REVIEW OF LITERATURE

Transcription

1 72 Chapter II REVIEW OF LITERATURE Domestication of rice first occurred in China (Candolle, 1883). However, Vavilov (1926, 1951) opined that rice was first domesticated in India. Reports on excavation at Mohenjodaro and Harrappa (2500 B.C.), Non Nok Tha, Thailand (prior to 3,500 B.C.), upper and lower basins at Yangtze river (3,395-2,000 B.C.) reveal the antiquity of rice cultivation in these areas. It is from these aggregate centres and noncentres that rice is supposed to have spread across Asia. According to Khush (1997), rice domestication began about 9,000 years ago within a broad geographic range spanning eastern India, Indo China and portions of Southern China. Rice varieties probably emerged 10,000 to 15,000 years ago along with southern and northern slopes of Himalayas. They withstood the periods of drought and pronounced variations in temperature and later spread from Himalayas to north-east and eastern India, northern southeast Asia and south China (Sharma, 1998). The Asian cultivated rice (Oryza sativa) is most important food crop that is staple food for nearly one-half of the world population. The genus Oryza is classified under the tribe Oryzae, subfamily Oryzoideae, of the grass family Poaceae. This genus has two cultivated species, O. sativa L. and O. glaberrima and more than 20 wild species distributed throughout the tropics and subtropics. Production and consumption are concentrated in Asia where more than 90 per cent of the rice is produced and consumed (Khush, 1997). The knowledge of parameters of variability, viz., genotypic co-efficient of variation (GCV), phenotypic co-efficient of variation (PCV), heritability and genetic advance is helpful for developing superior varieties with high yield potential. For effective breeding programmes it is important to know the association of yield with its component traits and direct and indirect effects on yield contributing components on yield. The relevant reports available in the literature pertaining to various aspects included in the present study are briefly reviewed under the following subheads: 2.1 Genetic variability 2.2 Correlation analysis 2.3 Path coefficient analysis

2 Genetic diversity Multivariate analysis Molecular characterization 2.1 Genetic variability Genetic variability is the basis of all plant improvement programmes. Sufficient genetic variability, if present, can be exploited for developing superior cultivar. Continuous variation exhibited by a quantitative character includes the heritable and non-heritable components (Fisher, 1918). Heritable component is the consequence of genotype, whereas the non-heritable part is due to environmental factors. Thus knowledge of heritability for different traits is essential for improvement in crop plants. Wright (1921) reported that heritability components comprised of additive and non-additive portion and it was the former which responds to selection. Wide range of variability for yield and associated characters in rice has been reported by a number of workers (Tripathi et al., 1973; Kaul and Kumar, 1982; Lal et al., 1983; De and Suriya Rao, 1988; Lokprakash et al., 1992). Estimation of expected genetic advance is important to have an idea of effectiveness of selection. Johnson et al. (1955a) found it more useful to estimate the heritability together with genetic advance in predicting the expected progress to be achieved through selection. The earlier studies on variability in rice are reviewed below: Deosthale and Pant (1970) studied nutrient composition of red rice. Protein content varied from 7.47 to Borthakur et al. (1974), in a study of ten tall and dwarf varieties reported that semidwarf varieties were generally superior for grain yield and yield components, while tall varieties showed greater genetic variability and estimated genetic advance for most of the traits. Basler (1985) reported milling percentage of 59.9 and 69.9 per cent, respectively, for red rice seed and cultivar BR-1GRA 409. Kim (1989) studied morphological characteristics of five varieties of red rice (weed plant to rice). Red rice was similar to japonica rice for plant type and showed vigorous tillering, long and slender culms, short and narrow leaf blades. Number of spikelets per panicle and secondary rachis of red rice was fewer than those of cultivated rice. Nagi et al. (1989) compared 13 aromatic rice lines for milling and cooking quality with Basmati 370. Three lines had higher grain length:breadth ratio than B370 and six lines exceeded B370 in amylose content (19.0%). Sarma et al. (1990) evaluated 43 traditional scented rice varieties for awns,

3 74 pigmentation and quality parameters and reported that 13 varieties had awns and 23 were pigmented. Grain length ranged from 5.7 to 9.9 mm, breadth from 1.8 to 30 mm and length:breadth ratio from 2.4 to 4.3. Bai et al. (1991) analyzed eleven red rice genotypes for grain qualities. Milling percentage ranged from 64.5 to 74.5 per cent; grain length from 5.11 to 7.05 mm; length:breadth ratio from 1.95 to 2.82 and protein content ranged from 7.5 to 10.9 per cent. Alvarado and Pedreros (1991) revealed that red rice showed variation in morphological characteristics and in awn and node colour. Fischer and Ramirez (1993) evaluated two red rice biotypes and rice cvr Oryzica. Two biotypes of red rice grew taller than Oryzica but had similar leaf area. Physiochemical properties of Japanese native red-kernel, non-glutinous japonica lines were examined by Matsue et al. (1997). The protein and amylose content ranged from 6.0 to 9.7 and 9.7 to 26.4 per cent, respectively, while for white kernel rice ranged from 6.5 to 6.9 and 15.6 to 20.9 per cent, respectively. Yang et al. (1998) studied micronutrient concentration in polished grain of 285 rice varieties. Cu and Zn concentration of indica rice were 2 times higher than japonica rice, while Fe concentration of japonica rice were slightly higher than indica rice. Among indica rice genotypes, red rice contained higher Zn than white rice. Noldin et al. (1999) evaluated red rice ecotypes including 11 straw hulled, 5 black-hulled, 2 gold hulled and 1 brown hulled types. Most of the ecotypes were found to be uniform but manifested considerable genetic variability. Red rice plants had pubescent leaves, were taller with lighter green colour and produced more tillers and panicles per plant than normal rice cultivars. Red rice ecotypes matured, on average, 9 days later than rice cultivars. However, rice cultivars produced more seeds per panicle than red rice. Ortiz et al. (2000) evaluated the performance of red rice populations and local rice cultivars in Venezuela. Red rice exhibited greater variation in hull colour (black to gold), pericarp colour (red to brown) and awn length than rice cultivars. Sinohin and Borromeo (2002) studied variability among traditional purple rice collected from four provinces of the Philippines and Malaysia. Several accessions were found to have twice the protein content of modern varieties. Vanaja and Babu (2006a) reported that most of the high yielding varieties evaluated lacked awn. Out of 56 rice genotypes evaluated, 25 genotypes were with straw apiculus colour, 20 were with grains having apiculus colour gold and gold furrows on straw background. High heritability coupled with moderate to high genetic advance for 1000-grain weight and grains per panicle was reported by Lok Prakash et al. (1992). Hemareddy et al. (1994) reported that yield

