Correlation and Variability among Yield and Yield Attributes of Advance Rice Genotypes in Rainfed Condition

TABLE OF CONTENTS

LIST OF TABLES

ACRONYMS

ABSTRACT

1. INTRODUCTION
1.1 Back ground
1.2 Objectives

2. LITERATURE REVIEW
2.1 Rice production in World
2.2 Drought and Its effect on Rice
2.3 Variability (Genetic and Phenotypic)
2.4 Heritability
2.5 Genetic advance and genetic advance as percentage of mean
2.6 Association of character

3. MATERIALS AND METHODS
3.1 Research site
3.2 Experiment Design and layout
3.3 Treatments
3.4 Cropping Pattern
3.5 Nursery bed preparation
3.6 Water Management
3.7 Nutrient recombination and Soil Character
3.8 Field Preparation and Transplanting
3.9 Weed Management
3.10 Data Collection
3.11 Statistical analysis

4. RESULTS AND DISCUSSION
4.1 Mean performance and analysis of variance
4.2 Heritability (Broad sense heritability)
4.3 Genetic advance and genetic advance as percentage of mean
4.4 Genotypic and phenotypic variance
4.5 Genotypic and phenotypic coefficient of variation
4.6 Correlation among character
4.7 Correlation between grain yield and other traits

5. CONCLUSIONS

REFERENCES

LIST OF TABLES

1 Genotypes used in research

2 RCBD ANOVA model

3 Mean square from analysis of variance of twelve rice genotypes in rainfed condition at NARC, 2017

4 Estimates of Phenotypic (σ2P) and Genotypic (σ 2g) Variance, Phenotypic coefficient of variability (PCV) and Genotypic coefficient of variability (GCV), Broad sense heritability (H2bs), Expected genetic advances (GA) and Genetic advance as percent of mean (GAM)

5 Correlation between grain yield and yield component of twelve rice genotype at Kumaltar,Lalitpur,2017

ACRONYMS

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ACKNOWLEDGEMENTS

I remain grateful to my major advisor Mr. Ankur Poudel, Department of Agronomy and Plant breeding, institutes of agriculture and animal science for motivating and providing initiative guidelines for the conduction of research and providing constructive comments during the preparation of the report. I also owe my gratitude to Technical officer Mr. Atit Prajuli and Ms. Bidya Maharjhan from NARC, Agri-Botany Division Kumaltar,Lalitpur for their support, encouragement and guidance during field work, report preparation and also to provide the needed resources for my research.

My heartiest gratitude to Mr. Subarna Sharma for his support, encouragement and valuable suggestion for my entire research period.

I would like to thank Prof. Dr. Bishnu Bilas Adhikari ,Campus chief of IAAS, Lamjung for his Administrative support for my research work.

Finally, I must express my very profound gratitude to my parents, to my friends Prativa Gaire, Rabin Aryal, Nikii Shrestha ,Keshab Thapa Magar and Shiva Acharaya for helping in my research.

ABSTRACT

The present research consists of 12 rice (Oryza sativa L.) genotypes and the experiment was conducted during June- November, 2017 in Randomized Block Design with three replication at NARC, Kumaltar. The data were recorded for ten quantitative character to study genetic variability, heritability, genetic advance and correlation coefficient analysis.Analysis of variance among 12 genotypes showed significant difference for all characters studied except non- effective tiller. Grain yield had positive significant correlation with SPAD and positive correlation with 1000 grain weight, flag leaf area, days of 50% flowering, plant height, number of effective tiller and panicle length and negative significant correlation with number of non-effective tiller and negative correlation with days of 90%maturity. Moderate genotypic coefficient of variation (GCV) and phenotypic coefficient variation (PCV) was observed for flag leaf area and followed by grain yield and number of effective tiller, that these characters could be used as selection for crop improvement. High estimates of heritability were observed for days of 50% flowering and followed by panicle length, flag leaf area, test weight and days of 90% maturity. High genetic advance were found in number of non effective tiller, grain yield per meter square, no of effective tiller and flag leaf area indicating pre-dominance of additive gene effects and possibilities of effective selection for the improvement of these characters.

Keywords: Correlation, Genetic Advance, Genetic Variability Heritability and Rice

1. INTRODUCTION

1.1 Back ground

Rice (Oryza sativa L.) 2n=24 (Hooker,1979) is monocot, annual, semi aquatic cereal crop and a member of family Poaceae. The genus, to which it belongs, Oryza,contains 25 species , only two of which are referred to as cultivated rice : Oryza sativa (2n=24=AA) sown in South-East Asian country and Japan, and Oryza glaberrima (2n=24=AA) in West Africa (Singh et al.,2015) . Gobal production of rice is about 740 million ton with 4,539 kg/ha productivity and habitable in 117 country covering a total area of 163 million hectares(FAOSTAT, 2014) and in Asia for 100 million household it is main source of income and employment(Singh et al.,2015). Diversified ecological condition from rainfed to irrigated and low land to upland to deep water system rice is being cultivated.

Rain fed rice, based grown in Asia is about 46million ha (around 30%) of total rice area in the world (Haefele and Hijmans,2007). Asia alone has around 23 million ha (20% of total rice area) prone to drought, where climate change will cause severe water scarcity;drought affects regularly (about one year in five) 13 million hectares of rainfed rice which is about half of total rainfed low land rice area in South and South East Asia Pandey et al. (2005). Average productivity of rainfed rice in North East India is 0.8 to 1.2 t/ha under different situations which is much lower compared to irrigated rice (2.9 t/ha) (Das, 2006). Rice yields are poor in rainfed situations mainly because of erratic rainfall and drought stress (Reddy et al., 1998; Courtois and Lafitte, 1999; Singh, 2006). It imposes a varying socio-economic problem, causing food shortage and famine in different part of Asia.

