GENETIC VARIABILITY, CORRELATION AND PATH

ANALYSIS OF F4 POPULATIONS OF RICE (Oryza sativa

L.)

TASNIA FERDOUS

DEPARTMENT OF GENETICS AND PLANT BREEDING

SHER-E-BANGLA AGRICULTURAL UNIVERSITY

DHAKA-1207, BANGLADESH

JUNE, 2015

GENETIC VARIABILITY, CORRELATION AND PATH

ANALYSIS OF F4 POPULATIONS OF RICE (Oryza sativa

L.)

BY

TASNIA FERDOUS

Registration No. 09-03371

A Thesis

submitted to the Faculty of Agriculture,

Sher-e-Bangla Agricultural University, Dhaka

in partial fulfillment of the requirements

for the degree of

MASTER OF SCIENCE

IN

GENETICS AND PLANT BREEDING

SEMESTER: JANUARY- JUNE, 2015

Approved by:

Prof. Dr. Md. Shahidur Rashid Bhuiyan Prof. Dr. Md. Sarowar Hossain Supervisor Co-supervisor

(Prof. Dr. Md. Sarowar Hossain) Chairman

Examination Committee

Dr. Md. Shahidur Rashid Bhuiyan

Professor

Department of Genetics and Plant Breeding

Sher-e - Bangla Agricultural University

Dhaka-1207, Bangladesh

Mob: 01552467945

E-mail: ipbscfc2009@gmail.com

CERTIFICATE

This is to certify that the thesis entitled “GENETIC VARIABILITY,

CORRELATION AND PATH ANALYSIS OF F4 POPULATIONS OF RICE”

submitted to the Faculty of Agriculture, Sher-e-Bangla Agricultural University

(SAU), Dhaka, in partial fulfillment of the requirements for the degree of MASTER

OF SCIENCE (MS) in genetics and plant breeding, embodies the results of a piece

of bonafide research work carried out by TASNIA FERDOUS, Registration no.

09-03371, under my supervision and guidance. No part of the thesis has been

submitted for any other degree or diploma.

I further certify that such help or source of information, as has been availed of

during the course of this investigation has duly been acknowledged.

Prof. Dr. Md. Shahidur Rashid Bhuiyan

Supervisor

Dated: June, 2015

Place: Dhaka, Bangladesh

DEDICATED TO

MY

PARENTS AND TEACHERS WHO

LAID THE FOUNDATION OF MY

SUCCESS

i

ACKNOWLEDGEMENTS

First and foremost I express my deepest and sincerest gratitude to the omniscient,

omnipresent and omnipotent Allah who enabled me to pursue education in

Agriculture discipline and to complete this thesis for the degree of Master of Science

(MS) in Genetics and Plant Breeding Department.

I wish to offer my cordial appreciation and best regards to my supervisor, Professor

Dr. Md. Shahidur Rashid Bhuiyan, Department of Genetics and Plant Breeding,

Sher-e-Bangla Agricultural University, Dhaka, who has supported me through out

my research and thesis with his patience and knowledge whilst allowing me the

room to work in my own way. I attribute the level of my Masters degree to his

encouragement and effort and without him this thesis would not have been

completed or written. One simply could not wish for a better supervisor. I am very

much grateful to my co-supervisor Professor Dr. Md. Sarowar Hossain, Department

of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, for

his valuable advice, constructive criticism and factual comments in upgrading the

research with all possible help during the research period and preparation of the

thesis.

I would like to express my deepest respect and boundless gratitude to my honorable

teachers of the Department of Genetics and Plant Breeding, Sher-e-Bangla

Agricultural University, Dhaka, for their valuable teaching, sympathetic co-

operation throughout of this research work. I want to give special thanks to elder

brother of SAU Rafiqul Islam who has helped me and co-operated during my

research work I also express my cordial thanks to some of my friends and seniors

Golam Robbani, Mahbuba Jamil, Kamrul Islam, Salma Sadia, Samsun Naher,

Zillur Rahman Arif Hossain and Monirul Haque Romel for their valuable help

ii

during conducting my research. I am also grateful to Ministry of Science and

Technology, Bangladesh for providing the fund of MS research work.

I am indebted to my last but not least profound and grateful gratitude to my

beloved parents and friends for their inspiration, blessing and encouragement that

opened the gate of my higher studies in my life.

June 2015 The Author

SAU, Dhaka

iii

GENETIC VARIABILITY, CORRELATION AND PATH

ANALYSIS OF F4 POPULATION OF RICE (Oryza sativa L.)

TASNIA FERDOUS

ABSTRACT

A field experiment was conducted with 16 F4 materials and 4 check varieties of Oryza

sativa L. at the experimental field of Sher-e-Bangla Agricultural University, Dhaka to

study the genetic variability, correlation, path coefficient analysis and selection of

advance genotypes during April 2014 to July 2014. Thirteen characters were studied

to find out the suitable traits for the improvement of rice yield. The selected

genotypes were found significantly variable for all of the characters. The lowest days

to maturity (92.00 days) was observed in G11 (BR 21 × BRRI dhan 29, F4, S7P2)

following as G4 (BR 21 × BRRI dhan 29, F4, S1P2) (95.33 days) and G5 (BR 21 ×

BRRI dhan 29, F4, S1P5) (96 days). The highest yield among F4 population was

recorded in G6 (BR 21 × BRRI dhan 29, F4, S6P3) (6.08 t/ha) followed by G10 (BR 21

× BRRI dhan 29, F4, S7P1) (5.30 t/ha) and G7 (BR 21 × BRRI dhan 29, F4, S6P8) (4.68

ton/ha). Comparatively phenotypic variances were higher than the genotypic

variances for all the characters studied. Also PCV were higher than the GCV for all

the characters studied. All characters showed high heritability.High heritability

coupled with moderate to high genetic advance in percent mean is governed by

additive gene action which is better for selection. The significant positive correlation

with grain yield per hectare was found in number of total tiller per plant, number of

effective tiller per plant, number of filled grains per panicle, total number of spikelet

per panicle and grain yield per plant. Path coefficient analysis revealed that days to

maturity, number of effective tillers per plant, total number of spikelet per panicle,

number of filled grains per panicle and yield per plant had the positive direct effect on

yield per hectare. The residual effect was found 0.398 which indicated that 60.2% of

the variability was accounted for thirteen yield and yield contributing traits in the

present studies. So direct selection based on these traits would be effective for

improvement of these F4 population. By comparing check varieties with segregating

populations some better genotypes as G6, G7, G9, G10, G12 and some individual

plants from different populations were selected as short duration and high yielding T.

Aus rice for future trial.

iv

TABLE OF CONTENTS

CHAPTER TITLE PAGE NO.

ACKNOWLEDGEMENTS i-ii

ABSTRACT iii

TABLE OF CONTENTS iv-v

LIST OF TABLES vi

LIST OF FIGURES vii

LIST OF PLATES viii

LIST OF APPENDICES ix

SOME COMMONLY USED ABBREVIATIONS x-xii

I. INTRODUCTION 1-3

II. REVIEW OF LITERATURE 4-33

2.1 Center of genetic diversity and biology of the crop 4

2.2 Genetic variation 4-5

2.3 Heritability ,genetic advance and selection

2.4 Correlation among different characters

2.5 Path co-efficient analysis

5-17

18-25

25-28

III. MATERIALS AND METHODS 29-40

3.1 Experimental site 29

3.2 Soil and Climate 29

3.3 Experimental materials 29-30

3.4 Methods 31

3.4.1 Germination of seed 31

3.4.2 Seedbed preparation and seedling rising 31

3.4.3 Land preparation for transplanting 31

3.4.4 Application of manure and fertilizer 31

3.4.5 Experimental design and layout 31-32

3.4.6 Transplanting 32

3.4.7 Intercultural operations and after care 32-33

3.4.8 Crop harvesting 33

3.4.9 Data collection 33-35

3.4.10 Statistical analysis 35-40

v

CHAPTER TITLE PAGE NO.

IV. RESULTS AND DISCUSSION 41-94

4.1 Analysis of variance among 16 F4

populations, and four check varieties of rice

for yield related traits

41-59

4.2 Estimates of genetic parameter 60-70

4.3 Correlation coefficient 71-79

4.4 Path Coefficient analysis 79-85

4.5 Selection of advanced lines for further trial from

F4 populations

85-93

V. SUMMARY AND CONCLUSION 94-99

REFERENCES 100-115

APPENDICES 116-118

vi

LIST OF TABLES

Table

No.

Title Page

No.

1. Materials ( T. Aus genotypes ) used in the experiment 30

2. Dose and method of application of fertilizers used in rice field 32

3. Analysis of variance (ANOVA) for yield and yield related

characters of 4 check varieties and 16 populations (F4 generation)

of Oryza sativa L

42

4. Mean performance of yield and yield related characters of 16

populations and 4 check varieties of Oryza sativa L.

43-44

5. Estimation of genetic parameters for yield related traits of 16

populations (F4 generation) with their and 4 check varieties in

rice (Oryza sativa L.)

61

6. Estimation of heritability and genetic advance of 16 populations

(F4 generation) with their 4 check varieties in rice (Oryza sativa

L.)

67

7. (a) Phenotypic and (b) genotypic correlation coefficient among

different pair of yield and yield contributing characters of 20

genotypes of rice (Oryza sativa L.)

73-74

8.

Path coefficient analysis showing direct and indirect effects of

different characters on yield of rice (Oryza sativa L.)

80

9. Comparison between selected F4 population and check varieties

for further trial

87

10. Mean performance table of important traits of four check varieties

of rice (Oryza sativa L.)

87

11. Selection of promising early high yielding plants from F4

materials of different genotypes

93

vii

LIST OF FIGURES

Figure

No.

Title Page

No.

1. Variation on days to maturity of 20 rice genotypes 46

2. Variation on number of effective tillers per plant of 20 rice

genotypes

52

3. Variation on number of filled spikelet per panicle of 20 rice

genotypes

55

4. Variation on number of total number of spikelet per panicle of

20 rice genotypes

57

5. Variation on yield per plant (gm) of 20 rice genotypes 57

6. Variation on thousand seed weight (gm) of 20 rice genotypes 59

7. Variation on yield per hectare (ton/ ha) of 20 rice genotypes 59

8. Genotypic and phenotypic co-efficient of variation in Oryza

sativa L.

62

9. Heritability and genetic advance as percent of mean in Oryza

sativa L.

70

viii

LIST OF PLATES

Plate

No.

Title Page

No.

1.

Photograph showing raising of seedling at seedbed

36

2. Photograph showing An overview of experimental field 36

3. Photograph showing variation at flowering stage 47

4. Photograph showing flowering stage in parent materials and

check varieties

48

5. Photograph showing variation at 80% maturity stage 49

6. Photograph showing 80% maturity stage in check varieties 50

7. Photograph showing (a) 80% maturity stage and (b )panicle

length (c) grain size of G6 (BR 21×BRRI dhan 29,F4, S6P3)

with check varieties

88

8. Photograph showing (a) 80% maturity stage and (b)panicle

length (c) grain size of G10 (BR 21×BRRI dhan 29,F4,S7P1)

with check varieties

89

9. Photograph showing (a) plant height and effective tiller and (b)

panicle length (c) grain size of G12 (BR24×BRRI dhan 29,F4,

S5P8) with check varieties

90

10. Photograph showing (a) plant height and effective tiller and (b)

panicle length (c) grain size of G7 (BR24×BRRI dhan

29,F4,S6P8) with check varieties

91

ix

LIST OF APPENDICES

Appendix No. Title Page No.

1. Map showing the experimental site under the study 116

2. Morphological, physical and chemical characteristics of

initial soil (0-15 cm depth) of the experimental site

117

3. Monthly average Temperature, Relative Humidity and

Total Rainfall of the experimental site during the period

from April, 2014 to July, 2014

118

x

SOME COMMONLY USED ABBREVIATIONS

Full word Abbreviation

% Percent

⁰C Degree Celsius

@ At the rate

p Phenotypic variance

g Genotypic variance

e Environmental variance

h b Heritability in broad sense

AEZ Agro-ecological zone

Agric. Agriculture

Agril. Agricultural

Agron. Agronomy

ANOVA Analysis of variance

BARI Bangladesh Agricultural Research Institute

BBS Bangladesh Bureau of Statistics

BD Bangladesh

BES Bangladesh Economic Survey

Biol. Biological

BINA Bangladesh Institute of Nuclear Agriculture

BR Bangladesh Rice

Breed. Breeding

BRRI Bangladesh Rice Research Institute

cm Centi meter

CV% Percentage of coefficient of variation

Df Degrees of freedom

D50F Days to flowering

DM Days to maturity

EC Emulsified concentrate

Ecol. Ecology

ECV Environmental co-efficient of variation

Env. Environment

et al. And others

xi

(Continued…)

Full word Abbreviation

etc. Etcetera

F1 The first generation of a cross between two dissimilar

homozygous parents

F4 The fourth generation of a cross between two dissimilar

homozygous parents

F5 The fifth generation of a cross between two dissimilar

homozygous parents

FAO Food and Agricultural Organization

FGP Filling spikelet percentage

g Gram

G Genotype

GA Genetic advance

GCV Genotypic coefficient of variation

GDP Gross domestic product

Genet. Genetics

GW Spikelet weight

GYP Grain yield per plant

HI Harvest Index

J. Journal

K Potassium

Kg Kilogram

m Meter

M1 First generation of mutant line

M3 Third generation of mutant lines

MSS Mean sum of square

MP Murate of Potash

MOA Ministry of Agriculture

m² Square meter

N Nitrogen

N North

n Number of chromosome

NET Number of effective tiller per plant

NFG Number of filled grain per panicle

xii

(Continued…)

Full word Abbreviation

NPB Number of primary branches per panicle

NSB Number of secondary branches per panicle

P Phosphorous

PCV Phenotypic coefficient of variation

PH Plant height

pH Negative logarithm of hydrogen ion

PL Panicle length

RCBD Randomized Complete Block Design

Res. Research

RIL Recombinant inbreed lines

S Sulfur

SAU Sher-e-Bangla Agricultural University

Sci . Science

TS Total number of spikelet per panicle

TSP Triple super phosphate

TGW 1000- grain weight

TSW 1000 seed weight

1

CHAPTER I

INTRODUCTION

Rice is central to Bangladesh’s economy and staple food for the people from time

immemorial. Rice continues to dominate the cropping system, three quarter of the

total cultivated area are used for rice production. It provides 75% of the calories and

55% of the proteins in the average daily diet of the people. Calories from rice are

particularly important, where it accounts for 50-80% of daily calorie intake. Rice is

rich in carbohydrate. The protein content is about 8.5 percent. The thiamin and

riboflavin contents are 0.27 and 0.12 micrograms, respectively (Bhuiyan et al., 2002).

Bangladesh is the 4th largest country in the world in respect of rice area and

production. It grows in all the three crop growing seasons of the year, namely aus,

amon and boro. It occupies about 77% (11.42Mha) of the total cropped area of about

14.94 Mha. Modern varieties (MV) of rice cover about 98% of Boro rice, 98% of T

Aman and 75% of Aus. The total rice production is 34.49 million metric tons (Mmt).

The national yield of MV cleaned rice in Aus, T. Aman and Boro seasons are 23.28,

130.23 and 190.07 Mmt respectively.

Aus is one of the major crop in Bangladesh. It has been contributing to food

production in addition to other two rice (Aman and Boro) crops. It has shorter life

cycle with lower yield in comparison to Aman and Boro. As Aus rice is rain fed, it

does not require withdrawing ground water. Amount of rainfall in Aus season (May-

July) is the highest in Bangladesh. Rainfall during this season reduces the cost of

irrigation. On the other hand, huge amount of ground water is required for Boro

cultivation, which is costly and is against the environment. Ground water level has

been decreasing by 4 cm every year due to utilization of ground water for Boro rice

cultivation, causing a serious threat to environment (Niogi, 2014). In future, enough

water will not be available to irrigate the entire area for Boro cultivation. As a

resource saving option, Aus based cropping pattern appears to be quite prospective.

Around 20% areas of Boro rice (around 0.9 Mha) can be shifted to Aus rice areas. In

order to compensate the reduced amount of Boro production, the cumulative Aus

areas should be increased to 1.8 Mha and the total production of Aus will have to be

5.2 million metric ton. To harvest this production, grain yield of modern Aus at

2

farmers’ field should be around 4.0 t/ha for which, in addition to other technologies,

needs the assurance of partial or supplemental irrigation facilities (Salam et al. 2014).

Nothing is without any drawbacks; there are still many limitations with strategic

planning for increasing Aus production and areas. As unit cost of production in Aus

season is higher than those of T. Aman and Boro seasons,farmers will be reluctant for

adopting Aus rice cultivation instead of Boro rice. Sustainable food Security of the

nation may be under question if total production is reduced due to sacrificing of Boro

areas for increasing Aus cultivation as the production of Aus rice per unit area is

lower compared to Boro rice. Lack of potential short duration (90 days) varieties

having higher yield in Aus season. Finally, lack of emphasis on Aus rice development

that is given by research organization. To overcome these issues, sacrificing boro

areas by Aus areas should be scientifically analyzed, partial irrigation for T. Aus

should be ensured, short duration T. Aus rice varieties having 90 days growth

duration and good yielding capacity should be developed and research organization

(GO, NGO and private sectors) should put more effort on Aus rice development.

Improved Aus varieties having short duration and good yielding are needed to be

introduced. Therefore short duration and higher yielding varieties of Aus rice with the

best utilization of rainwater will be promising practice for our farmers. Improved

varieties of Aus can be developed by exploiting the existing Aus, Boro genotypes.

The purpose of inter-varietal crosses between Aus-Boro is to develop progeny with

high yielding like Boro and short life cycle as Aus. Present investigation was

conducted to evaluate segregating F4 population of Aus-Boro crosses. Most promising

lines from segregating population were selected on the basis of earliness of maturity

and higher yield for future trial.

Grain yield is a complex polygenic quantitative trait which is greatly affected by

environment and determined by the magnitude and nature of their genetic variability

(Singh et al., 2000). In addition, grain yield is related with other characters such as

plant type, growth duration and yield components (Yoshida, 1981). Hence, selection

of superior genotypes based on yield as such is not effective. Selection has to be made

for the components of grain yield. The systematic breeding program involves the

steps like creating genetic variability, practicing selection and utilization of selected

3

genotypes to evolve promising lines. Estimates of heritability and genetic advance

will help in knowing the nature of gene action affecting the concerned trait (Sravan,

2012).Yield component traits show association among themselves and with yield.

Plant breeder have to find significant correlations among yield and yield component

traits, and effect of yield component traits on grain yield to predict the superior cross

combinations and to select ideal plant type with increased yield (Nagarajun et al.,

2013).

In view of the above discussion, the present study was undertaken to investigate the

genetic variability, heritability, genetic advance and association between grain yield

and yield related traits as a basis for selection of high yielding and short duration T.

Aus rice genotypes from sixteen genotypes. So, following are the main objectives of

this study:

To assess the extent of genetic variability for yield and yield related traits in

the F4 segregating populations.

To know the interrelationship of yield contributing characters and their direct

and indirect effect on yield.

To select high yielding and short duration F5 T. Aus materials for further trial.

4

CHAPTER II

REVIEW OF LITERATURE

2.1 Center of genetic diversity and biology of rice

Rice belongs to family Poaceae and genus Oryza and most probably originated in

India or southeastern Asia. It is the world’s second most important cereal crop next to

wheat. It has two cultivated and 22 wild species. The cultivated species are the Asian

rice, Oryza sativa L. and the African rice, Oryza glaberrima Steud. The Asian rice is

grown all over the world while African rice has originated and been cultivated in

West Africa for about more than 3500 years (Martin et al., 2006). Rice, a diploid

species with a chromosome number of 2n = 24, is normally a self pollinated crop but

up to 3% natural out crossing may occur depending on the cultivar and the

environment, although about 0.5% is the average out-crossing level (Poehlman and

Sleper,1995). Oryza sativa is a grass with a genome consisting of 466 Mb across 12

chromosomes with an estimated 46,022 to 55,615 genes (Jun et al, 2002).

2.2 Genetic Variation

Kumar et al. (2014); conducted experiment with 40 genotypes of rice. Analysis of

variance revealed significant difference among 40 rice genotypes for all characters

indicating the existence of variability. High GCV and PCV were observed for grain

yield per plant and biological yield per plant. On the other hand, Rafiqul (2014),

conducted experiment with 19 genotypes of rice, existence of variance in 14 yield

contributing character including days to maturity, no. of effective tiller per plant, no.

of filled grain of main tiller and yield (ton/ha) were found in analysis of variance.

Sadeghi (2011), also observed positive significant association of grain yield with

grains per panicle, days to maturity, number of productive tillers and days to

flowering.

Ullah et al. (2011); noted that grain yield was positively and significantly associated

with panicle length and grains per panicle. Hairmansis et al. (2010); recorded a

positive and significant association of grain yield with filled grains per panicle,

spikelet per panicle and spikelet fertility.

5

Pandey and Awasthi (2002), studied genetic variability in 21 genotypes of aromatic

rice for yield contributing traits. Significant genetic variability was observed among

the 21 genotypes for the entire yield for contributing traits. They concluded that traits

plant height, days to flowering effective tillers per plant, panicle length, number of

spikelet per panicle, test weight and spikelet yield per plant play a major role in the

enhancement of production of spikelet yield.The relationship between rice yield and

yield component traits has been studied widely at phenotypic level. Diaz et al. (2000)

noted wide variation in panicle length, panicle type, spikelet per panicle and panicle

weight and secondary branches per panicle.

Dabholkar (1999), reported that information on the nature and magnitude of genetic

variability present in a crop species is important for developing effective crop

improvement program. Chaudhury and Das (1998), estimated genetic variability in 11

deep water rice varieties for yield and yield related characters like effective tillers per

plant. They found a large difference between genotypic and phenotypic co-efficient of

variation for effective tillers per plant.

If the character expression of two individuals could be measured in an environment

identical for both, differences in expression would result from genetic control and

hence such variation is called genetic variation (Falconer and Mackay, 1996).

Awatshi and Sharma (1996), recorded considerable genetic variability for plant height

in 15 of high quality aromatic Oryza sativa genotypes. Allard (1960), showed

variability occurred among individuals due to differences in their genetic composition

and/or the environment in which they were raised.

2.3 Heritability, Genetic Advance and Selection

Ketan and Sarkar (2014), studied 26 indigenous aman rice cultivars and found that

high heritability in days to flowering, plant height, 1000 spikelet weight, panicle

length. Number of spikelet per panicle recorded the highest genetic advance followed

by plant height and number of secondary branches. High heritability in conjunction

with high genetic advance was registered for plant height, days to flowering and

number of secondary branches. High heritability in conjunction with low genetic

advance was observed for panicle length. Spikelet yield per plant was significantly

6

correlated with number of secondary branches per panicle at phenotypic level while

number of spikelet per panicle and fertility percentage at genotypic and phenotypic

level.

Dutta et al. (2013); studied 68 genotypes for twelve agronomical important characters

to estimate variability and genetic parameters. Considering genetic parameters, high

genotypic and phenotypic coefficient of variations, high heritability (broad sense) and

genetic advance as percentage of mean were shown by eight characters viz. tillers per

plant, days to flowering, harvest index, spikelet per panicle, spikelet density, panicle

per plant and spikelet yield. Thus, these characters were under the influence of

additive gene action and a satisfactory selection program.

Singh et al. (2013); observed forty-eight genotypes to examine genetic variability.

