highest value of P coupled with G was recorded in number of filled grains per panicle (63.52; 63.75), followed by total spikelet per panicle (62.51;

112 ), number of unfilled grains per panicle (62.86; 63.81), grain weight per plant (41.59; 42.34), panicle weight (40.86; 42.56), harvest index (27.17; 28.06), milled grain length (25.22; 25.71),100 seed weight (24.87; 24.94), number of panicles per plant (24.02; 29.32), decorticated grain length, gel consistency (22.34; 23.08), decorticated grain width (20.58; 21) and milled grain width (20.8; 21). The values of P are higher than G, indicating the apparent variation is not only due to genotypes but also due to the influence of environment. The high magnitude of genotypic coefficient of variation reveals the high genetic variability present in the material studied. In the present investigation phenotypic coefficient of variation was recorded higher than genotypic coefficient of variation and was in accordance with the Sarkar et al. (2007) and Lingaiah et al. (2015). The high magnitude of genotypic coefficient of variation for grain yield was also obtained by Tuhina-Khatun et al. (2015). High phenotypic and genotypic for 1000-grain weight were also obtained by Sarkar et al. (2007). The rest of the traits recorded moderate to low phenotypic in association with Genotypic.

113 95 Table 4.4: Genetic parameters for 27 yield and quality traits of 47 rice germplasm accessions Traits Mean (X) Min. Range Max. GCV (%) PCV (%) h 2 (bs) (%) GA as % mean Leaf: Length of blade (cm) leaf: width of blade (cm) Plant height (cm) Stem: Length(excluding panicle) Panicle length (cm) Panicle weight (g) Number of panicles per plant seed weight (g) Number of filled grains/panicle Number of unfilled grains/panicle Total spikelets/ panicle Spikelet Fertlity (%) Grain weight/plant (g) Shoot dry weight/plant (g) Harvest Index (%) Paddy length (mm) Paddy width (mm) Decorticated grain length(mm) Decorticated grain width(mm) Milled grain length (mm) Milled grain width (mm) Hulling (%) Milling (%) HRR (%) Gel consistency Alkali spreading value Amylose content (%) Heritability and Genetic advance as percentage of mean: Heritability estimates provide the information regarding the amount of transmissible genetic variation to total variation and determine genetic improvement and response to selection. Thus, heritability is the heritable portion of the phenotypic variance. It is a good index of the transformation of characters from parent to their offsprings. Heritability and genetic advance are important selection parameters. Heritability estimates along with genetic advance are normally more helpful in predicting the gain under selection than heritability estimates alone. Improvement in the mean genotypic value of selected plants over the parental population is known as genetic advance. It is the measure of genetic gain under selection. The success of genetic advance under selection depends on genetic variability, heritability and selection intensity. In the present investigation

114 96 heritability in broad sense and genetic advance were calculated for 27 yield and quality characters under study and are presented in Table 4.4. High estimate of heritability was found for all characters except for panicle length (44.47%). The highest heritability was estimated for decorticated grain length (99.45%) followed by 100-grain weight (99.44%), total spikelet per panicle (99.32%), number of filled grains per panicle (99.28%) and number of unfilled grains per panicle (97.04). This finding is in agreement with Choudhary et al. (2004) and Tuhina Khatun et al. (2015). Highest heritability for length of decorticated grain is similar with the findings of Shrivastava et al. (2012). Genetic advance is a measure of genetic gain under selection. The success of genetic advance under selection depends on heritability of the character under consideration. This indicates that though the character is less influenced by environmental effects, the selection for improvement of such trait may not be useful because, heritability is based on total genetic variance which includes fixable (additive) and non fixable (dominance and epistatic) variance. The magnitude of genetic advance as percent of mean was recorded high for all the traits. Only some traits observed moderate genetic advance namely width of leaf blade (18.61%), panicle length (11.18%), spikelet fertility (18.06%), (19.21%) hulling% (10.33%). All the traits possessing high values of genetic advance indicate that the characters are governed by additive genes and selection will be rewarding for improvement of such trait. Out of 27 yield and quality traits, twenty characters namely, leaf: length of blade (95.77; 35.30), plant height (90.93; 27.58) stem length (93.38; 34.31), panicle weight (96; 82.83), panicle: number per plant (67.12; 40.54), 100 grain weight (99.44; 51.09), number of filled grains per panicle (99.28; 50) number of unfilled grains per panicle (97.04; 45.8), total spikelet per panicle (99.32; 40.8), grain weight per plant (96.46; 84.13), shoot dry weight per plant (92.91; 39.63), harvest index (93.80; 54.21), paddy length (91.91; 25.45) decorticated grain length (99.45; 43.99), decorticated grain width (98.08-;.43), length of milled grain (96.19; 50.95), width of milled grain (98.08; 42.43), milling% (93.89; 23), HRR (83.52; 36.55), amylose content (89.70; 26.97) and gel consistency (99.69;

115 ) exhibited high heritability coupled with high genetic advance. It clearly indicates that most likely the heritability is due to additive gene effects and selection may be effective. High heritability with moderate or low genetic advance as percentage of mean was observed for width of leaf blade (76.43; 18.61), spikelet fertility (84.66; 18.06), width of paddy (88.06; 19.21), hulling % (82.06; 0.33). This indicates non-additive (dominance and epistasis) gene action. These findings are in agreement with findings of Veni and Rani (2006) and Rahman et al. (2016). 4.3 Association analysis: Correlation analysis Association analysis is an important approach in a breeding programme. It gives an idea about relationship among the various characters and determines the component characters, on which selection can be based for genetic improvement in the grain yield. Degree of association also affects the effectiveness of selection process. The degree of association between independent and dependent variables was suggested by Galton 1888, its theory was developed by Pearson (1904) and their mathematical utilization at phenotypic, genotypic and environmental levels was described by Searle (1961). The association between any two variables is termed as simple correlation or total correlation or zero order correlation coefficient. It is of three types viz, phenotypic, genotypic and environmental correlations. The correlation coefficient analysis is the index of association between two variables. These have been dealt in all possible combination for important characters at phenotypic and genotypic level and are presented in Table-4.5a and 4.5b. Grain yield showed positive and significant correlation with panicle length (0.97), number of panicle (0.41), number of filled grains per panicle (0.77), number of unfilled grains per panicle (0.63), total spikelet per plant (0.75) spikelet fertility (0.40), shoot dry weight (0.86), harvest index (0.88), decorticated grain

116 98 length (0.40), milled grain length (0.41), hulling% (0.62), milling% (0.34) and HRR (0.63). However, it showed negative and significant association with plant height (-0.33), stem length (-0.35) and paddy length (0.59). Milling (%) showed positive and significant correlation with panicle weight (0.39) followed by grain weight per plant (0.34), shoot dry weight (0.31), harvest index (0.27), hulling (0.53), shoot dry weight (0.86), harvest index (0.88), decorticated grain length (0.409), milled grain length (0.41), hulling % (0.62),and milling% (0.63). it showed negative and significant association with length of leaf blade (-0.37). HRR showed positive and significant correlation with panicle weight (0.65), number of filled grains per panicle (0.61), number of unfilled grains per panicle (0.55), total spikelet per panicle (0.61), spikelet fertility (0.28), grain weight per plant (0.63) and alkali spreading value (0.21). However, it showed negative but significant association with paddy length (-0.52) and paddy width (- 0.39). Hulling % possessed positive and significant association with number of filled grains per panicle (0.48), panicle weight (0.63), number of unfilled grains (0.45), grain weight per plant (0.62), total spikelet pe plant (048), shoot dry weight (0.41), harvest index (0.60), decorticated grain length (0.26), milled grain length (0.26). Content of amylose showed positive and significant association with number of panicle per plant (0.04), 100 seed weight (0.36), and paddy length (0.33). ). However, it showed negative but significant association with gel consistency (-0.85). Alkali spreading value showed positive and significant association with panicle length (0.30), panicle weight (0.45), and number of filled grains per panicle (0.45), number of unfilled grains per panicle (0.34) total spikelet per panicle (0.43), spikelet fertility (0.36), and grain weight per panicle (0.47), shoot dry weight (0.38), harvest index (0.38), and paddy length (0.95). Milled grain length showed positive and significant association with number of filled grains per panicle (0.29), panicle weight (0.39), spikelet fertility

117 99 (0.29), grain weight (0.41), shoot dry weight (0.36), harvest index (0.38), paddy length (0.45), decorticated grain length (0.98) and decorticated grain width (0.50). Milled grain width possessed positive and significant association with paddy length (0.78), decorticated grain length (0.54), decorticated grain width (1.00) and milled grain length (0.51) Decorticated grain length showed positive and significant association with number of filled grains per panicle (0.27), panicle weight (0.36), spikelet fertility (0.31), grain weight (0.40), shoot dry weight (0.34), harvest index (0.38), paddy length (0.51). Decorticated grain width showed positive and significant association with number of filled grains per panicle (0.23), paddy length (0.79), number of unfilled grains per panicle (0.23), decorticated grain length (0.54) and total spikelet per panicle (0.24). It showed negative but significant association with width of leaf blade (-0.28). Paddy length possessed positive and significant association with panicle length (0.75), number of panicle per plant (1.51), 100 seed weight (1.03) and spikelet fertility (0.73). Width of paddy showed positive and significant association with 100 seed weight and paddy length. Harvest index showed positive and significant association with number of panicle per plant (0.33), number of filled grain per panicle (0.77), number of unfilled grain per panicle (0.59), total spikelet per panicle (0.77), spikelet fertility (0.43) and grain weight per plant (0.88). Shoot dry weight possessed positive and significant association with panicle weight (0.91), Panicle length (0.30), number of panicles per plant (0.35), number of filled grains per panicle (0.62), number of unfilled grains per panicle (0.52), total spikelet per plant (0.60), spikelet fertility (0.28) and grain weight (0.86) Spikelet fertility possessed positive and significant association with panicle weight (0.37), number of panicle per plant (0.33), 100 seed weight (0.31),

118 100 Total spikelet per panicle showed positive and significant association with panicle weight (0.71), number of filled grains per panicle (0.99) and unfilled grains per plant (0.95). Grain yield observed high positive and significant correlation with shooy dry weight. High positive and significant correlation between grain yield and biological yield is in agreement with the findings of Girish et al. (2006). A highly significant and positive correlation of number of panicle per plant with grain yield is in confirmation with the findings advocated by Madhavilatha et al. (2005); Muthuswamy and Ananda kumar (2006); Ambili and Radhakrishnan (2011) and Rashid et al. (2014). ). Grain length showing significant and positive correlation with grain yield is in agreement with the findings of Gananasekaran et al. (2008) while, the same result for correlation between grain yield and grain breath is in confirmation of the finding of Girish et al. (2006). A significant positive correlation of grain length with grain breath are in agreement with the findings of Seraj et al. (2013). Head rice recovery had significant and positive correlation with milling percent is in confirmation with the findings of Ekka et al. (2011). The association between two variables which can be directly observed is termed as phenotypic correlation and it includes Genotypic and E nvironmental effects therefore, it differs under environmental conditions. The inherent or heritable association between two variables is known as genotypic or genetic correlation. This may be either due to pleotropic action of genes or due to linkage or both. The main genetic cause of such association is pleotropy, which refers to manifold effects of a gene (Falconer, 1960). This type of correlation is more stable and is of paramount importance to bring about genetic improvement in one character by selecting the other character of a pair that is genetically correlated.

119 101 per plant, number of unfilled grain per plant, HRR, decorticated grain length, milled grain length, hulling%, milling%, total spikelet per plant, spikelet fertility and harvest index had positive and highly significant correlation with grain yield per plant. It indicates strong correlation of these traits with grain yield and selection of these traits will be useful in improving grain yield.