Rice is most important cereal and stable food crops of Nepal and fulfilled about 50- 80% of caloric requirement of Nepalese people with an average per capita annual consumption of 122kg (MoAD 2013) .It is important cereal crop, both in terms of cultivated area , production and supporting livelihoods of 66% of farm households and (FAOSTAT database online and AQUASTAT database online, as of November 2012) and contributes 33.91% to GDP and 48.8% to AGDP(MOF). Rice is grown on more than 1.533 million hectares, producing a total of about 5.230 million tons and average yield of 3.369 ton per hectares in Nepal (MoAD,2017). Mostly, in Nepal rice’s cultivation is done in rainfed condition and thus, production is depends on amount and duration of rainfall,which lasts from June to September.(MoAD,2014) indicates that, only 3.48 million hectare of cereal crop only 1.33 million hectare of land is irrigated. Still 79% of rice is grown under rainfed condition of which 70% under rainfed lowland and 9% is under upland condition but only about 21% of rice area is irrigated ,either fully or partially (NARC,2004).Most of the rainfed area in Nepal are stress-prone (drought and cold prone) where, low production and high variability occur in rice production due to uncertain weather condition resulting from drought during monsoon season.

Drought has been recognized as the primary constraint to rainfed rice production (Datta, et al., 1975). Drought is a major abiotic stress that causes severe yield loss in rice as a staple food crop affecting 20% of the total rice growing area in Asia (Pandey and Bhandari, 2009).Drought condition at flowering is most serious and devastating to yield,it effect on pollination and causes grain abscission, abortion of flower and increase of percentage of unfilled grain(Hsiao, et al., 1976). Several workers viz., Kumar, et al. 2006 and Devatgar et al.,(2009) also observed that the percentage of unfilled grains were significantly higher in sites that were affected by drought at reproductive stage. Pantuwan, et al. (2000) also observed that grain yield of genotype under drought condition was reduced from 18 percent to 54 percent as compare to irrigated condition.

In drought prone environment breeding programme must combine selection under stress condition with selection for yield potential because farmers want cultivars that are both drought-tolerant and have high yield potential in favourable years Evidence indicates that these goals are not mutually exclusive. Breeding for drought tolerance in rice is primarily aimed at identifying genotypes with optimal reproductive capacity with high yield potential under drought-stress condition (especially at flowering stage) as compared to well watered conditions

1.2 Objectives

1.2.1 Broad Objective

- To find out the nature and magnitude of genetic variability and association between different traits in rice genotypes under rainfed condition.

1.2.2 Specific Objective

- To access the extent association of yield attributing traits to the grain yield in rice genotypes under rainfed condition.

- To estimate phenotypic and genotypic coefficient of variation and, heritability of yield and yield related traits

2. LITERATURE REVIEW

2.1 Rice production in World

Rice is grown in 117 countries across all habitable continents covering a total area of about 163 mha with a global production of about 740 mt and an average yield of about 4,539 kg / ha (FAOSTAT, 2014). The Asian continent ranks first with over 90.1% of world production, followed by the American continent (5.1%), African continent (4.2%), Europe (0.5%) and Oceania (0.1%). The major producing countries are China (206.5 million tons), India (157.2 million tons), Indonesia (70.8 million tons), Bangladesh (52.2 million tons) and Viet Nam with 44.9 million tons (FAOSTAT, 2014). The developing countries account for 95% of total production and among them China and India alone responsible for nearly half of the world output. (FAO, 2012). Being a vital for nutrition of many populations of Asia, Latin America and Caribbean and Africa, rice is central to the food security of over half world of population. In a span of ten years the cultivated area has almost doubled reaching 10 million hectares with current annual production of approximately 23 million tones (FAO, 2015).

2.2 Drought and Its effect on Rice

Drought is the most important limiting factor for crop production in many regions of the world (Passioura, 1996, 2007).One of the main constraints of rice cultivation and production is water shortage during periods of low rainfall, which affects the vegetative growth rate and grain yield (Tao et al., 2006). It is estimated that more than 50% of the world rice production area is affected by drought (Bouman et al., 2005). Drought stress during the vegetative growth, flowering, and terminal stages of rice cultivation can cause spikelet sterility and unfilled grains (Kamoshita et al., 2004). Rice is one of the most drought-susceptible crops, especially at the reproductive stage. It was reported that at rain-fed conditions, water deficit has a serious effect, especially at the booting stage, during which plants are particularly drought-susceptible, leading to low-crop productivity (Pantuwan et al. 2002).In rice the effect of drought varies with the variety, degree and duration of stress and growth stages (Sikuku et al., 2010)

Drought is especially damaging when it occurs immediately before and during flowering. If water stress occurs at tillering stage then number of reproductive tiller and panicle per hill was found to be reduced. Drought stress during each of the rice growth stage (vegetative growth, flowering and terminal stage) causes spikelet sterility that lead to unfilled grains (Kamoshita et al., 2004). Pantuwan et al. (2002) observed longer flowering delay when drought occurred during early tillering than when it occurred in mid-tillering stage made and concluded that under prolonged drought, flowering time is an important determinant of rice grain yield. Drought in general reduces the dry weight accumulation in all plant organs and shortens the life cycle of the plants causing yield reduction (Kamoshita et al., 2008). High sensitivity of rice to water stress with any intensity (mild or sever) during the reproductive stage (booting, flowering and panicle initiation). This effect might be due to decrease in translocation of assimilates towards reproductive organs (Rahman, et al., 2002). Kumar, et al. (2006) and Devatgar et al. (2009) also observed that the percentage of unfilled grains were significantly higher in sites that were affected by drought at reproductive stage. Among the major constraints to rice cultivation, water shortage significantly increases the vegetative growth rate and reduces grain yield (Tao et al., 2006). Usually, drought reduces the grain-filling period and induces early senescence by redirecting remobilization of assimilates from the straw to the grains (Plaut et al., 2004). Swain, et al., 2010 evaluated eighteen rice genotypes and they found the reduction in panicle number (72%) and grain yield (12%).