4 75 components except tillers per plant (42.19%) had high heritability which ranged from per cent (harvest index) to per cent (days to 50% flowering). Chaubey and Singh (1994) evaluated 20 rice varieties for 8 yield traits. Heritability was the highest for total number of spikelets, while genetic advance was the highest for grain yield per plant. Variability, heritability and genetic advance were the highest for grains per panicle, grain yield per plant and plant height (Govindarasu and Natarajan 1995; Sawant and Patil, 1995). Mani et al. (1997) reported that heritability was high for number of filled grains per panicle in 24 genotypes of basmati rice. Dhananjaya et al. (1998) evaluated 121 elite homozygous rice genotypes and observed that variability was maximum for productive tillers per plant, number of fertile spikelets and grain yield per plant. Chikkalingaih et al. (1999) studied quality traits and plant characters in 23 scented and one non-scented rice genotype and found high heritability and genetic advance for amylose content, kernel elongation, total number of tillers and effective number of tillers. Awasthi and Pandey (2000) observed significant genetic variability among 21 aromatic rice genotypes for plant height and days to 50 per cent flowering while, evaluating 128 rice line. Gregorio et al. (2000) found the range in Fe and Zn concentrations in brown rice within the eight sets of rice genotypes to be g/g and g/g, respectively. The highest Fe and Zn concentrations (18-22 and g/g, respectively) were found in several aromatic rice varieties. Sarawgi et al. (2000) revealed that heritable component of variation was high in milled grain length, length:breadth ratio, normalized grain weight and gelatinization temperature. Heritability was moderate to high for yield and grain quality characters in 26 aromatic rice genotypes (Sadhukhan and Chattopadhyay, 2000). In their study, Sadhukhan and Chattopadhyay (2001) again reported that aromatic rice had more number of grains per panicle and small grain size than basmati types. Basmati has kernel length, kernel length:breadth ratio and kernel elongation ratio greater than 7.0 mm, 3.5 and 1.5, respectively. Alkali spreading value ranged from and amylose content ranged from 15.6 to 25.0 per cent. Pandey and Awasthi (2002) observed significant variability for plant height, days to 50 per cent flowering, panicle length and total number of tillers per plants. In study of genetic variability and heritability of quality parameters in 150 rice germplasm in rice Yadav et al., 2002 reported high heritability and genetic advance for water uptake, volume expansion ratio, kernel elongation and gel consistency and maximum variability for test weight in long and medium

5 76 slender groups and for amylose content in medium and short slender groups. Nayak et al. (2002) evaluated 200 scented rice genotypes for yield and nine yield contributing characters and reported that, in general, all traits showed high heritability and genetic advance. Mohmmad et al. (2002) reported high heritability with moderate to high genetic advance for grain width, 1000-grain weight, grain length:breadth ratio and productive tillers. The phenotypic and genotypic variability in 15 quantitative characters of 52 rice genotypes were evaluated by Singh et al. (2002). Heritability in the broad sense was 3.61 for number of effective tillers per plant and for grain length. Patil et al. (2003) in a study of 128 traditional aromatic rices reported variability in amylose content ( %), alkali spreading value ( ) and kernel length:breadth ratio of cooked rice ( ). High heritability with high genetic advance was found for alkali spreading value, unfilled grain, 1000-grain weight, grain yield per plant and brown rice length:breadth ratio. Behari et al. (2004) in their study observed high heritability with high genetic advance for grain yield. Chand et al. (2004) evaluated 19 genotypes of aman paddy for eleven characters and observed high genotypic and phenotypic coefficient of variation for grains per panicle and grain yield per plot. Mishra and Pravin (2004) recorded the highest magnitude of genotypic coefficient of variation for kernel elongation ratio, while heritability and genetic advance were high for kernel elongation ratio and sterility percentage. Sinha et al. (2004) reported high genotypic coefficient of variation for grain yield, followed by test weight and panicles per plant. High heritability and high genetic advance was found for grain yield. Sixty four rice genotypes were evaluated for morphological and agronomical character by Dorosti et al. (2004). They observed high heritability for number of grains per panicle and flag leaf area. Variability with regards to grain yield, yield components and quality parameters were studied by Madhavilatha et al. (2005). The results revealed high variability, heritability and genetic advance for number of grains per panicle, grain yield per plant and kernel length:breadth ratio, while kernel elongation had high heritability with low genetic advance. Phenotypic coefficient of variation was higher than genotypic coefficient of variation for all the traits studied in 36 different rice genotype (Suresh and Anbuselvam et al., 2005). Saxena et al. (2005) evaluated 48 scented rice genotypes and concluded that high genotypic coefficient of variation and phenotypic coefficient of variation were recorded for all the