120 102 Table 4.15a: Association analysis (phenotypic and genotypic) of 27 yield and quality traits of 47 rice germplasm accessions P G 2 P 0.11 G P G P G P G P G P G P G P G P G P G P G P G P G P G

121 P G P G P G P G P G P G P G P G P G P G P G P G = Leaf: Length of blade ; 2 = Leaf: Width of blade ; 3= Plant height ;4= Stem: Length(excluding panicle ; 5= Panicle length ; 6= Panicle weight (g) ; 7= Number of panicles per plant ; 8= 100 seed weight (g) ; 9= Number of filled grains/panicle ;10= Number of unfilled grains/panicle ;11= Total spikelets/ panicle; 12= Spikelet Fertlity % ; 13= Grain weight/plant (g) ; 14= Shoot dry weight/plant (g) ; 15=Harvest Index (%) ; 16= Paddy length ; 17= Paddy width ; 18= Decorticated grain length (mm) ; 19=Decorticated grain width(mm) ; 20= Milled grain length ; 21 ; Milled grain width ; 22= Hulling% ; 23= Milling% ; 24=HRR% ; 25= Gel consistency ; 26=Alkali spreading value ; 27= Content of amylose %

122 104 Table 4.15a: Association analysis (phenotypic and genotypic) of 27 yield and quality traits of 47 rice germplasm accessions P 0.49 G P G P * G P ** G P ** G ** 20 P ** ** G ** ** 21 P ** ** ** G ** ** ** 22 P * * G ** ** P ** G ** 24 P ** ** ** ** G ** ** ** ** 25 P G P * * * G * * * * P ** G ** = Leaf: Length of blade ; 2 = Leaf: Width of blade ; 3= Plant height ;4= Stem: Length(excluding panicle ; 5= Panicle length ; 6= Panicle weight (g) ; 7= Number of panicles per plant ; 8= 100 seed weight (g) ; 9= Number of filled grains/panicle ;10= Number of unfilled grains/panicle ;11= Total spikelets/ panicle; 12= Spikelet Fertlity % ; 13= Grain weight/plant (g) ; 14= Shoot dry weight/plant (g) ; 15=Harvest Index (%) ; 16= Paddy length ; 17= Paddy width ; 18= Decorticated grain length (mm) ; 19=Decorticated grain width(mm) ; 20= Milled grain length ; 21 ; Milled grain width ; 22= Hulling% ; 23= Milling% ; 24=HRR% ; 25= Gel consistency ; 26=Alkali spreading value ; 27= Content of amylose %

123 Figure 4.23a: Graph representing significant correlation between grain yield and other traits Figure 4.23b: Graph representing significant correlation between HRR and other trait

124 106 Table 4.6: Summarised data representing the relationship of different traits on grain yield and HRR at genotypic level Traits Grain yield Correlation HRR Length of leaf blade (cm) Width of leaf blade (cm) Plant height (cm) Stem length (cm) Panicle length (cm) Panicle weight (g) Number of panicles per plant seed weight (g) Number of filled grains per panicle Number of unfilled grains per panicle Total spikelets per panicle Spikelet fertility (%) grain yield (g) _ 0.63 Shoot dry weight (g) Harvest index (%) Paddy length (mm) Paddy width (mm) Decorticated grain length (mm) Decorticated grain width (mm) Milled grain length (mm) s Milled grain width (mm) Hulling (%) Milling (%) HRR (%) 0.63 _ Gel consistency Alkali spreading value Amylose content (%) Bold values are significant at 5 % level of significance 4.4 Principal Component Analysis: Principal component analysis (PCA) is a powerful tool in modern data analysis because it is a simple, non-parametric method for extracting relevant information from confusing data sets. With minimal effort, PCA provides a roadmap for how to reduce a complex data set to a lower dimension to reveal sometimes hidden, simplified structures that often underlie it. It reduces the

125 107 dimensionality of the data while retaining most of the variation in the data set. PCA accomplishes this reduction by identifying directions, called Principal Components (PCs), along which the variation in the data is maximal. By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables. Thus, the primary benefit of PCA arise from quantifying the importance of each dimension for describing the variability of a data set in more interpretable and more visualized dimensions through linear combinations of variables that accounts for most of the variation present in the original set of variables. Therefore, principal component analysis is a variable reduction procedure. In the present investigation, PCA was carried out by using 47 rice genotypes and 27 morphological and grain quality traits. The outcome of the PCA described the genetic diversity of the rice genotypes for these traits. Eigen values measure the importance and contribution of each component to total variance, whereas each coefficient of eigen vectors indicates the degree of contribution of every original variable with which each principal component is associated. The higher the coefficients, regardless of the direction (positive or negative), the more effective they will be in discriminating between the genotypes. There are no standard tests to prove significance of Eigen values and the coefficients (Jolliffe, 2002; Sanniet al., 2012). Principal component analysis discriminated the 47 genotypes into 25 principal components. A data table (Table: 4.9) and scree plot (Figure 4.30) of eigen values and cumulative variability of all 25 principal components showed that the first eight components having more than 1 eigen values, accounts for more than 80% of total variation. This indicates that the identified traits within the principal components exhibited larger influence on the phenotype of population panel. PC 1 with eigen value of 7.87 accounted for 29.16% of the total variability, while PC 2 with eigen value 3.27 accounted for 12.11% of the total variation observed among the 47 rice genotypes. PC 3 had eigen value of 2.84 with variability of 10.53% while PC 4, PC 5, PC 6, PC 7 and PC 8 had eigen values of 2.03, 1.61, 1.50, 1.34 and 1.13respectively and accounted for 7.53%, 5.96%, 5.56%, 4.97%and 4.19% respectively of the total variability.

126 108 The results of the PCA explained the genetic diversity of the 47 accessions of rice. Each principal component had a number of contributory characters which accounted for the total variation presented in Table Panicle weight (0.33), grain weight/plant (0.33), harvest index (0.30) and head rice recovery percentage (0.27) were the important traits contributing to the PC1. As a result, the first component differentiated those genotypes that had high panicle weight, grain weight/plant, and harvest index and head rice recovery percentage. The second principal component was contributed mostly milled grain width (0.43), decorticated grain width (0.43), milled grain length (0.30) and plant height (0.27) which explained that PC2 discriminated those genotypes having high milled grain width, decorticated grain width, milled grain length and plant height. Gel consistency (0.48), amylose content (-0.45), Number of panicles per plant (-0.30)and leaf width of blade (0.29)were the main traits contributing to PC3 which showed that PC3 differentiating the rice genotypes having high gel consistency, amylose content, number of panicles per plant and leaf width of blade. Spikelet fertlity % (0.33), leaf: length of blade (0.32) and alkali spreading value (0.28) were the main contributing traits for PC4. Paddy width (0.41), number of panicles per plant (0.29) and number of unfilled grains/panicle (0.22) were the main contributing traits for PC5. Leaf: Length of blade (0.42), panicle length (0.31) and decorticated grain length (0.19) were the major contributing traits for PC6. Similarly, Paddy length, leaf: width of blade and panicle length were the main contributing traits for PC7 whereas spikelet fertlity %, gel consistency and 100 seed weight were the main contributing traits for PC8. Traits that have positive as well as negative impact on the PCs can be said to be the main source of variation and contributed mostly to differentiate the rice genotypes. Therefore, these traits can be used in the selection of diverse genotypes from particular principal component (Abimbola et al., 2016; Ashfaq et sal., 2012). Scree plot explained the percentage of variation associated with each principal component to obtain by drawing a graph between eigen values and principal component numbers. First 8 components explains the 80.01%variation and eign value >1. The PC-1showed 29.16% variability with eigen value 7.87 which then declined gradually. Elbow type line is obtained which after 9 th PC tended to straight with little variance observed in each PC. From the graph, it is clear that the

127 109 maximum variation was observed inpc-1 (Fig.4.24). The genotype wise scatter diagram of the 47 rice genotypes (scores) and the trait wise scatter diagram of 27 traits (loadings) along with the first two principal component axes, are shown in Figure The ordination of the rice genotypes (Figure-4.25) revealed that genotypes Basmati I, IGKVR-1244, MTU1010, Dhaur, Badalphool, Parra, Dhaura, Jaya, Nallawadlu, Beo-I and Dhamna panda were very divergent for the traits under study. The spread of the different genotypes across the plot showed variations among the genotypes, although most accessions from the same PC were closely distributed and overlapped with each other.

128 110 Table 4.7: Eigen values of 25 yield and quality traits of 47 rice germplasm accessions Parameter PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 Eigen value Variability (%) Cumulative (%) Parameters PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 Eigen value Variability (%) Cumulative (%)

129 111 Table 4.8: Eigen vectors of 47 rice germplasm accessions for yield and quality characters Traits Components PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Leaf: Length of blade (cm) leaf: width of blade (cm) Plant height (cm) Stem: Length(excluding panicle) Panicle length (cm) Panicle weight (g) Number of panicles per plant seed weight (g) Number of filled grains/panicle Number of unfilled grains/panicle Total spikelets/ panicle Spikelet Fertlity (%) Grain weight/plant (g) Shoot dry weight/plant (g) Harvest Index (%) Paddy length (mm) Paddy width (mm) Decorticated grain length(mm) Decorticated grain width(mm) Milled grain length (mm) Milled grain width (mm) Hulling (%) Milling (%) HRR (%) Gel consistency Alkali spreading value Amylose content (%) Values in bold are highly weighted vectors in respective PCs

130 112 Scree plot Eigenvalue Cumulative variability %) 1 0 F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16 F17 F18 F19 F20 F21 F22 F23 F24 F25 Principle Components 0 Figure 4.24: Scree plot showing eigen values and percentage of cumulative variability Principal component scores (PC scores) of initial 8 PCs for all the accession sand top 10 PC scores of each PC were presentedintable-4.12.these scores can be utilized top propose precise selection indices whose intensity can be decided by variability explained by each of the principal component.high PC score for a particular accession in a particular component denotes high values for the variables in that particular accession. Obtained results revealed that the Jaya, MTU-1010, IGKVR 1, TN 1 and Safri 17and Mahamaya in PC 1 indicated that they had highest grain yield, harvest index and HRR. In PC2, Basmati (I), Dhamna panda, No: 21 (A), Basmati, Amakoyali and Machhariankhi were the top genotypes for grain yield and quality parameters. The highestpcscoreofpc- 3recorded by Machhri kata, Lalbatra, Badshabhog selection 1, Tarunbhog selection 1 and Bhataajan indicated that they had high grain quality and high number of panicle per plant. On the basisoftop10 PC scores in each principal component, accessions are selected and presented in summarized form in Table

131 113 Thus, it is cleared that the principal component analysis highlights the characters with maximum variability. So, intensive selection procedure scan be designed to bring about rapid improvement of yield and quality traits. PC also helps in ranking of genotypes on the basis of PC scores in corresponding component. From the above results, it is cleared that Jaya is the best accession for both quality and yield attributing traits followed by MTU 1010, IGKVR 1, TN 1 and Safri 17. This result corroborates with the finding of Kumar et al. (2013). Above discussion revealed that identified accessions may be used as donor to improve the yield and quality traits in varietal development programme. Figure 4.25: Distribution of genotypes among two different principal components 5 Observations (axes F1 and F2: %) 4 Basmati (I) 3 Dhamna panda F2 (12.11 %) Beo (I) Amakoyali No :21 (A) Machhari ankhi Badi barik Kondi ajan Bodi Basmati Sakra Pinna basengi Lal batra Rambhoj Kakdo Churhala dhan Lallo-14 Bhata ajan Banko II Basmati IGKVR 1244 TN 1 Mahamaya IGKVR 1 Safri 17 Jaya -1 Nalla wadlu Satka Kadam phool Badshabhog IGKVR selection 2 1 Dubraj selection 1 JS-5 Indira aerobic dhan MTU Dhaura -3 Parra dhan Shyam jira Machhri kata Kanji Changadi Parra Tarunbhog selection 1 Swarna sub 1 Karma mahsuri Dhaur Badal phool F1 (29.16 %)