2.3 Variability (Genetic and Phenotypic)

Genetic variability in any crop is prerequisite for selection of superior genotypes over the existing cultivars .Genetic variability is the coefficient of variation which partitions the total variation into phenotypic, genotypic and environmental components. The genotypic coefficient of variation measures the extent of genetic variability present in the crop species for particular trait. The phenotypic coefficient of variation of characters is the manifestation of genotype, environment and interaction between the genotypes and environment. Most of the rice genotypes available in our country is average yielding, grown during kharif season in rainfed low land ecosystem. The quantitative measurement of individual character provides the basis for an interpretation of analysis of variance. According to Allard (1960), yield is polygenically controlled quantitative character and is highly influenced by environment. Partitioning of observed variability into heritable and non-heritable components is very much essential to get a true indication of the genetic coefficient of variability Panigrahi et al. (2018) identified large difference between PCV and GCV for character, no of productive tiller ,single plant yield indicating the environmental influence in expression of these character .Low GCV value is recorded for days 50% flowering, plant height and panicle length.

Tiwari et al. (2011) identified high GCV and PCV were observed for grain yield per plant. Mamata et al. (2018) investigation found out, grain yield ,productive tiller per plant had high GCV and PCV ,medium to plant height, days of 50% flowering and panicle length and low in 1000 grain weight.

Edukondalu et al. (2017) investigated on 40 rice genotypes in Kharif season and identified that plant height ,no of effective tiller ,grain yield per plant and 1000 grain weight had high PCV value and days of 50% flowering ,days of maturity and panicle length had low PCV value . The GCV was also observed in same pattern as PCV. The large difference between the values of PCV and GCV of characters like grain yield ,plant height, 1000 grain weight and no of effective tiller under kharif season, indicated that environmental factors significantly influenced the expression of these traits while other remaining traits indicating the less influence of environment in expression of these traits because of lower difference between of PCV and GCV.

Yadav et al. (2018) investigated on 30 rice genotypes and reveled that ,plant height ,panicle length ,1000 grain weight and grain yield had medium PCV and GCV value and low in days of 50% flowering .

High to low GCV was found in Grain yield per plant ,moderate in plant height ,panicle length,flag leaf area and number of effective tiller and low incase of days of flowering (Dhanwani et al.,2013). Islam et al. (2015) estimated of genotypic coefficient of variation (GCV) was 5.75% in plant height, 7.27% in days to 50% flowering, 6.08% in days to maturity, 8.03% in 1000 grain weight, 5.75 % in grain length, 8.12% in grain width and 22.13 % in grain yield.

Singh et al. (2018) found high GVC and PCV in grain yield and no of effective tiller ,moderate GCV and PCV in plant height and 1000 grain weight and low in days of 50% flowering ,days of maturity ,panicle length and SPAD .PCV and GCV difference was more in case of number of effective tiller; which indicates that environment factors significantly influenced. Tetwar et al. (2014) found high GVC and PCV grain yield low in days of 50% flowering ,plant height and panicle length.

2.4 Heritability

The heritability refers to as an index of transmissibility, to measure the genetic relationship between the parents and their offspring. Heritability indicates as to how much emphasis should be placed in for selection of a particular trait.

Sabesan et al. (2009) noted high values of heritability along with genetic advance for grain yield per plant, 100 grain weight, productive tillers per plant, panicle length and plant height.

Yadav et al (2018) investigated for thirty rice genotypes and indentified high heritability for test weight ,plant height ,panicle length ,days of 50% flowering and moderate for grain yield. Panigrahi et al. (2018) investigated for 32 rice variety and found that days of 50% flowering ,grain yield per plant ,100 grain weight had high heritability and medium fo panicle length and no of productive tiller and low heritability for plant height. Mamata.,et al (2018) found that ,grain yield ,days of 50% flowering ,plant height, panicle length and 1000 grain weight had high heritability and productive tiller had medium heritability.

High value of heritability in broad sense indicates that the character is least influenced by environmental effects. Edukondalu et al. (2017) revealed that, days of 50% flowering, days of maturity, plant height, panicle length, grain yield per plant 1000 grain weight and no of effective tiller. Dhanwani et al. (2013) found that days of flowering ,plant height ,no of effective tiller ,panicle length ,flag leaf area and grain yield.

Among the traits, high heritability was observed for days to 50% flowering, days to maturity, 1000 grain weight, and yield per hectare and low heritability for plant height (Islam et al., 2015). Singh et al. (2018) observed high heritability in days of 50% of flowering, days of maturity, plant height, no of effective tiller, panicle length, grain yield and medium heritability in SPAD and low heritability in 1000 grain weight. High heritability does not always indicate high genetic gain. Heritability and genetic advance are important selection parameters.

2.5 Genetic advance and genetic advance as percentage of mean

The estimates of genetic advance as per cent of mean provide more reliable information regarding the effectiveness of selection in improving the traits. Genetic advance denotes the improvement in the genotypic value of the new population over the original population.

Edukondalu et al. (2017) revealed that, grain yield per plant, 1000 grain weight, days of 50% flowering, plant height and no of effective tiller had high genetic advance and medium genetic advance found in days of maturity and panicle length. Yadav et al. (2018) identified moderate genetic advance for days of 50% flowering and grain yield and high genetic advance in plant height, 1000 grain weight and panicle length. Mamata et al. (2018) identified high genetic advance for grain yield, plant height and panicle length and medium heritability for days of 50% flowering, panicle length and 1000 grain weight. Dhanwani et al. (2013) revealed that, grain yield, plant height, panicle length, no of effective tiller and flag leaf area and medium incase of days of 50% flowering.