6 77 characters. However, genetic advance was high for seed yield per plant and number of unfilled spikelets. High heritability and high genetic advance was observed for plant height, number of tillers per plant, number of panicles per plant, number of spikelet per panicle and flag leaf length (Patil and Sarawgi, 2005 and De et al., 2005). Meng et al. (2005) reported that iron content in black rice is higher than red rice. High heritability accompanied with moderate genetic advance was recorded for days to 50 per cent flowering, plant height and grain yield per plant (Singh et al., 2006; Sankar et al., 2006). Monalisa et al. (2006) evaluated 20 lowland rice cultivars and reported high heritability and high genetic advance in filled grains and grains per panicle. Bajpai et al. (2006) evaluated ten germplasm lines of Kalanamak rice variety for quality and grain characteristics. In all germplasm, the 1000-grain weight was in the range of g, kernel length was in the range of mm. Amylose content and alkali digestion value were and per cent, respectively. Virk and Bouis (2006) screened lines for micronutrient content in the polished grains and identified lines with almost double the iron content than the popular varieties in polished grain. Vanaja and Babu (2006b), in their study on 56 high yielding rice genotypes revealed a wide range of variability for 10 quality parameters. Broad sense heritability and expected genetic advance were high for alkali spreading value, length:breadth ratio, milling percentage, amylose content and water uptake. Shobha et al. (2006) all the four cultivars studied by them for quality parameter have moderate to high head rice ( %), intermediate amylose (21.84 to 24.49%), medium gel consistency (43.3 to 58.8 mm) and intermediate alkali spreading value (4.5 to 5.1). Abdel et al. (2006) studied anthocyanin pigments from black, purple and red rices. The total anthocyanin content varied significantly and exhibited a range of g/g. Sixteen red rice accessions from the Southern United State were studied for their physical, milling and chemical composition related to cultivated medium and long-grain rice varieties. All red rice samples were medium grain and amylose and crude protein contents were generally higher (Patindol et al., 2006). Sharma et al. (2008) revealed that among the 33 aromatic rice cultivars, 10 possessed significantly higher yield, 17 had milling quality comparable to the controls and 2 possessed physiochemical characteristics as good as those of basmati controls. Deepa et al. (2008) evaluated nutritive components of dehusked rice of Njavara (a medicinal rice) with two rice varieties Jyothi (red coloured) and IR 64 (brown coloured). The carbohydrates, fats, and amylose content were comparable to Jyothi and IR 64. However, Njavara had 16.5 per cent higher protein, and contained higher amounts of thiamine (27-

7 78 32%) compared to the other two rice varieties. The cooking time of dehusked Jyothi and IR 64 varieties were found to be 30 minute, while Njavara needed longer time to cook. 2.2 Correlation analysis The correlation coefficient is a measure of the degree of association between two traits worked at the same time (Hayes et al., 1955). Yield is a complex trait. It is polygenically controlled as well as subject to the changing environment. Hence, the selection of superior genotypes based on yield as such is usually not very effective. Therefore, superior genotypes are selected on the basis of phenotypic expression. The knowledge of correlation between traits is important, as most of the traits of economic importance are complex involving several related traits (Robinson et al., 1951). The correlation also play important role in the selection of two or more traits simultaneously. The extent of observed relationship between two character is known as phenotypic correlation. As such it does not give the true genetic picture of the relationship between two characters of interest, because it includes environmental influence on the co-variance between the characters, the measure of which is termed as environmental correlation. For studying genetic variation in different characters and the manner in which environment variation affects the expression of such variation, genotypic correlation is essential. Genotypic correlations are mainly due to genetic causes through the pleiotropic effect of genes and/or linkage particularly in populations derived from cross between divergent strains. The degree of genetic correlation arising from pleiotropy, is the overall or the net effect of all the segregating genes that affect both characters (Falconer, 1961). Estimates of genotypic and phenotypic correlations among characters are useful in evaluating breeding programmes (Johnson et al., 1955b). Singh et al. (1980) found positive correlation of plant height with number of grains per panicle and negative correlation with number of ear bearing tillers and number of sterile grains per panicle. Amirthadevarathinam (1983) reported that grain yield per plant was positively correlated with total tillers and productive tillers and negatively with plant height and days to ripening. Negative correlation of yield of total brown rice and total milled rice in red rice was reported by Keisers (1984). Husain et al. (1987) reported that protein content was negatively correlated with volume expansion, water uptake and length:breadth ratio except grain breadth and phosphorus content. Lee et al. (1987) revealed that the grain yield was highly correlated with the number of panicles of red rice. Positive correlation between panicle