132 114 Table 4.9: List of selected accessions in each principal component on the basis of top 10 PC scores PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Jaya Basmati (I) Machhri kata Parra dhan Lalbatra Badalphool No :21 (A) Nallawadlu MTU-1010 Dhamna panda Lalbatra Basmati (I) No :21 (A) Basmati (I) Dhamna panda Bodi IGKVR 1 No :21 (A) Badshabhog selection 1 Shyamjira TN 1 Lallo-14 IGKVR 1 No :21 (A) TN 1 Basmati Tarunbhog selection 1 IGKVR 1 Machhariankhi TN 1 Badshabhog selection 1 Sakra Safri 17 Amakoyali Bhataajan Basmati Jaya Swarna sub 1 Karma Swarna sub 1 mahsuri Mahamaya Machhariankhi Jaya Kanji Sakra Badibarik Badalphool Karma mahsuri Tarunbhog selection 1 Bodi TN 1 Changadi Kanji Indira aerobic dhan 1 Machhri kata Indira aerobic dhan 1 JS-5 Kondiajan Basmati TN 1 Swarna sub 1 Kadamphool Parra dhan Kondiajan Indira aerobic Basmati Rambhoj JS-5 Banko II Karma mahsuri Lallo-14 Beo (I) dhan 1 IGKVR 1244 Badibarik Bodi Basmati Shyamjira JS-5 MTU-1010 Dubraj selection 1

133 115 Table 4.10: Principal component score of 47 rice germplasm accessions Accessions PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Name Dhaur JS Badalphool Amakoyali Rambhoj Churhaladhan Lalbatra Lallo Machhri kata Parra dhan Pinna basengi Sakra Satka Shyamjir Banko II Changadi Dhaura Kadamphool Kakdo Kanji Machhariankhi Nallawadlu Parra Basmati Dhamna panda No :21 (A) Badibarik Basmati Kondiajan Basmati (I) Beo (I) Bodi Bhataajan Swarna sub IGKVR IGKVR Karma mahsuri Badshabhog selection 1 Dubraj selection 1 Tarunbhog selection 1 Mahamaya Safri IGKVR Indira aerobic dhan 1 TN Jaya MTU

134 Cluster Analysis: Cluster analysis among 47 rice germplasm accessions/ genotypes was studied. The clustering pattern of all genotypes has been presented in Table 4.13 and Fig The analysis of overall pattern of genetic diversity and relationships among rice genotypes facilitates the selection of parents with diverse genetic background (Murphy et al., 1986 and Souza and Sorrels, 1991). Total 47 rice genotypes under this study were grouped into 8 clusters based on UPGMA method and Euclidean distance (Figure 4.26). Cluster wise genotypes are depicted in Table Cluster I comprises 7 genotypes (14.8%) out of 47 rice genotypes. Cluster II, Cluster III and Cluster IV consisted 2 genotypes (4.2%), 11 genotypes (23.4%) and 11 genotypes (23.4%) respectively. Cluster III and cluster IV has highest number of genotypes (11 in each). Cluster V and VI comprises of 10 (21.3%) and 1(2.1%) rice genotypes respectively. Cluster VII and VIII cluster consisted of 2 (4.2%) and 3 (6.3%) rice respectively. It means genotypes of same cluster have more similarity whereas genotypes between different clusters have more dissimilarity. This indicates significant genetic diversity for studied traits among these rice genotypes with lower within class and higher between class variations. This will help breeder to do targeted breeding programme by selecting most desirable and diverse parents to increase the selection and breeding efficiency. The pattern of group constellation proved the existence of significant amount of variability. The inter- and intra cluster distances among ten clusters were computed and are given in Table The intra cluster distance ranged from 0 to and the highest intra cluster distance was shown by Cluster I having seven genotypes. Distance between the clusters centroids are presented in Table Minimum distance was observed between cluster I and cluster IV (63.04) followed by cluster IV and cluster VI (66.03). This indicates the genotypes in these clusters are closely related by descent with respect to traits under study. Maximum distance was observed between cluster VII and cluster V (338.05) followed by cluster VII and cluster III (285.22) as well as between cluster VII and cluster V as well as cluster II and cluster III are suitable for further breeding programme to improve the traits under study. Hybridization between the

135 117 genotypes of these clusters will generate better Segregants and increase the efficiency of selection. Hence, the use of genotypes with high genetic distance should be given primary importance while those with the least genetic distance should be avoided (Odewale et al., 2012). Cluste r Table 4.11: Clustering pattern of 47 rice genotypes No. of genotype s Name of Genotypes I 7 Dhaur, JS-5, Swarna Sub 1, IGKVR 1, IGKVR 1244, Mahamaya, IGKVR 2 II 2 Badalphool, Karma Mahsuri III 11 Amakoyali, Rambhoj, Lallo-14, Sakra, Shyamjira, Banko II, Changadi, Kadamphool, Kanji, No :21 (A), Basmati IV 11 Churhaladhan, Lalbatra, Machhri kata, Machhari Ankhi, Kondiajan, Basmati (I), Bodi, Bhataajan, Badshabhog Selection 1, Dubraj Selection 1, Safri 17 V 10 Parra Dhan, Pinna Basengi, Satka, Dhaura, Kakdo, Nallawadlu, Parra, Dhamna panda, Badibarik, Beo (I) VI 1 Basmati VII 2 Tarunbhog Selection 1, Jaya VIII 3 Indira Aerobic Dhan 1, TN 1, MTU-1010

136 118 Figure 4.26: Dendogram of eight clusters based of Euclidean distance and UPGMA 250 Dendrogram 200 Eucledean Distance MTU-1010 Indira aerobic dhan 1 TN 1 Tarunbhog selection 1 Jaya No :21 (A) Banko II Kanji Basmati Changadi Amakoyali Shyam jira Rambhoj Sakra Lallo-14 Kadam phool Dhamna panda Satka Dhaura Pinna basengi Kakdo Parra Nalla wadlu Parra dhan Badi barik Beo (I) Badal phool Karma mahsuri Dhaur IGKVR 1 Mahamaya IGKVR 2 Swarna sub 1 JS-5 IGKVR 1244 Basmati Lal batra Machhri kata Machhari ankhi Kondi ajan Bodi Safri 17 Badshabhog selection 1 Dubraj selection 1 Basmati (I) Churhala dhan Bhata ajan

137 119 Table 4.13: Estimates of intra (Diagonal and bold) and inter cluster distances among eight clusters I II III IV V VI VII VIII I II III IV V VI VII VIII In order to visualize the pattern of clustering among rice genotypes, the mean performance of the clusters was calculated (Table 4.13). Cluster I showed higher mean value for total spikelet/panicle which indicates that genotypes of cluster I had higher total spikelet/panicle. Cluster II showed higher mean value for plant height and total spikelet/panicle. Cluster III had higher mean value for stem length and plant height. Cluster IV had higher mean value for plant height and total spikelet/panicle. Cluster V had higher mean value for plant height. Cluster VI, cluster VII and cluster VIII had higher mean value for plant height, total spikelet/panicle and number of fertile spikelet/plant. Cluster analysis is one of the useful tools for selection and efficient use of parents in hybridization program to develop high yielding potential cultivars/hybrids. The value of percentage contribution of 27 characters (Table: 4.16), included in cluster analysis, towards divergence ranged from hulling% (0.77) to number of filled grains per plant (10.41). Highest percentage contribution towards divergence was recorded by trait number of filled grains per plant (10.41) followed by, total spikelet per panicle (8.27), grain yield (g) (7.54), number of unfilled grains per plant (6.37), number of panicle per plant (5.54), panicle weight (5.48), alkali spreading value (4.85), harvest index (3.65), milled grain length(3.36), 100 seed weight (3.29), gel consistency (3.00), decorticated grain length (2.83), milled grain length (2.76), decorticated grain width (2.73), shoot dry weight (2.69), length of leaf blade (2.34), stem length (2.32), plant height (1.9), amylase content (1.88), milling% (1.55), width of leaf blade (1.47),

138 120 panicle length (1.37), panicle weight (1.36), spikelet fertility (1.31) and harvest index (0.77.). The result is in agreement with Rashid et al. (2014) and Ayesha et al. (2015). Genetic relations in crop species is a significant component of crop species, as it serves to provide information about genetic diversity and also a platform for stratified sampling of breeding populations (Mohammadi & Prasanna, 2003). These findings are in agreement with studies on different crops like sugarcane (Tahiret al., 2013), sesame (Seymus and Uzun, 2010), soybean and oil-palm (Abimbola et al., 2016). For the selection of parents, genetic diversity is one of the important decisive factors. Our investigation highlighted that these genotypes have broader genetic base for the traits under study which can serve the future rice breeding programme.

139 121 Table 4.14: Percent contribution of each character towards divergence S. No. Variables % Contribution 1 Leaf: Length of blade (cm) leaf: width of blade (cm) Plant height (cm) Stem: Length(excluding panicle) (cm) Panicle length (cm) Panicle weight (g) Number of panicles per plant seed weight (g) Number of filled grains/panicle Number of unfilled grains/panicle Total spikelets/ panicle Spikelet Fertlity (%) Grain weight/plant (g) Shoot dry weight/plant (g) Harvest Index (%) Paddy length (mm) Paddy width (mm) Decorticated grain length(mm) Decorticated grain width(mm) Milled grain length (mm) Milled grain width (mm) Hulling (%) Milling (%) HRR (%) Gel consistency Alkali spreading value Amylose content (%) 1.88

140 122 Table: 4.15a: Cluster mean for quantitative traits in 47 rice germplasm accessions Cluster LLB LWB PH SL PL PW NPP SW NFP NSP TSP SF GWP SWP I II III IV V VI VII VIII Table 4.15b: Cluster mean for quantitative traits in 47 rice germplasm accessions Cluster HI PLN PWT DGL DGB MGL MGB HLL MLL HRR GC ASV AC I II III IV V VI VII VIII LLB : Leaf: Length of blade SW : 100 seed weight (g) HI: Harvest Index (%) HLL: Hulling% LWB leaf: width of blade NFP : Number of filled grains/panicle PLN: Paddy length MLL: Milling% PH : Plant height NSP : Number of unfilled grains/panicle PWT: Paddy width HRR: HRR% SL : Stem: Length(excluding panicle) TSP: Total spikelets/ panicle DGL: Decorticated grain length(mm) GC: Gel consistency PL : Panicle length SF: Spikelet Fertlity % DGB: Decorticated grain width(mm) ASV: Alkali spreading value PW : Panicle weight (g) GWP: Grain weight/plant (g) MGL: Milled grain length AC: Content of amylose % NPP : Number of panicles per plant SWP: Shoot dry weight/plant (g) MGB Milled grain width

141 Molecular Characterization: The semi-dwarf phenotype has been extensively selected during modern crop breeding as an agronomically important trait. Introduction of the semi-dwarf gene semi-dwarf1 (sd1) has made significant contribution to the green revolution in rice (Oryza sativa L.). SD1 was involved not only in modern breeding including the green revolution, but also in early steps of rice domestication. The sd-1 gene was first identified in the Chinese variety Dee-geowoo-gen (DGWG), and was crossed in the early 1960s with Peta (tall) to develop the semi-dwarf cultivar IR8 which produced record yields throughout Asia and formed the basis for the development of new high-yielding, semi-dwarf plant types (Hargove and Cabanilla, 1979; Dalrymple 1986). Since the 1960s, sd-1 has remained the predominant semi-dwarfing gene present in current rice cultivars. More studies have been performed related to short plant stature because semidwarf rice cultivars with short, thick culms, raises the harvest index, improves lodging resistance and responsiveness to nitrogen fertilizer, resulting high yields without affecting panicle and grain quality. In the 1960s, the rapid expansion of the world population and dramatic decrease in cultivated lands raised concern that food production would not meet the growing demand, leading to a global food crisis (Khush, 1999). However, the development and widespread adoption, if wheat and rice le to major increases in food production, and consequently large scale famine was averted. This remarkable achievement was referred to as the green revoltuion (Hargove and Cabanilla, 1979; Dalrymple 1986, Khush, 1999). A major factor for the success of the green revolution was the introduction of high yielding semi-dwarf varieties in combination with application of large amounts of nitrogen fertilizer. Nitrogen fertilization is essential for the increase in grain yield, but it also promotes stem and leaf elongation, resulting in an overall increase in plant height. Under high nitrogen fertilization, most conventional varieties of wheat and rice grow extensively tall and are easily flattened by wind rain resulting in significant yield losses. By contrast, the semi-dwarf varieties respond to fertilizer inputs to produce an increased yield because their short stature confers lodging resistance even under high nitrogen fertilization. This is a