Singh et al. (2018) had high genetic advance in plant height ,no of effective tiller,1000 grain yield and grain yield ,medium in days of 50% flowering ,days of maturity and panicle length and low in SPAD . High genetic advance coupled with high heritability in plant height, no of effective tiller and grain yield .This traits which indicates that improvement in these characters is possible through mass selection and progeny selection. Low estimates of genetic advance expressed as percentage of mean was observed for days to maturity (Taya, 2014).

2.6 Association of character

2.6.1 Correlation coefficient

Correlation analysis was performed to understand the association of the yield with the agronomic characters of studied rice cultivars. Some morphological traits associated with plant architecture of rice have been found to have close relationship with yielding ability of rice varieties(Yang et al.,2007).Selection of traits contributing simultaneously to a character will improve it in subsequent segregation population (Nor et al., 2013). The correlation analysis is therefore necessary to determine the direction of selection and the numbers of characteristics need to be considered in improving any character such as grain yield. Investigation of the interrelationship between yield and its components will improve the efficiency of breeding program with appropriate selection criteria. A correlation coefficient tells the whether there is relationship between two variables and whether the relationship is positive or negative and how strong or weak the relationship (Bello and Igze,2010). Positives correlated between yield and yield components are requires for effective yield component breeding increasing grain yield in rice (Ogunbayo et al., 2014). So, it is important for plant breeders to understand the degree of correlation between yield and its components.

Zahid et al. (2006) reported that paddy yield had significant positive correlation with the, days to maturity and 1000-grain weight. Agahi et al. (2007) observed that grain yield was positively significantly correlated with number of productive tillers , days to maturity, flag leaf length , flag leaf width and plant height. Kole et al. (2008) reported positive and significant correlation of grain yield with plant height, but negative and significant correlation with days to 50% flowering at both genotypic and phenotypic levels. Plant height was significantly and positively correlated with panicle length. Akhtar et al. (2011) reported negative non-significant association of plant height and positive significant correlation of 1000-grain weight with paddy yield at phenotypic level. Fiyaz et al. (2011) reported number of productive tillers, days to 50 percent flowering and plant height had significant positive association with grain yield. Yadav et al. (2011) studied the extent of genetic association between yield and its components. Grain yield was significantly and positive correlation with number of effective tiller per hill, panicle length and 1000 grain weight at phenotypic levels and negative correlation with days of 50% flowering and plant height .

Laxuman et al. (2011) reported, days to 50 per cent flowering, number of productive tiller per plant, and panicle length had positive correlation with grain yield at phenotypic level. Days to 50% flowering was positively and non-significantly correlated with number of productive tillers whereas plant height was negatively and significantly correlated with productive tillers per hill. Sadeghi (2011) reported positive and significant correlation of grain yield with days to maturity, the number of productive tillers per plant, days to flowering, plant height, panicle length, flag leaf width and flag leaf length. Plant height was positively and significantly correlated with panicle length. Ravindra Babu et al. (2012) revealed significantly positive association of grain yield per plant with number of productive tillers per plant, negative significant correlation with days to 50% flowering. Plant height was significantly and positively correlated with panicle length and negatively with productive tillers per hill.

Seyoum et al. (2012) reported days to 50% flowering, days to 85% maturity, plant height and 1000-grain weight had negative and non-significant correlation with grain yield at phenotypic levels.

Edukondalu et al.(2017) studies revealed that the characters grain yield per plant showed significant and positive association with number of tillers per plant, panicle length and positive non significant correlation with 1000 grain weight, days of flowering and days of maturity. Afrin et al., (2017) studied 10 rice genotypes in 15 morphological trait and revealed that the character grain yield per plant showed significant and positive correlation with SPAD, 1000 grain weight, plant height, flag leaf area and panicle length and positive non-significant correlation with days of 50% flowering and with number of non effective tiller.

Lakshmi et al. (2014) studied 70 rice genotypes and found grain yield per plant was observed to be positively and significantly associated with days to maturity, number of effective tillers per plant and plant height indicating the importance of these traits as selection criterion in yield enhancement programmes and positive non significant correlation with days of 50% flowering and 1000 grain weight and negatives non significant with panicle length.

Phenotypic correlation coefficients showed that grain yield had negative non-significant correlation with plant height, days of 50% flowering, days of maturity and panicle length and positive and significant correlation with 1000 grain weight and no of effective tiller. Plant height had positive and significant correlation with number of days to 50% flowering and number of days to maturity and had negative and significant correlation with number of tillers, number of grains per panicle. Number of days to 50% flowering had positive and significant correlation with number of days to maturity. Number of days to maturity had positive and significant correlation with 1000-grain weight ( Osundare et al.,2017). Dilruba, et al. (2014) studies revealed that, grain yield showed positive and significant correlation with plant height and 1000 grain weight and positive non- significant with panicle length and days of 50% flowering and days of maturity .Days of maturity showed negative non significant correlation with panicle length plant height

3. MATERIALS AND METHODS

3.1 Research site

The experiment was conducted at the research block of Nepal Agriculture Research Council (NARC), Kumaltar, Lalitpur .The station is situated at an altitude of 1350 meter above mean sea level at 85010’ E and 27039’N.

3.2 Experiment Design and layout

This experiment of CVT (Co-ordinate varietal trial) was conducted in RCBD design where the genotypes were treated as treatments. There were total of 12 treatments or genotypes and 3 replication. Each genotype received the plots of 8m2(4m×2m) area with net plot area of 6.4m2(3.2m×2m). Two replication were separated with 75cm from each other and 40cm within blocks of replication. Plant geometry RR×PP (20cm×15cm)

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3.3 Treatments

There were 12 genotypes and their parent and source are listed below and Kumal-4 is standard check variety.