High genotypic and phenotypic coefficient of variation, heritability and genetic

advance as percent of mean was recorded for total number of spikelet per panicle,

filled grains per panicle, number of effective tillers, leaf width and spikelet yield per

plant. Positive and significant association was recorded by days to 50% maturity, leaf

length, leaf width, filled grains per panicle and total number of spikelet per panicle.

Spikelet yield per plant showed positive and significant correlation at genotypic and

phenotypic levels. Days to maturity, plant height, number of filled grains per panicle

and test weight exhibited positive direct effect both at genotypic and phenotypic

levels.

Tuwar et al. (2013) studied twenty nine genotypes of rice from diverse locations to

estimate the genetic components of variability. Analysis revealed that plant height had

high estimates of GCV and PCV proceeded by number of tillers and grain weight per

panicle. Heritability was higher for days to flowering followed by days to maturity,

plant height and panicle length which suggested that these traits would respond to

selection owing to their high genetic variability and transmissibility. High heritability

coupled with high genetic advance as percent of mean was recorded for number of

grains per panicle and grain effects in their expression and would respond to selection

effectively as they are least influenced by environment.

7

Chanbeni et al. (2012) studied 70 rice genotypes by considering 13 quantitative

characters. They showed that high estimates of genotypic coefficient of variation

(GCV) and phenotypic coefficient of variation (PCV) were for spikelet yield per hill

followed by tillers per hill and harvest index. High heritability with high genetic

advance was recorded for spikelet per panicle.

Seyoum et al. (2012); studied on the genetic variability, heritability of fourteen rice

genotypes for grain yield and yield contributing characters. Highly significant

(P<0.01) variations were found for days to 50% flowering, days to 85% maturity,

plant height, panicle length, spikelet per panicle and 1000-grains weight. Significant

difference (P<0.05) were found for panicles per plant, grains per panicle, total spikelet

fertility and grain yield. Relatively high genotypic coefficient of variation (GCV) and

phenotypic coefficient of variation (PCV) were found for days to 50% flowering,

plant height, grains per panicle, spikelet per panicle, 1000-grain weight and grain

yield. High heritability was found for plant height (97.17%), followed by 50%

flowering (90.16%), 1000-grains weight (83.17%), days to 85% maturity (82.45%),

panicle length (79.25%) and spikelet per panicle (60.25%).According to Akinwale et

al. (2011); heritability of a trait is important in determining its response to selection. It

was found out earlier that genetic improvement of plants for quantitative traits

requires reliable estimates of heritability in order to plan an efficient breeding

program. Moreover, knowledge of heritability is essential for selection-based

improvement as it indicates the extent of transmissibility of a character into future

generations (Sabesan et al., 2009, Ullah et al., 2011).

In order to estimate genetic variability and relationships among some agronomic traits

of rice an experiment were conducted with 30 varieties of rice under two irrigation

regimes by Abarshahr et al. (2011). Broad-sense heritability varied from 0.05 for

brown grain width to 0.99 for plant height and number of spikelet for panicle under

optimum irrigation and from 0.1 for brown grain width to 0.99 for plant height.

Evaluation of phenotypic and genotypic coefficient of variations (CV) showed that

the lowest and highest phenotypic CV under optimum irrigation regime was observed

to panicle fertility percentage and paddy yield and genotypic CV was related to brown

grain width and plant height, respectively, while under drought stress condition, days

to 50% flowering had the lowest phenotypic and genotypic CV and paddy yield and

8

plant height had the highest phenotypic and genotypic CV. Furthermore, the lowest

and highest expected genetic advance using selection intensity of 10% (i =1.75) were

evaluated for brown grain width and plant height under optimum irrigation regime,

respectively.

Akhtar et al.(2011), studied on the variance and heritability for yield contributing

characters in ten rice genotypes. The heritability was found to be high for number of

grains per panicle, days to maturity, plant height and paddy yield while lower for

number of tillers per plant. Hasan et al. (2011); studied twenty four rice varieties for

genetic variability, correlation and path analysis. The PCV values were greater than

GCV revealing little influence of environment in character expression. High values of

heritability along with moderate genetic advance were observed for days to flowering

and plant height. Spikelet yield showed positive significant association with number

of effective tillers/hill, panicle/m2, spikelet fertility and thousand spikelet weight at

both genotypic and phenotypic levels. Same traits had highest significant and positive

effect on yield.

Singh et al. (2011); evaluated eighty one rice (Oryza sativa L.) genotypes during

kharif, 2010, for 13 quantitative traits to examine the nature and magnitude of

variability, heritability (broad sense) and genetic advance. The genotypes were

significantly different for all the characters except flag leaf width. High estimates of

genotypic coefficient of variation (GCV) and phenotypic coefficient of variation

(PCV) were found for number of spikelet per panicle followed by harvest index, grain

yield per hill and number of panicles per hill. Broad sense heritability was highest for

biological yield per hill, which suggested that these traits would respond to selection

owing their high genetic variability and transmissibility. Maximum genetic advance

as percent of mean was recorded for number of spikelet per panicle with high value of

heritability.

Subbaiah et al. (2011); studied on the extent of variability and genetic parameters

with 16 check varieties and 48 hybrids for nine yield and yield related components

and twenty five quality characters. The magnitude of difference between phenotypic

coefficient of variation (PCV) and genotypic coefficient of variation (GCV) was

relatively low for all the traits. There was less environmental effect. High GCV and

9

PCV were found for harvest index, total number of productive tillers per plant in

check varieties and for total number of productive tillers per plant, number of grains

per panicle in hybrids. High heritability coupled with high genetic advance as percent

of mean were recorded for harvest index, total number of productive tillers per plant,

number of grains per panicle and grain yield per plant in case of check varieties and

total number of productive tillers per plant, number of grains per panicle and harvest

index in case of hybrids indicating the additive gene effects in the genetic control of

these traits and can be improved by simple selection in the present breeding material.

Prajapati et al. (2011); assessed thirty eight rice genotypes at field experimentation

centre, Department of Genetics and Plant Breeding, Allahabad School of Agriculture,

Allahabad during kharif, 2009. The experiment was conducted to study the 12

quantitative traits to examine the nature and magnitude of variability, heritability and

genetic advance. High estimates of heritability coupled with high genetic advance as

percent of mean was observed for harvest index followed by number of spikelet per

panicle, number of panicles per hill and number of tillers per hill. High estimates of

heritability coupled with moderate genetic advance as percent of mean was observed

for flag leaf width followed by days to 50% flowering, panicle length and biological

yield per hill.

Sadeghi (2011), also used 49 rice varieties (Oryza sativa L.) in an experiment to

determine variability, heritability and correlation between yield and yield components

for 2 years. He found broad sense heritability range from 69.21% (plant height) to

99.53% (grain width). Selvaraj et al. (2011); studied variability, correlation and path

coefficient on 21 rice genotypes for grain yield and other yield attributes. Analysis of

variance revealed considerable variability among the genotypes for all the characters.

The phenotypic correlation coefficient (PCV) values were slightly greater than

genotypic correlation coefficient (GCV), revealing negligible influence of

environment in character expression. High heritability coupled with high genetic

advance and high GCV were observed for number of tillers/plant followed by number

of productive tillers per plant, plant height and grain yield / plant.

Subbaiah et al. (2011); studied the extent of variability and genetic parameters with

16 parents and 48 hybrids for nine yields and its components and 25 quality

10

characters. The magnitude of difference between PCV and GCV was low for all the

traits, indicating less environmental influence. High GCV and PCV were recorded for

harvest index, total number of productive tillers per plant and gelatinization

temperature in parents, number of grains per panicle, gelatinization temperature and

amylase content in hybrids. High heritability coupled with high genetic advance as

percent of mean were recorded for gelatinization temperature, harvest index, total

number of productive tillers per plant, number of grains per panicle, kernel length,

kernel L/B ratio and grain yield per plant in case of parents and gelatinization

temperature, amylase content, total number of productive tillers per plant, number of

grains per panicle and harvest index in case of hybrids indicating the additive gene

effects in the genetic control of these traits and can be improved by simple selection

in the present breeding material.

Singh et al. (2011); evaluated 81 rice genotypes during kharif, 2010 for 13

quantitative traits to examine the nature and magnitude of variability, heritability and

genetic advance. Analysis of variance revealed that the differences among 81

genotypes were significant for all the characters except flag leaf width. Among the all

traits number of spikelet per panicle exhibited high estimate of genotypic coefficient

of variation (GCV) and phenotypic coefficient of variation (PCV) followed by harvest

index, grain yield per hill and number of panicles per hill. Broad sense heritability

was highest which suggested that this trait would respond to selection owing their

high genetic variability and transmissibility. Maximum genetic advance as percent of

mean was recorded for number of spikelet per panicle with high value of heritability.

Pandey et al. (2010); studied on the genetic variability among forty rice genotypes for

yield and yield contributing components. High significant difference was found for all

the characters for the presence of substantial genetic variability. The maximum

genotypic and phenotypic coefficient of variability was found for harvest index, grain

yield per hill, plant height and biological yield per hill. High heritability coupled with

high genetic advance was found for plant height and number of spikelet per panicle.

Akhatar et al. (2011); estimated the genetic variability, character association and path

analysis of 52 exotic rice genotypes for reproductive traits. There was found

significant genetic variability among genotypes. The highest genotypic variance and

phenotypic variance were found for pollen sterility and filled grains per panicle. High

11

heritability and genetic advance were recorded for pollen sterility. This study

suggested that selection could be based on filled grains per panicle only according to

genetic parameters, association and path analysis.

Bisne et al. (2009); conducted an experiment on 44 rice genotype in Raipur,

Chhattisgarh in kharif 2005 for 13 characters. Low, moderate and high genotypic and

phenotypic coefficient of variations was observed. High genotypic and phenotypic

coefficients of variations were expressed by harvest index, total number of filled

spikelet per panicle, 100-grain weight and spikelet fertility percentage. High

heritability coupled with high genetic advance was exhibited by harvest index, total

number of chaffy spikelet per panicle, grain yield per plant, total number of filled

spikelet per panicle and spikelet fertility percentage and selection may be effective for

these characters.Kumar et al. (2009); carried out an experiment to study the selection

criteria for selecting high yielding genotypes in two different early segregating F2 and

F3 populations by estimating heritability and genetic correlation between yield and its

main economic traits in their subsequent F3 and F4 generations of two crosses in rice.

The heritability estimates were high for spikelet/main panicle and 100-grain weight,

whereas it was medium to low for grain yield and low for panicles/plant.

Rita et al. (2009); observed high genotypic and phenotypic coefficient of variations

with harvest index, total number of filled grain per panicle, 100-spikelet weight and

spikelet fertility percentage. High heritability coupled with high genetic advance was

exhibited by harvest index, total number of chaffy spikelet per panicle, spikelet yield

per plant, total number of filled grain per panicle and spikelet fertility percentage and

selection may be effective for these characters. Vange (2009), conducted a field

experiments in 2005 in the Experimental Farm Station of the University of

Agriculture, Makurdi, Nigeria to evaluate the performance and genetic diversity of

some upland rice accessions. Genotypic coefficient of variability (GCV) was

generally lower than phenotypic coefficient of variability (PCV). Days to 50%

heading, days to maturity, flag leaf area, panicle weight, panicle length, number of

branches/panicle, number of seeds/panicle, grain weight/panicle and seed yield

showed very low differences between their PVC and GCV values. Also these traits

had high estimates for heritability and genetic advance.

12

Genetic advance gives clear picture and precise view of segregating generations for

possible selection. Higher estimates of heritability coupled with better genetic

advance confirms the scope of selection in developing new genotypes with desirable

characteristics (Ajmal et al., 2009). Kole et al. (2008); studied variability for twelve

morphological characters of 18 morphologically distinct mutants in M4 generation

along with their two mother genotypes (IET 14142 and IET 14143), which were

developed from Tulaipanja, an aromatic non-basmati rice cultivar of West Bengal.

Genotypic and phenotypic coefficients of variation were high for flag leaf angle and

panicle number; moderate for grain number per panicle, straw weight, harvest index

and grain yield per plant; and low for days to flower, plant height, panicle length,

spikelet number, spikelet fertility (%) and test weight. High heritability accompanied

by high to moderate genetic advance for flag leaf angle, panicle number, grain

number, straw weight and grain yield indicated the predominance of additive gene

action for the expression of these characters.

Sabouri et al. (2008); studied the traits of the parents (30 plants), F1 (30 plants) and

F2 generations (492 individuals), which were evaluated at the Rice Research Institute

of Iran (RRII) during 2007. Heritability of the number of panicles per plant, plant

height, days to heading and panicle exertion were greater than that of grain yield. The

selection indices were developed using the results of multivariate analysis. To

evaluate selection strategies to maximize grain yield, 14 selection indices were

calculated based on two methods (optimum and base) and combinations of 12 traits

with various economic weights. Results of selection indices showed that selection for

grain weight, number of panicles per plant and panicle length by using their

phenotypic and/or genotypic direct effects (path coefficient) as economic weights

should serve as an effective selection criterion for using either the optimum or base

index.

Heritability, a measure of the phenotypic variance attributable to genetic causes, has

predictive function of breeding crops (Songsri et al., 2008). It provides an estimate of

the genetic advance a breeder can expect from selection applied to a population under

certain environment.

13

Generally, heritability indicates the effectiveness with which selection of genotypes

could be based on phenotypic performance. Most effective yield component breeding

to increase grain yield could be achieved, if the component traits are highly heritable

and positively correlated with grain yield. However, it is very difficult to judge

whether observed variability is highly heritable or not. Most of the important

agronomic traits are quantitative in nature and manifested in terms of degree rather

than kind. Plant breeders require knowledge that will help them to identify superior

genotypes efficiently to select them and concentrate their genes in a line or variety

that is commercially acceptable. To do this, it is essential to learn first whether the

trait is heritable and then to understand the kind and extent of the genetic components

of the variation.

Ashvani et al. (2007); studied the genetic parameters of variability and heritability of

different characters in 32 genotypes of rice, grown in Ghaziabad, Uttar Pradesh, India,

in Kharif 1992. The heritability and high genetic advance as percentage of mean

estimates were highest for days to flowering. Ingale et al. (2007); conducted an

experiment Effect of seedling age on 50% flowering of parental lines of Sahyadri rice

hybrid. The experiment was formulated to assess the effect of seedling age at

transplanting on 50% flowering of A, B, and R lines of Sahyadri rice hybrid. The 50%

flowering was delayed in both younger and older aged seedlings than the

recommended age of seedling (25 days old) at transplanting by approximately half the

number of days by which the seedlings are younger and older than the recommended

age.

Karim et al. (2007); studied on variability and genetic parameter analysis of 41

aromatic rice genotypes. The phenotypic variance was higher than the corresponding

genotypic variance for the characters. These differences were in case of number of

panicles per hill, number of primary branches, number of filled grains per panicle,

spikelet sterility (%) and grain yield per hill indicating greater influence on

environment for expression of these characters. 1000-grain weight and days to

maturity showed least difference between phenotypic and genotypic variance, which

indicated additive gene action for expression of the characters. High genotypic

coefficient of variation (GCV) value was revealed for 1000-grain weight followed by

14

spikelet sterility (%), grain yield per hill and number of filled grains per panicle,

whereas days to maturity showed very low GCV.

Tang et al. (2007); studied the agronomic traits and heterosis of javanica varieties and

the indica- javanica genotypes of rice in Changsha, Hunan, China. Javanica rice

exhibited long panicles, big grains, less panicle per plant, a long growth duration and

high plant height in Changsha. The hybrid of (Pei`ai RNT 711, RNT 24, RNT 1, PNL

2, PLANTG 1, RTN 2, KJT 2, RNT 3, KJT 147 and RNT 68) were evaluated for

heterosis, heterobeltiosis and yield advantages in percent. Heterosis, heterobeltiosis

and yield advantages in percent for productive tiller number per plant ranged from -

26.10 to 124.32%, -44.79% to 90.80% and 58.50% to 80.43% respectively. On the

other hand, Wang et al. (2007); recorded the effects of panicle type and source-sink

relation on the variation in grain weight (GW) and quality within a panicle were

investigated using four japonica (Oryza sativa L.) varieties differing in grain density

and two source-sink adjusting treatments. There were significantly differences in GW

and filling grain percentage ( FGP) among superior and inferior grains for compact-

panicle varieties ( Xiushui 994 and Xiushui 63), while not for loose-panicle ones

(Xiushui 11 and Chunjiang 15).

Sankar et al. (2006); conducted an experiment with 34 rice genotypes and high

heritability as well as genetic advance was obtained for productive tillers per plant.

Sharma et al. (2006); evaluated 39 upland rice genotypes for the estimation of genetic

variability. The significant mean sum square indicated strong variability for days to

50% flowering. Though days to 50% flowering had high heritability (92.8%), it had

low GCV. Sanjeev (2005), conducted an experiment with 19 mutant lines (M3)

derived from Pusa Basmati and Taraori Basmati and observed higher heritability for

days to flowering compared to other characters. Satyanarayana et al. (2005); studied

variability, correlation and path coefficient analysis for 66 restorer lines in rice and

observed low heritability for panicle length as well as high variability, heritability and

genetic advance for plant height.

Shashidhar et al. (2005); reported positive association of spikelet yield with plant

height, number of productive tillers hill-1

, dry matter plant-1

and harvest index at

15

phenotypic and genotypic level. Patil and Sarawgi (2005), evaluated 128 aromatic

rice accessions and estimate genetic variation and correlation for 7 traits and found

that number of ear-bearing tiller hill-1

had high genotypic and phenotypic coefficient

of variation. High heritability coupled with high genetic advance was also estimated

for this character. Patil and Sarawgi (2005), studied genetic variability in traditional

aromatic rice accessions and found that the genetic and phenotypic coefficients of

variation were high for 100- grain weight.According to Bihari et al. (2004); who

conducted an experiment with seventeen aromatic rice genotypes observed the days to

flowering and test weight were highly heritable traits. Sarma and Bhuiyan (2004),

studied genetic variation and divergence in 58 aus rice genotypes and observed

highest broad- sense heritability for plant height.

Chand et al. (2004); studied nineteen genotypes of aman paddy (Oryza sativa)

emanating from different sources different sources for spikelet yield and their

components during kharif. Heritability and genetic advance as percentage of mean

were high for 1000 spikelet weight. Hossain and Haque (2003) reported that both

genotypic and phenotypic variances were found highly significant in all the traits with

little higher phenotypic variations as usual. Similarly the low differences between the

phenotypic and genotypic coefficient of variations indicated low environmental

influences on the expression of the characters. High heritability coupled with high

genetic advance of yield, grains per panicle, days to flowering and height suggested

effective selection for improvement of these characters could be made.

Kumari et al. (2003); reported that plant height showed high heritability coupled with

modern genetic advances. Patil et al. (2003); evaluated 128 traditional aromatic rice

genotypes and found high heritability (>70%) in broad sense for all the characters

expected panicle length (54.9). Patil et al. (2003); evaluated 128 traditional aromatic

rice genotypes and found high heritability for 100-grain weight associated with

yield/hectare. According to Ali et al. (2002);since high heritability does not always

indicate high genetic gain, heritability with genetic advance considered together

should be used in predict-ing the ultimate effect for selecting superior varieties.

Pandey and Awasthi (2002), studied genetic variability in 21 genotypes of aromatic

rice for yield contributing traits, significant genetic variability was observed for days

16

to 50% flowering. Pandey and Awasthi (2002), studied genetic variability in 21

genotypes of aromatic rice for yield contributing traits. Significant genetic variability

was observed among the 21 genotypes for the entire yield for contributing traits. They

concluded that traits plant height, days to 50% flowering effective tillers per plant,

panicle length, number of grains per panicle, test weight and grain yield per plant

playing a major role in the enhancement of production of grain yield. Singh (2001),

stated that genetic advance expected from selection refers to the improvement of characters in

genotypic value for the new population compared with the base population under one cycle of

selection at a given selection intensity.

Jiang et al. (2000); observed the importance of number of tillers/plant influencing

yield. Productive tillers/hill showed significant positive correlations with correlations

with grain yield (Reddy and Kumar, 1996). Chen-Liang et al. (2000); showed that the

cross between Peiai 64s and the new plant type lines had strong heterosis for filled

grains per plant, number of spikes per plant and grain weight per plant, but heterosis

for spike fertility was low. Xiao et al. (1996) indicated that heterosis in F1 hybrids for

spikelets/panicle showed a positive and significant correlation with genetic distance in

indica x indica but not in indica x japonica crosses. Kamal et al. (1998); performed an

experiment to assess the yield of nine modern varieties (MV) and six improved

varieties (LIV). They observed that modern variety BR11 gave the highest grain yield

followed by BR10, BR23, Binasail and BR24.

Mehetre et al. (1996); concluded that information on heritability, yield correlations

and genetic variability is derived from data on 8 characters in M2 generation of 8

upland rice varieties with gamma radiation (10, 20, 30, 40 or 50 kilo roentgen).

Significant differences occurred among genotypes for all characters. Estimates of

heritability ranged from 91.2 (plant height) to 35.6% (sterility). Expected genetic

advance ranged from 6.9 (panicle length) to 54.9% (grain yield/plant). Mishra et al.

(1996); studied genetic parameters in some rice genotypes and found high value of

heritability and genetic advance for 1000-grain weight. Sawant and Patil (1995),

evaluated 75 genotypes of rice and found high coefficient of variation for spikelet

yield per plant. High value of heritability coupled with high expected genetic advance

was observed for spikelet yield per plant.

17

Chaubey and Singh (1994), evaluated 20 rice varieties and reported high heritability

for total number of spikelet followed by grain yield per plant and 1000-grain weight.

Genetic advance in percent of mean were higher for grain yield per plant followed by

panicle weight and total number of spikelet. Manual and Prasad (1993), observed

little differences between phenotypic and genotypic coefficient of variation indicating

less environmental influences. They reported low value of genotypic coefficient of

variation, high heritability and low genetic advance for panicle length.Thirty rice

genotypes were evaluated for variability by Das et al. (1992). Fertile tillers plant-1

showed high GCV. Fertile tillers plant-1

also showed high heritability with high

genetic advance in percent of mean.

A study was conducted by Yadav (1992), on 11 plant characters in 16 rice genotypes

and revealed that heritability estimate was high for days to flowering and for

yield/plant. Li et al. (1991); worked on 9 rice cultivars and estimated that genetic

coefficient of variation was high for yield per plant but they got moderate heritability

and moderate genetic advance.Vishwakarme et al. (1989); estimates moderate

heritability and moderate genetic advance for spikelet yield per plant in 82 population

of rice. Choudhury and Das (1988); worked out on estimates of genetic variability,

heritability and genetic in 11 deepwater rice varieties for yield and its attributing

traits. High genotypic coefficient of variation was observed in spikelet yield. High

heritability with high genetic advance was also found for spikelet yield.

Yield in cereals is a complex character and determined by some yield component.

Grafius (1964), suggested that these yield components express their genetic and

environmental effects finally through spikelet yield. Allard (1960), stated that the

broad sense heritability is the relative magnitude of genotypic and phenotypic

variance for the traits and it gives an idea of the total variation accounted to genotypic

effect. Ghose and Ghatge (1960), also stated that tiller number, panicle length

contributed to yield.

18

2.4 Correlation among different characters

Rangare et al. (2012); evaluated forty exotic and Indian rice germplasm including one

local check for their efficiency with respect to eleven yield and yield contributing

characters from Kharif, 2009 under normal conditions. Associated studies have

indicated that for an improvement in grain yield, the intensive selection should be

positive for biological yield per plant, number of fertile tillers per plant, number of

spikelet per panicle, test weight, panicle length and days to maturity as these traits

showed significantly strong positive association with grain yield, but days to 50%

flowering, days to initial flowering, harvest index and plant height through had

positively non significant association with grain yield.