142 124 major reason why the green revolution could double the crop yield in wheat and rice (Khush, 1995). IR8, a rice variety known as miracle rice which was developed by the International Rice Research Institute (IRRI), contributed to the green revolution in Asia. Likewise, the high yielding varieties Taichung Native 1 (TN1) in Taiwan (Aquino and Jennings, 1966) and Tongil in Korea (Suh and Heu, 1978) also harbor the sd1 allele from DGWG and contributed to food security in these countries. Similarly, the native semi-dwarf rice varieties Reimei in Japan (Futsuhara et al., 1967) Calrose 76 in the USA (Foster, 1978) carry different sd1 alleles and have been widely used in rice breeding programs in these countries. The sd1 mutants have been analyzed genetically and physiologically, and these studies have been applied in rice breeding programs (Futsuhara et al., 1967; Suge, 1975; Kikuchi et al., 1985) Development of genotypic data using SSR markers: Total genomic DNA was extracted from 47 lines of rice using CTAB method (Zheng et al., 1995). Fresh and healthy leaves were used for extraction of DNA. The DNA samples were quantified by using Nano Drop Spectroscopy (NANODROP 2000c). The quantity of the samples was found in the range from ηg/μl. DNA samples were then diluted with sterilized water such that the final concentration of DNA became 50 ηg/μl. Plant height variation: Phenotypic analysis of 47 rice accessions, showed statistically significant differences in plant height. Plant height variation ranged from cm to cm. The average plant height was cm was found among these accessions. Maximum plant height was found in Basmati ( cm) SSR marker analysis: Genetic associations among 47 accessions were analyzed, based on phenotypic variation of plant height with the help of 69 SSR markers of chromosomes one. Out of 69 SSR markers, six primers were found monomorphic across all accessions. A total of 177 alleles were amplified

143 125 and the number of alleles per locus generated by each marker ranged from 1 to 8 alleles with an average number of 2.56 alleles per locus. Maximum number of alleles (8) was amplified by marker RM marker. The PIC value across markers ranged from 0 to 0.99 with an average of Maximum PIC on chromosome 1 was 0.99 at marker RM 6340 followed by RM (0.97) and RM 1003 (0.95).

144 126 S. No. Table 4.15: List of 69 microsatellite markers of chromosome #1 with number of alleles, allele size and PIC value found among 47 rice accessions Marker Amp Size (bp) No. of alleles PIC S. No. Marker Amp Size (bp) No. of alleles 1 RM RM HvSSR RM RM RM RM RM RM RM HvSSR RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM RM HVSSR RM RM RM RM RM RM RM RM HVSSR RM RM RM RM RM RM RM RM HVSSR RM RM RM RM RM RM RM RM RM RM RM RM RM RM Sd1 E RM SD1 E HVSSR SD1 E RM a Similarity coefficient analysis and Clustering: Many studies have also reported significantly greater allelic diversity of microsatellite markers than other molecular markers (Mc Couch et. al., 2001). PIC

145 127 Similarity coefficient analysis and clustering for 47 rice genotypes was performed by Jaccard s similarity coefficient and Euclidean distance. The figure shows that the Jaccard s similarity coefficient ranges from 0.04 to 0.75 with highest similarity coefficient between Dhaur and Badalphool (0.75) and lowest between Dhaur and Machhari Ankhi. Total 47 rice genotypes taken under study were divided into two main clusters, cluster A and cluster B at Jaccard s similarity coefficient Cluster A contains 19 genotypes namely Amakoyali, Rambhoj, Churhaladhan, Lalbatra, Lallo 14, Machharikata, Satka, Parradhan, Changadi, Shyamjira, Banko-II, Dhaura, Kadamphool, Kakdo, Parra, Basmati, Kanji, Nallawadlu and Machhariankhi. Genotypes under cluster B contains 28 genotypes which includes Dhaur Badalphool, JS-5, Pinnabasengi, Sakra, Badshahbhog selection 1, Dubrajselection 1, Tarunbhog selection 1, Safri 17, IGKVR1, IGKVR1244, Karma mahsuri, Mahamaya, IGKVR2, Swarnasub 1, Indira Aerobic Dhan, TN1, Jaya and MTU 1010, Dhamnpinda, Basmati, No 21 (A), Beo (I) and Bhatajan. From this all the three dwarf genotypes i.e, includes Dhaur, Badalphool and JS-5 is grouped into the same cluster also the maximum similarity coefficient was found between Dhaur and Badal phool (0.75) which shows high considerable similarity among the three genotypes. Cluster A is further subdivided into cluster A1 and cluster A2 at Jaccard s similarity coefficient Cluster A1 has four genotypes namely Amakoyali, Rambhoj, Churhaladhan and Lalbatra in which Amakoyali is in separate cluster and remaining three in separate cluster. In A1 highest similarity percentage (59%) was found Churhala dhan and Rambhoj and lowest similarity percentage (47%) was observed between Amakoyali and Lal Batra. Cluster A2 has 15genotypes namely Lallo 14, Machharikata, Satka, Parradhan, Changadi, Shyamjira, Banko-II, Dhaura, Kadamphool, Kakdo, Parra, Basmati, Kanji, Nallawadlu and Machhariankhi. A2 is further divided into two sub sub clusters A2a and A2b. Cluster A2a has four genotypes with highest Jaccard s similarity coefficient 0.69 between machhari kanta and Satka and lowest in between lallo 14 and Parradhan as A2b has 11 genotypes with highest Jaccard s similarity coefficient 0.71 between kadamphool and Kakdo and lowest in between chandari and

146 128 Machhari ankhi. Cluster B is further subdivided into cluster B1 and cluster B2 at Jaccard s similarity coefficient Cluster B1 has 17 genotypes namely Dhaur Badalphool, JS-5, Pinnabasengi, Sakra, Badshahbhog selection 1, Dubrajselection 1, Tarunbhog selection 1, Safri 17, IGKVR1, IGKVR1244, Karma mahsuri, Mahamaya, IGKVR2, Swarnasub 1, Indira aerobic dhan, TN1, Jaya and MTU B1 is firther subdivided into two subgroups B1a and B1b. Cluster B1a has 5 genotypes with highest at Jaccard s similarity coefficient 0.75 between Dhaur and Badalphool and lowest as 0.47 in between Cluster B1b has 12 genotypes in which highest Jaccard s similarity coefficient was observed between IGKVR1 and IGKVR1244 (0.73) and lowest in between Badshah Bhog selection 1 and MTU1010 (0.47). Whereas B2 has 9 genotypes namely Dhamnpinda, Basmati, No 21 (A), Beo (I) and Bhatajan. Highest Jaccard s similarity coefficient was observed between No 21 (A) and Bodi (0.72) and lowest in between Dhamnapinda and Bhataajan (0.53).

147 129 Fighure 4.27: UPGMA based molecular dendogram of SSR markers showing 47 rice germplasm

148 b Polymorphism Information Content of SSR markers: Polymorphism Information Content provides an estimate of determining power of a marker based on the number of alleles at a locus and relative frequencies of these alleles. PIC value represents the relative informativeness of each marker and in the present study, PIC value was 0 for RM 6575, RM 6950, RM5781, RM 6980, HvSSR 1-33, RM 329 and highest for RM 6340 with an average PIC value of 0.43 (Fig: 4.34) PIC Values for Used SSR Markers RM 104 RM 431 RM 212 RM1003 RM1282 RM 6236 RM6887 RM5389 RM 8084 RM 6575 RM8085 RM 5 HVSSR 1-49 RM 6504 RM 8068 RM 448 RM 3825 RM 486 RM 499 RM 243 RM 3447 RM5781 RM 6117 RM 102 RM 5389 RM 6340 RM 495 HVSSR-1-33 RM 129 RM 6980 RM 1141 RM 302 RM 84 Sd1 E1 SD1 E3 Fig 4.28: Graphical representation of PIC value of SSR markers The sd1 allele in IR 8, an indica variety, contains a 383bp deletion from exon 1 to exon 2 which induces a frame shift that creates a new stop codon. It therefore is considered a null allele. On the other hand, the japonica varieties such as Jikkoku, Remei and Calrose 76 carry single nucleotide substitutions in SD1 leading to a single amino acid exchanges (Sasaki et al., 2002a, Ashikari et al. 2002, Spielmeyer et al., 2002). In general, native indica varieties are taller than japonica varieties, and the strong sd1 allele has been used in indica breeding to develop semi-dwarf varieties such as IR8.

149 Fig 4.29: PCR amplification of 47 rice germplasm accessions with SSR primers RM 431, RM 3825, RM 12091, RM 212, RM 315 and RM

150 132 Table 4.16: Characterization of dwarf, semi-dwarf, semi-tall and tall accessions at sd1 locus with SDE1, SDE2 and SDE3 exon sites along with SSR markers near sd1 gene Genotypes PH (cm) RM 431 RM 3825 RM RM 212 RM 315 RM SD1 E1 SD1 E2 SD1 E3 Dhaur NA NA 155 JS NA NA 155 Badal Phool NA 155 Amakoyali Rambhoj Churhala Ddhan NA Lal Batra NA NA 155 Lallo NA NA 155 Machhri kata NA 155 Parra Dhan Pinna Basengi NA NA NA NA Sakra NA Satka NA 155 Shyam Jir NA Banko II NA Changadi Dhaura NA 155 Kadam phool NA 155 Kakdo Kanji NA 250 NA 155 Machhari Ankhi Nalla Wadlu NA 155 Parra Basmati NA 155 Dhamna Panda NA NA NA No :21 (A) NA NA 610 NA Badi Barik NA 610 NA Basmati NA 610 NA Kondi Ajan NA 610 NA Basmati (I) NA Beo (I) NA Bodi NA 155 Bhata Ajan NA 155 Swarna Sub NA NA IGKVR NA NA NA IGKVR NA NA Karma Mahsuri NA 155 Badshah Bhog NA 155 selection 1 Dubraj Selection NA 155 Tarunbhog Sselection NA Mahamaya NA NA Safri NA 155 IGKVR NA NA NA Indira Aerobic Dhan NA NA NA TN NA NA NA Jaya NA 155 MTU NA NA NA

151 133 Forty seven rice germplasm accessions (33 landraces and 14 released varieties) were included in the study which had plant height in the category of dwarf, semidwarf, semitall and tall. In order to find the allelic variations in plant height based on sd1 gene we used three exons of sd1 gene along with six SSR markers which are in close vicinity of sd1 gene. Allele sizes for nine markers in all the 47 genotypes are given in the table The results obtained were shows the allelic variations of genotypes for plant height. For plant height, RM 431 reported two allelic variations (200 and 225bp), RM 3825 showed seven allelic variations (120,130, 140, 150, 155, 180 and 400bp), RM 12091exhibited eight variations (70, 80, 85, 90, 100, 105, 120 and 130bp), RM 212 had two alleles of 130 and 150bp. Likewise, RM 315 (133 and135bp), RM (90 and 92bp), SD1E1 (250, 275bp) and SD1E2 reported 600 and 610bp allelic variations c Single marker Analysis: Single marker analysis of all the 47 genotypes for nine markers (all present in chromosome one) was done for 27 traits including plant height viz., RM 431, RM 3825, RM 12091, RM 212, RM 315, RM 12023, SD1E1, SD1E2 and SD1E3 and significant t value was obtained for 19 traits which include plant height as well (Table 4.19). Marker RM 212 was observed to be linked with many traits such as Plant height, stem length (excluding panicle), number of filled grain per panicle, number of unfilled grain per panicle, total spikelet per panicle, spikelet fertility, Panicle weight per plant, Shoot dry weight per plant, Harvest index, Length of paddy and HRR. Marker RM was found to be linked with HRR, hulling%, milled grain length, milled grain width and length of paddy.