Table 1: Genotypes used in Research

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3.4 Cropping Pattern

There was many more research in this filed and cropping pattern was Maize-Rice-Wheat

3.5 Nursery bed preparation

Seed were sown on 2074/2/12 inside the NARC botany division and seed were sowed on well-drained, fines leveled and weed free seed bed by broadcasting method. Each plot size used were 1m×1m and Malathion was used during the nursery establishment for fungal protection.

3.6 Water Management

The field was dependent on the rainfall. Further, irrigation condition was not maintained, the substantial care was applied so the field would not receive water from irrigated channels in flowering conditions.

3.7 Nutrient recombination and Soil Character

Soil was Clay, Loamy and fertile. The recommended doses of NPK were 60:30:30 kg ha-1. Fertilizers used were Urea (46% N), DAP (18% P2O5) and M0P (60% KCl). DAP equal amount of fertilizer was applied in each plot. Half of Urea, full dose of DAP and MOP was applied as basal dose while remaining dose of nitrogen were applied in the single dose at 45 days after transplanting ( DAT) , no micro-nutrients were applied.

3.8 Field Preparation and Transplanting

Filed was prepared by tractor in working field in 2074/04/06 .45 days olds seedling was transplanted in same day of field preparation where spacing were RR×PP (20cm×15cm) at a depth of 4-5cm with 4-5 seedling per hills.

3.9 Weed Management

Single hand weeding were done at 42DAT. No any chemical weedicide was applied 3.10 Data Collection

3.10.1 Phenological characters

- Flowering Days

For 50% flowering days were taken by observing 50%flowering in plant was calculated by duration between seed sowing to 50% flowering days .

- Maturity Days

Maturity days was calculated by duration between seed sowing to maturity index like milk in grains ,soft dough stage etc .acquired by plant in days .

3.10.2 Biometric character

- Plant height(cm)

5 random plants were selected from each plot and height of plant was taken from the base of the plant to the tip of the longest panicle length in centimeter .Scale of 1m was used for this purpose.

- Flag leaf area(cm2)

Flag leaf area was taken using the flag leaf area meter. Flag leaf area of five plants was taken randomly and mean was taken as flag leaf area.

- Panicle length

5 panicles were randomly selected from each plot and average length was calculated. Panicle length was taken from tip of panicle to base of panicle using centimeter scale.

- Chlorophyll content

It was measured from flag leaf area of plants during the 50% heading of plants in a plot using SPAD meter. It was taken from five plants and then mean was taken as chlorophyll content.

3.10.3 Yield and yield attributing character

- Number of effective tiller per m2

- Number of non- effective tiller per m2

- 1000 grain weight

1000 grain weight in electronic balance. It was expressed in gram.

- Grain yiled(tons ha-1)

The grains was weighed after threshing and cleaning .The moisture % of grain was calculated by using digital moisture meter. The standard moisture percentage 14% was used for adjustment to find the final yield per hectare.

Grain yield (ton ha-1) at 14% MC= x10000 m2

Where, MC is the moisture content of fresh sample in percent.

3.11 Statistical analysis

3.11.1 Analysis of variance

Table 2:RCBD ANOVA model

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3.11.2 Genotypic and phenotypic coefficient of variation

The phenotypic and genotypic variance components and coefficients of phenotypic and genotypic of variation to compare the variation among traits were calculated by the methods suggested by (Lush, 1940) and (Chaudhary and Prasad, 1968).

Genotypic variance σ 2g=TMSS-EMSS/R

Error variance =σ 2e

Phenotypic variance σ 2p=σ 2e+σ 2g

Genotypic coefficient of variation (GCV) = [illustration not visible in this excerpt]

Phenotypic coefficient of variation (PCV) = [illustration not visible in this excerpt]

Where,

σg = Genotypic standard deviation

σp = Phenotypic standard deviation

X = General mean of the trait

Sivasubramanian and Madhavamenon (1973) categoried the value of GCV and PCV as follows:

0 – 10 %: Low

10 – 20 %: Moderate

>20 %: High.

3.12.3 Broad sense heritability (h2bs)

The ratio of genotypic variance (Vg) to the phenotypic variance (Vp) is called broad sense heritability and expressed in percentage Hanson, G. H., Robinson, H. F., & Cornstock, R. E. (1956).

Heritability in broad sense for all characters was computed using the formula given by Falconer (1996) as:

H = [illustration not visible in this excerpt]

Where:

H = heritability in broad sense

Vp = phenotypic variance

Vg = genotypic variance

The heritability percentage categorized as low, moderate and high as followed by, Robinson, H. F., Cornstock, R. E., & Harvey, P. H. (1949) as follows:

0 – 30% : Low

30 – 60% : Moderate

> 60% : High

3.11.4 Genetic advances (GA) and genetic advance as percent of mean

Under selection expected genetic advances where for each character at 5% selection intensity was computed by the formula described by (Johnson et al,.1955a).

Genetic Advances (GA) = k.σp.H

Where: k = constant (selection differential where k = 2.056 at 5% selection intensity)

σp = phenotypic standard deviation

H = broad sense heritability

Genetic advances as percent of mean was calculated to compare the extent of predicted advances of different traits under selection, using the formula GAM =GA/X *100 (Falconer, 1996).

Where:

GAM=genetic advances as percent of mean

GA=Genetic advances under selection

X = Mean of population in which selection will be employed.

The GA as percent of mean (GAM) was categorized as low, moderate and high as followed by (Johnson et al., 1955)

0-10%= low, 10-20%=Moderate, 20% and above=High

3.11.5 Correlation

Genotypic and phenotypic coefficient of correlation between two characters was determined by using variance and covariance components.(Weber and Moorthy ,1952).

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Where: rg (xy) and rp(xy) are genotypic and phenotypic correlation coefficients, respectively. Covg (xy) and covp (xy) are genotypic and phenotypic covariance of xy.

σ1/2 g(x),σ1/2 p(x) and σ1/2 g(y),σ1/2 p(y) are genotypic and phenotypic standard deviations of x and y respectively.