Satheeshkumar et al. (2012); estimated correlation in fifty three genotypes of rice for

fifteen characters. It revealed grain yield per plant exhibited high significant and

positive genotypic correlation with number of productive tillers per plant, filled grains

per panicle and total number of grains. Acording to Yadav et al. (2011); it is apparent

that information of morphological and physiological aspects of crop is also a key

feature to plan a creative breeding program. Thus, the genetic reconstruction of plant

architecture is required for developing high yielding crop varieties. Akhtar et al.

(2011); studied on the genotypic and phenotypic correlation for yield contributing

characters in ten rice genotypes. Paddy yield had strong genetic correlation with

number of grains per panicle, days to maturity and 1000 grain weight. Paddy yield

had significant positive correlation with number of grains per panicle and 1000 grain

weight.

Akinwale et al. (2011); evaluated twenty rice genotypes in the International Institute

of Tropical Agriculture, Ibadan, Nigeria during 2008-2009 cropping season. They

reported that grain yield had significantly positive correlation with the number of

tillers per plant (r = 0.58**), panicle weight (r =0.60*) and number of grains per

panicle (r= 0.52*). Therefore, the results suggest that these traits can be used for grain

yield selection. Sadeghi (2011), used 49 rice varieties (Oryza sativa L.) in an

experiment to determine variability, heritability and correlation between yield and

yield components for 2 years. Grain yield was found to be positively and significantly

correlated with grains per panicle, days to maturity, panicle weight and number of

19

productive tillers, days to flowering, plant height, panicle length indicating the

importance of these characters for yield improvement in this population.

Tripathi et al. (2011); stated that resourceful crop improvement scheme refers to the

collection of superior alleles into single targeted genotype. Chakraborty et al. (2010);

studied on the genotypic and phenotypic correlation along with coheritability between

two characters of 29 genotypes of boro rice. Correlation analysis revealed significant

positive genotypic correlation of yield per plant with plant height (0.21), panicles per

plant (0.53), panicle length (0.53), effective grains per panicle (0.57) and harvest

index (0.86). The study suggested that five component characters, namely harvest

index, effective grains per plant, panicle length, panicles per plant and plant height

influenced the yield of boro rice. A genotype with higher magnitude of these

component characters could be either selected from the existing genotypes or evolved

by breeding program for genetic improvement of yield in boro rice.

Ghosal et al. (2010); evaluated eighteen advanced breeding lines for yield and yield

contributing characters to observe their variability, associations and direct and indirect

effect on yield during boro season 2009. Path coefficient analysis revealed that

effective tillers/m2, thousand grain weight (g) and growth duration (days) had higher

direct effects on yield (t/ha). Nandeshwar et al. (2010); evaluated twenty five F2

progenies derived from the crosses involving HYV and quality rice during kharif

2005. Grain yield plant -1

possessed significant positive correlation with panicle

number plant -1

, panicle weight and grain number panicle -1

while it had significant

negative correlation with plant height.

Wattoo et al. (2010); conducted an experiment in order to determine the associations

among yield components and their direct and indirect influence on grain yield of rice.

For this purpose, 30 genotypes collected from different sources were tested. The

phenotypic correlations among the yield traits were estimated. Grain yield was

significantly correlated with its component characters, number of productive tillers

per plant, number of grains per panicle and flag leaf area.

20

Kumar et al. (2009); carried out an experiment with 42 genotypes derived from seven

crosses of rice and reported that phenotypic coefficient of variation was

comparatively higher than the corresponding genotypic coefficient of variation form

number of panicle plant-1

. Vange (2009), conducted a field experiments in 2005 in the

Experimental Farm Station of the University of Agriculture, Makurdi, Nigeria to evaluate the

performance and genetic diversity of some upland rice accessions. Genotypic correlation

analysis of yield with other traits revealed that yield had a significantly positive correlation

with flag leaf area, number of tillers, number of panicles, panicle weight, panicle length,

number of branches/panicle, number of seeds/panicle and seed weigh/panicle, grain length

and 1000 seed weight. Agahi et al. (2007); conducted an experiment to investigate

correlation coefficient of grain yield and sixteen yield-related traits among 25 lines.

The results showed that grain yield was significantly correlated with days to heading,

total tillers, number of productive tillers, days to maturity, number of grain per

panicle, flag leaf length, flag leaf width and plant height.

Akter et al. (2007); evaluated thirty advanced breeding lines of deep-water rice during

T. Aman season with a view to finding out variability and genetic association for

grain yield and its component characters. The highest genetic variability was obtained

in filled grains/panicle followed by plant height. Panicles/plant, filled grains/panicle

and grain yield had genetic coefficient of variation and heritability in broad sense

coupled with high genetic advances in percentage of mean. Panicle length,

panicles/plant, plant height, filled grains/panicle and harvest index showed significant

positive association with grain yield. Path coefficient analysis also revealed maximum

positive and direct contribution of filled grain yield followed by panicles/plant, 1000-

grain weight and flag leaf area. Moreover, plant height had the highest indirect effect

on grain yield through filled grains/panicle. Flag leaf area, harvest index and panicle

length also had higher positive indirect effect on grain yield through filled

grains/panicle.

Mustafa and Isheikh (2007), evaluated fourteen rice (Oryza sativa L.) genotypes at the

Gezira Research Station Farm (GRSF), Sudan for correlation coefficient between

yield and yield components among phenotypic markers and polygenic trait analysis.

Phenotypic correlations between grain yield and number of filled grain panicle-1,

number of panicle m-2

and 1000 grain weight were 0.52, 0.36 and 0.27 respectively.

These results suggested that improvement in yield could be attained by selecting rice

21

plants for higher number of filled grain panicle-1, number of panicle m-2, and 1000

grain weight. Sankar et al. (2006); studied on correlation on single plant yield and its

components in 34 rice genotypes. They concluded that single plant yield was

positively and significantly correlated with days to 50 per cent flowering, productive

tillers/plant, panicle length and grains/panicle and hence can be taken as indices for

improving yield in rice. Singh et al. (2006); conducted an experiment with 37 rice

genotypes and reported that there were highly significant differences among the

genotypes for plant height and the estimates of phenotypic coefficient of variation and

genotypic coefficient of variation were of the same magnitude for the character but

high heritability was recorded for the character.

Habib et al. (2005); evaluated 10 local biroin rice varieties with a view to find out

variability and genetic association for spikelet yield and its component characters. The

highest genetic variability was obtained in flag leaf area and filled grain/panicle. High

heritability associated high genetic advance was observed in filled grains/panicle,

1000 spikelet weight, harvest index and spikelet yield. Genotypic correlation

coefficients were higher than the corresponding phenotypic correlation coefficients in

most of cases. Plant height, day to maturity and filled grain/panicle showed significant

positive correlation with spikelet yield.

Patil and Sarawgi (2005), evaluated 128 aromatic rice accessions and estimate

genetic variation and correlation for 7 traits and found that number of ear-bearing

tiller hill-1

had high genotypic and phenotypic coefficient of variation. High

heritability coupled with high genetic advance was also estimated for this character.

Satyanarayana et al. (2005); studied variability, correlation and path coefficient

analysis for 66 restorer lines in rice and observed low heritability in number of

effective tillers plant-1

. Zahid et al. (2005); studied 14 genotypes of basmati rice and

observe high heritability couple with high genetic advance for plant height and 1000-

spikelet weight. They also reported that plant height has negative correlation with

yield. In addition he observed the positive relationship of plant high with spikelet

quality.

22

Shrama and Haloi (2004), studied genetic variation and divergence in 58 aus rice and

found the highest genotypic as well as phenotypic coefficient of variation for number

of effective tillers plant-1

. Mahto et al. (2003); evaluated twenty six early maturing

rice genotypes and found that the difference between phenotypic and genotypic

coefficient of variation was minimum for days to flowering (2.13) high values for

heritability (97.33) and high genetic advance. Guimara (2002), indicated that the

plants with comparatively large panicles tended to have high number of filled grains.

However, in most of the cases positive correlation was observed between number of

panicle per plant and panicle length.

The correlation between heterosis over better parent and inbreeding depression

showed that yield can be improved by direct selection for days to flowering and

number of productive tillers per plant (Verma et al., 2002). Araus et al. (2001); stated

that, it was imperative in the improvement of grain yield traits of rice to have a clear

understanding of the relationships between grain yield and other agronomic characters. Grain

yield was the most integrative character because it was influenced by all factors that

determine productivity. Iftekharuddaula et al. (2001); studied twenty-four modern rice

varieties of irrigated ecosystem with a view to finding out variability and genetic

association for grain yield and its component characters. All the characters tested

were showed significant variation among the varieties. The highest genetic variability

was obtained in spikelet per panicle and grains per panicle. High heritability together

with high genetic advance in percentage of mean was observed in plant height, 1000-

grain weight, grains/panicle and spikelet/panicle.

Ismail et al. (2001); reported that the nature and extent of genetic variation governing

the inheritance of characters and association will facilitate effective genetic

improvement.Prasad et al. (2001); studied eighty fine rice genotypes to observe

genetic variability and selection criteria for some yield contributing characters

through correlation and path coefficient analysis. Correlation coefficient study

revealed high positive correelation of spikelet yield with effective tillers/plant, fertile

spikelets/panicle and 1000 spikelet weight. A significant negative correlation was

obtained between spikelet yield and plant height. Path coefficient analysis revealed

maximum contribution of fertile spikelets/panicle to spikelet yield.

23

Shanthi and Singh (2001), observed that plant height exhibited low variation between

phenotypic and genotypic coefficient of variation. High heritability coupled with high

genetic advance was observed in plant height, which governed largely through the

additive effect of genes. Prasad et al. (2001); conducted an experiment where eight

fine rice genotypes were studied. Correlation coefficient study revealed high positive

correlation of grain yield with effective tillers/plant, fertile grains/panicle and 1000-

grain weight. A significant negative correlation was obtained between grain yield and

plant height. Satyavathi et al. (2001); evaluated 15 rice varieties and found moderate

to high coefficient of variation for plant height.

Shanthi and Singh (2001), found significant variation among the genotypes for all

characters studied. Panicle length exhibited low variation between phenotypic and

genotypic coefficient of variations. Atwal and Singh (2001), studied genetic

variability and observed that genetic coefficient of variation (GCV) was higher than

the phenotypic coefficient of variation (PCV) for days to flowering. They also found

high heritability and genetic advance for this character. Sangeeta-Mahitkar et al.

(2000); conducted an experiment during the kharif season of 1998 in Akola,

Maharashtra, India to investigate the correlation between the growth and yield

contributing characters, and crop yield of upland rice. A positive and significant

correlation was observed by Singh and Chaudhury (1996) estimated genetic

variability, heritability and genetic advance for 12 characters in 100 genotypes of rice.

1000 spikelet weight showed high value of genotypic coefficient of variation (GCV)

than phenotypic coefficients of variation (PCV), while low heritability for 1000

spikelet weight was reported by Honarnejad (1995).

Chen-Liang et al. (2000); showed that the cross between Peiai 64s and the new plant

type lines had strong heterosis for filled grains per plant, number of spikes per plant

and spikelet weight per plant, but heterosis for spike fertility was low. Xiao et al.

(1996) indicated that heterosis in F1 hybrids for spikelet/panicle showed a positive and

significant correlation with genetic distance in indica x indica but not in indica x

japonica crosses. Tomar et al. (2000); found that the correlation estimates were

highest between harvest index and 1000-grain (48.71) followed by yield/plant and

number of grains/panicle (44.71), flag leaf length and plant height (43.15), and

number of grains/panicle and panicle length (41.72). A negative correlation was found

between biological yield and harvest index (−39.41), 1000-grain weight and number

24

of grains/panicles (−33.31), 50% flowering and panicle length (−2.74) and 50%

flowering and number of primary branches/panicle (−2.94). The yield/plant had

positive association with plant height, number of effective tillers, panicle length,

primary branches/panicle, number of grains/panicle and 1000-grain weight, harvest

index, biological yield, flag leaf length and its width and days to 50% flowering.

Jiang et al. (2000); observed the importance of number of tillers/plant influencing

yield. Productive tillers/hill showed significant positive correlations with correlations

with spikelet yield (Reddy and Kumar, 1996). Mehetre et al. (1996); concluded that

correlation analyses showed that filled grains/panicle, plant height and panicle length

are important characters for selection in breeding programs. Multivariate analysis

showed that the 75 M2 families had formed 14 genetically diverse groups. Sawant and

Patil (1995), evaluated 75 genotypes of rice and found high coefficient of variation for

spikelet yield per plant. High value of heritability coupled with high expected genetic

advance was observed for spikelet yield per plant.

Chaubey and Richharia (1993), studied simple correlation on eight quantitative

characters in 80 indica rice varieties, including HYV and indigenous high quality rice

in two environments each at two locations during rainy season. Grain yield per plant

showed significant positive correlation with plant height, panicle length, spikelet per

panicle, panicle weight, and test weight. Bai et al. (1992); reported that spikelet yield

per plant positively correlated with numbers of positively tillers and number of

spikelet per plant. Dhaliwal et al. (1992); revealed that number of grains per panicle,

number of panicles per plant, panicle length and 100-grain weight showed positive

and significant correlation with grain yield.

Palaniswamy and Kutty (1990), showed that panicle was negatively correlated with

flowering duration and positively with tiller height. Ghosh and Hossain (1988),

reported that effective tillers/plant, number of spikelets/panicle and spikelet weight as

the major contributory characters for spikelet yield it had positive correlations with

number of productive tillers per plant. Associations of various yield components in

rice (Padmavathi et al., 1986) indicated that the plants with large panicles tend to have

a high number of fertile spikelet. Similarly, a positive correlation was observed

between number of panicle/plant and panicle length.

25

The degree of correlation among the characters is an important factor especially in

economic and complex character such as yield. Steel and Torrie (1984), stated that

correlations are measures of the intensity of association between traits. The selection

for one trait results in progress for all characters that are positively correlated and

retrogress for traits that are negatively correlated. Knowledge of correlation between

yield and its contributing characters is absolutely essential to find out guidelines for

plant selection. The existing relationships between traits are, generally determined by

the genotypic, phenotypic and environmental correlations.In rice, grain yield depended

on various growth and component traits, and was the outcome of a combination of different

yield components, such as the panicle number per plant, the filled grains per panicle and the

weight per grain (Yoshida, 1983a).

Kaul and Kumar (1982), reported high genotypic coefficient of variation and high

heritability of plant height. Plant height is considered as an important plant character

related to yield in rice. Plant height was found to vary from variety to variety in rice.

In addition, a number of other agronomic such as plant height, leaf area, dry-matter yield,

heading date, lodging resistance and proneness to shattering influence grain yield directly or

indirectly (Griffiths, 1965). Grain yield is the product of number of tillers per plant, thousand

grain weight and number of grains per panicle when each of these characters were measured

without error (Johnson et al., 1955a). It is therefore, valued to estimate the magnitude of

correlations among the yield and yield component trait parameters.

2.5 Path coefficient analysis

Path coefficient analysis is a very important statistical tool that indicate which

variables (causes) exert influence on other variables, while recognizing the impacts of

multicollinearity (Akanda and Mundt, 1996). Path coefficient analysis separates the

direct effects from the indirect effects through other related traits by partitioning the

correlation coefficient (Sravan, 2012). It requires a cause and effect situation among

variables. Mulugeta et.al. (2012); stated that, from the path coefficient analysis in

rice, it was revealed that grains per panicle (2.226) exhibited maximum positive direct

effect on grain yield followed by days to 50% flowering (1.465), panicle length

(0.641), total spikelet fertility (0.269) and plant height (0.087). The direct effects of

days to 85% maturity, tillers per plant, panicles per plant, spikelets per panicle and

thousand grains weight were negative. Panicle length, tillers per plant, panicles per

26

plant, spikelet per panicle and total spikelet fertility had positive indirect effect on

grain yield through grains per panicle. The indirect effects of grains per panicle

through other traits indicated that direct selection using grains per panicle to select

high yielding genotypes would be effective. Grains per panicle showed the highest

positive direct effect and genotypic correlation (r = 0.906) with grain yield. This

strong genetic correlation resulted in high positive direct effect on grain yield.

Rarangare et al. (2012); evaluated forty exotic and Indian rice germplasm including

one local check for their efficiency with respect to eleven yield and yield contributing

characters from kharif, 2009 under normal condition. Thus study for improvement of

yield was used through path coefficient analysis and result revealed that biological

yield per plant, harvest index, number of fertile tiller per plant, days to 50%

flowering, test weight, days to maturity and panicle length all had important role in

the improvement of grain yield in rice at genotypic and phenotypic levels.

Seyoum et al (2012); conducted a field experiment using rice genotypes during the

main rainy season of 2009 and 2010 at three rain fed upland locations of Southwest

Ethiopia to estimate the path coefficient of grain yield and yield contributing traits in

upland rice. They showed that grain per panicle had maximum positive direct effect.

In order to estimate genetic variability and relationships among some agronomic traits

of rice an experiment were conducted with 30 varieties of rice under two irrigation

regimes by Abarshahr et al. (2011). Path analysis for paddy yield indicated that the

number of spikelet per panicle and flag leaf length had positive direct effects and days

to complete maturity and plant height had negative direct effects on paddy yield under

optimum irrigation condition, while flag leaf width and number of filled grains per

panicle had positive direct effects and days to 50% flowering had negative direct

effect on paddy yield under drought stress condition.

According to Akinwale et al. (2011); Sadeghi (2011), the path coefficient analysis

furnishing the cause and effect of different yield component would provide better

index for selection rather than mere correlation coefficients. Path coefficient analysis

partitions the genetic correlation between yield and its component traits into direct

and indirect effects and hence has effectively been used in identifying useful traits as

selection criteria to improve grain yield in rice.

27

Chakraborty et al. (2010); studied on the path analysis of 29 genotypes of rice. Path

analysis based on genotypic correlation coefficients elucidated high positive direct

effect of harvest index (0.86), panicle length (0.2560) and 100-grain weight (0.1632)

on yield per plant with a residual effect of 0.33. Plant height and panicles per plant

recorded high positive indirect effect on yield per plant via harvest index whereas

effective grains per panicle on yield per plant via harvest index and panicle length.

Hairmansis et al. (2010); evaluated agronomic characters and grain yield of nine

advanced rice breeding lines and two rice varieties for four cropping seasons in dry

season (DS) 2005, wet season (WS) 2005/2006, DS 2006, and DS 2007. Result from

path analysis revealed that the following characters had positive direct effect on grain

yield, i.e. number of productive tillers per hill (p = 0.356), number of filled grains per

panicle (p = 0.544), and spikelet fertility (p = 0.215). Plant height had negative direct

effect (p = -0.332) on grain yield, while maturity, number of spikelet per panicle and

1000-grain weight showed negligible effect on rice grain yield.

Yadav et al. (2010); carried out a field experiment was to establish the extent of

association between yield and yield components and others characters in rice. They

found that the path coefficient at genotypic level revealed that harvest index,

biological yield, number of tillers per hill, panicle length, number of spikelet per

panicle, plant height and test weight had direct positive effect on seed yield per hill

indicating these are the main contributors to yield. Wattoo et al. (2010); conducted an

experiment in order to determine the associations among yield components and their

direct and indirect influence on grain yield of rice. 30 genotypes collected from

different sources were tested. Path analysis revealed that days to maturity had the

highest direct effect (0.751) on grain yield per plant. In addition, the yield components

had positive direct effect on grain yield except the days to heading (-0.834). The order

of yield components was the number of productive tillers per plant, flag leaf area and

1000 grain weight.

Kumar et al. (2009); carried out an experiment to study the selection criteria for

selecting high yielding genotypes in two different early segregating F2 and F3

populations by estimating heritability and genetic correlation between yield and its

28

main economic traits in their subsequent F3 and F4 generations of two crosses in rice.

Path coefficients analysis confirmed that spikelet/main panicle was important yield

determinants followed by 100-grain weight as evident from their magnitude of direct

contribution to grain yield. Rokonuzzaman et al. (2008); evaluated twenty modern

Boro rice varieties with a view to find variability and genetic association for grain

yield and yield components character. The experiment was conducted at BRRI farm

during the Boro season of 2004. Path coefficient showed that number of effective

tiller per plant and plant height are the characters that contribute largely to grain yield.

Ashvani et al. (2007); carried out Path analysis in 22 genotypes of rice, grown in

Hardwar, Uttaranchal, India, during kharif, 1990-91. They found that days to

flowering had the highest positive direct effect on spikelet yield. Thus greater

emphasis should be given for selection of these characters. Kishore et al. (2007);

conducted an experiment during kharif, 2004 in Hyderabad Andhra Pradesh, India

with 70 rice genotypes, including aromatic and non-aromatic lines. Path coefficient

analysis revealed that days to flowering showed positive direct effects on spikelet

yield.

Path coefficient analysis is used in plant breeding programs to determine the nature of

relationships between yield and yield components that are useful as selection criteria

to improve the crop yield. The goal of the path analysis is to accept descriptions of the

correlation between the traits, based on a model of cause and effect relationship and to

estimate the importance of the affecting traits on a specific trait. Guo et al. (2002),

studied the genetic relationships between rice yield and its components using

correlation and path analyses involving a set of 241recombinant inbreed lines (RIL)

population of Shanyou 63, Data were recorded for 1000- spikelet weight (TGW) and

it showed tremendous transgressive variation.

Iftekharuddaual et al. (2001) reported that high number of grins per panicle, bold

grains, more panicles per m2 and higher harvest index had positive and higher direct

effect on grain yield. Moreover, days to maturity, days to flowering, plant height and

spikelet per panicle had positive and higher indirect effect on grain yield through

grains per panicle.

29

29

CHAPTER III

MATERIALS AND METHODS

The details of different populations used and methodology followed during the

experimental period are described in this chapter as follows:

3.1 Experimental site

The experiment was conducted at the experimental field of Sher-e-Bangla Agricultural

University, Dhaka-1207. The location of the experimental site was situated at 23045/ N

latitude and 90023/ E longitude with an elevation of 8.45 meter from the sea level.

Photograph showing experimental sites (Appendix I).

3.2 Soil and climate

The experimental site was situated in the subtropical zone. The soil of the experimental

site belongs to Agro-ecological region of “Madhupur Tract” (AEZ No. 28). The soil was

clay loam in texture and olive gray with common fine to medium distinct dark yellowish

brown mottles. The pH was 5.47 to 5.63 and organic carbon content is 0.82% (Appendix

II). The records of air temperature, humidity and rainfall during the period of experiment

were noted from the Bangladesh Meteorological Department, Agargaon, Dhaka

(Appendix III).

3.3 Experimental Materials

Sixteen (16) populations of F4 generation including four check varieties (BR21, BR26,

BRRI dhan 42 and BRRI dhan 43) were used as experimental materials.