152 134 Table 4.17: The t-test table from single marker analysis for yield contributing traits S. No. Traits C # Markers t value 1 Plant height (cm) 1 RM * 2 stem length (excluding panicle) (cm) 1 RM * Grain weight (g) 1 SDE ** 1 SDE2 0.02* 4 number of filled grain per panicle 1 RM * 5 number of unfilled grain per panicle 1 RM * 1 SDE ** 1 SDE2 0.02* 6 total spikelet per panicle 1 RM ** 7 Spikelet fertility (%) 1 RM ** 8 Panicle weight per plant (g) 1 RM ** 1 SDE * 9 Shoot dry weight per plant (g) 1 RM * 1 SDE * 10 Harvest index (%) 1 RM *** 11 Paddy width (mm) 1 RM * 1 RM *** 1 SDE * 12 Paddy length (mm) 1 RM * 1 RM * 13 Grain weight per plant (g) 1 RM *** 14 Milled grain width (mm) 1 RM * 1 SDE * 15 Milled grain length (mm) 1 RM * 16 Hulling (%) 1 RM * 17 Milling (%) 1 SDE ** 1 SDE ** 18 HRR (%) 1 RM * 1 RM * 1 SDE * 1 SDE * 19 Gel consistency 1 RM * * Significant at 5% level; ** at 1% and *** at 0.1% probability level The three exons of sd1 gene i.e., SDE1, SDE2 and SDE3 along with six other SSR markers present close to the sd1 gene were used to study the allelic variation based on the gene. Table 4.15 shows the presence of allele size obtained in all 47 rice genotypes for all the nine markers. But none of the markers among these were linked with the trait of interest i.e., plant height. Three genotypes

153 135 namely, Dhaur, JS-5 and Badalphool were designated as dwarf lines. The results depicts that not a single marker was found to be linked to these three lines. The different alleles present in these three lines were also found in semi-dwarf and as well as in tall lines. This clearly indicates that among nine markers, none of them were responsible for dwarfism in specifically these three genotypes (Dhaur, JS-5 and Badalphool). The results show that there might be some other gene held responsible for dwarfness in Dhaur, JS-5 and Badalphool instead of the expression of semidwarf sd1 gene. Also studies have been reported that there are other genes controlling dwarf stature in rice. In the present study, it is clear that the dwarfness in Dhaur, JS-5 and Badalphool is not because of sd1 gene. Recently, the sd1 gene was found to encode GA20 oxidae-2(ga20ox-2) which catalyses late steps of gibbellerin (GA) biosynthesis (Sasaki et al., 2002a; Ashikari et al., 2002 and Spielmeyer et al., 2002). There are various determinants for plant height, but a defect of GA is one of the major reasons for the dwarf phenotype. Sasaki et al., (2002a) and Ashikari et al., (2002) concluded that low GA production due to the loss of function or reduced function of GA20ox-2 causes plant height reduction in sd1mutants.

154 CHAPTER-V SUMMARY AND CONCLUSION The present study DNA Fingerprinting of rice genotypes (Oryza sativa L.) along with allelic variations based on sd1 Gene was carried out by using forty seven rice genotypes, with the objective of their characterization at morphological, quality and molecular level also for the allelic variations for plant height based on sd1 gene. The experiment was conducted at Research cum Instructional farm of IGKV, Raipur during Kharif The experiment was conducted in Randomized Block Design (RBD), with two replications. The plants were observed regularly at different growth stages in order to find out the diagnostic descriptors of each genotype which were uniformly present in the population and to be stable. Apart from morphological descriptors and quality analysis, molecular markers were also applied in order to characterize these rice germplasm lines at DNA level. In order to propose elite plant type with desired characters i.e. mean, range, phenotypic and genotypic coefficient of variances, heritability, genetic advance and genetic advance as percentage of mean were also studied. Rice genetic diversity assessed so far suggests a broad genetic base in India. The landraces available today preserve the allelic richness. Commercial cultivars are genetically homogenous while the landraces studied revealed composite genetic structure. Forty seven genotypes of rice from Chhattisgarh were selected for this study; the results can be summarized as below: To establish distinctiveness among rice genotypes qualitative (DUS) characters have been used. Qualitative characters are considered as morphological markers in the identification of germplasm accessions of rice because they are less influenced by environment. In the present investigation, among the qualitative characters observed, leaf blade pubescence, leaf blade colour, anthocyanin colouration of area below apex on lemma, stem length, panicle length, curvature of main axis of panicle, lemma and palea colour, altitude of branches of panicle and density of pubescence on lemma recorded highest variation among accessions. Analysis of variance revealed the existence of significant variability for all the characters included for study. The high magnitude of phenotypic coefficient of variation was higher in magnitude than the genotypic coefficient of variation suggesting the influence of environment 136

155 137 on expression of characters. The highest value of PCV coupled with GCV was recorded in number of filled grains per panicle, followed by total spikelet per panicle number of unfilled grains per panicle, grain weight per plant, panicle weight, harvest index, milled grain length,100 seed weight, number of panicles per plant, decorticated grain length, gel consistency, decorticated grain width and milled grain width. High heritability coupled with high genetic advance was observed in twenty characters namely, leaf: length of blade, plant height, stem length, panicle weight, number of panicles per plant,100 grain weight, number of filled grains per panicle, number of unfilled grains per panicle, total spikelet per panicle, grain weight per plant, shoot dry weight per plant, harvest index, decorticated grain length, w i d t h o f decorticated grain, length of milled grain, width of milled grain, milling%, HRR, amylose content, and gel consistency. High estimate of heritability was found for all the quantitative characters related to yield and quality characters under study showed more than 82% heritability estimate. Grain yield had significant and positive correlation with panicle length, number of panicle, number of filled grains per panicle, total spikelet per plant, spikelet fertility, shoot dry weight, harvest index, decorticated grain length, decorticated grain width, plant height, HRR, hulling% and milled grain length. High significant genotypic correlation with grain yield per plant indicating the co-segregation of the concerned characters either due to linked gene or pleiotropy effect or both. On the basis of cluster analysis rice accession lines were grouped into 8 clusters. The highest numbers of accessions were in cluster 3 and 4 had 11 genotypes. Maximum distance was observed between cluster VII and cluster V and Minimum distances was observed between cluster I and cluster IV. The inter cluster distances in present study were higher than the intra cluster distance in all cases reflecting wider diversity among the breeding lines of distant groups. The result of the PCA revealed that Jaya is the best accession for both quality and yield attributing traits followed by MTU 1010, IGKVR1, TN1 and Safri 17.. These identified accessions may be used as donor to improve the yield and quality traits in varietal development programme. Also the genotypes Basmati-1, IGKVR-1244, MTU1010, Dhaur, Badalphool, Parra, Dhaura,

156 138 Nallawadlu, Beo-1 and Dhamna panda were very divergent for the traits under study which can be utilized for hybridization programme. Similarity coefficient analysis and clustering for 47 rice genotypes was performed by Jaccard s similarity coefficient and Euclidean distance. Jaccard s similarity coefficient ranges from 0.04 to 0.75 with highest similarity coefficient between Dhaur and Badalphool (0.75) and lowest between Dhaur and Machhariankhi. Total 47 rice genotypes taken under study were divided into two main clusters, cluster A and cluster B at Jaccard s similarity coefficient After analysis the data generated from 69 microsatellite markers (SSR), 63 markers showed polymorphic reaction in forty eight rice accessions. PIC value represents the relative informativeeness of each marker and in the present study, PIC value was 0 for RM 6575, RM 6950, RM5781, RM 6980, HvSSR 1-33, RM 329 with an average PIC value of The accessions that are derivatives of genetically similar dropped in one group. In UPGMA tree, two major clusters were formed having 19 and 28 genotypes with SSR marker. Also single marker analysis of all the 47 genotypes for nine markers was done for 27 traits including plant height which shows that RM 212 is significantly linked with the plant height. Thus, RM 212 which is in close vicinity of the sd1 gene and is responsible for allelic variations present in the materials under study. CONCLUSIONS: Agro-morphological and quality descriptors showed remarkable differences in their distribution and amount of variations within them. Significant variation in all 27 yield and yield attributing traits were obtained. High variation (PCV and GCV along with high heritability and genetic advance) was observed for 100 seed weight, number of filled grains per panicle, total spikelets per panicle, number of unfilled grains per panicle and grain weight per plant, length and width of decorticated and milled grain and gel consistency. The magnitude of genetic advance as percent of mean was recorded high for almost all the traits. These high values of genetic advance indicate that the characters are governed by additive genes and selection will be rewarding for improvement of such trait. Grain yield had significant and positive correlation with panicle length, number of panicle, number of filled grains per panicle, total spikelet per plant, spikelet fertility, shoot dry

157 139 weight, harvest index, decorticated grain length, decorticated grain width, plant height, HRR, hulling% and milled grain length. Very high positive direct effect on grain yield was exhibited by panicle length, harvest index, shoot dry weight, number of unfilled grains per panicle total spikelet per plant, HRR, number of unfilled grains per panicle, hulling% and number of panicle per plant. Principal component analysis discriminated the 47 genotypes into 25 principal components. Results of eigen values and cumulative variability of all 25 principal components showed that the first three components accounts for more than 51% of total variation. First 8 components explains the 80.01% variation and eign value >1. Cluster analysis rice accession lines were grouped into 8 clusters. The highest numbers of accessions were in cluster III and IV had 11 genotypes. Maximum distance was observed between cluster VII and cluster V and Minimum distance was observed between cluster I and cluster IV. The inter cluster distances in present study were higher than the intra cluster distance in all cases reflecting wider diversity among the breeding lines of distant groups. The maximum inter cluster distance was observed between cluster 7 and 5 and minimum distance was observed between cluster 1 and cluster 4. The inter cluster distances in present study were higher than the intra cluster distance in all cases reflecting wider diversity among the breeding lines of distant groups and less divergent genotypes within the cluster. Similarity among 47 rice genotypes was performed for 69 SSR markers by Jaccard s similarity coefficient and Euclidean distance. Total genotypes taken under study were divided into two main clusters, cluster A and cluster B at Jaccard s similarity coefficient Highest similarity coefficient was observed in between Dhaur and Badalphool and lowest between Dhaur and Machhariankhi. Also single marker analysis of all the 47 genotypes for nine markers was done for 27 traits including plant height which shows that RM 212 is significantly linked with the plant height. Thus RM 212 which is in close vicinity of the sd1 gene and is responsible for allelic variations present in the materials under study. PIC value was 0 for RM 6575, RM 6950, RM5781, RM 6980, HvSSR 1-33, RM 329 indicating no polymorphism and highest 0.99 for with an average PIC value of Among nine markers, none of them were responsible for dwarfism in specifically these three genotypes (Dhaur, JS-5 and Badalphool). The results show that there might be some other gene

158 140 held responsible for dwarfness in Dhaur, JS-5 and Badalphool instead of the expression of semidwarf sd1 gene.