This coefficient of correlation will be tested for their statistical significance using‘t’ test as:

[illustration not visible in this excerpt] where n=number of treatments

The calculated value of t was compared with‘t’ table value at n-2 degrees of freedom at 1 and 5 percent level of significance .

4. RESULTS AND DISCUSSION

4.1 Mean performance and analysis of variance

Mean values and significant levels of yield and yield attributing traits of twelve drought tolerance rice genotypes are presented in Table 3 .The traits test weight, grain yield per meter square, plant height, panicle length ,flag leaf area ,SPAD, days of 50% flowering and no of effective tiller were found significant, which indicates sufficient genetic variability.

Table 3: Mean square from analysis of variance of twelve rice genotypes in rainfed condition at NARC, 2017

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Significance probability levels 0.001 *** 0.01** 0.05*

NS= Non Significant

DF=Days of 50% flowering, PH= Plant height ,FA= Flag leaf area, SPAD= Chlorophyll units,

PL=Panicle length, DM= Days of 90% maturity, ET= No of effective tiller per meter square, NET=non effective tiller per meter square,1000gw =Thousand grain weight ,GY= Grain yield per meter square

Table.4. Estimates of Phenotypic (σ 2P) and Genotypic (σ 2g) Variance, Phenotypic coefficient of variability (PCV) and Genotypic coefficient of variability (GCV), Broad sense heritability (H), Expected genetic advances (GA) and Genetic advance as percent of mean (GAM) of twelve rice genotypes in rainfed condition at NARC, 2017.

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DF=Days of 50% flowering, PH= Plant height ,FA= Flag leaf area, SPAD= Chlorophyll units, PL=Panicle length, DM= Days of 90% maturity, ET= No of effective tiller per meter square, NET=non effective tiller per meter square,1000gw =Thousand grain weight ,GY= Grain yield per meter square

4.2 Heritability (Broad sense heritability)

In the present investigation heritability in broad sense was calculated for all characters under study and is presented in Table 4. Heritability is classified as high (above 60%), medium (30%-60%) and low (below 30%) as suggested by Johnson et al. (1955). The traits studied in the present investigation expressed low to high heritability estimates ranging from 15.42 to 99.8 percentages . Heritability was found to be highest days to 50% flowering (87.45%) and followed by panicle length (77.70%), flag leaf area (74.40%), test weight (74.74%) ,days of 90% maturity and plant height (60.95%) where as moderate heritability was found in trait like grain yield (49.00%), no of effective tiller (45.98%), SPAD(33.24%)and lowest in number of non effective tiller(15.42%) as presented in (Table 4) .Similar findings for high heritability estimates were also obtained by (Tiwari et al., 2011) and Dhanwani et al. (2013) found 97.97% heritability for days of 90% maturity .Similar results were earlier reported by Bihari et al. (2004) for days to 50 per cent flowering and test weight; Sankar et al. (2006) for days to 50 per cent flowering, plant height, panicle length, and test weight and Karthikeyan et al. (2009) for days to 50 per cent flowering and 1000 grain weight, Konate et al.,(2016) for flag leaf area and Karande et al (2017) for grain yield . However, in a previous study, Patel et al. (2012) found higher heritability for plant height. High heritability values indicate that the traits under study are less influenced by environment in their expression and these characters could be successfully transferred to offsprings if their selection is performed in hybridization programme. The plant breeder, therefore, may make his selection safely on the basis of phenotypic expression of these traits in the individual plant by adopting simple selection methods.

4.3 Genetic advance and genetic advance as percentage of mean

The genetic advance was highest for grain yield (51.01) followed by number of non effective tiller per meter square (31.11), effective tiller per meter square(14.41), flag leaf area (12.17),plant height(8.53), days to 50% flowering (7.59) and SPAD (4.30). Low genetic advance was expressed in 1000 grain weight (3.58) followed by panicle length (3.26). Both heritability estimates and genetic advance were observed higher for Grain yield, no of effective tiller and flag leaf area. Based on these results, it is suggested that the high heritability is most likely due to additive gene effects , which indicates that improvement in these characters is possible through mass selection and progeny selection. SPAD showed low heritability as well as low genetic advance , it indicated that these characters are highly influenced by environmental factors. Therefore selection would be ineffective. In the present research, days of 50% flowering ,panicle length, plant height ,flag leaf area and 1000 grain weight spreading values exhibited high heritability but low genetic advance which indicated that these traits are governed by non-additive gene action and phenotypic selection may not effective . It also indicates greater role of non-additive gene action in their inheritance suggesting heterosis breeding could be useful for improving these traits. Singh et al. (1980), Patel et al. (2014) and Singh et al.(1990) for number of effective tillers per square meter and by Fukrei et al. (2011) for grain yield per square meter. Panicle length, days to 50% flowering, days to maturity, test weight ,days of 90% maturity has low genetic advance indicates non-additive gene effects; suggesting that these characters could be improved by developing varieties through recurrent selection method (Ogunbayo et al., 2014). High heritability and medium genetic advance of mean shown by 1000 grain weight and panicle length, which are largely control of additive gene action and selection of this traits may be done in early generation and recurrent selection can be done .Medium heritability and medium genetic advance of mean shown by grain yield and effective tiller which suggest that these traits are primarily under genetic control and selection for them can be achieve through their phenotypic performance.

4.4 Genotypic and phenotypic variance

A wide range of variance was observed for all the characters. Phenotypic variance was higher than genotypic variance for the entire yield and its contributing characters indicate the influence of environmental factors on these traits. Grain yield (1251.33) showed highest genetic variability followed by no of effective tiller per square meter (106.56), flag leaf area (46.93), plant height (28.16), days of 50% flowering(15.55), and SPAD(13.15). Low genetic variability was recorded for 1000 grain weight (4.04), panicle length (3.22),days of 90% maturity (0.96) and non effective tiller (0.14) present in Table, Similar findings were reported earlier by (Devi et al.,2006) and Prajapati et al. (2011) for grain yield . Limbani et al. (2017) for grain yield, no. of effective tiller and for panicle length, 1000 grain weight with low genetic variability.