8 79 length and yield was observed by Dewande et al. (1991). Grain yield per plant was positively correlated with number of productive tillers, plant height, panicle length and number of grains per panicle at genotypic and phenotypic levels (Chaubey and Richharia, 1993; Bai et al., 1992). Abd-El-Samic and Hasan (1994) observed positive correlation of yield with eight yield components in rice while, evaluating 48 lines of rice. Positive and significant correlation of number of ear bearing tillers, plant height and panicle weight with grain yield per plant was observed by Chaubey and Singh (1994), while positive correlation was found between panicle length and plant height. Paramasivan et al. (1995) studied character association in 14 short duration rice genotypes and revealed that grains per panicle was significantly and positively correlated with number of spikelets per panicle and high density grain index. Positive correlation of yield with number of tillers per hill and number of grains per panicle was observed by Mishra et al. (1996). Sawant et al. (1996) revealed that ear bearing tillers per plant, grain weight, panicle length and grains per panicle were positively and significantly correlated with grain yield. Rao et al. (1997) found that all correlation coefficients at genotypic level were significant whereas, at phenotypic level, productive tillers per plant showed a significant association with 1000-grain weight and straw yield per plant. Sarawgi et al. (1997) revealed significant positive correlation of grain yield with number of fertile spikelets per panicle, grain width, 1000-grain weight and harvest index. Yang et al. (1998) evaluated 285 promising rice accessions for micronutrient concentration and revealed no close relationship between micronutrients and protein. Singh et al. (1998) analyzed 45 indigenous genotypes of rice for eight grain quality traits and found that grain physical characters were positively correlated with three quality characters (amylose content, volume expansion and water uptake). Positive correlation of rice yield with total number of grains per panicle, filled grains per panicle and 1000-grain weight was observed by Suhartini et al. (1999). During their study in 29 accessions of aromatic rice, Sadhukhan and Chattopadhyay (2000) revealed that amylose content and elongation ratio had positive correlation with kernel length and test grain weight. Positive correlation of grain yield per plant with days to 50 per cent flowering, number of total grains, plant height and number of filled grains per panicle were reported by Bhandarkar et al. (2002) and Allahgholipour and Salehi (2003). Chaudhary and Motiramani (2003) evaluated 44 traditional aromatic rice genotypes for 19 grain quality and yield attributes and reported that grain yield per plant showed significant

9 80 positive correlation with effective tillers per plant, spikelet density and biological yield per plant. Zhou et al. (2003) carried out correlation analysis with rice genotype Qinglinai No. 1 and showed that yield was highly significantly and positively related to productive panicles and negatively to spikelets per panicle and filled grains per panicle. Significant positive correlations were also found among the length of the third and second top leaves, grain length as well as gel consistency. Nineteen local mid land rice landraces with IR36 as standard control were evaluated for yield and eight yield attributing traits (Sinha et al., 2004). Significant positive genetic correlations were observed for panicles per plant and test weight with yield. Zang et al. (2004) found that grain length and length:width ratio were positively correlated with amylose content and negatively with protein content of the grain. Souroush et al. (2004) reported that grain yield had a positive and significant correlation with number of panicles per plant, number of filled grains per panicle, grain weight and days to 50 per cent flowering. Positive correlation of grain yield per plant with number of panicles per plant, number of tillers and negative correlation of number of panicles with number of grains per panicle in forty one rice genotypes grown in Nadia, West Bengal was reported by Sarkar et al. (2005). Patil and Sarawgi (2005) evaluated 128 aromatic rice accessions and reported positive correlation of rice yield with number of days to 50 per cent flowering, plant height and number of filled grains per panicle. Borbora et al. (2005), while analyzing 30 rice genotypes, comprising 16 local cultivars and 14 high yielding varieties revealed that grain yield was positively correlated with grains per panicle and negatively with plant height, panicles per plant and chaffy grains per panicle. Wang et al. (2005) during their study on rice grain quality traits reported significant, positive correlation among brown rice, milled rice and head rice while, negative correlation was recorded for length:breadth ratio and milled rice. Zeng et al. (2005) observed highly significant correlation among Fe content and gel consistency, Zn content and seed setting rate and Cu content and number of grains per panicle. In their study on 14 Basmati rice genotypes Zahid et al. (2006) revealed the positive correlation of grain yield with grains per panicle, and negative correlation with plant height and 1000-grain weight. Yield and yield attributing traits were evaluated (Singh et al., 2006; Sankar et al., 2006) and it was reported that only grain yield was positively and significantly correlated with number of grains per panicle followed by days to 50 per cent flowering, effective tillers per plant and panicle length. Monalisa et al. (2006) observed significant positive correlation of effective tillers per plant and high density grains per

10 81 panicle with grain yield. Tayeng and Singh (2006) observed significant positive correlation of grain yield with panicle length and number of spikelets per panicle, while number of days to 50 per cent flowering was positively correlated with number of spikelets per panicle, panicle length and 1000-grain weight. Dheeraj et al. (2006) analyzed a set of aromatic rice germplasm alongwith four checks. Hulling per cent was significantly positively correlated with length:breadth ratio and elongation ratio and significantly negatively with milled rice kernel breadth. Milled rice kernel length was positively correlated with length:breadth ratio, cooked rice kernel length and 1000-grain weight. Shobha et al. (2006) reported that grain yield was positively and significantly associated with fertile spikelets and negatively with sterility per cent. Jiang et al. (2007) investigated the relationship between mineral element content and cooking quality traits in milled rice of 274 genotypes. Mg, Fe and Mn content were significantly correlated with most of the other mineral element contents, while Cu content had significant negative associations with K and Mg contents of rice. Amylose content was significantly associated with Cu content. Significant associations were found between protein content and Na, Zn and Cu content. Shivani et al. (2007) reported that hulling percentage showed positive significant correlation with milling percentage. Correlation of amylose content with alkali spreading value was positive but non-significant. 2.3 Path coefficient analysis Although the correlations are helpful in determining the components of complex traits like yield, yet they do not provide an exact picture of the relative importance of direct and indirect influences of each of the component traits, because as the number of independent variables influencing a particular dependent variable increases, there is bound to be certain amount of independence. Due to their mutual association the development of the dependent variable is determined by the degree of independent variables and indirect effect exerted via other characters, arising inevitability as an integral part of the growth pattern. Under such complex situations, the total correlation is insufficient to explain the real association for an effective manipulation of the characters. It is the path analysis that splits the correlation coefficient into direct and indirect effects thus specifying the relative contribution of each character (Shanthala et al., 2004). Dewey and Lu (1959) used path coefficient analysis in breeding programme for the first time.