30

Table 1. Materials (T. Aus rice genotypes) used in the experiment

Genotypes Populations Source

G1 BR 21×BR 26, F4,S6 P6 SAU

G2 BR 21× BRRI dhan 28,F4, S5 P8 SAU

G3 BR 21× BRRI dhan 28,F4,S5P9 SAU

G4 BR 21× BRRI dhan29,F4,S1 P2 SAU

G5 BR 21× BRRI dhan 29,F4,S1 P5 SAU

G6 BR 21× BRRI dhan 29,F4,S6 P3 SAU

G7 BR 21× BRRI dhan 29,F4,S6 P8 SAU

G8 BR 21× BRRI dhan 29,F4,S6 P9 SAU

G9 BR 21× BRRI dhan 29,F4,S6 P10 SAU

G10 BR 21× BRRI dhan 29,F4,S7 P1 SAU

G11 BR 21× BRRI dhan 29,F4,S7 P2 SAU

G12 BR 24× BRRI dhan 29,F4,S5 P8 SAU

G13 BR 24× BRRI dhan 29,F4,S5 P9 SAU

G14 BR 24× BRRI dhan 29,F4,S5 P10 SAU

G15 BR24× BRRI dhan 36,F4,S7 P8 SAU

G16 BR26× BRRI dhan 36,F4, S7 P10 SAU

G17 BR 21 BRRI

G18 BR26 BRRI

G19 BRRI dhan 42 BRRI

G20 BRRI dhan 43 BRRI

31

3.4 Methods

The following precise methods have been followed to carry out the experiment:

3.4.1 Germination of seed

Seed of all collected rice genotypes soaked separately for 24 hours in clothes bag. Soaked

seeds were picked out from water and wrapped with straw and gunny bag to increase the

temperature for facilitating germination. After 72 hours seeds were sprouted properly

3.4.2 Seedbed preparation and seedling rising

The seed bed was prepared well by puddling the wetland with repeated ploghing

following by laddering. Sprouted seeds were sown seperately in the previously wet seed

bed on April, 2013. Proper care was taken so that there was no infestation of pest and

diseases and no damage by birds.

3.4.3 Land preparation for transplanting

The experimental plot was prepared by several ploughing and cross ploughing followed

by laddering and harrowing with tractor and power tiller to bring about good tilth. Weeds

and other stubbles were removed carefully from the experimental plot and leveled

properly.

3.4.4 Application of manure and fertilizer

The fertilizers N, P, K, S and B in the form of urea, TSP, MP, Gypsum and Borax

respectively were applied. The entire amount of TSP, MP, Gypsum, Zinc Sulphate and

Borax were applied during final preparation of field. Urea was applied in two equal

installments before sowing and flowering (Table 2).

3.4.5 Experimental design and layout

Field lay out was done after final land preparation. The experiment was laid out in

Randomized Complete Block Design (RCBD) with three replications. The total area of

32

the experiment was 28 m26.79 m = 750 m2. The spacing between lines to line was 75

cm. Seeds were sown in lines in the experimental plots on May, 2014.

Table 1.Dose and method of application of fertilizers used in rice field

Fertilizers Dose( kg/ha) Applantication (%)

Basal 1st installment 2

nd installment

Urea 127 33.33 33.33 33.33

TSP 52 100 -- --

MP 60 100 -- --

Gypsum 0 100 -- --

Borax 0 100 -- --

Source: BRRI ( 2014)

3.4.6 Transplanting

The check varieties and parents were first transplanted randomly in each block. Then

experimental genotypes (16 F4 cross materials) were tranplanted randomly to the

remaining plots. Each entry was grown as single seedling per hill in the rows on May,

2014 with a spacing of 25 cm between rows and 20 cm between plants.

3.4.7 Intercultural operations and after care

After establishment of seedlings, various intercultural operations were accomplished for

better gowth and development of the rice seedlings.

i. Irrigation and drainage

Flood irrigation was given to maintain a constant level of standing water upto 6

cm in the early stages to enhance tillering, proper growth and development of the

seedlings and 10-12 cm in the later stage to discourge late tillering. The field was

finally dried out 15 days before harvesting.

33

ii. Gap filling

First gap filling was done for all of the plots at 10 Days after transplanting (DAT).

iii. Weeding

Weeding was done to keep the plots free from weeds, which ultimately ensured

better growth and development. The newly emerged weeds were uprooted

carefully at tillering stage and at panicle initiation stage by mechanical means.

iv. Top dressing

After basal dose, the remaining doses of urea were top-dressed in 2 equal

installments. The fertilizers were applied on both sides of seedlings rows with the

soil.

v. Plant Protection

Diazinon 57 EC was applied at the time of final land preparation and later on

other insecticides were applied as and when necessary.

3.4.8 Crop harvesting

Harvesting was done depending upon the maturity. When 80% of the plants showed

symptoms of maturity i.e., straw color of panicles, leaves, stems desirable seed color, the

crop was assessed to attain maturity. Ten plants were selected at random from F4

progenies in each replication. The plants were harvested by uprooting and then they were

tagged properly. Data were recorded on different parameters from these plants. Variation

at flowering and ripening stage of different genotypes are presented in Plate no. 3, 4, 5

and 6.

3.4.9 Data collection

For studying different genetic parameters and inter-relationships, fourteen characters

were taken into consideration. The data were recorded on ten selected plants for each

cross and ten selected plants for each parent on the following traits-

34

i. Days to flowering

Difference between the dates of transplanting to the date of 50% flowering of a

plot was counted and was recorded when 50% plant of a plot were at the

flowering stage. Plate 1(a) showing variation at days to flowering.

ii. Days to maturity

Maturities of the crops of different combination were recorded considering the

symptom such as moisture content of rice, color changing of the plant from

greenish to straw colored appearance. Plate 2(b) showing variation at maturity

stage.

iii. Plant height (cm)

The height of plant was recorded in centimeter (cm) at the time of harvesting. The

height was measured from the ground level to the tip of the panicle.

iv. Number of total tillers per plant

The total number of panicle bearing tillers were counted from each of the sample

hills and average was taken.

v. Number of effective tillers per plant

The number of effective tiller per plant was counted as the number of panicle

bearing tillers per plant and average value was recorded.

vi. Panicle length (cm)

The length of panicle was measured with a meter scale from 10 selected plants

and the average value was recorded as per plant.

vii. Number of primary branches per panicle

Primary branches were counted from one panicle of each of the randomly selected

10 plants and the average value was recorded.

viii. Number of secondary branches per panicle

Secondary branches were counted from one panicle of each of the randomly

selected 10 plants and the average value was recorded.

ix. Number of filled grains per panicle

Presence of endosperm in spikelet was considered as filled grain and total number

of filled grains present on main panicle was counted and average was taken.

35

x. Total number of spikelet per panicle

The total number of filled grains and unfilled grains were collected randomly

from selected 10 plants of a plot and then average numbers of total spikelet per

panicle was recorded.

xi. Yield per plant (g)

Grains obtained from each plant were sun dried and weighted carefully. The dry

weight of gains per plant was then recorded.

xii. 1000-seed weight (g)

One thousand seeds were counted randomly from the total cleaned harvested

seeds and then weighted in grams and recorded.

xiii. Yield per hectare (t)

Grains obtained from each unit plot were sun dried and weighted carefully and

converted to ton per hectare.

3.4.10 Statistical analysis

All the collected data of the study were used to statistical analysis for each character,

analysis of variance (ANOVA), mean, range were calculated by using MSTATC

software program and then phenotypic and genotypic variance was estimated by the

formula used by Johnson et al. (1955). Heritability and genetic advance were measured

using the formula given by Singh and Chaudhary (1985) and Allard (1960). Genotypic

and phenotypic co-efficient of variation were calculated by the formula of Burton (1952).

Genotypic and phenotypic correlation coefficient was obtained using the formula

suggested by Johnson et al. (1955); and path co-efficient analysis was done following the

method outlined by Dewey and Lu (1959).

36

Plate 1. Photograph showing raising of seedling at seedbed

Plate 2. An overview of experimental field

37

i) Estimation of genotypic and phenotypic variances

Genotypic and phenotypic variances were estimated according to the formula of Johnson

et al. (1955).

a. Genotypic variance, r

MSEMSGgδ2

Where, MSG = Mean sum of square for genotypes

MSE = Mean sum of square for error and

r = Number of replication

b. Phenotypic variance, egp 222

Where, g2 = Genotypic variance,

g2 = Environmental variance = Mean square of error

ii) Estimation of genotypic co-efficient of variation (GCV) and phenotypic co-

efficient of variation (PCV)

Genotypic coefficient of variation (GCV) and Phenotypic coefficient of variation (PCV)

were calculated following formula as suggested by Burton (1952):

Genotypic coefficient of variance (%) = 𝜎𝑔

𝑥 × 100

Where,

𝜎𝑔 = genotypic standard deviation

𝑥 = population mean

Phenotypic coefficient of variance (% )= 𝜎𝑝ℎ

𝑥 × 100

Where,

𝜎𝑝ℎ= phenotypic standard deviation

𝑥 = population mean

38

iii) Estimation of heritability

Heritability in broad sense was estimated following the formula as suggested by Johnson

et al. (1955):

Heritability (%) = 𝜎𝑔

2

𝜎𝑝2 × 100

Where,

𝜎𝑔2 = genotypic variance

𝜎𝑝2 = phenotypic variance

iv)Estimation of genetic advance

The following formula was used to estimate the expected genetic advance for different

characters under selection as suggested by Allard (1960):

𝐺𝐴 =𝜎𝑔

2

𝜎𝑝ℎ2 × 𝐾. 𝜎𝑝

Where,

GA = Genetic Advance

𝜎𝑔2 = genotypic variance

𝜎𝑝2= phenotypic variance

𝜎𝑝 = phenotypic standard deviation

𝐾 = Selection differential which is equal to 2.64 at 5% selection intensity

v) Estimation of Genetic advance in percentage of mean

Genetic advance in percentage of mean was calculated by the following formula given by

Comstock and Robinson (1952):

Genetic advance in percentage of mean = Genetic advance

𝑥 × 100

Where,

𝑥 = population mean

39

vi) Estimation of Correlation

Simple correlation was estimated with the following formula (Singh and Chaudhury,

1985):

𝑟 = 𝑥𝑦 −

𝑥. 𝑦𝑁

𝑥2 − 𝑥 2

𝑁 𝑦2 − 𝑦 2

𝑁

12

Where,

= Summation

x and y are the two variables

N= Number of observations

vii) Path co-efficient analysis

Path co-efficient analysis was done according to the procedure employed by Dewey and

Lu (1959) also quoted in Singh and Chaudhury (1985), using simple correlation values.

In path analysis, correlation co-efficient is partitioned into direct and indirect of

independent variables on the dependable variable.

In order to estimate direct and indirect effect of the correlated characters, say x1, x2, x3

yield y, a set of simultaneous equations ( three equations in this example) is required to

be formulated as given below:

ryx1= Pyx1+ Pyx2rx1x2+ Pyx3rx1x3

ryx2= Pyx1rx1x2+ Pyx2+ Pyx3rx2x3

ryx3= Pyx1rx1x3+ Pyx2rx2x3+ Pyx3

Where, r’s denotes simple correlation co-efficient and P’s denote path co-efficient

(unknown). P’s in the above equations may be conveniently solved by arranging them in

matrix form. Total correlation, say between x1 and y is thus partitioned as follows:

Pyx1 = the direct effect of x1 on y

Pyx1rx1x2 = the indirect effect of x1 via x2 on y

Pyx1rx1x3 = the indirect effect of x1 via x3 on y

40

After calculating the direct and indirect effect of the characters, residual effect(R) was

calculated by using the formula given below (Singh and Chaudhury, 1985):

P2RY = 1 – Piy. riy

Where,

P2RY = (R

2); and hence residual effect, R = (P2RY)

1

2

Piy = Direct effect of the character on yield

riy = Correlation of the character with yield

Statistical packaged used

The various statistical packages were used for data analysis and these are MS Excel 2007

(Microsoft) MSTATC for windows.

41

CHAPTER IV

RESULTS AND DISCUSSION

The present study was carried out with a view to determine the variability among 16

F4 rice populations and four check varieties of Oryza sativa L. and also to study the

correlation and path coefficient for seed yield and different yield contributing

characters. The data were taken down on different characters such as days to 50%

flowering, days to 80% maturity, plant height (cm), total no. of tiller per plant, no. of

effective tiller per plant, panicle length (cm) per plant, no. of primary branches per

plant, no of secondary branches per plant, total no. of spikelet per panicle, no. of filled

grain per panicle, yield per plant (g) dry, 1000 seed weight (g) and yield (t/ha). The

data were statistically scrutinized and thus obtained results are illustrated below under

the following heads:

4.1 Analysis of variance among 16 F4 populations and four check

varieties of rice for yield related traits

The analysis of variance of 20 genotypes (16 F4 populations and 4 check varieties) of

rice for yield related different characters are presented in Table 3. The analysis of

variance for the characters indicated the extant of significant differences among the

genotypes examined revealing that sufficient variability was present and selection

would be potential to develop the varieties. Rice genotypes under this experiment are

listed in Table 1.

4.1.1 Days to 50% flowering

The analysis of variance (ANOVA) showed significant mean sum of square due to

genotypes difference (191.62**) for days to 50% flowering (Table 3). Highly

significant differences in days to 50% heading was observed among rice genotypes

ranging from 66.33 to 97.67 days. The genotypes G4, G5, G6, G7, G8, G9 and G11

headed earlier with 66-79 days. Whereas, days to 50% flowering in check varieties

were observed in 86.33 days in BR21, 85.67 days in BR 26, 81.33 days in BRRI dhan

42 and 81.33 days in BRRI dhan 43 (Table 3). So, days to flowering of G11 (BR 21 ×

BRRI dhan 29, S7P2) was minimum in comparison to four check varieties. The others

least days of flowering was recorded in G5 (BR 21 × BRRI dhan 29, S1P5), G4 (BR

21 × BRRI dhan 29, S1P2) and in G7 (BR 21 × BRRI dhan 29, S6P8). The remaining

genotypes showed different flowering time (Table 4) (Plate 3 & 4).

42

Table 3. Analysis of variance (ANOVA) for yield and its related characters of 20

genotypes in Oryza sativa L.

Sl.No.

Characters

Mean sum of squares (MSS)

Replication Genotypes Error

2 (df) 19 (df) 38 (df)

1 50%flowering 8.15 191.62** 0.68

2 80%maturity 0.52 178.67** 2.24

3 Plant Height (cm) 20.37 334.77** 14.06

4 Total no. of tiller/ plant 0.14 6.38** 0.14

5

No. of effective tiller/

plant 0.07 6.43** 0.14

6 Panicle length (cm)/plant 0.25 10.58** 1.47

7

No. of primary

branches/panicle 0.43 3.45** 0.27

8

No. of secondary

branches/ panicle 1.69 45.82** 2.24

9

Total no. of spikelet/

panicle 341.84 1136.15** 25.15

10

No. of filled grain of

main tiller 107.01 1138.28** 25.38

11 Yield/ Plant (gm) 3.14 34.33** 0.75

12 1000 seed weight (gm) 3.81 14.74** 1.60

13 Yield ( ton/ hectare) 0.08 2.09* 0.03

* = Significant at 5% level of probability, ** = Significant at 1% level of probability

43

Table 4. Mean performance of yield and yield contributing characters of 16 F4 rice population and 4 check varieties

Genotypes 50%flowering(d

ays)

80%maturity(d

ays)

Plant height

(cm)

Total no. of

tiller/ plant

No. of effective

tiller/ plant

Panicle length

(cm)/plant

No. of primary

branches/panicle

G1 82.67 104.67 126.04 10.63 9.57 22.71 10.23

G2 78.33 104.67 116.64 10.80 9.73 21.60 9.20

G3 92.33 118.67 151.63 9.93 8.87 23.64 11.60

G4 73.00 95.33 112.76 11.63 10.57 20.52 9.17

G5 72.67 96.00 113.02 10.27 9.20 21.03 9.17

G6 79.00 104.00 125.49 15.17 14.10 23.58 9.10

G7 75.33 101.00 119.84 12.70 11.63 22.49 9.07

G8 75.67 99.67 124.56 11.33 10.27 21.63 10.00

G9 77.00 100.33 123.21 11.27 10.20 21.52 10.73

G10 84.00 108.33 129.31 13.33 12.27 23.40 11.73

G11 66.33 92.00 124.38 10.63 9.57 24.46 10.40

G12 78.67 103.67 129.57 12.53 11.43 26.31 12.43

G13 98.33 118.33 138.98 10.87 9.77 26.21 11.43

G14 81.33 105.67 116.85 10.87 9.77 25.13 10.30

G15 97.67 122.33 145.60 10.67 9.57 25.41 10.60

G16 84.00 107.33 124.28 10.53 9.43 26.22 10.07

G17 86.33 109.67 123.24 9.27 8.17 22.31 8.40

G18 85.67 109.00 108.82 8.47 7.37 23.89 9.73

G19 81.33 103.33 122.12 10.53 9.43 21.38 9.17

G20 81.33 104.67 120.86 11.40 10.30 21.33 9.50

Maximum 97.67 122.33 151.63 15.17 14.10 26.31 12.43

Minimum 66.33 92.00 108.82 8.47 8.17 20.52 8.40

Mean 81.55 105.43 124.86 11.14 10.06 23.24 10.10

CV %( Coefficient

of variation) 1.01 1.42 3.00 3.39 3.78 5.22 5.14

44

Table 4 (cont’d)

Genotypes No. of secondary branches/

panicle

Total no. of spikelet/

panicle

No. of filled grains of

main tiller

Yield/ Plant (g) 1000 seed weight (g) Yield ( ton/ hectare)

G1 25.10 111.03 99.13 20.99 20.99 4.08

G2 27.97 84.33 72.43 17.40 17.40 3.45

G3 28.20 95.33 83.43 17.49 17.49 4.13

G4 22.73 114.00 102.10 23.43 23.43 2.52

G5 26.37 94.57 82.67 19.96 19.96 3.37

G6 30.67 147.23 135.33 27.54 27.54 6.08

G7 27.07 137.03 125.13 23.55 23.55 4.68

G8 21.07 119.63 107.73 22.35 22.35 3.61

G9 24.57 127.23 115.33 23.48 23.48 4.24

G10 29.33 147.80 135.90 24.81 24.81 5.30

G11 21.90 124.13 112.23 22.45 22.45 3.93

G12 33.33 139.50 127.60 24.13 24.13 4.37

G13 34.83 96.73 84.67 16.89 16.89 2.69

G14 25.83 121.33 109.27 18.33 18.33 3.34

G15 31.43 83.37 71.30 16.60 16.60 3.50

G16 27.13 125.70 113.63 27.12 27.12 3.26

G17 26.97 126.33 114.27 25.00 25.00 4.21

G18 24.90 130.37 118.30 24.14 24.14 3.44

G19 20.00 105.30 93.23 23.36 23.36 3.36

G20 24.67 105.17 93.10 25.35 25.35 4.10

Maximum 34.83 147.80 135.90 27.54 27.43 6.08

Minimum 20.00 83.40 71.30 16.60 18.87 2.52

Mean 26.70 116.81 104.84 22.22 22.67 3.88

CV % 5.61 4.29 4.81 3.89 5.57 4.23

45

4.1.2 Days to maturity

Analysis of variance for days to 80% maturity showed significant mean sum of square

(178.67**) due to genotypes difference (Table 3). The mean value ranged from 92 to

122 days. G11 was earlier in maturity (92 days) followed by G4 (95.33days), G5

(96.00 days) and G8 (99.67 days). Days to 80% maturity among the check varieties

were observed 103.33 days in BRRI dhan 42, 104.67 days in BRRI dhan 43, 109.00

days in BR 26 and 109.67 days in BR 21 (Table 4). So, days to maturity of G11 (BR

21×BRRI dhan 29, S7P2) was lower than check varieties. Therefore, G11 (BR

21×BRRI dhan 29, S7P2) was suitable for selection as early Aus lines. Variation in

days to maturity in different genotypes had also been reported by Sabouri et al.

(2008). Days to maturity showed almost the same trend with days to heading. Yaqoob

et al. (2012) also observed that early headed genotypes matured earlier. Variation of

yield per plant in 20 genotypes of F4 generation is presented in Figure 1 (Plate 5 & 6).

4.1.3 Plant height (cm)

Analysis of variance for plant height showed significant mean sum of square

(334.77**) due to genotypes difference (Table. 3). Genotypes significantly differed in

plant height that ranged from 108.82 to 151.63 cm. Maximum height (151.63 cm) was

recorded in G3 (110.8 cm) and G18 (BR 26) was the shortest (108.82 cm) (Table 2).

G2 (BR 21×BRRI dhan 28, F4, S5P8), G4 (BR21 × BRRI dhan29,F4, S1P2), G5 (BR

21×BRRI dhan 29,F4,S1P5) and G14 (BR 24×BRRI dhan 29 F4,S5 P10) showed shorter

statures with 111.64, 112.76, 113.02 and 116.85 cm of plant height respectively. The

rest of the genotypes showed varying plant height (Table 4). Sabouri et al. (2008)

recommended plant height as an important trait for selection of high yielding rice

plants where as kumar et al. (2014) also reported significant variation in plant height.

46

Figure 1. Variations in days to maturity of 20 genotypes

104.67104.67

118.67

95.33 96104 101 99.67100.33

108.33

92

103.67

118.33

105.67

122.33

107.33109.67 109103.33104.67

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

Days to maturity

47

Plate 3. Photograph showing variation at flowering stage

48

Plate 4. Photograph showing flowering stage in check varieties

49

Plate 5. Photograph showing variation at 80% maturity stage

50

Plate 6. Photograph showing 80% maturity stage in check varieties

51

4.1.4 Number of total tillers per plant

Analysis of variance for days number of total tillers per plant showed significant

(6.38**) mean sum of square due to genotypes difference (Table 3). Out of 20

genotypes of F4 population, the maximum number of tillers per plant (13.33) was

observed in G10 (BR 21 × BRRI dhan 29, F4, S7P1) and the minimum number of

tillers per plant (9.93) was recorded in G3 (BR 21 × BRRI dhan 28, F4, S5P9).

Whereas, the check variety BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 had the

value of number of total tiller per plant of (9.27), (8.47), (10.53) and (11.40)

respectively. The number of total tillers per plant of G6 (BR 21 × BRRI dhan29, S6,

P3) was the highest of other the check varieties. The second maximum number of

tillers per plant (13.33) was observed with G10 (BR 21 × BRRI dhan 29, F4, S7P1)

which was also higher than four check varieties. The rest of the genotypes showed

varying number of tillers per plant (Table 4).

4.1.5 Number of effective tillers per plant

Number of tiller in rice is a major determinant for panicle production and as a result,

it affects total yield. The genotypes, which produced higher number of effective tillers

per plant showed higher grain yield in rice (Dutta et al., 2002). Analysis of variance

for number of effective tillers per plant showed significant (6.43**) mean sum of

square due to genotypes difference (Table 3). The lines with more number of total

tillers showed better number of productive tillers per plant. Significant variations in

number of fertile tillers ranging from 7.37 to 14.10 per plant were observed among the

genotypes under this study. Higher number (14.10) of tillers per plant produced by G6

(BR 21 × BRRI dhan 29, F4, S6P3) followed by 12.27 in G10 (BR 21 × BRRI dhan 29,

F4, S7P1) and 11.63 in G7 (BR 21 × BRRI dhan 29, F4, S6P8). Whereas, the check

variety BR 21, BR 26, BRRI dhan42 and BRRI dhan 43 had effective tillers per plant

(8.17), (7.37), (9.43)and (10.30) respectively. Medium number of fertile tiller per

plant (10.52), (10.27) and (11.43) were observed in G4 (BR 21 × BRRI dhan 29, F4

,S1P2), G8(BR 21× BRRI dhan29, F4, S6P9) and G12 (BR 24 × BRRI dhan 29, F4,

S5P8) respectively (Table 4). Variation of yield per plant in 20 genotypes of F4

generation is presented in Figure 2.