4.5 Genotypic and phenotypic coefficient of variation

Coefficient of variation studied indicated that estimates of phenotypic coefficient of variation (PCV) were higher than the corresponding genotypic coefficient of variation (GCV) for all the traits (Table 4) indicating that they all interacted with the environment to some extent. Similar results were reported by (Dutta et al., 2013);( Singh et al., 2014) ;(Tuhina- Khatun et al., 2015); Bhadru et al.,(2012) and Paikhomba et al.,(2014) in rice. The estimates of genotypic coefficient of variation (GCV) was,17.77% in flag leaf area, 16.16% in no of non effective tiller 10.79% in grain yield per meter,8.14% for no of effective tiller, 7.57%in 1000 grain weight,7.36% in panicle length ,4.90% in SPAD, 4.34% in plant height,3.10%in days to 50% flowering and 0.59% in days of 90% maturity. The higher estimate of phenotypic coefficient of variation (PCV) was obtained for all the characters corresponding to GCV and these character could be used as selection for crop improvement. A comparison of estimates of GCV (%) with their corresponding PCV (%) for different traits (Table 4) revealed that in general, the GCV (%) were close to the estimates of PCV (%) for all characters except no of non-effective tiller , no of effective tiller ,flag leaf area, SPAD and grain yield per plot were least influenced by environment. The traits did not exhibit close estimates of GCV (%) and PCV (%) in the present study pointing out the fact that it was by environment. This was in accordance with the findings of Akinwale et al, (2011) and Sarkar et al. (2005) for grain yield for meter square. Senapati (2005) and Akinwale et al. (2011) for, 1000 grain weight and no of effective tiller.Limbani et al. (2017 for no of non effective tiller per meter square and Akinwale et al,(2011) for lowest GCV and PCV difference in days of 90% flowering .

Table 5: Correlation between grain yield and yield component of twelve rice genotypes at Kumaltar, Lalitpur, 2017

illustration not visible in this excerpt

*Significant at P=0.05, **significant at P=0.01

PL=panicle length, 1000g=Thousand grain weight, SY=Straw yield , FA= Flag leaf area , PH= Plant height, DF= Days of 50% flowering, ET= No of effective tiller per meter square, NET= No of non effective tiller per meter square, SPAD= Chlorophyll units , GY= Grain yield per meter.

4.6 Correlation among character

4.6.1 Days of 50% flowering

Days of 50% flowering exhibits positive correlation with panicle length (r=0.519), 1000gw (r=0.345) and number of effective tiller and significant negative correlation with plant height (r=-0.598*) and negative correlation with flag leaf area (r=-0.071), non effective tiller (r= -0.178) and SPAD (-0.301) . The observed positive correlation of date of flowering was supported by earlier researchers as Khan et al.(2014) for panicle length. The days to 50 per cent flowering recorded a non-significant negative phenotypic correlation with, plant height (r=-0.598*) similar results were reported by Meenakshi et al., (1999). Afrin .et al,.(2017) also reported positive correlation with panicle length,1000gw,grain yield and for no of effective tiller and Rathod et al.,(2017) also reported positive non significant correlation with of effective tiller and grain yield .

4.6.2 Days of 90% maturity

Days of 90% maturity shows positive correlation with flag leaf area ,plant height, number of effective tiller and non-effective tiller ,and shows negative correlation with panicle length,1000 grain weight, days of 50% flowering and SPAD .Nandhan et al., (2010) and Karande et al (2017) for 1000 grain weight and grain yield. Dilruba, et al.,(2014) showed negative non significant with panicle length and plant height.

4.6.3 1000gwt

1000 grain weight shows positive correlation with plant height (r=0.035) days of flowering (r=0.342) and effective tiller(r=0.389) and shows highly significant negative correlation with non effective tiller (r=-0.650**) and negative correlation with panicle length(r=-0.004),flag leaf area (r=-0.192).The observed positive correlation of 1000 grain weight was supported by earlier researcher Ravindra Babu et al. (2012) for number of productive tillers per plant,.. Nandhan et al. (2010) for plant height. Afrin .et al,.(2017) also reported same results for days of 50% flowering ,effective tiller and plant height ,1000 grain weight ,days of 50%flowering .

4.6.4 Plant height

Plant height showed positive correlation grain yield(r=0.287) and with chlorophyll contain (r=0.397) contain indicating yield could be improved by increasing plant height , slightly positive correlation with 1000 grain weight(0.035). This character had siginificant negative correlation with days of 50% flowering (-0.598**) and negative correlation number of effective tiller(r=-0.306) ,panicle length (-0.372) and with non effective tiller also (-0.282) which indicated that increased of plant height decreased no of effective tiller per unit area. Afrin .et al,.(2017) also reported same results for chlorophyll contain . Rahman et al., (2014) also reported same negative correlation results for panicle length.

4.6.5 Effecticve tiller

Number of effective tiller per meter square exhibited positive phenotypic correlation with grain yield per meter(0.239),days of 50% flowering (0.558),1000 grain weight(0.389) and shows negative correlation with panicle length (-0.095),Flag leaf area(-0.025),plant height(-0.306) ,non effective tiller(-0.358) and chlorophyll contain(-0.197). Patel et al., (2014)found same result for grain yield per plant Ravindra Babu et al. (2012), Rahman et al. (2014) also reported same results for panicle length.