11 82 They calculated direct and indirect effects on seed yield. Seed size and spikelets per panicle were found to be relatively important though these traits were negatively associated. Amirthadevarathinam (1983) on path analysis indicated great contribution of total tillers and days to flowering towards grain yield. Chauhan and Tandon (1984) observed that grain yield was most strongly dependent on the number of spikelets per panicle. Reuben and Katuli (1989) examined direct and indirect association between selected variables in eleven advance rice breeding lines and a check. Plant height and filled grains per panicle had high positive direct effects on grain yield. The relationship between filled grains per panicle and plant height, panicles per m 2 and tillers per plant were negative, suggesting a compromise in selection for the optimum combination of these characters. Mirza et al. (1992) suggested that panicle length, number of grains per panicle and number of panicles per plant should be used as selection criteria for grain yield. Pantone et al. (1992) the path analysis quantified direct effects of red rice (a weed) and Mars rice (cultivated rice) densities on the yield components (grain weight and per cent filled florets). Paul and Nanda (1994) reported that panicle number per m 2 had the highest direct positive effect on yield. The direct effect of number of filled grains per panicle, although negative, was counterbalanced by indirect effect of panicle number per m 2. In their study on 20 genotypes of rice Rao et al. (1997) reported that productive tillers per plant had the highest direct effect on grain yield, followed by plant height, panicle length and flag leaf area. Path coefficient analysis by Sharma (1998) revealed that the following characters exerted the greatest direct effects on total biomass, grain weight per panicle and leaf weight per panicle in dwarf varieties, panicle weight in semi-dwarf and leaf weight followed by panicle weight in tall varieties. Six qualitative traits in the F 2 generation of eight crosses of basmati rice were studied by Gupta et al. (1999). They reported that number of panicles, grains per panicle and panicle weight were the most important components of grain yield of basmati rice. Allahgholipour and Salehi (2003), path analysis demonstrated that the increase in grain yield was largely due to the increase in plant weight. Shanthala et al. (2004) analyzed eight hybrids grown alongwith their parents and found that spikelet number, spikelet density, 100-grain weight, harvest index, days to maturity and number of productive tillers showed a direct positive effect on grain yield. Plant

12 83 height, panicle weight, panicle length and days to maturity recorded negative direct effect on grain yield. Madhavilatha et al. (2005) evaluated forty four elite rice genotypes and indicated the high direct effect of the number of effective tillers per plant, plant height and harvest index on grain yield and indirect effect of different yield components and quality traits through plant height and harvest index. Sarkar et al. (2005) path analysis revealed that number of panicles per plant had the highest direct effect on grain yield, followed by number of grains per panicle. Patil and Sarawgi (2005) examined direct and indirect association between selected variables in 128 aromatic rice accessions grain weight had the maximum positive direct effect on grain yield, followed by number of ear-bearing tillers per plant and number of filled grains per panicle. De et al. (2005) revealed that the positive effect of kernel breadth and protein content on yield was nullified by high negative indirect effect of kernel length and plant height, respectively. Borbora et al. (2005) reported the highest positive direct effect of grain yield per panicle on grain yield per plant, followed by secondary branches per panicle and plant height. Chaffy grain numbers per panicle showed the highest negative direct effect on grain yield, followed by panicle number, days to 50 per cent flowering, primary branches per panicle and 1000-grain weight. Filled grains per panicle, primary branches per panicle and 1000-grain weight showed the highest indirect effect on grain yield per plant. Vaithiyalingan and Nadarajan (2005) reported that number of grains per panicle had the highest positive direct effect on yield, followed by productive tillers per plant. Zahid et al. (2006) evaluated 14 basmati rice genotypes and reported that the number of tillers per plant, grains per panicle and 1000-grain weight contributed maximum direct effects on yield. Monalisa et al. (2006) studied path analysis in 20 low land rice cultivars and reported high positive direct effects coupled with significantly positive association of effective tillers per plant and high density grains per panicle with grain yield per plant. Tayeng and Singh (2006) reported direct effect of number of grains per panicle, harvest index and panicle length on grain yield. Panicle weight, number of spikelets per panicle and number of effective tillers had negative direct effects on grain yield. Sankar et al. (2006) carried out path analysis in 34 rice genotypes and indicated that the correlated traits, except for panicle length, exhibited high positive direct effects towards single plant yield. Shobha et al. (2006) while, studying twenty five short grain and six long grain basmati varieties, reported that grain yield was influenced mainly