52

Figure 2 .Variations in total number of effective tiller per panicle of 20 genotypes

9.57 9.73

8.87

10.57

9.2

14.1

11.63

10.27 10.2

12.27

9.57

11.43

9.77 9.77 9.57 9.43

8.17

7.37

9.43

10.3

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

53

4.1.6 Panicle length (cm)

Panicle length also differed significantly in different genotypes with a range of 20.52-

26.31 cm in this study. Maximum panicle length 26.31 cm was recorded in G12 (BR

24 × BRRI dhan 29, F4,S5P8) followed by 26.22cm in G16 (BR 26 × BRRI dhan 36,

F4, S7P10)and 26.21 in G13 (BR 24 × BRRI dhan 29, F4, S5P9). It was observed that

genotypes showing longer plant height had also shown long panicles and vice versa.

This might be ascribed due to positive association between plant height and panicle

length. The panicle length of check varieties were 22.31, 23.89, 21.38 and 21.33cm

recorded in BR21, BR 26, BRRI dhan 42 and BRRI dhan 43 respectively. So, panicle

length of G12 (BR 24 × BRRI dhan29, F4, S5P8), G16 (BR 26 × BRRI dhan 36, F4, S7

P10) and G13 (BR 24 × BRRI dhan 29, F4, S5P9) were higher than the check varieties.

The remaining genotypes showed differential panicle length (Table 4). Ullah et al.

(2011) reported similar result.

4.1.7 Number of primary branches per panicle

Analysis of variance for number of primary branches per panicle showed significant

(3.45*) mean sum of square due to genotypes difference (Table. 3). Number of

primary branches per panicle varied significantly in different genotypes with a range

of 8.40-12.43 in this study. Out of 20 genotypes of F4 generations, the highest number

of primary branches per panicle 12.43 was noted in G12 (BR 24 × BRRI dhan 29, F4,

S5P8) which was followed by G10 (BR 21 × BRRI dhan 29, F4, S7P1) (11.73) and the

minimum number of primary branches per panicle 9.07 was recorded in G7 (BR 21 ×

BRRI dhan 29, F4, S6P8). On the other hand, the numbers of primary branches per

panicle of check varieties were 8.40, 9.73, 9.17 and 9.50 observed in BR 21, BR 26,

BRRI dhan 42, and BRRI dhan 43 respectively. So, number of primary branches per

panicle of G12 (BR 24 × BRRI dhan 29, F4, S5P8) and G10 (BR 21 × BRRI dhan 29,

F4, S7P1) was higher than all other the check varieties (Table 4).

54

4.1.8 Number of secondary branches per panicle

Number of secondary branches per panicle differed significantly in different

genotypes with a range of 20.00-34.83 in this study. Out of 20 genotypes of F4

generations, the highest number of secondary branches per panicle was 34.83

observed in G13 (BR 24 × BRRI dhan 29, F4, S5P9) and the lowest number of

secondary branches per panicle was 21.07 recorded in G8 (BR 21 × BRRI dhan 29,

F4, S6P9). Whereas, the number of secondary branches per panicle of the check

varieties BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 were 26.97, 24.90, 20.00

and 24.67 respectively. Hence, the number of secondary branches per panicle of G13

(BR 24 × BRRI dhan 29, F4, S5P9) was higher than four check varieties. The rest of

the genotypes showed varied number of secondary branches per panicle (Table 4).

Diaz et al. (2002) noted wide variation in number of secondary branches per panicle.

4.1.9 Number of filled grains per panicle

Analysis of variance for number of number of filled grains per panicle showed

significant (1138.28**) mean sum of square due to genotypes difference (Table. 3). In

this study, out of 20 genotypes of F4 generations, the highest number filled grains per

panicle was 135.90 observed in G10 (BR 21 × BRRI dhan 29, F4, S7P1) followed by

G6 (BR 21 × BRRI dhan 29, F4, S6P3) (135.33) and the minimum number of filled

grains per panicle was 71.30, noted in G15 (BR 24 × BRRI dhan 36, F4, S7P8).

Whereas, number of filled grains per panicle were 178.6, 171.1, 111.3 and 153.1 in

check varieties BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 respectively. So, the

number filled grains per panicle of G10 (BR 21 × BRRI dhan 29, F4, S7P1) and G6

(BR 21 × BRRI dhan 29, F4, S6P3) were higher than the check varieties. The

remaining genotypes showed differential number of filled grains per panicle (Table

4). Variation of yield per plant in 20 genotypes of F4 generation is presented in Figure

3. Hairmansis et al. (2010) also reported variation in number of filled grains per

panicle in respect of yield.

55

Figure 3 .Variations in total number of filled spikelet per panicle of 20 genotypes

99.13

72.43

83.43

102.1

82.67

135.33

125.13

107.73

115.33

135.9

112.23

127.6

84.67

109.27

71.3

113.63114.27118.3

93.23

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19

56

4.1.10 Total number of spikelet per panicle

Like other traits, total number of spikelet per panicle also differed significantly in

different rice genotypes which ranged from 83.37 to 147.80. Maximum total number

of spikelet per panicle was 147.80, recorded in G10 (BR 21 × BRRI dhan 29, F4, S7P1)

followed by G6 (BR 21 × BRRI dhan 29, F4, S6P3) (147.23), G12 (BR 24 × BRRI

dhan 29, F4, S5P8) (139.50) ) and G7 (BR 21 × BRRI dhan 29, F4, S6P8) (137.03) were

significantly better than rest of the genotypes . On the other hand, number of total

spikelet per panicle were 126.33, 130.37, 105.30 and 105.17 in check varieties BR 21,

BR 26, BRRI dhan 42 and BRRI dhan 43 respectively. The remaining genotypes

showed differential total number of spikelet per panicle (Table 4). Variation of yield

per plant in 20 genotypes of F4 generation is presented in Figure 4. Pandey and

Awasthi (2002) observed similar result.

4.1.11 Yield per plant (g)

In this study yield per plant (g) varied notably in 20 rice genotype. The highest yield

per plant was 27.54 g recorded in G6 (BR 21 × BRRI dhan 29, F4, S6P3) and the

lowest number of yield per plant was recorded 16.60 g in G15 (BR 24 × BRRI dhan

36, F4, S7P8). Whereas, the yield per plant of check varieties BR 21, BR 26, BRRI

dhan 42 and BRRI dhan 43 were 25.00, 24.14, 23.36 and 25.35 g respectively per

plant. Hence, G6 was suitable for selection as it exhibited the better performance over

the check varieties. The rest of the genotypes showed varied number of yield per plant

(Table 4). Variation of yield per plant in 20 genotypes of F4 generation is presented in

Figure 5. Kumar et al. (2014) also observed similar findings.

57

Figure 4.Variations in total number of spikelet per panicle of 20 genotypes

Figure 5.Variations in Yield per plant (g) of 20 genotypes

111.03

84.33

95.33

114

94.57

147.23

137.03

119.63127.23

147.8

124.13

139.5

96.73

121.33

83.37

125.7126.33130.37

105.3105.17

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

20.99

17.417.49

23.43

19.96

27.54

23.5522.35

23.4824.81

22.45

24.13

16.8918.33

16.6

27.12

2524.14

23.36

25.35

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

58

4.1.12 1000 seed weight (g)

Like other traits, thousand grain weight also differed significantly in different rice

lines which ranged from 18.87 to 27.43 g. Maximum grain weight was recorded 27.3g

in G12 and the minimum 1000 seed weight was recorded 18.87 g in G13 (BR 24 ×

BRRI dhan 29, F4, S5P3). The check varieties BR 21, BR 26, BRRI dhan 42 and BRRI

dhan had 1000 seed weight of 23.00, 25.00, 20.75, 23.36 and 25.35 g respectively.

So, the 1000 seed weight of G7 (BR 21 × BRRI dhan 28, F4, S5P2) was higher than all

the check varieties. The rest of the genotypes exhibited differential 1000 seed weight

in (Table 4). Variation of thousand seed weight in 20 genotypes of F4 generation is

presented in Figure 6.

4.1.13 Yield (t / ha)

Yield is the most excellent trait and all the research work and objectives are

dependent on yield. In this study grain yield of genotypes ranged from 2.52 to 6.08

ton per hectare. Higher grain yield (6.08 ton/ ha) was harvested from G6 (BR21×

BRRI dhan 29, F4, S6P3), followed by G10 (BR21× BRRI dhan 29, F4, S7P1) (5.30 t/

ha) and G7 (BR21× BRRI dhan 29, F4, S6P8) ( 4.68 t/ ha ) and the minimum number

of yield was recorded 2.52 t/ha in G4 (BR21× BRRI dhan 29, F4, S1P2). Lafarge et al.,

(2009) reported significant variation in yield and other traits of rice genotypes grown

under irrigated and rain fed conditions. The yield of four check varieties were 42.21

t/ha in BR 21, 3.44 t/ha in BR 26, 3.36 t/ha in BRRI dhan 42 and 4.10 t/ha in BRRI

dhan 43. Therefore, G6 (BR21× BRRI dhan 29, F4, S6P3) was promising for selection

as it showed the better performance over the check varieties. Additionally, the yield of

G10 (BR21× BRRI dhan 29, F4, S7P1) (5.30 t/ha) and G7 (BR21× BRRI dhan 29, F4,

S6P8) (4.68 t/ha) were also higher than check varieties. Thus, G10 and G7 were also

suitable for selection. The rest of the genotypes showed differential yield (t/ha) (Table

4). Rafiqul (2014) observed similar findings. Variation of yield per hectare in 20

genotypes of F4 generation is presented in Figure 7.

59

Figure 6. Variations in thousand seed weight (g) of 20 genotypes

Figure 7. Variations in Yield per hectare (ton/ha) of 20 genotypes

19.93

22.5

25.27

19.4

22.9323.6721.722.2722.5

21.3722.16

27.43

18.8719.33

22.8723.66 2325 24.5325.1

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

4.08

3.45

4.13

2.52

3.37

6.08

4.68

3.61

4.24

5.3

3.93

4.37

2.69

3.343.5

3.26

4.21

3.44 3.36

4.1

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20

60

4.2 Estimation of genetic parameter

4.2.1 Estimates of Variance Components

In present investigation, phenotypic variance was higher than the genotypic variances

for all the characters indicating the influence of the environmental factors on these

traits. The slight deviation between GCV and PCV was also reported by Mustafa and

Eisheikh (2007), Kole et al. (2008) and Seyoum et al. (2012). Large differences

reflect high environmental influence, while small differences reveal high genetic

influence. Phenotypic coefficients of variation were slightly higher than the genotypic

co-efficients of variation for all the traits studied. This indicates the presence of slight

environmental influence to some degree in the phenotypic expression of the

characters Pandy et al (2010) and Mulugeta et al. (2012) also observed similar

findings. Genotypic, phenotypic and environmental variance and genotypic,

phenotypic and environmental coefficients of variation are depicted in Table 5.

4.2.1.1 Days to flowering

Phenotypic and genotypic variance for days to flowering was recorded as 96.15 and

95.47 respectively with slight differences between them (Table 5). The genotypic

coefficient of variation (GCV) (11.98) was lower than the phenotypic coefficient of

variation (PCV) (12.02), which exhibited that environment had less effect but gene

had great role on the expression of this trait. There was a little difference between

GCV and PCV on this trait, which might facilitate selection.

4.2.1.2 Days to maturity

Phenotypic and genotypic variance for days to maturity was noted 90.45 and 88.22

respectively with moderate deviation between them, suggested moderate effect of

environment on the expression of the genes controlling this character (Table 5). The

moderate phenotypic coefficient of variation (PCV) (9.02%) was close to genotypic

coefficient of variation (GCV) (8.91%), which indicated that environment had a role

on the expression of this trait that might facilitate selection.

61

Table 5. Estimation of genetic parameters for yield related traits of 16 F4 rice

population and four check varieties

Parameters

σ2p

σ2g

σ2e

PCV

(%)

GCV

(%)

ECV

(%)

Days to flowering 96.15 95.47 0.68 12.02 11.98 1.01

Days to maturity 90.45 88.22 2.24 9.02 8.91 1.42

Plant Height

(cm) 174.42 160.36 14.06 10.58 10.14 3.00

Total no. of

tillers/ plant 3.26 3.12 0.14 16.21 15.85 3.39

No. of effective

tillers/ plant 3.29 3.14 0.14 18.02 17.62 3.78

Panicle length

(cm) 6.02 4.55 1.47 10.56 9.18 5.22

No. of primary

branches/panicle 1.86 1.59 0.27 13.50 12.48 5.14

No. of secondary

branches/ panicle 24.03 21.79 2.24 18.36 17.48 5.61

No. of filled

grains /panicle 580.65 555.50 25.15 20.63 20.18 4.29

Total no. of

spikelet / panicle 581.83 556.45 25.38 23.01 22.50 4.81

Yield/ Plant (g) 17.54 16.79 0.75 18.85 18.44 3.89

1000 seed weight

(g) 8.17 6.57 1.60 12.61 11.31 5.57

Yield (ton/

hectare) 1.06 1.03 0.03 26.51 26.18 4.23

2p = Phenotypic variance, 2g = Genotypic variance and 2e = Environmental variance, GCV =

Genotypic Coefficient of Variation, PCV = Phenotypic Coefficient of Variation and ECV =

Environmental Coefficient of Variation

62

Figure 8. Genotypic and phenotypic coefficient of variation in Oryza sativa L.

12.02

9.02

10.58

16.21

18.02

10.56

13.5

18.36

20.63

23.01

18.85

12.61

26.51

11.98

8.91

10.14

15.85

17.62

9.18

12.48

17.48

20.18

22.5

18.44

11.31

26.18

PCV % GCV%

63

4.2.1.3 Plant height (cm)

The analysis of variance showed highly significant difference among 20 genotypes

(334.77**), studied for plant height at 1% level of probability (Table 3). The

phenotypic variance (174.42) was comparatively higher than the genotypic variance

(160.36). The moderate difference revealed that the environmental factors had

moderate impact on plant height traits of these genotypes. The phenotypic and

genotypic coefficient of variations were 10.58% and 10.14% respectively (Table 5)

(Figure 8), which denoted that the genotypes had relatively moderate and environment

had low influence on this character expression. Ketan and Sarker (2014) also noted

that the PCV was higher than the GCV in this character.

4.2.1.4 Total number of tillers per plant

Phenotypic variance and genotypic variance were noted down as 3.26 and 3.12 (Table

5) respectively. The phenotypic variance appeared to be higher than the genotypic

variance indicated considerable impact of environment on the expression of the genes

controlling this trait. The PCV (16.21) and GCV (15.85) were relatively adjacent

which indicated that the genetic variation existed among the genotypes and

environment had less influence on this character expression. So there might be

available scope for selection. Tuwar et al. (2013) found high GCV and PCV value in

this character.

4.2.1.5 Number of effective tillers per plant

The phenotypic variance of number of tillers per plant (3.29) was greater than

genotypic variance of tillers per plant (3.14) (Table 5). This difference between

phenotypic variance and genotypic variance suggested that environmental impact

(0.14) had present on number of tillers per plant but less. Values of PCV and GCV

were 18.02% and 17.62% respectively. The moderate difference between PCV and

GCV indicated that the genetic variation was minimal among the genotypic variation

and environment had medium influence on this character expression. So there was a

possibility for selection. Similar result for GCV was also observed by Li et al. (1991).

64

4.2.1.6 Panicle length (cm)

Phenotypic variance and genotypic variance for panicle length were 6.02 and 4.55

respectively (Table 5). Lower difference between them indicating that they were less

responsive to environmental aspects for their phenotypic expression and relatively

lower PCV (10.56%) and GCV (9.18%) indicating that the genotype had less

variation for this trait. Padmaja et al (2008) recorded low GCV and PCV along with

little difference between them.

4.2.1.7 Number of primary branches per panicle

Phenotypic variance and genotypic variance were recorded as 1.86 and 1.59,

respectively. The phenotypic variance appeared to be higher than the genotypic

variance indicating considerable influence of environment on the expression of the

genes controlling this trait and relatively low difference between PCV (13.50%) and

GCV (12.48%) value suggested that the apparent variation not only due to genotypes

but also due to the influence of environment (Table 5) (Figure 8). Karim et al. (2007)

observed higher differences between GCV and PCV for this character.

4.2.1.8 Number of secondary branches per panicle

Number of primary branches per panicle exhibited little differences between

phenotypic variance (24.03) and genotypic variance (21.79) indicating lower

environmental (2.24) influence on this trait. Difference between PCV% (18.36 %) and

GCV% (17.48 %) value denoted that the apparent variation not only due to genotypes

but also because of the influence of environment (5.61%) (Table 5). No doubt,

selection would be rewarded for this trait.

4.2.1.9 Total number of spikelets per panicle

Number of spikelet per plant exhibited highest phenotypic variance (580.65) with

genotypic variance (555.50) and low environmental (25.15) influence. The difference

between the PCV% (20.63 %) and GCV% (20.18 %) denoted extant of less

environmental influence among the genotypes (Table 5). Thus selection could be

considered by this trait.

65

4.2.1.10 Number of filled grains per panicle

The phenotypic and genotypic variances for this number of filled grains per panicle

were 581.83 and 556.45 respectively. The phenotypic variance appeared to be higher

than the genotypic variance suggested that considerable influence of environment on

the expression of the genes controlling this character. The value of PCV and GCV

were 23.01% and 22.50% respectively for number of filled grain per panicle which

denoted that medium variation existed among different genotypes (Table 5)

(Figure.8). Similar variability was also found by Fukrei et al. (2011) and Rangare et

al. (2011).

4.2.1.11 Yield per plant (g)

The phenotypic variances and genotypic variances for Yield per plant (g) were 17.54

and 16.79 respectively. The values are adjacent to each other indicated moderate

environmental influences on this trait. The values of PCV and GCV were 18.85% and

18.44% (Table 5), suggested that the genotype had minimal environmental variation

for this trait. High PCV and GCV were reported for this character by Mulugeta et al.

(2012).

4.2.1.12 1000 seed weight (g)

Thousand seed weight showed phenotypic (8.17) and genotypic (6.57) variance with

less differences suggested that they were low responsive to environmental elements.

Phenotypic coefficient of variation (12.61%) and genotypic coefficient of variation

(11.31%) were very close to each other (Table 5). There was a very slight difference

between phenotypic and genotypic coefficient of variation, denoting low

environmental influence on this trait.

4.2.1.13 Yield (ton/ ha)

The phenotypic variance (1.06) tended to be slightly higher than the genotypic

variance (1.03), indicating less influence of environment on the expression of this

trait. The phenotypic coefficient of variation (26.51 %) was higher than the genotypic

coefficient of variation (26.18 %). This suggested that environment had a minor role

(4.23%) on the expression of this trait (Table 5) (Figure.8).

66

4.2.2 Estimation of broad-sense heritability and genetic advance as

percent of mean

4.2.2.1 Days to 50% flowering

High heritability (99.30%), high genetic advance (20.06) with high genetic advance as

percent of mean (24.60) were recorded in 50% flowering days which suggested that

the character was controlled by additive gene action and high heritability denoted that

this character was least influenced by the environmental factors. So selection might be

considered basis of this trait (Table 6).

4.2.2.2 Days to 80% maturity

Days to 80% maturity exhibited high heritability (97.53%) with moderate genetic

advance (19.11) and genetic advance in percentage of mean (18.12%) denoted that

this trait was governed by additive gene action and selection for such trait might be

effective (Table 6).

4.2.2.3 Plant height (cm)

The degree of heritability of this trait was high heritability (91.94%) with high genetic

advance (25.01) and genetic advance in percent of mean (20.03%) (Table 6). These

findings indicated that trait was possibly controlled by additive gene action.

Therefore, selection based on this character might be effective. High heritability and

high genetic advance reported by Subbaiah et al. (2011).

4.2.2.4 Total number of tillers per plant

Number of tiller per plant exhibited high heritability (95.62%) with low genetic

advance (3.56) and high genetic advance in percentage of mean (31.92) (Table 6).

These results denoted the possibility of predominance of additive gene action in the

inheritance of this trait. There was both environmental and genotypic impact on the

trait. This character possessed high variation; it would serve high potential for

effective selection for further genetic improvement.

67

Table 6. Estimation of heritability and genetic advance of 16 F4 populations with

four check varieties of rice

Parameters

Heritability Genetic advance

(5%)

Genetic advance

(% mean)

Days to

50%flowering 99.30 20.06 24.60

Days to

80%maturity 97.53 19.11 18.12

Plant Height (cm) 91.94 25.01 20.03

Total no. of tiller/

plant 95.62 3.56 31.92

No. of effective tiller/

plant 95.60 3.57 35.49

Panicle length (cm) 75.58 3.82 16.44

No. of primary

branches/panicle 85.49 2.40 23.77

No. of secondary

branch/ panicle 90.67 9.16 34.30

No. of filled grain

/panicle 95.67 47.49 40.65

Total no. of spikelet/

panicle 95.64 47.52 45.33

Yield/ Plant (g) 95.73 8.26 37.17

1000 seed weight (g) 80.45 4.74 20.89

Yield ( ton/ hectare) 97.46 2.07 53.23

68

4.2.2.5 Number of effective tiller per plant

Number of effective tiller per plant high heritability (95.60%) with low genetic

advance (3.57) and high genetic advance in percentage of mean (35.49) (Table 6).

These results indicated the possibility of predominance of additive gene action in the

inheritance of this character. Both environmental and genotypic factors influenced the

trait. Therefore, selection based on this character might be effective for increasing

grain yield.

4.2.2.6 Panicle length (cm)

The degree of heritability of panicle length was high (75.58%) with very low genetic

advance (3.82) and moderate genetic advance in percent of mean (16.44) (Table 6).

Which indicated that environmental effect was more than the genotypic effect and due

to additive gene action, selection for further improvement of the trait might be

effective.

4.2.2.7 No .of primary branches per panicle

High heritability (85.49%) and very low genetic advance (2.40) and high genetic

advance in percent of mean (23.77) were shown by this trait (Table 6) .Which

determined the presence of additive gene effect on the character. Thus, selection for

further trail might be wise. Moderate heritability was reported by Biswas et al. (2001).

4.2.2.8 Number of secondary branches per panicle

High heritability (90.67%) with low genetic advance (9.16) and moderate genetic

advance in percentage of mean (34.30%) were found in number of secondary

branches per panicle (Table 6). These findings revealed that the action of additive

gene involved on the expression of this character as well as a scope of improvement

through selection might be rewarding.

69

4.2.2.9 Number of spikelet per panicle

Number of spikelet per panicle exhibited high heritability (95.67%) with high genetic

advance (47.49) and high genetic advance in percent of mean (40.65%) (Table 6). The

trait was controlled by additive genes and selection for this trait might be rewarding.

4.2.2.10 Number of filled grain of main tiller

High heritability (95.64%) with high genetic advance (47.52%) and high genetic

advance in percent of mean (45.33%) were recorded in respect of number of filled

grain of main tiller (Table 6) (Figure 9). The trait was governed by additive genes and

selection for this trait might be rewarding. High heritability with high genetic advance

was also reported by Bisne et al. (2009).

4.2.2.11 Yield per plant

High heritability (95.73%) along with low genetic advance (8.26%) with high genetic

advance in percent of mean (37.17%) indicated that additive gene effect was there

(Table 6) (Figure 9). So selection for this trait might be rewarding. High heritability

with high genetic advance was reported by Chakraborty and Chakraborty (2010).

4.2.2.12 Thousand seed weight (g)

The magnitude heritability of thousand seed weight (g) was high (80.45%) and

genetic advance was low (4.74) and advance in percent of mean was also high

(20.89%) (Table 6) (Figure 9). These result revealed that additive genes involvement

in the expression of the trait and there is a scope of improvement by direct selection.

Similar result was revealed by Ullah et al. (2011).