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172 154 Appendix A: Weekly Meteorological Data during Crop Growth Period of Kharif -2016) Wk. No. Date Max. Temp. Min. Temp. Rainfall (mm) Rainy days Relative Humidity (%) Vapour Pressure (mm of Hg) Wind Velocity Evaporation (mm) Sun Shine ( C) ( C) I II I II (Kmph) (hours) 26 June July Aug Sep Oct Nov

173 155 Appendix B: Description of agro-morphological characters S. No. 1. (+) 2 (*) Characteristics States Note Stage of observation Type of Assessment Coleoptile: Colour Colourless 1 10 VS Green 2 purple 3 Basal leaf: Sheath Green 3. Leaf: Intensity of green colour 4. Leaf: Anthocyanin colourization 5. Leaf: Distribution of anthocyanin Colouration 6. Leaf Sheath: anthocyanin (+) Colouration 7. Leaf sheath: Intensity of anthocyanin Colouration 8. (*) 9 (*) (+) 10. (*) Leaf; pubescence of blade surface Leaf: Auricles Leaf: Anthocyanin colourization of auricles 11 Leaf: collar (+). 12. Leaf: Anthocyanin colourization of collar 13. Leaf: Ligule (+) 14. Leaf: Shape of Ligule (+) (*) 15. Leaf: Colour of Ligule (*) Green Light purple Purple lines Uniform purple Light Medium dark Absent present On tips only On margins only In blotches only Uniform Absent present Very weak Weak Medium Strong Very strong Absent Weak Medium Strong Very strong Absent Present Colourless Light purple purple Absent Present Absent Present Absent Present Truncate Acute Split White Light purple purple 16. Leaf: length of blade Short(<30cm) Medium(30-40cm) Long(>45cm) VS 40 VG 40 VG 40 VG 40 VG 40 VG 40 VS 40 VS 40 VS 40 VS 40 VS 40 VS 40 VS 40 VS 40 MS

174 Leaf: Width of blade Narrow(<1cm) Medium(1-2cm) Broad(>2cm) 18. Culm: Attitude (for Non procumbent 19 (+). 20 (*) 21 (*) (+) 22 (*) floating rice only) Culm: Attitude Time of heading (50% plants with panicles) Flag leaf: attitude of blade(early observation) Spikelet: Density of pubescence of lemma Procumbent Erect Semi-erect Open spreading Very early Early Medium Late Very late Erect Semi-erect Horizontal Drooping Absent Weak Medium Strong Very strong 23 Male sterility Absent Present 24 Lemma: Anthocyanin (+) colouration of keel 25 (+) 26 (*) (+) 27 (*) (+) Lemma: Anthocyanin colouration of area below apex Lemma: Anthocyanin colouration of apex Spikelet: colour of stigma Absent or very weak Weak Medium Strong Very strong Absent Weak Medium Strong Very strong Absent Weak Medium Strong Very strong White Light green Yellow Light purple purple 28 Stem: Thickness Thin Medium Thick 29 (*) 30 (*) Stem: length Stem: Anthocyanin colouration of nodes Very short Short Medium Long Very long Absent Present MS 40 VS 40 VS 55 VG 60 VG VS 65 VG 65 VS 65 VS 65 VS 65 VS 70 MS

175 Stem: Intensity of Anthocyanin colouration of nodes 32 Stem: Intensity of Anthocyanin colouration 33 (*) (+) 34 (*) (+) 35 (*) (+) of internodes Panicle: length of main axis Flag leaf: Attitude of blades Panicle: Curvature of main axis 36 Panicle: number per plant 37 (*) 38 (+) 39 (*) (+) 40 (*) Spikelet: colour of tip of lemma Lemma and palea: colour Panicle: Awns Panicle: colour of awns (late observation) Weak Medium strong Absent Present Very short Short Medium Long Very long Erect Semi-erect Horizontal Deflexed Straight Semi-Straight Deflexed Dropping Few Medium many White Yellowish Brown Red Purple Black Straw Gold and gold furrows on straw background brown spots on straw Absent Present Yellowish White Yellowish brown Brown Reddish brown Light red Red Light purple Purple Black MS 90 VG 90 VG MS VS VG 90 VG 90 VS 41 Panicle: length of longest Awns 42 (*) 43 (+) Panicle: Distribution of Awns Panicle: Presence of secondary branching Very short Short Medium Long Very long Tip only Upper half only Whole length Absent Present VG-MS 90 VS 90 VG

176 (+) 45 (+) (*) 46 (*) (+) Panicle: secondary branching Panicle: attitude of branches Panicle: Exertion Weak Strong clustered Erect Erect to semi erect Semi-erect Semi erect to spreading spreading Partly exerted Mostly exerted Well exerted 47 Time maturity(days) Very early Early Medium Late Very late 48 Leaf: Senescence Early medium late 49 (*) (+) Sterile lemma: colour 50 Grain: Weight of 1000 fully developed grains 51 (+) Grain: Length Straw Gold Red purple Very low Low Medium High very high Very short Short Medium Long Very long 52 Grain: Width Very narrow Narrow Medium Broad Very broad 53 (+) 54 (*) (+) 55 (*) (+) 56 (*) (+) Grain: Phenol reaction of lemma Decorticated grain: length Absent Present Short Medium Long Extra long Decorticated grain: width Narrow Medium broad Decorticated grain: shape(in lateral view) Short slender Short bold Medium slender Long bold Long slender Extra Long slender VG 90 VG 90 VG 90 VG 92 VG 92 VS 92 MG 92 MS 92 MS 92 VG 92 MS 92 MS 92 MS

177 (*) 58 (+) 59 (*) (+) 60 (+) 61 (+) 62 (*) (+) Decorticated grain: colour Endosperm: presence of amylose Endosperm: content of amylose Varieties with endosperm of amylose absent only polished grain: Expressed of white core Gelatinization temperature through alkali spreading value Decorticated grain: Aroma White Light brown Variegated brown Dark brown Light red Red Variegated purple Purple Dark purple Absent Present Very low Low Medium High very high Absent or very small Small Medium Large Fully chalky low Medium High Medium High Absent Present VG 92 MG 92 MG 90 MG 92 MG 92 MG

178 160 Appendix C (i) : Agromorphological characters studied in 47 germplasm of rice CGR Number Genotypes Coleoptile colour Basal leaf: Sheath colour Leaf: Intesity of green colour Leaf:Anthocyanin coloration Leaf: Distribution of anthocyanin colouration Leaf sheath anthocyanin colouration Leaf sheath: intensity of anthocyanin colouration Leaf: Pubescence of blade surface Leaf: Auricles Leaf: Anthocyanin colouration of auricles CGR NO Dhaur White Green Dark Absent Absent Absent Absent medium present colourless CGR NO JS-5 White Green Dark Absent Absent Absent Absent medium present colourless CGR NO Badal Phool White Green Medium Absent Absent Absent Absent strong present colourless CGR NO-1 Amakoyali White Green Medium Absent Absent Absent Absent very strong present colourless CGR NO- 14 Rambhoj Purple Green Medium Absent Absent Absent Absent strong present colourless CGR NO- 72 Churhala Dhan White Green Medium Absent Absent Absent Absent weak present colourless CGR NO- 218 Lal Batra White Green Medium Absent Absent Absent Absent medium present colourless CGR NO- 242 Lallo-14 White Green Dark Absent Absent Absent Absent medium present colourless CGR NO- 255 Machhri Kata White Green Medium Absent Absent Absent Absent medium present colourless CGR NO- 300 Parra Dhan White Green Dark Absent Absent Absent Absent medium present colourless CGR NO- 320 Pinna Basengi White Green Dark Absent Absent Absent Absent weak present colourless CGR NO- 369 Sakra Purple Green Dark Absent Absent Absent Absent medium present colourless CGR NO- 389 Satka White Green Dark Absent Absent Absent Absent weak present colourless CGR NO- 447 Shyam jir White Green Medium Absent Absent Absent Absent medium present colourless CGR NO- 529 Banko II White Green Dark Absent Absent Absent Absent strong present colourless CGR NO- 659 Changadi White Green Dark Absent Absent Absent Absent weak present colourless CGR NO- 746 Dhaura White Green Dark Absent Absent Absent Absent weak present colourless CGR NO- 930 Kadam phool White Green Dark Absent Absent Absent Absent medium present colourless CGR NO- 953 Kakdo White Green Dark Absent Absent Absent Absent medium present colourless CGR NO- 993 Kanji White Green Medium Absent Absent Absent Absent strong present colourless CGR NO Machhari Ankhi White Green Dark Absent Absent Absent Absent medium present colourless CGR NO Nalla Wadlu White Green Dark Absent Absent Absent Absent weak present colourless CGR NO Parra White Green Medium Absent Absent Absent Absent medium present colourless CGR NO- 569 Basmati White Green Dark Absent Absent Absent Absent weak present colourless CGR NO- 741 Dhamna Panda White Green light Absent Absent Absent Absent medium present colourless CGR NO No :21 (A) purple Green Dark Absent Absent Absent Absent weak present colourless CGR NO Badi Barik White Green Medium Absent Absent Absent Absent weak present colourless CGR NO Basmati Purple Green Dark Absent Absent Absent Absent weak present colourless CGR NO Kondi ajan margins present present Purple Purple Dark only medium weak present light purple

179 161 CGR NO Basmati (I) White Green Dark Absent Absent Absent Absent medium present colourless CGR NO Beo (I) Purple Green Dark Absent Absent Absent Absent weak present colourless CGR NO Bodi Purple Green Dark Absent Absent Absent Absent weak present colourless CGR NO Bhata Ajan margins present present White Purple Dark only strong weak present colourless Swarna Sub 1 White Green Dark Absent Absent Absent Absent weak present colourless IGKVR 1 Purple Green Dark Absent Absent Absent Absent medium present purple IGKVR 1244 Purple Green Dark Absent Absent Absent Absent medium present light purple Karma Mahsuri White Green Medium Absent Absent Absent Absent medium present colourless Badshabhog Selection 1 White Green Medium Absent Absent Absent Absent medium present colourless Dubraj sslection 1 White Green Medium Absent Absent Absent Absent weak present colourless Tarunbhog Selection 1 White Green Light Absent Absent Absent Absent weak present colourless Mahamaya Purple Green Dark Absent Absent Absent Absent medium present purple Safri 17 White Green Light Absent Absent Absent Absent weak present colourless IGKVR 2 Purple Green Dark Absent Absent Absent Absent very strong present colourless Indira Aerobic Absent Absent Absent Absent weak present colourless Dhan 1 White Green Dark TN 1 White Green Light Absent Absent Absent Absent weak present colourless Jaya White Green Dark Absent Absent Absent Absent medium present colourless MTU-1010 White Green Dark Absent Absent Absent Absent medium present colourless

180 162 Appendix C (ii) : Agromorphological characters studied in 47 germplasm of rice CGR Number Genotypes Leaf: Collar Leaf: Anthocyanin colouration of collor Leaf: Ligule Leaf: Shape of Ligule Leaf: Colour of ligule Culm: Attitude Time of heading (50% plants with panicles) Flag leaf: Attitude of blade(early observation) Spikelet: Density of pubescence of lemma CGR NO Dhaur present absent present split white erect late semierect medium CGR NO JS-5 present absent present split white semierect late semierect strong CGR NO Badal Phool present absent present split white erect late semierect medium CGR NO-1 Amakoyali present absent present split white semierect late erect very strong CGR NO- 14 Rambhoj present absent present split white semierect medium semierect very strong CGR NO- 72 Churhala Dhan present absent present split white semierect medium semierect medium CGR NO- 218 Lal Batra present absent present split white semierect medium semierect strong CGR NO- 242 Lallo-14 present absent present split white semierect medium erect very strong CGR NO- 255 Machhri Kata present absent present split white semierect medium erect medium CGR NO- 300 Parra Dhan present absent present split white semierect medium semierect very strong CGR NO- 320 Pinna Basengi present absent present split white erect medium erect medium CGR NO- 369 Sakra present absent present split white erect late erect strong CGR NO- 389 Satka present absent present split white semierect late semierect strong CGR NO- 447 Shyam jir present absent present split white erect late erect very strong CGR NO- 529 Banko II present absent present split white semierect medium erect medium CGR NO- 659 Changadi present absent present split white erect medium erect weak CGR NO- 746 Dhaura present absent present split white erect medium erect strong CGR NO- 930 Kadam phool present absent present split white erect medium erect medium CGR NO- 953 Kakdo present absent present split white semierect medium semierect medium CGR NO- 993 Kanji present present present split white semierect medium semierect medium CGR NO Machhari Ankhi present absent present split white semierect late erect strong CGR NO Nalla Wadlu present absent present split white semierect late semierect medium CGR NO Parra present absent present split white semierect late erect weak CGR NO- 569 Basmati present absent present split white semierect late horizntal weak CGR NO- 741 Dhamna Panda present absent present split white semierect late horizntal strong CGR NO No :21 (A) present absent present split white semierect late horizntal strong CGR NO Badi Barik present absent present split white semierect late horizntal weak