13 84 by the direct effect of fertile spikelets followed by plant height. 2.4 Genetic diversity molecular techniques. Genetic diversity among the genotypes was assessed using multivariate analysis and Multivariate analysis Statistical procedures such as multivariate analysis based on Mahalanobis s D 2 statistics analysis serves as important tool in quantifying the genetic divergence in different crops (Rao, 1952a). This analysis also provides a measurement of relative contribution of different components of diversity both at inter and intra cluster levels. D 2 statistics is used to select diverse parents through a study of genetic divergence. The D 2 statistics for multivariate analysis has been successfully used in a variety of crops to select divergent genotypes in order to exploit heterosis and for bringing together higher frequency of desirable genes in a strain. Higher inter-cluster distance can be utilized for selecting genotypes for intervarietal hybridization programme to exploit the available diversity for having good heterotic effect (Murthy and Quadri, 1966; Somayajulu et al. 1970; Lee and Kaltsikes, 1973; Joshi and Singh, 1979). Singh et al. (1987) presented information on genetic diversity in fifty lowland rice cultivars using D 2 statistics. Cluster I, II and III were the largest and morphological characters namely panicle length, spikelets number and plant height were responsible for the diversity. In another study, 40 varieties were grouped into 11 clusters for 8 yield characters. Genetic variability was not related to geographical diversity (Selvakumar et al., 1989). Number of effective tillers and number of grains per panicle contributed the most to divergence. Sarma et al. (1996), on the basis of data on eleven quality traits grouped 39 upland rice genotypes into 10 distinct clusters. The highest genetic divergence was between clusters 5 and 6. Mishra and Dash (1997) studied genetic diversity in 10 genotypes of aromatic rice. Four clusters of genotypes were formed on the basis of D 2 statistics. Crossing selected genotypes of cluster II (ORP655-7 and Kasturi) with the genotypes of cluster IV (Badena) was useful for recombining genes for stability and high yield. Genetic diversity using Mahalanobis D 2 technique for grain quality traits was measured by Sarawgi et al. (1998). Based on eighteen quality characters they grouped these genotypes into ten clusters. Cluster VIII had the maximum intra-cluster distance, whereas cluster VI and VIII were identified as

14 85 genetically divergent. Ahmed and Borah (1999) evaluated 85 indigenous glutinous rice varieties for genetic diversity in 13 agronomic characters. Panicles per hill, grains per panicle, grain fertility and grain yield accounted for major portion of divergence. Thirty six genotypes of low land rice were grouped into 12 clusters using Mahalanobis D 2 statistics. Number of grains per panicle, plant height, 1000-grain weight and grain length played a major role in the formation of clusters (Reddy et al., 2002). The pattern of distribution of genotypes within various clusters was random and independent of geographical isolation in a study involving 28 yield and morphological traits for hundred aromatic rice genotypes (Sharma et al., 2002). Mishra and Pravin (2004) using Mahalanobis D 2 statistics grouped sixteen rice cultivars and their seventy two F 1 s into 12 clusters based on their genetic distance. The hybrids and one of their parents occupied the same cluster (as in clusters VI, IX, XI and XII). The pattern of distribution of genotypes from diverse geographical region into different clusters was random. Dorosti et al. (2004) assessed genetic diversity in 64 promising lines and cultivars of rice and grouped them into 4 clusters. They suggested that hybridization between group 1 and 2 will provide more diversity and stronger heterosis. Genetic diversity of 62 cultivars of irrigated rice originating from BRRI, IRRI and China were studied by Islam et al. (2004), using the Mahalanobis D 2 statistical method. They grouped cultivars into five clusters. The highest intercluster distance was observed between cluster I and cluster IV followed by cluster I and V; cluster I and III; cluster III and cluster IV; and cluster IV and cluster V. Genetic diversity of quantitative and qualitative traits of 36 lines of rice were studied by Souroush et al. (2004). Based on divergence analysis, the genotypes were grouped into 5 clusters. Genotypes of the third cluster including 4 pure lines had high yield, high number of panicles and filled grains per panicle, intermediate amylose and lower plant height, therefore, these traits can be transferred through hybridization programmes. Genotypes of the first and fifth clusters can also be used to improve yield and grain cooking quality, respectively. Purabi et al. (2004) determined genetic divergence using Mahalanobis D 2 statistics in 50 land races of rice. The genotypes were grouped into 10 clusters. Intra-cluster distance was the highest in cluster IX, followed by cluster I, which included twelve genotypes of diverse origin. The maximum inter-cluster D 2 value was recorded between cluster IV and IX. Pradhan and Mani (2005) estimated genetic divergence in 38 elite basmati rice genotypes for 12 characters using Mahalanobis D 2 statistics. The genotypes were grouped into ten clusters. Maximum