4.2.2.13 Yield per hectare (ton)

High heritability (97.47%) coupled with low genetic advance (2.07%) and high

genetic advance in percent of mean (53.23%) was recorded in respect of yield per

hectare (Table 6) (Figure 9). These findings exhibited that it was predominated by

additive genes and the environmental influence was low on the trait .Selection for this

trait might be effective for farther improvement.

70

Figure 9. Heritability and Genetic Advance as Percentage of Mean in Oryza sativa L.

99.397.53

91.9495.62 95.6

75.58

85.49

90.67

95.67 95.64 95.73

80.45

97.46

24.6

18.1220.03

31.9235.49

16.44

23.77

34.3

40.65

45.33

37.17

20.89

53.23

Heritability Genetic Advance(% mean)

71

4.3 Correlation coefficient

Most of the characters of interest to breeders are complex and are the result of the

interaction of a number of components. Complete knowledge on interrelationship of

plant character like grain yield with other characters is of paramount importance to

the breeder for making improvement in complex quantitative character like grain

yield for which direct selection is not much effective. Correlation coefficient, forms

the basis for selecting the desirable plant, aiding in evaluation of relative influence of

various component characters on grain yield. Steel and Torrie (1984) stated that

correlations are measures of the intensity of association between traits. The selection

for one trait results in progress for all characters that are positively correlated and

retrogress for traits that are negatively correlated. Correlation coefficient analysis has

been used by breeders to reveal a positive relationship between yield and other traits

that enhance yield in rice genotypes. Grain yield, being a quantitative trait, is a

complex character of any crop. Various morphological and physiological plant traits

contribute to yield. These yield-contributing components are interrelated with each

other showing a complex chain of relationship and highly influenced by the

environmental conditions (Prasad et al., 2001). Breeding strategy in rice mainly

depends upon the degree of associated traits as well. In present study, the correlation

analysis revealed that, the genotypic correlation coefficients were higher than the

phenotypic correlation coefficients demonstrating that, the observed relationships

among the various traits were due to genetic causes indicating that the phenotypic

expression of correlations is reduced under the influence of environment. This is also

in accord with the findings of Sabesan et al. (2009), Jayasudha and Sharma (2010)

and Patel et al. (2014). Estimates of phenotypic, and genotypic, correlation

coefficients between each pair of traits are presented in (Table 7a & 7b) respectively.

The magnitudes of genotypic correlation coefficients for most of the traits were higher

than their corresponding phenotypic correlation coefficients, except in a few cases,

which indicate the presence of inherent or genetic association among various traits.

72

4.3.1 Days to 50% flowering

Days to flowering exhibited highly significant and positive correlation with days to

maturity (G = 0.986**, P = 0.980**), plant height (G= 0.686**, P = 0.666**) and

number of secondary branch per panicle (G=0.603**, P=0.586**) and suggested that

if days to flowering increased then days to maturity, number of secondary branch per

panicle and panicle length also will be increased (Table 7a & 7b). It showed positive

and significant correlation with panicle length (G = 0.511*, P = 0.479*). But it had

significant and positive correlation with number of primary branch per panicle

(G=0.358, P=0.343) and thousand seed weight (G=0.007, P=0.004). It had

insignificant and negative correlation with number of total number tiller per plant (G=

- 0.251,P=- 0.249), total number of effective tiller per plant (G=-0.257, P=0.254),total

number of spikelet per panicle(G=-0.350,P=-0.348) and number of filled grain per

panicle (G=-0.352,P=-0.350) at phenotypic level. Though, it had insignificant and

negative interaction with yield per plant (g) (G=--0.404, P=-0.399). It also showed

insignificant and negative correlation with yield per hectare (t/ha) (G=-0.133, P=-

0.130). Insignificant association of these traits revealed that the association between

these traits is largely influenced by environmental factors (Table 7a & 7b).

4.3.2 Days to maturity

Days to maturity exhibited highly significant and positive correlation with plant

height (G=0.740**, P=0.717**), and number of secondary branch per panicle

(G=0.643**, P=0.621**). It revealed that if maturity increased, then plant height and

number of secondary branch per plant also be increased. Significant positive

interaction with panicle length (G=0.541*, P=0.507), suggested that yield could be

improved by using this character, insignificant and positive interaction with panicle

length. It also showed insignificant and positive correlation with number of primary

branch per panicle (G=0.387, P=0.370) and thousands seed weight (G= 0.085, P=

0.084) at genotypic and phenotypic level (Table 7a & 7b). Positive significant

correlation of this character with grain yield was reported by Akhtar et al. (2011).

Insignificant association of these traits indicated that the association among these

traits was largely influenced by environmental factors. Rangare et al. (2012) reported

positive and significant correlation with yield.

73

Table 7a. Genotypic correlation coefficients among different pairs of yield and yield contributing characters for different

genotypes of Oryza sativa L.

DM PH TILL ETILL PL NPB NSB TS FG TSW GYP YIELD

D50F 0.986** 0.686** -0.251 -0.257 0.511* 0.358 0.603** -0.35 -0.352 0.007 -0.404 -0.133

DM 0.740** -0.211 -0.216 0.541* 0.387 0.643** -0.337 -0.339 0.085 -0.427 -0.041

PH 0.072 0.071 0.527* 0.656** 0.545* -0.272 -0.271 0.125 -0.42 0.157

TILL 1.000** 0.011 0.144 0.315 0.514* 0.516* -0.037 0.376 0.678**

ETILL 0.005 0.142 0.309 0.513* 0.514* -0.04 0.374 0.678**

PL 0.668** 0.683** 0.169 0.168 0.114 -0.147 -0.013

NPB 0.510* 0.086 0.086 0.116 -0.289 0.073

NSB -0.003 -0.004 0.051 -0.251 0.238

TS 1.000** 0.118 0.788** 0.641**

FG 0.118 0.787** 0.642**

TSW 0.367 0.327

GYP 0.493**

**, * Correlation is significant at the 0.01 and 0.05 level, respectively.

D50F= Days to 50% flowering, DM= Days to maturity, PH= Plant height(cm), TILL= Total number of tiller per plant, ETILL= Total number of effective tiller per

plant, PL= Panicle length (cm), NPB= Number of primary branch per panicle, NSB= Number of secondary branch per panicle, TS= Total number of spikelet per

panicle, FG= Number of filled grain per panicle, GYP= Yield per plant (g), TSW= Thousand seed weight (g) and YIELD= Yield (ton/ha)

74

Table 7b. Phenotypic correlation coefficients among different pairs of yield and yield contributing characters for different

genotypes of Oryza sativa L.

DM PH TILL ETILL PL NPB NSB TS FG TSW GYP YIELD

D50F 0.980** 0.666** -0.249 -0.254 0.479* 0.343 0.586** -0.348 -0.35 0.004 -0.399 -0.13

DM 0.717** -0.21 -0.215 0.507* 0.37 0.621** -0.334 -0.336 0.084 -0.423 -0.038

PH 0.071 0.07 0.473* 0.618** 0.517* -0.269 -0.269 0.114 -0.406 0.152

TILL 1.000** 0.021 0.129 0.304 0.509* 0.510* -0.052 0.367 0.664**

ETILL 0.015 0.128 0.3 0.508* 0.509* -0.055 0.366 0.665**

PL 0.614** 0.636** 0.155 0.153 0.073 -0.139 0.001

NPB 0.488* 0.086 0.087 0.11 -0.266 0.078

NSB -0.001 -0.001 0.058 -0.238 0.229

TS 1.000** 0.109 0.770** 0.628**

FG 0.108 0.770** 0.629**

TSW 0.342 0.306

GYP 0.480*

**, * Correlation is significant at the 0.01 and 0.05 level, respectively.

D50F= Days to 50% flowering, DM= Days to maturity, PH= Plant height(cm), TILL= Total number of tiller per plant, ETILL= Total number of effective tiller per

plant, PL= Panicle length (cm), NPB= Number of primary branch per panicle, NSB= Number of secondary branch per panicle, TS= Total number of spikelet per

panicle, FG= Number of filled grain per panicle, GYP= Yield per plant (g), TSW= Thousand seed weight (g) and YIELD= Yield (ton/ha)

75

4.3.3 Plant height (cm)

Plant height exhibited highly significant and positive correlation with number of

primary branch per panicle (G= 0.656**, P=0.618**).It indicated that if plant height

is increased, the number of primary branch per panicle will also be increased. It had

significant and positive correlation with panicle length (G=0.527*, P=0.473*) and

total number of secondary branch per panicle(G=0.545*, P=0.517*). It had

insignificant and positive correlation with total number of tiller per plant

(G=0.072,P=0.071), number effective tiller (G=0.071,P=0.07), thousand seed weight

(G=0.125,P=0.114) and yield per hectare (G=0.157,P=0.152). But Zahid et al. (2005)

found negative correlation. It also had insignificant and negative correlation with total

number of spikelet per panicle (G=-0.272,P=-0.269), number of filled grain(G=-

0.271,P=-0.269) and yield per plant (g) ( G=0.157,P=0.152) (Table 7a & 7b)

.Insignificant association of these traits suggested that the association between these

traits was largely influenced by environmental factors.

4.3.4 Total number of tillers per plant

Total number of tillers per plant exhibited highly significant and positive correlation

with total number of effective tiller per plant (G= 1.000**, P=1.000**) and yield per

hectare (G= 0.678**, P=0.664**) (Table 7a & 7b). It suggested that if total number of

tiller per plant is increased number of effective tiller per plant, yield per hectare (t/ha)

would also be increased. It had significant and positive correlation with total number

of spikelet per panicle (G=514*, P=0.509*) and number of filled grain (G=516*,

P=0.510*) (Table 7a & 7b). Akinwalel et al. (2011) recorded similar results. But it

denoted significant and positive correlation with total number of spikelet per panicle

and number of filled grain at genotypic and phenotypic level. It showed insignificant

and positive interaction with panicle length (G=0.011) at genotypic level and total

number of primary branch per panicle (G=0.144, P=0.129). But it showed

insignificant and positive interaction with number of secondary branch per panicle.

(G= 0.315) at genotypic level. It also showed insignificant and positive interaction

with yield per plant (P=0.367) at phenotypic level. It had insignificant and negative

correlation with thousand seed weight (P= -0.052) (Table 7a & 7b).Insignificant

association of these traits expressed that the association between these traits was

76

largely influenced by environmental factors. Agahi et al. (2007), Ghosal et al. (2010),

Watto et al. (2010) and Sadeghi et al. (2011) observed positive and significant

correlation of yield with effective tillers per plant.

4.3.5 Number of effective tillers per plant

Number of effective tillers per plant exhibited highly significant and positive

interaction only with yield per hectare (G=0.678**, P=0.665*) (Table 7a & 7b).

Prasad et al. (2001) showed that number of effective tiller per plant has significant

and positive interaction with yield. It indicated that if total number of effective tiller

per plant is increased, yield per hectare will also be increased which would be

effective for selection. It showed positive and significant correlation with number of

total number of spikelet per panicle (G=0.513*) and number of filled grain pre

spikelet (G=0.508*) at genotypic level only. It also showed insignificant and positive

interaction with panicle length (G=0.005) at genotypic level, number primary branch

per panicle (G=0.142, P=0.128), number secondary branch per panicle (G=0.309

P=0.300) and yield per plant (G=0.374, P=0.366). It had negative and insignificant

interaction with thousand seed weight (g) (G= -0.040, P= -0.055) (Table 7a & 7b).

Insignificant association of these traits suggested that the association between these

traits was largely influenced by environmental factors.

4.3.6 Panicle length (cm)

Highly significant and positive interaction with number of primary branches per

panicle (G=0.668**, P=0.614**) and number of secondary branch per panicle

(G=0.683**, P=0.636**) were found in respect of panicle length. It indicated that if

panicle length is increased, number of primary branches per panicle and number of

secondary branch per panicle will also be increased which would be effective criteria

for selection. Panicle length had insignificant and positive interaction with number of

total number of spikelet per panicle (G=0.169,P=0.155), number of filled grain per

panicle (G=0.168,P=0.153) and thousand seed weight (G=0.114,P=0.073) (Table 7a

& 7b). Insignificant and negative interactions were reported in yield per plant (g) ( G=

-0.147, P= -0.139) and yield per hectare (G= -0.013, P= 0.001). Insignificant

association of these trait indicated that the association between these traits was largely

influenced by environment. Akter et al. (2007) also noted that panicle length has

positive and significant relation with grain yield.

77

4.3.7 Number of primary branches per panicle

Number of primary branches per plant showed significant and positive interaction

only with number of secondary branch per panicle (G = 0.510*, P = 0.488) (Table 7a

& 7b). It indicated if number of primary branches per plant was increased, number of

secondary branch per panicle would also be increased. It had insignificant and

positive correlation with number of total spikelet per panicle (G = 0.086, P = 0.086),

number of filled grain per panicle (G = 0.086, P = 0.087) thousand seed weight (G =

0.116, P = 0.110) and yield per hectare (G = 0.073, P = 0.071). It also showed

insignificant and negative correlation with yield per plant (gm) (G= -0.289, P= -

0.266) (Table 7a & 7b). Insignificant association of these traits suggested that the

association between these traits is largely influenced by environmental factors.

4.3.8 Number of secondary branches per panicle

Number of secondary branches per panicle showed insignificant and positive

correlation with thousand seed weight (G = 0.051, P = 0.048) and yield per

hectare(G = 0.238, P = 0.229) (Table 7a & 7b), it also showed insignificant and

negative interaction with number total spikelet per panicle(G = -0.003, P = -

0.001),number of filled grain per panicle(G = -0.004, P = -0.001) and yield per

plant(g) ( G = -0.251, P = -0.238). Insignificant association of these traits suggested

that the association between these traits is largely influenced by environmental

factors.

4.3.9 Total number of spikelet per panicle

Total number of spikelet per panicle showed highly significant and positive

interaction with total number of filled grain per panicle (G=1.000**, P=1.000**),

yield per plant (g) (G= 0.788**,P =0.770**) and yield per hectare (G= 0.641**,P

=0.628**) (Table 7a & 7b). It indicated that if total number of spikelet per panicle

was increased then total number of filled grain per panicle, yield per plant and yield

per hectare would also be increased which might be desirable for selection. It showed

78

positive and insignificant interaction with thousand seed weight (G=0.118, P=0.109)

(Table 7a & 7b). Environmental factors influenced this insignificant association

between these traits.

4.3.10 Number of filled grains per panicle

Number of filled grain per panicle showed highly significant and positive interaction

with yield per plant (G=0.787**, P=0.770**) and yield per hectare (G=0.642**,

P=0.629**) (Table 7a & 7b). It indicated that if number of filled grain per panicle was

increased then yield per plant and yield per hectare would also be increased. Akter et

al. (2007) also observed similar result. It showed positive and insignificant correlation

with thousand seed weight (G=0.118, P=108) (Table 7a & 7b). Insignificant

association of these traits suggested that the association between these traits is largely

influenced by environmental factors. Singh et al. (2013) reported significant and

positive interaction of number of filled grain per panicle with yield per plant.

4.3.11 Yield per plant (g)

Yield per plant showed highly significant and positive interaction with yield per

hectare (G=0.493**, P=0.480**). Nandeshwar et al. (2010), Sadeghi et al. (2011) and

Akinwale et al. (2011) also reported that yield per plant was positively and

significantly correlated with yield. It indicated that if yield per plant increased, yield

per hectare would also be increased which might be desirable character for selection

(Table 7a & 7b). It also showed insignificant and positive interaction with thousand

seed weight (g) at phenotypic level (P=0.367). Insignificant association of these traits

suggested that the association between these traits is largely influenced by

environmental factors (Table 7a & 7b).

79

4.3.12 1000 seed weight (g)

Thousand seed weight exhibited insignificant and positive interaction with yield per

hectare (G=0.327, P=0.306) (Table 7a & 7b). It indicated that insignificant association

of these traits suggested that the association between these traits largely influenced by

environmental factors (Table 7a & 7b).

4.5 Path Coefficient analysis

Path coefficient analysis is used in plant breeding programs to determine the nature of

the relationships between yield and yield components that are useful as selection

criteria to improve the crop yield. The goal of the path analysis is to accept

descriptions of the correlation between the traits, based on a model of cause and effect

relationship and to estimate the importance of the affecting traits on a specific trait. In

correlation studies, with the increasing number of variables, the indirect association

becomes intricate and important. In such situation, path coefficient analysis is useful

to find out direct and indirect causes of associations. Path coefficient analysis at

genotypic level permits a critical examination to specific factors acting to produce a

given correlation and measures the relative importance of each factor. Grain yield per

plant was considered as a resultant (dependent) variable where, flowering, days to

maturity, plant height (cm), total number of tiller /plant, total number of effective

tiller/plant, panicle length (cm), number of primary branches per panicle, number of

secondary branches per panicle, number of filled grain per panicle, yield/ plant (g),

thousand seed weight (g) and yield (ton/ hectare) were causal (independent) variables.

Assessment of direct and indirect effect of path co-efficient analysis for Oryza sativa

L. is presented in Table 8.

In plant breeding, it is very difficult to have complete understanding of all component

traits of yield. The residual effect permits accurate explanation about the pattern of

interaction of other possible components of yield which was not included in the

investigation on the dependent variables. The residual effect was 0.398, denoted that

contribution of component characters on yield per hectare was 60.2% by the thirteen

characters studied in path analysis, the rest 39.8% was the contribution of other

factors which were not included in the study on the dependent variable .

80

Table 8. Path coefficient analysis showing direct and indirect effects of different characters on yield of Oryza sativa L.

Characters Direct

effect

Indirect effect Genotypic

correlation

with yield D50F DM PH ETILL NPB TS FG GYP TSW

D50F -0.191 - -0.189 -0.131 0.491 -0.684 0.669 0.673 0.772 -0.013 -0.133

DM 0.197 0.194 - 0.146 -0.426 0.763 -0.664 -0.669 0.842 0.168 0.421**

PH 0.358 0.245 0.265 - 0.025 0.234 -0.097 -0.100 -0.150 0.045 0.157

ETILL 0.348 -0.089 -0.075 0.025 - 0.049 0.179 0.178 0.130 -0.014 0.678**

NPB -0.334 -0.120 -0.129 -0.219 -0.048 - -0.029 -0.029 -0.010 -0.039 0.073

TS 0.210 -0.736 -0.708 -0.571 0.107 0.181 - 0.210 0.164 0.247 0.641**

FG -0.156 0.550 0.530 0.423 -0.802 -0.134 -0.156 - 0.122 -0.184 0.642**

GYP 0.046 -0.019 -0.019 -0.019 0.017 -0.013 0.036 0.036 - 0.017 0.493**

TSW 0.100 -0.001 0.008 0.012 -0.004 0.011 0.012 0.012 -0.037 -

Residual

effect

0.398

**, * Correlation is significant at the 0.01 and 0.05 level, respectively.

D50F= Days to 50% flowering, DM= Days to maturity, PH= Plant height (cm), ETILL= Total number of effective tiller/plant, NPB= Number of primary

branch/panicle, TS= Total number of spikelet/panicle, FG= Number of filled grain of main tiller, GYP= Yield per plant (gm), TSW= Thousand seed weight (gm)

and Yield= Yield (t/ha)

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4.5.1 Days to flowering

The genotypic path coefficient analysis revealed that, days to flowering had negative

direct effect (-0.191) on yield per hectare. This trait showed indirect positive effect on

total number of effective tiller per plant (0.491), total number of spikelet per panicle

(0.669), total number of filled grain per panicle (0.673), and yield per plant (gm)

(0.772). On the other hand, it exhibited indirect negative effect on days to maturity -

0.189), plant height (-0.131), total number of primary branches per panicle (-0.684),

and thousand weight (-0.013). Finally it expressed negative and insignificant

correlation with yield per hectare (-0.013) (Table 8). These results signified that if

days to flowering decreased then yield per hectare would be decreased mostly through

the positive indirect effect of flowering with other characters. Abarshahr et al. (2011)

reported that days to flowering had negative direct effect on yield per hectare.

4.5.2 Days to maturity

Path co-efficient analysis revealed that, days to maturity had significant positive direct

effect (0.421**) on yield per hectare. This particular trait had positive indirect effect

through days to flowering (0.194), plant height (0.146), number of primary branch per

panicle (0.763), thousand seed weight (0.168) and yield per plant (0.842).). On the

other hand, days to maturity had negative indirect effect via number of effective tiller

per plant (-0.426), total number of spikelet per panicle (-0.664), number of filled grain

per panicle (-0.669) and it expressed positive correlation with yield per hectare (Table

8). Thus, selection based on this character would be effective. These results signified

that if days to maturity increased, yield per hectare would be increased mostly through

the positive indirect effect of maturity with other characters. Habib et al. (2005)

revealed same result in case of days to maturity.

4.5.3 Plant height (cm)

Genotypic path analysis exhibited that plant height had positive direct effect (0.358)

on yield per hectare. It had positive indirect effect with days to flowering (0.358),days

to maturity (0.265), total number of effective tiller per plant (0.0.025), number of

primary branch per panicle (0.234), thousand seed weight (0.045) and yield per plant

(0.114) (Table 8). Plant height had negative indirect effect via number of total spikelet

per panicle (-0.097), number of filled grain per panicle (-0.100) and grain yield per

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plant (-0.150) (Table 8). Plant height finally expressed positive correlation with yield

per hectare (0.157).These results denoted that if plant height increased then yield per

hectare would be increased mostly through the positive indirect effect of plant height

with other characters. Rokonuzzaman et al. (2008) and Habib et al. (2005) also

showed direct positive result for this character.

4.5.4 Total number of effective tillers per plant

Path co-efficient analysis revealed that, total number of effective tillers per plant had

positive direct effect (0.348) on yield per hectare. This trait had positive indirect

effect through plant height (0.025), number of primary branches per panicle (0.049),

number of total spikelet per panicle(0.179), number of filled grains per panicle

(0.178), and yield per plant (0.130) and (Table 8). On the other hand, total number of

effective tillers per plant had negative indirect effect via days to flowering (-0.089),

days to maturity (-0.075) and thousand seed weight (-0.014). Finally it made highly

positive correlation with yield per hectare (0.678**) (Table 8). These results denoted

that if total number of effective tiller per plant increased then yield per hectare would

be increased mostly through the positive indirect effect of total number of effective

tiller per plant with other characters. Ghosal et al. (2010) observed that total effective

number of tillers per hill had positive direct effect on yield per hectare.

4.5.5 Number of primary branches per panicle

Path analysis exhibited that number of primary branches per panicle had direct

negative effect (-0.334) on yield per hectare. This trait had positive indirect effect

through number of effective tiller per plant (0.048), number of filled grain per panicle

(0.029), total number of spikelet per panicle (0.029) and yield per plant (0.10) (Table

8). On the other hand, it had negative indirect effect via days to flowering (-0.120),

days to maturity (-0.129), plant height (-0.219), and thousand seed weight (-0.039).

Finally it made positive correlation with yield per hectare (0.073) (Table 8).

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4.5.6 Total number of spikelet per panicle

Path analysis revealed that total number of spikelet per panicle had direct positive

effect (0.210) on yield per hectare. This trait had positive indirect effect via number of

effective tiller per plant (0.107) followed by, number of primary branches per panicle

(0.181), number of filled grain per panicle (0.201), yield per plant (0.164) and

thousand seed weight (0.247) (Table 8). On the other hand, it had negative indirect

effect through days to flowering (-0.427), days to maturity (-0.291) and plant height (-

0.242). Finally it expressed highly positive and significant correlation with yield per

hectare (0.641**) (Table 8). These results signified that if total number of spikelet per

panicle is increased then yield per hectare would be increased mostly through the

positive indirect effect of total number of spikelet per panicle with other characters.