181 163 CGR NO Basmati present absent present split white semierect late erect strong CGR NO Kondi ajan present absent present split white semierect late horizntal strong CGR NO Basmati (I) present absent present split white semierect late horizntal very strong CGR NO Beo (I) present absent present split white semierect medium horizntal very strong CGR NO Bodi present absent present split white semierect late horizntal strong CGR NO Bhata Ajan present absent present split purple open late horizntal strong Swarna Sub 1 present absent present split white semierect very late semierect medium IGKVR 1 present present present split white semierect late semierect very strong IGKVR 1244 present present present split white semierect late erect strong Karma Mahsuri present absent present split white semierect late horizntal weak Badshabhog Selection 1 present absent present split white semierect very late horizntal weak Dubraj sslection 1 present absent present split white semierect very late erect strong Tarunbhog Selection 1 present absent present split white erect very late erect medium Mahamaya present present present split white erect late erect medium Safri 17 present absent present split white semierect very late erect weak IGKVR 2 present absent present split white erect late erect strong Indira Aerobic Dhan 1 present absent present split white semierect late semierect weak TN 1 present absent present split white semierect late semierect strong Jaya present absent present split white semierect late erect strong MTU-1010 present absent present split white semierect late erect strong

182 164 Appendix-C (iii): Agromorphological characters studied in 47 germplasm of rice CGR Number Genotypes Male sterility Lemma: Anthocyanin colouration of keel Lemma: Anthocyanincolouration of area below apex Lemma: Anthocyanin colouration of apex Spikelet: colour of stigma Stem: Thickness Stem: Anthocyanin colouration of nodes Stem: Intensity of anthocyanin colouration of nodes Stem: intensity of Anthocyanin colouration of internode CGR NO Dhaur Absent Absent Absent Absent white thin Absent Absent Absent CGR NO JS-5 Absent Absent Absent Absent white medium Absent Absent Absent CGR NO Badal Phool Absent Absent Absent Absent white thin Absent Absent Absent CGR NO-1 Amakoyali Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 14 Rambhoj Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- 72 Churhala Dhan Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 218 Lal Batra Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- 242 Lallo-14 Absent Absent weak weak white thin Absent Absent Absent CGR NO- 255 Machhri Kata Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 300 Parra Dhan Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 320 Pinna Basengi Absent Absent Absent Absent white thin Absent Absent Absent CGR NO- 369 Sakra Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 389 Satka Absent Absent Absent Absent purple thin Absent Absent Absent CGR NO- 447 Shyam jir Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 529 Banko II Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- 659 Changadi Absent medium Absent Absent white thin Absent Absent Absent CGR NO- 746 Dhaura Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- 930 Kadam phool Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 953 Kakdo Absent Absent Absent Absent white medium Absent Absent Absent CGR NO- 993 Kanji Absent Absent strong weak white thin Absent Absent Absent CGR NO Machhari Ankhi Absent Absent Absent Absent white medium Absent Absent Absent CGR NO Nalla Wadlu Absent Absent Absent Absent white medium Absent Absent Absent CGR NO Parra Absent medium strong medium white medium Absent Absent Absent CGR NO- 569 Basmati Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- 741 Dhamna Panda Absent Absent Absent Absent white thick Absent Absent Absent CGR NO- No :21 (A) Absent strong Absent medium white medium Absent Absent Absent

183 CGR NO CGR NO CGR NO CGR NO CGR NO CGR NO CGR NO Badi Barik Absent Absent Absent medium white thick Absent Absent Absent Basmati Absent Absent medium strong purple thick Absent Absent Absent Kondi ajan Absent Absent Absent Absent white thick Absent Absent Absent Basmati (I) Absent Absent medium strong white thick Absent Absent Absent Beo (I) Absent Absent weak strong purple medium Absent Absent Absent Bodi Absent Absent Absent strong purple thick Absent Absent Absent Bhata Ajan Absent Absent strong medium white medium present strong present Swarna Sub 1 Absent Absent Absent strong white medium Absent Absent Absent IGKVR 1 Absent Absent Absent medium purple thick Absent Absent Absent IGKVR 1244 Absent Absent Absent strong purple thick Absent Absent Absent Karma Mahsuri Absent Absent Absent Absent white thick Absent Absent Absent Badshabhog Selection 1 Absent strong strong strong white thick Absent Absent Absent Dubraj sslection 1 Absent Absent Absent Absent white thick Absent Absent Absent Tarunbhog Selection 1 Absent Absent Absent Absent white thick Absent Absent Absent Mahamaya Absent Absent Absent strong purple medium Absent Absent Absent Safri 17 Absent Absent Absent very strong white thick Absent Absent Absent IGKVR 2 Absent Absent strong very strong purple medium Absent Absent Absent Indira Aerobic Dhan 1 Absent Absent Absent medium white medium Absent Absent Absent TN 1 Absent Absent Absent Absent white thick Absent Absent Absent Jaya Absent Absent Absent Absent white medium Absent Absent Absent MTU-1010 Absent Absent absent Absent white medium Absent Absent Absent

184 166 Appendix-C (iv): Agromorphological characters studied in 47 germplasm of rice CGR Number Genotypes Flag leaf: Attitude of blade(late observation) Panicle: Curvature of main axis Spikelet: Colour of tip of lemma lemma and palea colour Panicle: Awns Panicle: Colour of awns(late observation) CGR NO Dhaur erect deflexed yellow straw Absent Absent CGR NO JS-5 semierect deflexed yellow straw Absent Absent CGR NO Badal Phool erect deflexed yellow straw present yellowish white CGR NO-1 Amakoyali semierect deflexed gold and gold furrow on straw present yellow background yellowish white CGR NO- 14 Rambhoj horizontal drooping brown brown furrow on strow present yellowish white CGR NO- 72 Churhala Dhan semierect semistraight yellow brown Absent Absent CGR NO- 218 Lal Batra erect deflexed yellow straw Absent Absent CGR NO- 242 Lallo-14 semierect semistraight brown brown Absent Absent CGR NO- 255 Machhri Kata semierect deflexed yellow straw Absent Absent CGR NO- 300 Parra Dhan semierect straight black black Absent Absent CGR NO- 320 Pinna Basengi horizontal semistraight yellow straw Absent Absent CGR NO- 369 Sakra erect semistraight yellow straw Absent Absent CGR NO- 389 Satka erect semistraight gold and gold furrow on straw brown background Absent Absent CGR NO- 447 Shyam jir horizontal deflexed brown brown furrow on strow present yellowish white CGR NO- 529 Banko II semierect semistraight brown brown furrow on strow present yellowish white CGR NO- 659 Changadi horizontal semistraight gold and gold furrow on straw present yellow background yellowish white CGR NO- 746 Dhaura semierect deflexed yellow straw present yellowish white CGR NO- 930 Kadam phool semierect semistraight brown straw Absent Absent CGR NO- 953 Kakdo erect drooping yellow brown furrow on strow present yellowish white CGR NO- 993 Kanji erect straight yellow straw present yellowish white CGR NO Machhari Ankhi erect straight black black Absent Absent CGR NO Nalla Wadlu erect deflexed gold and gold furrow on straw brown background Absent Absent CGR NO Parra semierect deflexed black black Absent Absent CGR NO- 569 Basmati horizontal deflexed yellow straw Absent Absent CGR NO- 741 Dhamna Panda horizontal drooping yellow straw Absent Absent CGR NO No :21 (A) horizontal deflexed brown straw Absent Absent CGR NO Badi Barik semierect semistraight yellow straw present yellowish white CGR NO Basmati horizontal drooping brown straw present yellowish white CGR NO Kondi ajan erect deflexed brown black present yellowish white CGR NO Basmati (I) horizontal drooping brown brown furrow on straw Absent Absent

185 167 CGR NO Beo (I) semierect semistraight gold and gold furrow on straw brown background Absent Absent CGR NO Bodi horizontal drooping brown brown Absent Absent CGR NO Bhata Ajan horizontal deflexed brown brown spot on straw Absent Absent Swarna Sub 1 erect semistraight yellow straw Absent Absent IGKVR 1 semierect deflexed yellow straw Absent Absent IGKVR 1244 erect deflexed red straw present yellowish white Karma Mahsuri horizontal deflexed brown straw present yellowish white Badshabhog horizontal drooping Selection 1 brown brown spot on straw Absent Absent Dubraj sslection 1 horizontal deflexed yellow straw Absent Absent Tarunbhog Selection semierect semistraight 1 yellow straw Absent Absent Mahamaya semierect deflexed yellow straw present yellowish white Safri 17 erect drooping yellow straw Absent Absent IGKVR 2 semierect drooping gold and gold furrow on straw yellow background Absent Absent Indira Aerobic Dhan gold and gold furrow on straw erect deflexed 1 brown background Absent Absent TN 1 semierect deflexed yellow straw Absent Absent Jaya erect drooping yellow straw Absent Absent MTU-1010 horizontal drooping brown straw Absent Absent

186 168 Appendix-C (v): Agromorphological characters studied in 47 germplasm of rice CGR Number Genotypes Panicle: Length of longest awn Panicle: Presence of secondary branching Panicle: Secondary branching Panicle: Attitude of branches Panicle: Exertion Time Maturity(Days) Leaf: senescence CGR NO Dhaur Absent semierct to late Present Weak spreading mostly medium straw CGR NO JS-5 Absent Present Weak semierct mostly late medium straw CGR NO Badal Phool long Present Weak semierct well late medium straw CGR NO-1 Amakoyali medium Present Weak straight well late early straw CGR NO- 14 Rambhoj very long Present Weak erect to semierect well medium early straw CGR NO- 72 Churhala Dhan Absent Present Weak semierct well medium early straw CGR NO- 218 Lal Batra Absent Present strong semierct mostly medium early straw CGR NO- 242 Lallo-14 Absent Present strong semierct mostly medium early straw CGR NO- 255 Machhri Kata Absent Present weak semierct mostly medium early straw CGR NO- 300 Parra Dhan Absent semierct to medium Present weak spreading well early straw CGR NO- 320 Pinna Basengi Absent semierct to medium Present weak spreading mostly early straw CGR NO- 369 Sakra Absent Present strong spreading mostly late early straw CGR NO- 389 Satka Absent semierct to late Present strong spreading well early straw CGR NO- 447 Shyam jir very long Present strong spreading mostly late early straw CGR NO- 529 Banko II very long Present weak semierct partially medium early straw CGR NO- 659 Changadi medium Present weak spreading mostly medium early straw CGR NO- 746 Dhaura long Present strong spreading well medium early straw CGR NO- 930 Kadam phool Absent Present strong spreading well medium early straw CGR NO- 953 Kakdo small Present weak semierct partially medium early straw CGR NO- 993 Kanji semierct to medium medium Present weak spreading partially early straw CGR NO Machhari Ankhi Absent Present weak semierct partially late medium straw CGR NO Nalla Wadlu Absent Present strong spreading mostly late early straw CGR NO Parra Absent semierct to late Present weak spreading well medium straw CGR NO- 569 Basmati Absent semierct to late Present weak spreading well late straw CGR NO- 741 Dhamna Panda Absent semierct to late Present strong spreading well medium straw Sterile lemma: Colour