15 86 intra-cluster distance was observed in cluster III while minimum inter-cluster was between cluster VI and VIII. Suman et al. (2005) evaluated 114 rice genotypes from Andhra Pradesh for 16 characters to quantify the genetic diversity among them. The genotypes fell into 10 clusters. The maximum inter-cluster distance was observed between cluster V and X. Geographical diversity did not relate to genetic diversity. Genetic diversity was studied in 100 genotypes of rice germplasm by Patil et al. (2005). Among the characters studied, days to 50 per cent flowering, flag leaf length, flag leaf width, plant height were major components contributing to the total genetic diversity. Sankar et al. (2005) assessed the nature and magnitude of genetic divergence in 34 rice genotypes for 8 characters using Mahalanobis D 2 statistics. Composition of clusters indicated the non existance of correspondence between genetic diversity and geographical distribution. A total of 21 aromatic rice genotypes were grouped into 6 clusters for different characters (Awasthi et al., 2005). The inter-cluster distance was observed to be the highest between cluster II and III, indicating that the genotypes of these 2 clusters were genetically more diverse. Devi et al. (2006) evaluated fifty four rice cultivars for genetic diversity for yield and yield components. The genotypes were grouped into 9 clusters. Cluster V, which had 7 cultivars exhibited the greatest intra-cluster distance. The 7 cultivars in this cluster were the most heterogeneous, and this cluster was most suitable for within group hybridization. Gahalain (2006) grouped the 55 genotypes into 12 clusters based on D 2 values. The highest contribution to divergence was shown by total grains per panicle (22.6%), panicle weight (10.1%), days to 50 per cent flowering (7.6%) and shoot weight (7.2%). Other characters showed low contribution to divergence, ranging from 0.5 per cent for grain breadth to 5.9 per cent for grain yield per plant. Paramasivan et al. (2006) grouped 22 genotypes into six cluster using D 2 statistics. The result indicates that genetically diverse lines will certainly yield more heterotic hybrids when they one combined. Patindol et al. (2006) using Mahalanobis D 2 statistics grouped sixteen red rice accessions from the Southern United States into 2 clusters based on their genetic distance. One cluster with more resemblance to wells (a long grain rice cultivar) and another cluster with more resemblance to Bengal (a cultivated medium grain rice) for their kernel properties. Gupta and Hari (2006) grouped seventy nine aromatic and fine grained rice accessions collected from foot hills of Uttaranchal into eight clusters on the basis of 13 characters using non hierarchical Euclidean analysis. Clusters VII and VIII showed maximum

16 87 inter-cluster distance. Sandhyakishore et al. (2007) grouped 70 genotypes into nine different clusters using different yield and quality traits. The characters like water uptake, gel consistency and head rice recovery percentage contributed maximum towards genetic diversity. The maximum inter-cluster distance was recorded between cluster VII and cluster VIII. The genotypes in these clusters may serve as potential donors for future hybridization programmes to develop potential recombinants with high yield coupled with desirable grain quality Molecular characterization Advances in molecular biology techniques have provided the basis for uncovering virtually unlimited number of DNA markers. These markers have been recently utilized for many purposes including genome mapping, gene tagging, estimation of genetic diversity, varietal differentiation, resolution of uncertain parentage and purity testing (McCouch et al., 1997; Olufowote et al., 1997; Coburn et al., 2002). Since DNA is same in all the tissues, so these markers are independent of the stage or organ analyzed. Also, these are nearly infinite in number and independent of environmental effects. The choice of a molecular marker technique depends on its reproducibility and simplicity. The best markers have low cost and labour requirements and high reliability. PCR based approaches are in demand because of their simplicity and requirement for only small quantities of sample DNA Random Amplified Polymorphic DNA (RAPD) DNA markers, being independent of environmental interference are indispensable tools in the study of genetic variability. RAPD markers have been used to characterize variation at the DNA level, both within species and among closely related texa (Welsh and McClelland, 1990). These are, therefore, suitable for gene mapping, population genetics, phylogenetic studies and genotype identification (Martin et al., 1991; Mazzarella et al., 1992). Ko et al. (1994) conducted molecular characterization of 37 geographically diverse rice varieties, using PCR and 22 arbitrary oligonucleotide primers in RAPD. A total of 144 markers were generated, of which 67 per cent were found to be polymorphic, while, studying the genetic diversity in rice with RAPD markers, Mackill (1995) showed that indica and japonica cultivars were clustered into different

17 88 groups. Tropical japonicas were found to be usually clustering together though a firm boundary was absent between the tropical and temperate types. Genetic diversity among 18 Hansraj accessions was characterized based on RAPD markers by Raghunathachari et al. (1998). With 10 random primers, 144 bands were produced, of which 95 per cent were polymorphic, showing a high degree of molecular variation. Nadarajan et al. (1999) used RAPD profiling to analyze the genetic relationship among ten rice varieties and reported high level of polymorphism. Raghunathachari et al. (2000) conducted RAPD analysis of 18 rice accessions, derived from Indian scented rice germplasm, and constructed a dendrogram to observe genetic relationships at the DNA level. The study was based on 10 random primers generating 144 markers, of which 95.1 per cent were polymorphic. Baishya et al. (2000) reported sufficient variation at the DNA level in aromatic and nonaromatic rices varieties by RAPD based DNA fingerprinting. Choudhury et al. (2001) used 58 random decamer primers for identifying and classifying 48 aromatic rice genotypes. Most of these primers (96.5%) detected polymorphism among the genotypes. Estorninons et al. (2001) analyzed the genetic relationships among the 23 red rice populations and three rice cultivars using 15 RAPD markers. A total of 111 bands were produced, 89 of which were polymorphic. The 78 bands were polymorphic among the red rice populations while, only 45 were polymorphic among the three rice cultivars. Genetic diversity in a set of landraces was studied by Neeraja et al. (2002). The analysis of 36 accessions using 10 arbitrary decamer random primers revealed per cent polymorphism. Similarity values among landraces ranged from 0.58 to 0.89, indicating wide diversity. Stobdan et al. (2003) used RAPD technique to characterize and assess the genetic relationships among 32 accessions of aromatic and non-aromatic rices. The similarity coefficient ranged from 0.37 to 0.81 showing a diverse gene pool. Gholaki et al. (2003) used 66 primers for RAPD profiling, out of which 12 random primers produced suitable genetic polymorphisms. Among the 129 RAPD markers produced, 104 (80.62%) were polymorphic while, 25 (19.3%) were monomorphic. The size of generated bands ranged from 0.45 to 3.0 kb. The average band number for each polymorphic primer ranged from The genotypes were divided into 7 groups and genetic diversity among genotypes varied from 44 to 91 per cent. Barooah and Sarma (2004) analyzed 51 Sali rice accessions from the Assam collection based