Seyoum et al. (2012) observed that grains per panicle had direct positive effect on

yield per hectare.

4.5.7 Number of filled grains per panicle

Genotypic path analysis revealed that number of filled grain per panicle had direct

positive effect (0.156) on yield per hectare. This trait had positive indirect effect

through days to flowering (0.550), days to maturity (0.530), plant height (0.423) and

yield per plant (0.122) (Table 8). On the other hand, it had negative indirect effect via

total number of effective tiller per plant (-0.802), number of primary branches per

panicle (-0.134), number of total spikelet per panicle (-0.156) and thousand seed

weight (-0.184). Finally it showed highly positive and significant correlation with

yield per hectare (0.642**) (Table 8). These results denoted that if number of filled

grain per panicle increased then yield per hectare would be increased mostly through

the positive indirect effect of number of filled grain per panicle with other characters.

Prasad et al. (2001) showed that number of filled grain per panicle had direct positive

effect on yield.

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4.5.8 Yield per plant (g)

Path analysis exhibited that number of yield per plant had direct positive effect

(0.046) on yield per hectare. This trait had positive indirect effect through days to

number of effective tiller per plant (0.017), total number of spikelet per panicle

(0.036) number of filled grain per panicle (0.036) and thousand seed weight (0.017)

(Table 8). On the other hand, it showed negative indirect effect via flowering (-0.019),

days to maturity (-0.019), plant height (-0.019), number of primary branches per

panicle (-0.013). Finally it gained highly positive and significant correlation with

yield per hectare (0.493**) (Table 8). These results denoted that if yield per plant

increased then yield per hectare would be increased mostly through the positive

indirect effect of yield per plant with other characters.

4.5.9 Thousand seed weight (g)

Genotypic path analysis revealed that thousand seed weight had direct positive effect

(0.100) on yield per hectare. This trait had positive indirect effect via days to maturity

(0.008), plant height (0.12), total number of primary branch per panicle (0.011),

number of total spikelet per panicle (0.012) and number of filled grain per panicle

(0.0.012) (Table 8). On the other hand, it had negative indirect effect via days to

flowering (-0.001), number of effective tiller per plant (-0.004), and yield per plant (-

0.037). Finally it obtained positive correlation with yield per hectare (0.327) (Table

7). These results signified that if thousand seed weight increased then yield per

hectare would be increased mostly through the positive indirect effect of thousand

seed weight with other characters. Ghosal et al. (2010) denoted that thousand seed

weight had direct positive effect on yield ton per hectare.

Path analysis indicated that, number of effective tiller per plant, total number of

spikelet per panicle, number of filled grain per panicle and yield per plant (g) could be

used as selection criteria for better grain yield. The results of this study revealed that

the highest positive indirect effects of plant height, days to 80% maturity, fertile tillers

per plant and thousand-grain weight through seed yield and plant height through

fertile tiller per plant and plant height was recorded. Therefore, selection for high

yield in rice genotypes should place maximum emphasis on these four traits namely

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number of effective tiller per plant, total number of spikelet per panicle, number of

filled grain per panicle and yield per plant (g).

4.6 Selection of advanced lines for further trial from F4 populations

Some promising genotype and individual plants were found by making comparison

among the check varieties with segregating populations. These promising genotypes

and individual plants from different populations were selected for further trials which

are presented in Table 9 and Table 11.

4.6.1 Selection of promising genotypes

Genotype G6 (BR 21 × BRRI dhan 29, F4, S6P3) performed better as it required 104

days to mature, had 14 effective tiller per plant, 135 total number of filled spikelet per

panicle, 27.54 gram yield per plant and 6.08 t/ha of yield (Table 9), (Plate 7b and 7c).

Whereas, check varieties BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 required

109, 109,103 and 104 days to mature respectively, had 8.17, 7.37, 9.43 and 10.30

effective tiller respectively , had 114.27, 118.30, 93.23 and 93.10 total number of

filled spikelet per panicle respectively, had 25.00, 24.14, 23.36 and 25.35 gram yield

per plant respectively and 4.21, 3.44, 3.36 and 4.10 t/ha of yield respectively (Table

10). Thus, G6 (BR 21 × BRRI dhan 29, F4, S6P3) was better than the check varieties.

Genotype G10 (BR 21 × BRRI dhan 29, F4, S7P1) exhibited better performance as it

required 108 days to maturity, had 12 effective tiller per plant, 147 total number of

spikelet per panicle and 24.81 gram yield per plant and 5.30 ton/ha grain yield

(Table 9), (Plate 8b and 8c). It required 108 days to maturity but the heck variety BR

21 needed 109 days (Table 9), had 8.17 effective tiller, 114.27 total number of filled

spikelet , 25.00 gram of yield per plant and 4.21 t/ha of yield per hectare which

signified that, required time was less than check variety (Table 10) . Hence, genotype

G10(BR 21 × BRRI dhan 29, F4, S7P1) was selected in respect of grain yield per

hectare for future trial.

Genotype G7 (BR 21 × BRRI dhan 29, F4, S6P8) performed better as it required 101

days to maturity, had 11 effective tiller per plant, 137 total number of spikelet per

panicle and 23.55 gram yield per plant (Table 9) (Plate 9a,9b,9c). Whereas, check

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varieties BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 required 109, 109,103 and

104 days to mature respectively, had 8.17, 7.37, 9.43 and 10.30 effective tiller

respectively, had 114.27, 118.30, 93.23 and 93.10 total number of filled spikelet per

panicle respectively, had 25.00, 24.14, 23.36 and 25.35 gram yield per plant

respectively and 4.21, 3.44, 3.36 and 4.10 t/ha of yield respectively (Table 10). G7

required 101 days to mature which denoted that, required time was lesser than parent.

So, G7 (BR 21 × BRRI dhan 29, F4, S6P8) was selected as it performed better over the

check varieties in respect of maturity and yield.

Genotype G12 (BR 24 × BRRI dhan 29, F4, S5P8) exhibited better performance as it

required 103 days to maturity, had 11 effective tiller per plant, 139 total number of

spikelet per panicle and 4.37 ton/ha of seed yield (Table 9) (Plate 10a ,10b,10c).

Whereas, check varieties BR 21, BR 26, BRRI dhan 42 and BRRI dhan 43 required

109, 109,103 and 104 days to mature respectively, had 8.17, 7.37, 9.43 and 10.30

effective tiller respectively, had 114.27, 118.30, 93.23 and 93.10 total number of

filled spikelet per panicle respectively, had 25.00, 24.14, 23.36 and 25.35 gram yield

per plant respectively and 4.21, 3.44, 3.36 and 4.10 t/ha of yield respectively (Table

10). G12 required 103 days to maturity which signified that, required time was less

than check varieties (Table 9). Again its seed yield (t/ha) was higher than the check

varieties. So it was satisfactory for selection of high yielding materials for future use.

Therefore, genotype G12 (BR 21 × BRRI dhan 29, F4, S5P8) was chosen for future

trial.

Genotype G9 (BR 21 × BRRI dhan 29, F4, S6P10) exhibited better performance as it

required 100 days to maturity and 4.24 ton/ha of seed yield (Table 9). It required 100

days to maturity which signified that, required time was lesser than check varieties

(Table 9). Again, seed yield (ton/ha) was higher than the check varieties. So it was

suitable for selection of early high yielding materials for future use. Thus, Genotype

G9 (BR 21 × BRRI dhan 29, F4, S6P10) was chosen for further trial.

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Table 9. Comparison between selected F4 populations for further trial and check

varieties

Table 10. Mean performance table of important traits of four check varieties of

Oryza sativa L.

Genotypes Populations Days to

Maturity

Yield per

hectare (t/ha)

G6 BR 21×BRRI dhan29, F4, S6P3 104.00 6.08

G7 BR 21×BRRI dhan29, F4, S6P8 101.00 4.68

G9 BR 21×BRRI dhan29, F4, S6P10 100.33 4.24

G10 BR 21×BRRI dhan29, F4, S7P1 108.33 5.30

G12 BR 24×BRRI dhan29, F4, S5P8 103.67 4.37

G17 BR 21 109.67 4.21

G18 BR 26 109.00 3.44

G19 BRRI dhan 42 103.33 3.36

G20 BRRI dhan 43 104.67 4.10

Varieties Days to

maturity

Total number

of effective

tiller

Total number of

spikelet per

panicle

Yield

per

plant (g)

G17 (BR 21) 109.67 8.17 126.33 25.00

G18 (BR 26) 109.00 7.37 130.37 24.14

G19(BRRI dhan 42) 103.33 9.43 105.30 23.36

G20(BRRI dhan 43) 104.67 10.30 105.17 25.35

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Plate 7a. Photograph showing 80% maturity stage of G6 (BR 21× BRRI dhan 29,

F4, S6P3) with BRRI dhan 42

Plate 7b. Photograph showing panicle length of G6 with their check varieties

Plate 7c. Photograph showing grain size of G6 with check varieties

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Plate 8a. Photograph showing 80% maturity stage of G10 (BR 21×BRRI dhan

29,F4, S7P1) with BRRI dhan 42 and BRRI dhan 43

Plate 8a. Photograph showing panicle length of G10 with their check varieties

Plate 8c: Photograph showing grain size of G10 with check varieties

90

Plate 9a. Photograph showing plant height and effective tiller of

G7(BR24×BRRI dhan 29,F4,S6P8) and check varieties

Plate 9b. Photograph showing panicle length of G7 with check varieties

Plate 9c. Photograph showing grain size of G7 with check varieties

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Plate 10a. Photograph showing plant height and effective tiller of G12

(BR24×BRRI dhan 29, F4, S5P8) and check varieties

Plate 10b. Photograph showing panicle length of G12 with their check varieties

Plate 10c. Photograph showing grain size of G12 with check varieties

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4.5.2 Selection of individual plant

The highest yield per plant was scored 68.22 g by G12-3/1/104 (Replication number/

Plant number/ Days to maturity). The second maximum yield per plant was observed

65.9 g in G12-3/8/104. Individual plants G1-1/5/103 and G1-1/7/103 had 41.28 g and

33.48 g of yield per plant respectively. Plants under G4 genotype G4-2/6/100 and G4-

3/2/95 which yield/plant 35.49 g and 56.66 g respectively. Plant number G6-1/2/95,

G6-1/10/95, G6-1/4/93, G6-3/2/95 and G6-3/3/95 had 30.54g, 30.85g, 30.83g, 43.93g

and 35.20 g of yield per plant respectively. 38.83g and 40.49 g of yield per plant were

recorded in plant number G10-1/3/95 and G10-3/4/105 .Lastly plant no. G14-2/2/105,

G14-2/3/105 and G14-2/5/105 under G14 genotype exhibited 38.2 g, 45.42 g and

54.22 g yield per plant respectively (Table 10). All chosen plants under G1, G4, G6,

G10, G12 and G14 genotypes showed higher yield per plant than check varieties G17

(BR 21)( 25.00 g), G18 (BR26) (24.14 g), G19 (BRRI dhan42) (23.36g) and G20

(BRRI dhan 42) ( 25.35 g) respectively (Table 10).

Lower days to maturity was ranged from 93 to 95 days, which were observed in

genotype G4, G6 and G10. Again another lower range of day to maturity 100-105

days were recorded under G1 G12 and G14 genotypes (Table 11). All these selected

materials showed lesser maturity days than 4 check varieties (Table 10, Plate 5, 6).

Thus, considering days to maturity and yield per plant comparing with check varieties

the plant number 5, 7 under genotype G1; the plant number 6,2 under genotype G4;

the plant number 2, 10, 4, 2, 3 under genotype G6; the plant number 3, 9, 4, 3, 10

under genotype G10; the plant number 4, 6, 7, 1, 2, 4, 8 under genotype G12 and the

plant number 2, 3, 5 under genotype G14 were selected.

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Table 11. Selection of promising early high yielding plants from the F4 materials

of different genotypes

Cross combinations Replication

Number

Plant

Number

Days to

Maturity

Yield per

Plant (g)

G1 1 5 103 41.28

7 103 33.48

G4 2 6 100 35.49

3 2 95 56.66

G6 1 2 95 30.54

10 95 30.85

4 93 30.83

3 2 95 43.93

3 95 35.20

G10 1

3 95 38.83

9 95 38.65

3 4 105 40.49

2 3 93 31.23

10 93 30.82

G12 2 4 104 37.35

6 104 39.12

7 104 49.51

3 1 104 68.22

2 104 43.09

4 104 33.6

8 104 65.9

G14 2 2 105 38.24

3 105 45.42

5 105 54.22

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CHAPTER V

SUMMARY AND CONCLUSION

In this present study, twenty rice genotypes were evaluated in randomized complete

block design with three replications in the experimental farm, Sher-e-Bangla

Agricultural University (SAU), Dhaka, during April 2014 to July 2014. This

investigation involved two parents, two check variety and sixteen F4 cross materials of

rice .The objectives of the study were assessing the extent of variability in F4 rice

genotype, estimating association among grain yield and yield related traits and,

partitioning the correlation coefficients into direct and indirect effects and identifying

high yielding and short duration T. Aus genotypes for future trial. Data on different

yield attributing traits such as days to flowering, days to maturity, plant height (cm),

total number of tiller /plant, total number of effective tiller/plant, panicle length (cm),

number of primary branches per panicle, number of secondary branches per panicle,

number of filled grain per panicle, yield/ plant (gm), thousand seed weight (g) and

yield (ton/ hectare) were noted down.

The analysis of variance showed the presence of significant differences among the

tested genotypes for all characters considered, indicating the existence of variability

among the tested genotypes. The days to flowering were recorded the maximum

(97.67 Days) in G15 (BR 24 × BRRI dhan 36, F4, S7P8) and minimum (66.33 days)

was observed in G11 (BR 21 × BRRI dhan 29, F4, S7P2). The lowest days to maturity

(92 days) was scored by G11 (BR 21 × BRRI dhan 29, F4, S7P2) and the maximum

days to maturity (122.33 days) was noted in G15 (BR 24 × BRRI dhan 36, F4,S7 P8).

The minimum (108.82 cm) plant height was recorded in G18 (BR 26) and the

maximum (151.63 cm) plant height was scored by G3 (BR 21×BRRI dhan 28, F4,

S5P9). The highest number of total tiller per plant (15.17) was recorded in G6 (BR 21

× BRRI dhan 29, F4, S6P3); whereas the minimum number of total tiller per plant

(8.47) was observed in G18 (BR 26). In G6 (BR 21 × BRRI dhan 29, F4, S6P3)the

highest (14.10) number of effective tiller per plant was observed; whereas the

minimum number of effective tiller per plant (7.37) was recorded in G18 (BR 26).

99

In G12 (BR 24 × BRRI dhan 29, F4, S5P8), the highest (26.31 cm) panicle length was

observed and the minimum length of panicle (20.52 cm) was observed in G4 (BR 21

× BRRI dhan 29, F4, S1P2). The maximum number of primary branches per panicle

(12.43) was scored by G12 (BR 24 × BRRI dhan 29, F4, S5P8); whereas the minimum

number of primary branches per panicle (8.4) was observed in G17 (BR 21). The

highest number of secondary branches per panicle (34.83) was noted down in G13

(BR 24 × BRRI dhan 29, F4, S5P9), whereas the minimum number of secondary

branches per panicle (20.00) was recorded in G19 (BRRI dhan 42). Maximum total

number of spikelet per panicle (147.80) was observed in G10 (BR 21 × BRRI dhan

29, F4, S7P1), whereas the lowest total number of spikelet per panicle (83.40) was

observed in G15 (BR 24 × BRRI dhan 36, F4, S7P8). The number of filled grain per

panicle was scored the highest (135.90) by G10 ( BR 21 × BRRI dhan 29, F4, S7P1)

and the minimum number of filled grain per panicle (71.30) was recorded in G15(BR

24 × BRRI dhan 36, F4, S7P8).

The yield per plant was found maximum (27.54 g) in G6 (BR 21 × BRRI dhan 29, F4,

S6P3), whereas the minimum weight of yield per plant (16.60 g) was scored by G15

(BR 24 × BRRI dhan 36, F4, S7P8). Thousand seed weight was noted maximum (27.43

g) in G12 (BR 24 × BRRI dhan 29, F4, S5P8),whereas the minimum thousand seed

weight (18.87 g) was found in G13(BR 24 × BRRI dhan 29, F4, S5P9). The highest

yield (6.08 t/ha) was recorded in G6 (21 × BRRI dhan 29, F4, S6P3) and the lowest

yield (2.52 t/ha) was observed in G4 (BR 21 × BRRI dhan 29, F4, S1P2).

Phenotypic variance was higher than the genotypic variances for all the characters

indicating the influence of the environmental factors on these traits. However, the

phenotypic variances were higher than the corresponding genotypic variance for all

the characters under study. Total number of spikelet per panicle and filled grain per

panicle showed higher influence of environment on the expression of these characters.

Plant height showed moderate influence of environment for the expression of these

characters.

99

Moreover, days to flowering, days to maturity, total number of tiller per plant, total

number of effective tiller per plant, panicle length, number of primary branches per

panicle, number of secondary branches per panicle, yield per plant, thousand seed

weight and yield per hectare showed least difference phenotypic and genotypic

variance indicating additive gene action for the expression of the traits.

Phenotypic coefficients of variation were slightly higher than the genotypic

coefficients of variation for all the traits studied. This indicates the presence of

environmental influence to some degree in the phenotypic expression of the traits

.High phenotypic coefficient of variation was recorded for total number of spikelet

per panicle, filled grain per panicle and grain yield (t/ ha).

Estimates of heritability along with genetic advance are more useful in predicting the

value of selection than heritability alone. Days to flowering exhibits the highest value

of heritability (99.30%) while panicle length exhibits the lowest value of heritability

(75.58%). Estimates of heritability (>60.00%) coupled with high genetic advance as

percent of mean (>20.00%) for days to flowering, plant height, total number of tiller

per plant, effective tiller per plant, number of primary branches per panicle, number

secondary branches per panicle, total number of spikelet per panicle, filled grain per

panicle, yield per plant (g), thousand seed weight (g) and grain yield (t/ ha) revealed

that most likely the heritability due to additive gene effects and selection may be

effective. Such value of high heritability and high genetic advance might be attributed

to the action of additive genes. This suggests that such characters could be improved

by direct selection. High heritability with moderate genetic advance was observed for

days to maturity and panicle length indicating medium possibility of selecting

genotypes.

Correlation coefficient simply measures mutual association without regard to

causation .Genetic correlation is the association of breeding values (additive genetic

variance) of the two characters. Genotypic and phenotypic correlation coefficients

measure the extent to which the same genes or closely linked genes cause co-variation

99

in two different characters. Both tell us the association between and among two or

more characters.

The genotypic correlation coefficients were higher than the phenotypic correlation

coefficients demonstrating that, the observed relationships among the various traits

were due to genetic causes which suppressed the environmental effects. In few cases,

phenotypic correlation coefficient were higher than their corresponding genotypic

correlation co-efficient suggesting that both environmental and genotypic correlation

in these cases act in the same direction and finally maximize their expression at

phenotypic level.

Grain yield had significant and positive association with total tiller number per

plant(G= 0.678**, P= 0.664*),effective tiller per plant (G= 0.678**, P= 0.665**),

total number of spikelet per panicle (G= 0.641**, P= 0.628**), filled grain per

panicle(G= 0.642**, P=0.629**) and grain yield per plant (G= 0.493**, P= 0.480**)

at both genotypic and phenotypic levels. There was positive association of grain yield

with plant height (G= 0.157, P= 0.152), number of primary branches per panicle (G=

0.073, P= 0.078), number of secondary branches and per panicle (G= 0.238, P=

0.229). Thousand grain weight (G= 0.327, P= 0.306) had also positive correlation

with grain yield at phenotypic level. On the other hand, it was negatively correlated

with days to 50% heading (G= -0.113, P= -0.130), days to maturity (G= -0.041, P=

-0.038) at both level and panicle length (G= -0.013) at genotypic level.

Path coefficient analysis of grain yield per hectare revealed that days to maturity,

effective tiller per plant, total number of spikelet, filled grain per spikelet and grain

yield per plant were the major contributors of grain yield. Positive direct effects of

these traits on grain yield indicated their importance in determining these complex

traits and therefore, should be kept though practicing selection aimed at the

improvement of grain yield.

From the path coefficient analysis in F4 progeny, it was revealed that effective tiller

per plant (0.678**) exhibited maximum positive direct effect on grain yield followed

by filled grain per panicle (0.642), total number of spikelet per panicle (0.641) and

days to maturity (0.421). The direct effect of days to 50% flowering was negative.

99

Plant height and thousand seed weight also were the important contributors to yield

per hectare which could be taken in consideration for future hybridization program.

The residual effect was 0.398, indicated that yield attributing component characters on

yield per hectare was 60.2% by characters studied in path analysis; the remaining

39.8% was the effect of other factors such as characters not examined.

Therefore, considering the variability, heritability, correlation and path coefficient

analysis, the seven (7) genotypes viz. G6 (BR 21×BRRI dhan 29, F4, S6P3), G7 (BR

BR 21×BRRI dhan 29, F4, S6P8), G9(BR 21×BRRI dhan 29, F4, S6P10), G10 (BR

21×BRRI dhan 29, F4, S7P1), G12 (BR 24×BRRI dhan 29, F4, S5P8 ) were selected as

high yielding and short duration T. Aus lines for future trial.

Specially selection of individual plant such as plant number G1-1/5/103, G1-1/7/103

under genotype G1; the plant number G4-2/6/100, G4-3/2/95 under genotype G4;

plant numbers G6-1/2/95, G6-1/10/95, G6-1/4/93, G6-3/2/95 and G6-3/3/95 under

genotype G6; the plant numbers G10-1/3/95, G10-1/9/95, G10-3/4/105, G10-2/3/93,

G10-2/10/93 under genotype G10; the plant numbers G12-2/4/104, G12-2/6/104,

G12-2/7/104, G12-3/1/104, G12-3/2/104, G12-3/4/104, G12-3/8/104 under genotype

G12 and the plant numbers G14-2/2/105, G14-2/3/105, G14-2/5/105 under genotype

G14 might be rewarded for selection.

99

100

REFERENCES

Abarshahr, M., Rabiei, B. and Lahigi, H.S. (2011). Genetic Variability, Correlation

and Path Analysis in Rice under Optimum and Stress Irrigation Regimes. Nat.

Sci. Biol. 3(4): 134-142.

Ajmal, S.U., Zakir, N.and. Mujahid, M.Y 2009. Estimation of genetic parameters and

charac-ters association in wheat. J. Agric. Biol. Sci., 1: 15-18.

Agahi, K., Fotokian, M.H. and Farshadfar, E. (2007). Correlation and Path Coefficient

Analysis for Some Yield Related Traits in Rice Genotypes (Oryza sativa L.).

Asian J. Plant Sci. 6(3): 513-517.

Akanda, S.I. and Mundt, C.C. 1996. Path coefficient analysis of the effects of stripe

rust and cultivar mixtures on yield and yield components of winter wheat.

Theo. App. Genet. 92: 666-672.

Akinwale, M.G., Gregorio, G., Nwilene, F., Akinwele, B.O., Ogunbayo, S.A. and

Odiyi, A.C. (2011). Heritability and Correlation and Coefficient Analysis for

Yield and Its Components in Rice (Oryza sativa L.). African J. Plant Sci.

5(3):207-212.

Akhatar, N., Nazir, M.F., Rabnawaz, A., Mahomod, T., Safdar, M.E., Asif, M. and

Rehman, A. (2011). Estimation of heritability, correlation and path coefficient

analysis in fine grain rice (Oryza sativa L.). J. Plant Sci. 21(4): 60-64.