4.6.6 Non effective tiller

Non effective tiller shows significant negative correlation with 1000 grain weight(r=-0.650**) and grain yield (r=-0.799**), negative correlation with effective tiller(r=-0.358),flag leaf area(r=-0.312),plant height(r=-0.282),days of 50% flowering(r=-0.178) and slightly negative with panicle length(r=-0.087). Rahman et al,.(2014) also found negative correlation with panicle length ,1000 grain weight and grain yield which indicates with increased in non- effective tiller there will be decreased grain yield, 1000 grain weight and panicle length .

4.6.7 Panicle length

Panicle length show positive correlation with Flag leaf area(r=0.156) days of 50% flowering(r=0.519), chlorophyll contain(r=0.169) and grain yield (0.156) . Afrin .et al,.(2017) reported the same results, flag leaf area, days of 50% flowering and chlorophyll contain .It had negative non-significant correlation with 1000 grain weight (-0.004).Vange et al., (1999) for 1000 grain weight and Kalyan et al.,(2014). It shows negative correlation with plant height(-0.372),no of effective tiller(-0.095) per unit area and slightly negative correlation with non effective tiller per unit area(-0.087). Rahman et al., (2014) also reported same results for plant height .

4.6.8 Flag leaf area

Flag leaf area found to be highly positive correlation(r=0.743**) with chlorophyll contain and positive correlation with grain yield (r=0.419) and panicle length(r=0.156) and shows negative correlation with plant height(r=-0.007),days of 50% flowering(r=-0.0.071),no of effective tiller(r=-0.025)and non effective tiller(r=-0.312) . Faisal and Tahir(2014). Sharifi et al. (2013) found same result for chlorophyll contain.

4.6.9 SPAD

Chlorophyll contain was highly positive significant with grain yield (r=0.577 **) and flag leaf area (r=0.743**)and positive correlation with panicle length r=(0.169) an plant height(r=0.397).It showed non-significant negative correlation with 1000grain weight(r=-0.087),days of 50%flowering (r=-0.301),no of effective tiller(r=-0.197).and no of non-effective tiller(r=-0.246). Afrin et al. (2017) also found the highly significant positive correlation with grain yield and flag leaf area and positive correlation with panicle length and plant height.

4.7 Correlation between grain yield and other traits

Phenotypic correlations revealed that grain yield per plant had significant positive association with chlorophyll contain (r=0.577**), while plant height(r=0.287), number of effective tiller per meters square (r=0.239), 1000 grain weight (r=0.425), panicle length(r=0.156),flag leaf area(r=0.419) and days of 50% flowering(r=0.336) showed non-significant positive correlation with grain yield..This indicated that all these characters were important for yield improvement. Similar kind of association was revealed by Afrin .et al,.(2017) also found the highly significant positive correlation with chlorophyll contain , Mohanty et al. (2012), Reddy et al. (2013) and Patel et al. (2014) for plant height. Nagaraju et al. (2013) for days of 50% flowering ,Satish Chandra et al. (2009), Ravindra Babu et al. (2012 ),Patel et al. (2014) and Rao et al. (2014) for 1000 grain weight and number of productive tillers per plant. Grain yield per meter square showed highly negative significance with non effective tillers(r=-0.799**).Rahman et al. (2014) also reported the same result, non effective tiller showed highly significant negative correlation with grain yield. Lakshmi et al. (2014) also reported positive non significant with days of 50% flowering and 1000 grain weight. Grain yield shows negative correlation with days of 90% maturity(r=-0.291) and Seyoum et al. (2012) reported days to 90% maturity, negative and non-significant correlation with grain yield at phenotypic levels.

The character association studies revealed that the characters grain yield per plant showed significant positive association with chlorophyll contaion and positive correlation , number of productive tillers per plant,1000 grain weight, panicle length plant height and flag leaf area . Hence ,these characters could be considered as criteria for selection for higher yield as these were mutually and directly associated with grain yield( Meenakshi et al.,1999). Edukondalu et al. (2017) studies revealed that grain yield had positive non- significant correlation with 1000 grain weight and days of 50% flowering.

5. CONCLUSIONS

The analysis of variance showed the presence of significant difference among the tested genotypes for all the character except non –effective tiller per square and indicating the existence of variability among tested genotypes. Phenotypic variance was higher than the genotypic variances for all the characters indicating the influence of the environmental factors on these traits. A comparison of estimates of GCV (%) with their corresponding PCV (%) for different traits revealed that ,in general the GCV (%) were close to the estimates of PCV (%) for all characters except no of non-effective tiller , no of effective tiller ,flag leaf area, SPAD and grain yield per plot were least influenced by environment. Heritability was found to be highest for days to 50% flowering (87.45%) panicle length (77.70%), flag leaf area (74.40%), test weight (74.74%) , days of 90% maturity (67%) and plant height (60.95%) where as moderate heritability was found in trait like grain yield (49.00%), no of effective tiller (45.98%), SPAD(33.24%)and lowest in number of non effective tiller(15.42%). High heritability values indicate that the traits under study are less influenced by environment in their expression. The genetic advance was highest for grain yield (51.01) followed by number of non effective tiller per meter square (31.11), effective tiller per meter square(14.41), flag leaf area (12.17),plant height(8.53), days to 50% flowering (7.59) and SPAD (4.30). Both heritability estimates and genetic advance as mean were observed higher for flag leaf area. Based on these results, it is due to additive gene effects, which indicates that improvement in these characters is possible through mass selection and progeny selection. High heritability and low genetic advance was observed for days of 90% maturity, it is due to non-additive gene and this trait could be improved by hetrosis breeding program. Medium heritability and medium genetic advance obtained in grain yield and effective tiller and selection for them can be achieved through their phenotypic performance .

In our present study, grain yield shows significant positive correlation with SPAD, negative significant correlation with number of non- effective tiller and positive correlation with remaining trait .Hence, these characters could be considered as criteria for selection for higher yield as these were mutually and directly associated with grain yield. This condition indicates that there is good opportunity to improve this trait using the tested genotypes.

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