18 89 on 72 RAPD markers. Among the 11 primers, the percentage of polymorphic bands ranged from 33 (OPK- 14) to 100% (OPK-19), showing a higher degree of variability. Guo-qin et al. (2005) studied the population genetic structure and genetic diversity of weedy rice in Liaoning Province using RAPD markers. The results indicate that the level of genetic diversity of Liaoning weedy rice is very low, with polymorphic loci being only 3.70 per cent. Nipon et al. (2007) assessed genetic variability among 24 rice genotypes using ten RAPD primers. A total of 81 RAPD markers were generated with 92.5 per cent polymorphism. The similarity coefficient based on RAPD was Inter Simple Sequence Repeat (ISSR) Inter Simple Sequence Repeat (ISSR) markers were developed by Zietkiewicz et al. (1994). ISSRs are the regions that lie within microsatellite repeats and offer great potential to determine intra and inter-genomic diversity compared to other arbitrary primers, since they reveal variation within unique regions of the genome at several loci simultaneously. Several properties of microsatellites, such as a high copy number in eukaryotic genome, make ISSRs extremely useful markers. These are semi arbitrary markers amplified by PCR in the presence of one primer complementary to a target microsatellite. Amplification by these does not require genome sequence information and leads to multilocus and highly polymorphic patterns (Tsumura et al., 1996; Nagaoka and Ogihara, 1997). The technique has the advantages over RAPD and in addition shows higher level of polymorphism, reproducibility and costeffectiveness per polymorphism (Prashanth et al., 2002; Reddy et al., 2002). In rice, ISSR-PCR has been used for the analysis of micro-satellite motif frequency and fingerprinting of varieties, determining phylogenetic relationship among Oryzae species, in distinguishing basmati rice varieties, as marker for restorer genes, and in studies on comparing effectiveness of different molecular markers (Davierwala et al., 2000). Joshi et al. (2000) used ISSR polymorphism to study genetic diversity and phylogenetic relationship in Oryza species. Forty two genotypes including 17 wild species, two cultivated species, O. sativa and O. glaberrima, and three related genera were used in ISSR analysis. Eleven ISSR primers were used to determine the genetic diversity and construct a consensus tree. ISSR analysis suggested that Oryzae might have evolved following a polyphyletic pathway. Bornet and Branchard (2001) found this technique to be stable across a wide range of PCR parameters. Abundant polymorphisms were detected among seven

19 90 dicot species tested with two tri-nucleotide and two tetra-nucleotide primers. Nagaraju et al. (2002) analyzed three groups of rice genotypes [traditional basmati, evolved basmati (EB) and non-basmati rice varieties (NB)] using 19 simple sequence repeat (SSR loci) and 12 inter-ssr-pcr primers. A total of 70 SSR alleles and 481 inter-ssr-pcr markers were revealed in 24 varieties from three groups. The lowest genetic diversity was observed among the traditional basmati varieties, whereas the EB varieties showed the highest genetic diversity. Sarla et al. (2003) studied 24 accessions of O. nivara and four O. sativa varieties using ISSR-PCR. The primers based on AG and GA repeats were informative; their resolving power ranged from 4.2 to 10.8 and polymorphism information content from 0.64 to Navinder et al. (2004) evaluated the genetic diversity and pattern of relationship among the 18 rice genotypes using ISSR markers. A total of 240 (188 polymorphic) bands were detected using 25 UBC8 ISSR primers. Inter simple sequence repeat (ISSR) amplification was used to analyze micro-satellite motif frequency in the rice genome (Blair et al., 2004) and to evaluate genetic diversity among rice cultivars. A total of 32 primers, containing different simple sequence repeat (SSR) motifs, were tested for amplification on a panel of 59 varieties. ISSR results suggested that within the dinucleotide class, the poly (GA) motif was more common than the poly (GT) motif. Trinucleotide ISSR markers were found to be less polymorphic than either dinucleotide or certain tetranucleotides ISSR markers. Sarla et al. (2005) evaluated the potential of ISSR-PCR for diversity analysis in 86 accessions of Indian rice (50 land races, 20 improved varieties and 16 other accessions) using 14 anchored primers based on (AG) and (GA) repeats. In all, 220 band position (loci) and 5514 bands were generated and all loci were polymorphic. The frequency of clustered GA repeats was more in varieties than in land races. Polymorphism information content and resolving power were negatively correlated to mean genetic similarity among accessions. Sujatha et al. (2006) studied the phylogenetic relationship among wild Oryza species using ISSR and SSR markers. Thirty two accessions representing 17 species from the genus Oryza and Porteresia were examined for ISSR polymorphisms using eight informative primers showing 100 per cent polymorphism. Bao et al. (2006) employed ISSR markers to examine genetic diversity and relationship of 56 waxy rice (O. sativa) accessions. A total of 190 ISSR bands were generated, showing a very high level of polymorphism (92.2%). The dendrogram generated could clearly differentiate the indica and japonica groups.