187 169 CGR NO No :21 (A) Absent semierct to late Present strong spreading well early straw CGR NO Badi Barik semierct to late very long Present strong spreading well medium straw CGR NO Basmati very late short Present weak semierct well medium straw CGR NO Kondi ajan very semierct to late short Present strong spreading well medium straw CGR NO Basmati (I) Absent Present strong spreading well late medium straw CGR NO Beo (I) Absent semierct to medium Present weak spreading well medium straw CGR NO Bodi Absent Present strong semierct partially late medium straw CGR NO Bhata Ajan Absent semierct to late Present strong spreading well medium straw Swarna Sub 1 Absent Present strong erect to semierect partially very late very late straw IGKVR 1 Absent Present strong semierct well late very late straw semierct to IGKVR 1244 late short Present strong spreading partially very late straw Karma Mahsuri long Present strong spreading mostly late very late straw Badshabhog Selection 1 Absent Present strong semierct well very late very late straw Dubraj sslection 1 Absent semierct to very late Present strong spreading partially very late straw Tarunbhog Selection 1 Absent Present strong semierct partially very late very late straw Mahamaya semierct to late short Present strong spreading mostly late straw Safri 17 Absent Present strong semierct mostly very late late straw IGKVR 2 Absent semierct to late Present strong spreading mostly late straw Indira Aerobic Dhan 1 Absent semierct to late Present strong spreading partially late straw TN 1 Absent semierct to late Present strong spreading partially late straw Jaya Absent Present strong semierct mostly late late straw semierct to Absent late MTU-1010 Present strong spreading mostly very late straw

188 170 Appendix D1: Mean performance of 27 yield and quality traits of 47 rice genotypes S. Genotypes No Dhaur JS Badal Phool Amakoyali Rambhoj Churhala Dhan Lal Batra Lallo Machhri Kata Parra Dhan Pinna Basengi Sakra Satka Shyam jir Banko II Changadi Dhaura Kadam phool Kakdo Kanji Machhari Ankhi Nalla Wadlu Parra Basmati Dhamna Panda No :21 (A) Badi Barik Basmati Kondi ajan Basmati (I) Beo (I)

189 Bodi Bhata Ajan Swarna Sub IGKVR IGKVR Karma Mahsuri Badshabhog Selection Dubraj sslection Tarunbhog Selection Mahamaya Safri IGKVR Indira Aerobic Dhan TN Jaya MTU = Leaf: Length of blade ; 2 = Leaf: Width of blade ; 3= Plant height ;4= Stem: Length(excluding panicle ; 5= Panicle length ; 6= Panicle weight (g) ; 7= Number of panicles per plant ; 8= 100 seed weight (g) ; 9= Number of filled grains/panicle ;10= Number of unfilled grains/panicle ;11= Total spikelets/ panicle; 12= Spikelet Fertlity % ; 13= Grain weight/plant (g) ; 14= Shoot dry weight/plant (g) ; 15=Harvest Index (%) ; 16= Paddy length ; 17= Paddy width ; 18= Decorticated grain length (mm) ; 19=Decorticated grain width(mm) ; 20= Milled grain length ; 21 ; Milled grain width ; 22= Hulling% ; 23= Milling% ; 24=HRR% ; 25= Gel consistency ; 26=Alkali spreading value ; 27= Content of amylose %

190 172 Appendix D2: Mean performance of 27 yield and quality traits of 47 rice genotypes S. No. Genotypes Dhaur JS Badal Phool Amakoyali Rambhoj Churhala Dhan Lal Batra Lallo Machhri Kata Parra Dhan Pinna Basengi Sakra Satka Shyam jir Banko II Changadi Dhaura Kadam phool Kakdo Kanji Machhari Ankhi Nalla Wadlu Parra Basmati Dhamna Panda No :21 (A) Badi Barik Basmati Kondi ajan Basmati (I)

191 Beo (I) Bodi Bhata Ajan Swarna Sub IGKVR IGKVR Karma Mahsuri Badshabhog Selection Dubraj sslection Tarunbhog Selection Mahamaya Safri IGKVR Indira Aerobic Dhan TN Jaya MTU = Leaf: Length of blade ; 2 = Leaf: Width of blade ; 3= Plant height ;4= Stem: Length(excluding panicle ; 5= Panicle length ; 6= Panicle weight (g) ; 7= Number of panicles per plant ; 8= 100 seed weight (g) ; 9= Number of filled grains/panicle ;10= Number of unfilled grains/panicle ;11= Total spikelets/ panicle; 12= Spikelet Fertlity % ; 13= Grain weight/plant (g) ; 14= Shoot dry weight/plant (g) ; 15=Harvest Index (%) ; 16= Paddy length ; 17= Paddy width ; 18= Decorticated grain length (mm) ; 19=Decorticated grain width(mm) ; 20= Milled grain length ; 21 ; Milled grain width ; 22= Hulling% ; 23= Milling% ; 24=HRR% ; 25= Gel consistency ; 26=Alkali spreading value ; 27= Content of amylose %

192 174 Appendix E: Microsatellite (SSR) markers used for molecular characterization in 47 accessions of rice. S. No. Marker Name Amplicon size Forward Primer Reverse Primer Chromosome 1 RM GGAAGAGGAGAGAAAGATGTGTGTCG TCAACAGACACACCGCCACCGC HvSSR GTGTCCTATCAGGTGAGGTA ACACTCCTCCTCTGTTCTTTA RM TCCTGCGAACTGAAGAGTTG AGAGCAAAACCCTGGTTCAC RM GCGAAAACACAATGCAAAAA GCGTTGGTTGGACCTGAC RM CCACTTTCAGCTACTACCAG CACCCATTTGTCTCTCATTATG HvSSR GCTCTTCGATTCACTTCATC AGCAGAAGGTAACATGGAGA RM CCACTTTCAGCTACTACCAG CACCCATTTGTCTCTCATTATG RM TTCTCAGCTGCTTGTGCATC CCTCCAAGGTAAAGGGGTTC RM AAGCATGACAGCTGCAAGAC GGGGATGAAGGGTAATTTCG RM CACCAGCTTCATGCATGC AGCACTCAACTGATGCAGTG RM GTTCATCACGTGCAAAGGAG TGACGCTGGTGTACGGCTC RM CAGCAAACCAAACCAAGCC GCGAGGAGGAGGAGAAAAAG RM CTGTGTCCTTGTATCAGATG TAGATGAAACACTTGTCGAG RM CCCTCCCTTCTGTAAGCTCC GAAGAACAATGGGGTTCTGG RM TCTTGCATGAGAGCCAACAC GCTATTGCGCGAGATTATCC HVSSR ATATCATGCTATGCTGGGAC A AATTTCGGCATCTATATCCA RM CATGACAACATTGTTCTGAA CCTGTGATCAAGTCCTGTAG RM GTATGCATATTTGATAAGAG AAGTCACCGAGTTTACCTTC RM CTTTCCCTCCTCTTCAAACT AGCGACCTCAGATGAACTTA RM GTCTGTGTCACTAACCATGCC CATGGCGTCTCAACTACACC RM TGCGTTTCGATTTCTTTTTA GGAAAGTTGTGTTCTTTGGC RM TACTGGTGCAAGGATACCCC TGCTCCAAACCTCAGTCTCC RM TGCAACTTCTAGCTGCTCGA GCATCCGATCTTGATGGG RM TGCGTACCTCTGCTCCTCTCTGC GACGAAGCCGACCAAGTGAAGC HVSSR CTTTCCCTCCTCTTCAAACT AGCGACCTCAGATGAACTTA RM GCGCTTCTACTTCCACAAGG GGTTGGCGTACGTAGAGAGG RM GAGAGGCGGTAGTATGCTCG TCTCCAATTTCACCGTCACC RM CCCTCCCTTCTGTAAGCTCC GAAGAACAATGGGGTTCTGG RM AAACCTCTCGCTGTAATTAG TGAACATTTATTGATATGGTAAA RM CTGCTTCTTGCTCCGAGAAG CTGTTCTTGGCTTGGTTTCC RM TCTGATCTTGATGCAGGCAC TCTCCCGATTTGGACAGATC RM CTGTGTCCTTGTATCAGATG TAGATGAAACACTTGTCGAG RM AAAGCCCCCAAAAGCAGTAC GTGAAACTCTGGGGTGTTCG PIC value

193 34 HVSSR AGGAACAGCTACACCAGAAA ATTAGCCAAAGCATCCAAAC RM CCCCCCTCTCTCTCTCTCTC TAGCCACATCAACAGCTTGC RM GAAGTGTGATCACTGTAACC TACAGTGGACGGCGAAGTCG RM TACCAAACACCAACACTGCG ACCTGCAGTATCCAAGTGTACG RM GGAAGGTAACTGTTTCCAAC GAAATGCTTCCCACATGTCT RM GATCTGCAGACTGCAGTTGC AGCTGCAACGATGTTGTCC RM AACGACTGCTCCCTCTTCAG AGCTTGCAAGGCATTAGCTC RM AACCAGCAGATCGTCGAAAG GAAATGAGATGGCATCCGAG RM CGTGGTGCCTTCTTTCAAAG AAACAGGCGTAGCAGCAAAG RM ACCTCCTAAGCTCCAAACC AACGTCTTGTCCTCCACGAC RM GTCTACATGTACCCTTGTTGGG CGGCATGAGAGTCTGTGATG RM AGCACAAACCTAGCGGAGTG ACCTGAGCATGGATACTCGG RM TACTCTTTCGCGAAGGTTCG GAGAAGGGGTCATCCTCCTC RM AACTTTCCCACCACCACCGCGG GCAGCAGCAAGCCAGCAAGCG RM TACTCTTTCGCGAAGGTTCG GAGAAGGGGTCATCCTCCTC RM TCTTGCATGAGAGCCAACAC GCTATTGCGCGAGATTATCC RM ACACTCTCTCTCCTCGCTGC CGAGGAGAATACTCGTTCGG RM AACGGAGATCGAGATCGATG TGCTTCCTCATCTCCCTCAC RM GATGGTTTTCATCGGCTACG AGTCCCAGAATGTCGTTTCG RM AATCCAAGGTGCAGAGATGG CAACGATGACGAACACAACC RM TGCGTTTCGATTTCTTTTTA GGAAAGTTGTGTTCTTTGGC HVSSR AAACTGGAGATGAACTCGAA GTAACGAACTAGAGCATGGG RM AGCACAAACCTAGCGGAGTG ACCTGAGCATGGATACTCGG RM TCTCTCCGGAGCCAAGGCGAGG CGAGCCACGACGCGATGTACCC RM GTAAAGGCGAATCCGAACAC CACGTGGTGGCTTGAGAAC RM GTCCTTTGTTTCTGAAGACG CCTTTCTTTCACTTGATTGG RM TGCATTGCAGAGAGCTCTTG CCTGTGATCAAGTCCTGTAG RM TGCATTGCAGAGAGCTCTTG CAGGGCTTTGTAAGAGGTGC RM CACAACAGAACAATGCCTAA TTGGCAACATGAATTGTAGA RM TCATGTCATCTACCATCACAC ATGGAGAAGATGGAATACTTGC RM CATTCGGCTGCTGCTATTC GCTTGTCACATCTTGCACAG RM TAAGGGTCCATCCACAAGATG TTGCAAATGCAGCTAGAGTAC RM CGGTTAATGTCATCTGATTGG TTCGAGATCCAAGACTGACC Sd1 E1 250 CAACTCACTCCCGCTCAACACAGC TTTGAAATGCAATGTCGTCCACC SD1 E2 600 GCGCCAATGGGGTAATTAAAACG GGCATTCCATTGTTTGTGATTGG SD1 E3 155 GTTTGTCCTTGTCGCGTTGCTCAG TCTGTTCGTTCCGTTTCGTTCCG

194 176 RESUME Name : Richa Sao Date of Birth : 14/08/1991 Present Address : Room No. 21, Kadambari Girl s Hostel, College of Agriculture, IGKV, Krishak Nagar, Jora, Raipur (Chhattisgarh) Phone No. : E. mail : richasao.agro@gmail.com Permanenet address : D/O- Shri Krishna Kumar Sao HDD-II 79, Phase- III, Kabir Nagar, Raipur. Pin (Chhattisgarh) Academic Qualification : Degree Year University B. Sc. (Ag.) 2015 COA, IGKV, RAIPUR M. Sc. (Ag.) 2017 COA, IGKV, RAIPUR Professional Experience (If Any): RAWEP 2015 Member of Preofessional Societies (If any): NO Awards/ Recognitions (If any): No Publications (If any): Submitted-1

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