Akter, K., Habib, S.H., Bashar, M.K. and Nurunnabi, A.M. (2007). Genetic analysis

and selection criteria in advanced breeding lines of deep water rice.

Bangladesh J. Plant Breed. Genet. 20(1): 39-45.

Ali, A. Khan, S. Asad, MA 2002. Drought tolerance in wheat: Genetic variation and

heritability for growth and ion relations. Asian J. Plant Sci.1: 420-422.

Allard, R.W. (1960). Principles of Plant Breeding. John Willey and Sons. Inc. New

York.

Anonymous. (1989). Annual Report of Bangladesh Agricultrural Research Institute.

Joydebpur, Gazipur. p. 133.

101

Araus, J. L., Casadesus, J. and Bort, J. (2001). Recent tools for the screening of

physiological traits determining yield. In: Reynolds, M.P., J. I. Ortiz-

Monasterio and Mc Nab (eds). Application of Physiology in Wheat Breeding.

Mexico, D. F.: CIMMYT.

Ashvani, D.R., Panwar, P.S. and Kumar, V. (2007). Genetic variability and

heritability studies in rice. Int. J. Agric. Res. 20(1): 47-49.

Ashvani, D.R., Panwar, P.S. and Kumar,V. (2007). Path analysis of spikelet yield in

rice (Oryza sativa L.). Int. J. Agric. Res. 20(1): 27-28.

Atwal, D.S. and Singh, R.S. (2001). Genetic and variability and character association

in semi-dwarf indica rice. Madras Agril. J. 87(6): 183-191.

Awasthi, L.P. and Sharma, A. K. (1996). Studies on morphological traits of aromatic

rice ( Oryza sativa L.). New Agrl. 7(1): 79-83.

Bai, N.R., Devika, R., Regina, A. and Joseph, C.A. (1992). Correlation of yield and

yield components in medium duration rice cultivars. Env. Ecol. 10(2): 459-

470.

Bhuiyan, N.I., Paul, D.N.R. and Jabbar, M.A. (2002). Feeding the extra millions by

2025 challenges for rice research and extension in Bangladesh. A key note

paper presented on national workshop on rice research and extension, BRRI.

P. 9.

Bihari, P.K., Richharia, A.K. and Sahu, R.S. (2004). Genetic advance for yield

attributes in aromatic rice. J. App. Biol. 14(2): 1-5.

Bisne, R., Sarawgi, A.K. and Verulkar, S.B. (2009). Study of Heritability, Genetic

Advance and Variability for Yield Contributing Characters in Rice.

Bangladesh J. Agril. Res. 34(2): 175-179.

Biswas, S.K., Siddique, M.A., Kabir, K.A., Banu, B. and Choudhury, N.H. (2001).

Grain quality of some Binni rice varieties of Bangladesh. Bangladesh J. life

Sci. 13(1&2): 181-188.

102

BRRI (Bangladesh Rice Research Institute). (2012). Adhunik Dhaner Chash,

Jodebpur, Gazipur. p. 10.

BRRI (Bangladesh Rice Research Institute). (2014). Adhunik Dhaner Chash.

Bangladesh Rice Res. Ins. Joydebpur, Gazipur, Bnagladesh. pp.9-13.

Burton, G.W. (1952). Quantitative inheritance in grass pea. Proc. 6th Grassl. Cong. 1:

277-283.

Chakraborty,(2011). Riceproduction and productivityin Andhara Pradesh, Report

Prepared as part of the Summer Internship for Masters in Development

Studies, Tata Institute of Social Studies, Mumbai .

Chakraborty, R. and Chakraborty, S. (2010). Gnetic variation and coorelation of some

morphometric traits with grain yield in bold grain rice (Oryza sativa L.) gene

pool of Barak valley. American. Eurasia J. Sustain. Agric. 4(1): 26-29.

Chanbeni,Y., Ovung, G.M. Lal and Rai, P.K. (2012). Studies on genetic diversity in

Rice (Oryza sativa L.). J. Agril. Tech. 8(3): 1059-1065.

Chand, S.P., Roy, S.K., Mondal, G.S., Mahato, P.D., Panda, S., Sarker, G. and

Senapati, B.K. (2004). Gentic variability and character asociation in rainfed

lowland Aman paddy ( Oryza sativa L.). Env. Eco. 22(2): 430-434.

Chaubey, P.K. and Richharia, A.K. (1993). Genetic variability, correlations and path

coefficients in indica rices. Indian J. Genet. Plant Breed. 53(4): 356-360.

Chaubey, P.K. and Singh, R.P. (1994). Genetic variability, correlation and path

analysis of yield components of rice. Madras Agril. J. 81(9): 468-470.

Choudhury, P.K D. and Das, P.K. (1998). Genetic variability, correlation and path co-

efficient analysis in deep-water rice. Ann. Agril. Res. 19(2): 120-124.

103

Choudhury, P. K. D. and Das, P. K. (1988). Genetic variability, correlation and path

coefficient analysis in deep water rice. Ann. Agril. Res. 19(2): 120-124

Chen, L., Sun, C.Q., Li, Z. C. and Wang, X.K. (2000). Study on heterotic ecotype of

two-line japonica hybrid rice in north China. J. China. Agric. Univ. 5(3): 30-

40.

Comstock, K. and Robinson, P.R. (1952). Estimation of genetic advance. Indian J.

Hill. 6(2): 171-174.

Dabholkar, A.R. (1999).Elements of Biometrical Genetics. Concept Publishing

Company, New Delhi. Pp:151-175.

Das, R.K., Islam, M.A., Howlader, M., Ibrahim, S.M., Ahmed, H.U. and Miah, N.M.

(1992). Variability and genetic association in uplantand rice (Oryza sativa L.).

Bang. J. PIant. Breed. Genet. 5(1& 2): 51-56.

Dewey, D.R. and Lu, K.H. (1959). A correlation and path coefficient analysis of

components of crested wheat grass seed production. Agron. J. 51: 515-518.

Diaz, S.H., Castro, R. and Morejon, R. (2000). Morpho-agronomic characterization of

varieties of rice. Instituo Nacional. De ciencias.

Dhaliwal, T.S. and Sharma, H.L. (1992). Genetic Variation, Heritability, Correlations

and Path-Analysis Among Agronomic and Grain Characters in Rice. Thai J.

Agric. Sci. 25(3): 171-180.

Dutta, P., Dutta, P.N. and Borua, P.K. (2013). Morphological traits as selection

indices in rice: A statistical view. Uni. J. Agril. Res. 1(3): 85-96.

Dutta, R.K. and Khanam, S. (2002). Plant architecture and growth characteristics of

fine spikelet aromatic rice and their relation with grain yield. IRC Newsl. 5(1):

51-56.

Food and Agriculture Organization of the United Nations (FAO), 2015, p. 24

Edris, K.M., Islam, A.T.M.T., Chowdhury, M.S. and Haque, A.K.M.M. (1979).

Detailed Soil Survey of Bangladesh Agricultural University Farm,

Mymensingh, Dept. Soil Survey, Govt. People’s Republic of Bangladesh. p.

118

104

Falconer, D.S. and Trudy, F.C., Mackay,(1996). Introduction to Quantitative Genetics

4th ed. Longman Group Limited Malaysia.

Fukrei, P.K., Kumar A ,Tyagi , W.,Rai, M. and Pattanayak, A (2011). Genetic

Variability in Yield and its Components in Upland Rice Grown in Acid Soils

of North East India . J. Rice Res. 4( 1 & 2) : 4-7.

Garris,Tai, T.H., Coburn J., Kresovich, S., McCouc S. et al. (2004). Genetic structure

and diversity in Oryza sativa L . Genet. 169 (3): 1631–8.

Guimara, E.P. (2002). Genetic diversity of rice production in Brazil. In: V.N.

Nguyen, (ed.) Genetic diversity in rice production, Case studies from Brazil,

India and Nigeria. Food and Agriculture Organization of the United Nations

(FAO), Rome, Italy. pp. 11-35.

Ghosal, S., Biswas, P.L., Khatun, M. and Khatun, S. (2010). Genetic variability an

character associations in irrigated rice (Oryza sativa L.). Bangladesh J.Plant

Breed. Genet. 23(2): 23-27.

Ghose, R.L.M. and Ghatge, M.B. (1960). Rice in India. Karnataka J. Agric. Sci.

8(2):32.

Ghosh, P.K. and Hossain, M. (1988). Genetics evaluation and regression analysis of

yield and yield attributes in rice (Oryza sativa). Indian J. Agril. Sci. 25: 485-

509.

Grafius, J.E. (1964). A geometry of plant breeding. Crop Sci. 4: 241-246.

Griffiths, D.J. (1965). Breeding for higher seed yields from herbage varieties. J.Nat.

Inst. Agric. Bot. 10: 320-331

Guo, B., luo, Li., xing, Zh., Wang, Pi., Mei, Wei., QianQian., Ying, Shan. (2002).

Path anlysis for grain yield and its components in rice. CNRRI, Hangzhou

310006, China. Chinese Rice Res. Newsl. 10(2): 5-6.

Habib, S.H., Bashar, M.K., Khalequzzaman, M., Ahmed, M.S. and Rashid, E.S.M.H.

(2005). Genetic analysis and morpho-physiological selection criteria for

traditional biroin Bangladesh rice gramplasms. J. Biol. Sci. 5(3): 315-318.

105

Hairmansis, A., Kustianto, B., Supartopo and Suwarno, (2010).Correlation analysis of

agronomic characters and grain yield of rice for tidal swamp areas.

Indonesian J. Agric. Sci. 11: 11-15.

Hasan, M.J., Kulsum, A., Masuduzzaman, A.S.M. and Ramesha, M.S. (2011).

Genetic variability and charcter association for agronomic traits in hybrid rice

( Oryza saiva L.). Bangladesh J. Plant Breed. Gene. 24(1): 45-51

Hossain, M.A. and Haque, M.E. (2003). Genetic variability and path analysis in rice

genotypes. Bangladesh J. Plant. Breed. Genet. 16(1): 33-37

Hooker, J.D. (1979). The floral of British India. Reeve. Co. Kent. England. Vol. 21:

310-315.

Honarnejad, R. (1995). Study on combining ability and correlation among some

morphological characters in six indica rice genotypes. Seed plant. 11(4): 37-

52.

Iftekharudduaula, K.M., Badshah, M.S., Basher, M.K. and Akter, K. (2001). Genetic

variability, character association and path analysis of yield components in

irrigated rice (Oryza sativa L.). Bangladesh J. Plant Breed. Genet. 14(2): 43-

49.

Ingale, B.V., Waghmode, B.D., Dalvi, V.V. and Rewale, A.P. (2007).Effect of

seedling age on flowering of parental lines of Sahyadri rice hybrid. Crop

Improve. 34(2): 145-148.

Ismail, A, Khalifa, M.A. and Hamam, K.A. (2000). Genetic studies on some yield

traits of durum wheat. Asian J.Agric,Sci. 32: 103-120.

Jayasudha, S. and Sharma, D. (2010). Genetic parameters of variability, correlation

and path-coefficient for grain yield and physiological traits in rice (Oryza

sativa L.) under shallow low land situation. Ele. J. Plant Breed. 1(2): 1332-

1338.

Jiang, Z.X., Huang, Z.Q., Li, Y.S., Lou, Z.X. and Shi, F.Z. (2000). Relationship

between quantative and qualitative traits of population in hybrid rice. Chinese

J. Rice. Sci. 143(3): 179-182.

106

Johnson, H.W., H.F. Robinson and R. G. Comstock. (1955). Estimates of genetic and

envi-ronmental variability in soybean. Agron. J. 47: 314-318.

Jun, Y. ,Songniun, H. Jun, W. and Bin, L. (2002).A draft sequence of the rice genome

(Oryza sativa L. ssp indica).Science.296: 79-92.

Kamal, A.M.A., Azam, M.A. and Islam, M.A. (1998). Effect of cultivar and NPK

combination of the yield contributing characters of rice. Bangladesh J. Agril.

Sci. 15(1): 105-110.

Karim, D., Sarkar, U., Siddique, M.N.A., Khaleque, M.A. and Hasnat, M.Z. (2007).

Variability and genetic parameter analysis in aromatic rice. Int. J. Sustain.

Crop Prod. 2(5): 15-18.

Kaul, M. L. M. and Kumar, V. (1982). Genetic divergence in rice. Ann. Biol. 15(1):

35-39.

Ketan R. and Sarker G. (2014). Studies on variability, heritability, genetic advance

and path analysis in some indigenous Aman rice (Oryza sativa L.). J. Crop

Weed. 10(2): 308-315.

Kishore, N.S., Ansari, N.A., Babu, V.R., Rani, N.S., Rao, L.V.S. and Ravi C. (2007).

Correlation and path analysis in aromatic and non-aromatic rice genotypes.

Agril. Sci. Dig. 27(2): 122-124.

Kole, P.C., Chakraborty, N.R. and Bhat, J.S. (2008). Analysis of variability,

correlation and path coefficients in induced mutants of aromatic non-basmati

rice. Trop. Agric. Res. Ext. 11: 60-64.

Kumar R., Suresh B.G., Ravi K., Sandhya P.K.R. (2014). Genetic variability,

correlation and path coefficient studies for grain yield and other yield

attributing traits in rice (Oryza Sativa L.) Intl J. Life Sci. Res. 4: 229-234.

Kumar, M., Sharma, P. R., Krakash, N and Singh, P. K. (2009). Selection criteria for

high yielding genotypes in early generations of Rrice. SAARC J. Agri., 7(2):

37-42.

107

Kumari, R.U., Rangasamy, P. and Gomez, S.M. (2003). Phenotypic differentiation in

indica-japonica wide compatible varieties in rice (Oryza sativa L.). Plant

Arc. 3(1): 141-142.

Lafarge, T.and Bueno, C.S. (2009). Higher crop performance of rice hybrids than elite

inbreds in the tropics: 2. does sink regulation, rather than sink size, play a

major role? Field Crops Res. 10 (3):245-254.

Li, Q.L., Guo, G.Z., Jiang, G. and Zhu, V. (1991). Study on high yield breeding and

genetic analysis of yield components of main rice cultivars in Jilin. Heriditas

China. 13(5): 3-6.

Martin, J.H., Waldren, R.P. and David, L.S. (2006). Principles of field crop

production.4th ed.Pearson Prentice Hall, Upper saddle River, New Jrsey

Columbus, Ohio.

Mahto, R.N., Yadava, M.S. and Mohan, K.S. (2003). Genetic variation, character

association and path analysis in rain fed upland rice. Indian J. Dryland

Agril. Res. Dev. 18(2): 196-198.

Manual, W.W. and Prasad, M.N. (1993). Genetic variability in medium duration rice

Oryza sativa. 30: 355-356.

Mehetre, S.S., Mahajan, C.R., Patil, P.A., Lad, S.K. and Dhumal, P.M. (1996).

Variability, heritability, correlation, path analysis and genetic divergence

studies in upland rice. Intl. Rice Res. Notes. 19(1): 8-10.

Mishra, D., Mishra, N.C., Das, G.B. and Patra, G.J. (1996). Genetic variability,

interrelationship and performance of some scented rice genotypes. Env. Ecol.

14(1):150-153.

Mulugeta, S., Sentayehu, A. and Kassahun, B.(2012). Genetic variability, heritability,

correlation coefficient and path analysis for yield and yield related traits in

upland rice (Oryza sativa L.). J. Plant Sci. 7: 13-2.

108

Mustafa, M.A. and Elsheikh, M.A.Y. (2007). Variability, correlation and path co-

efficient analysis for yield and its components in rice. African Crop Sci. J.

15(4): 183-189.

Nagarajun, C., Reddi S. K. M., Hariprasad, R. and Sudhakar, P.(2013). Correlation

between traits and path analysis coefficient for grain yield and other

components in rice (oryza sativa L) genotypes. Intl. J. App. Biol. Pharm.

Tech.. 2: 137-142.

Nanda, J.S. (2002). Rice Breeding and Genetics: Research priorities and challenges.

Oxford and IBH Publishing Co. Pvt. Ltd., New Delhi. pp. 1-15.

Nandeshwar, B.C., Pal, S., Senapati, B.K. and De, D.K. (2010). Genetic variability

and character association among biometrical traits in F2 generation of some

rice crosses. Ele. J. Plant Breed. 1(4): 758-763.

Neogi, G. M. (2014). Why Aus rice cultivation needs to be re-introduced. The daily

sun. Editorial (November 1).

Padmaja, V., Radhika, K., Rao, L.S. and Padma, V. (2008). Studies on variability,

heritability and genetic advance for quantitative characters in rice (Oryza

sativa L.). J. Plant Genet. Res. 21(1): 196-198.

Padmavathi, N., Mahadevappa, M. and Reddy, O.U.R. (1986). Associations of

various yield components in rice (Oryza sativa L.) Crop Res. Hisar. 12(3):

353-357.

Palaniswamy, K.M. and Kutty, K.K. (1990). Effect of tiller height and flowering

duration on panicle length in three rice varieties. (Oryza sativa L). Rice. Sci.

27(4): 433-435.

Pandey, P.and. Anurag P.R. (2010). Estimation of genetic parameters in indigenous

rice. J.Bioflux Society. 2: 79-84.

Pandey, V.K. and Awasthi, L.P. (2002). Studies on genetic variability for yield

contributing traits in aromatic rice. Crop Res. Hisar. 23(2): 214-218.

109

Pandey, V.K. and Awasthi, L.P. (2002). Studies on genetic variability for yield

contributing traits in aromatic rice. Crop Res. Hisar. 23(2): 214-218.

Patel, J.R., Saiyad, M. R., Prajapati, K. N., Patel, R. A. and Bhavani, R. T.(2014).

Genetic variability and character association studies in rainfed upland rice

(Oryza sativa L.) .Electronic J.Plant Breed., 5(3): 531- 537.

Patil, P.V., Saragi, A.K. and Sharivastava, M.N. (2003). Genetic analysis of yield and

quality traits in traditional aromatic rice accessions of rice. J. Maharashtra

Agril. Univ. 28(3): 255-258.

Patil, P.V. and Sarawgi, A.K. (2005). Studies on genetic variability, aromatic rice

accessions. Ann. Plant Physiol. 19(1): 92-95.

Poehlman, M. J. and Sleper, D.A. (1995). Breeding field crops 4th Ed. Iowa State

University Press. Ames, Iowa. p.218.

Prajapati, M., Singh, C.M., Suresh, B.G., Lavanya, G.R. and Jadhav, P. (2011).

Genetic parameters for grain yield and its component characters in rice. Ele. J.

Plant Breed. 2(2): 235-238.

Prasad, B., Patwary, A.K. and Biswas, P.S. (2001). Genetic variability and selection

criteria in fine rice (Oryza sativa L.). Pakistan J. Biol. Sci. 4(10): 1188-1190.

Purseglove, J.W. (1985). Tropical crops: Monocotyledons.English Language Book

Society,Longman, England. p.168.

Rafiqul, I.(2014). Selection of short duration high yielding aus materials in F4

generation of rice (Oryza Sativa L.) through aus-aus cross. MS Thesis.

Institute of Seed Tehnology, Sher-e-Bangla Agricultural University. Dhaka.

Rangare, N.R., Krupakar, A., Ravichandra, K., Shukla, A.K. and Mishra, A.K. (2012).

Estimation of characters association and direct and indirect effects of yield

contributing traits on grain yield in exotic Indian rice (Oryza sativa L.)

germplasm.Int. J. Agric. Sci. 2(1): 54-61.

Ray, J.K. (1985). Introduction to botany of the rice plant. 2nd Ed. Rice Research

Institute in India. Indian Council of Agricultural Research, New Delhi, India.

p. 5.

110

Reddy, Y.S. and Kumar, P.V.R. (1996). Studies on genetic variability, correlation and

path analysis in rice. New Botanist. 23: 129-133.

Rita, B., Sarawgi, A.K. and Verulkar, S.B. (2009). Study of heritability, genetic

advance and variability for yield contributing characters in rice. Bangladesh

J. Agril. Res. 34(2): 175-179.

Rokonuzzaman, M., Zahangir, M.S., Hussain, I. and Hossain, S. (2008). Genotypic

variability of the components and their effects on rice yield: Correlation and

path analysis study. Italian J. Agron. 3(2): 131-134.

Sabesan, T., Suresh, R. and Saravanan, K. (2009). Genetic variability and correlation

for yield and grain quality characters of rice grown in coastal saline low land

of Tamilnadu. Ele. J. Plant Breed. 1: 56-59.

Sabouri, H., Rabiei, B. and Fazalalipour, M. (2008). Use of selection indices based on

multivariate analysis for improving grain yield in rice. Rice Sci. 15 (4): 303-

310.

Sadeghi, S.M. (2011). Heritability, phenotypic correlation and path coefficient studies

for some agronomic characters in landrace rice varieties. World Appl. Sci. J.

13(5): 1229-1233.

Salam, M. A., Iftekharuddaula, K. M., and Siddique, A. B. (2014). Strategic plan for

increasing aus and aman rice cultivation in Bangladesh. Paper presented at the

workshop of BRRI on March, 2014.

Sanjeev, S. (2005). Genetic analysis of creation mutant lines of bashmati rice in M3

generation. Crop Res. Hisar. 29 (3): 462-465.

Sangeeta, M., Girl, D.G., Jahagirdar, S.W., Giri, M.D. and Mahitkar, S. (2000).

Correlation studies in upland rice. Annals plant physiol. 14(2): 195-197.

Sankar, P.D., Sheeba, A. and Anbumalarmathi, J. (2006). Variability and character

association studies in rice (Oryza sativa L.). Agric. Sci. Digest 26(3): 182-184.

Sarma, M.K. and Bhuyan, J. (2004). Genetic variability and divergence studies in Aus

rice (Oryza sativa L.). Adv. Plant Sci. 17(1): 323-328.

111

Satheeshkumar, P. and Saravanan, K. (2012). Genetic variability, correlation and path

analysis in rice (Oryza sativa L.). Int. J. Curr. Res. 4(9): 82-85.

Satyanarayana, P.V., Srinivas, T., Reddy, P.R., Madhavilatha, L. and Suneetha, Y.

(2005). Studies on variability, correlation and path coefficient analysis for

restorer in rice (Oryza sativa L.). Res. Crops. 6(1): 80-84.

Satyavathi, C.I., Bharadwaj, C. and Subramanyam, D. (2001). Vraiability, correlation

and path analysis in rice varieties under different spacing. Indian J. Agril.

Res. 32(2): 79-84.

Sawant, D.S. and Patil, S.L. (1995). Genetic variability and heritability in rice. Ann.

Agril. Res. 16(1): 59-621.

Selvaraj, I.C., Nagarajan, P., Thiyagarajan, K., Bharathi, M. and Rabindran, R.

(2011). Genetic parameters of variability, correlation and path coefficient

studies for grain yield and other yield attributes among rice blast disease

resistant genotypes of rice (Oryza sativa L.). African J. Biol. 10(17): 3322-

3334.

Seyoum, M., Alamerew, S. and Bantte, K. (2012). Genetic variability, heritability,

correlation coefficient and path analysis for yield and yield related traits in

upland rice. J. Plant Sci. 7(1): 13-20.

Sharma, S.K. and Haloi, B. (2006). Characterization of crop growth variables in some

scented rice cultivars of Assam. Indian J. Plant Physiol. 6(2): 166-171

Sharma, S.K. and Haloi, B. (2006). Characterization of crop growth variables in some

scented rice cultivars of Assam. Indian J. Plant Physiol. 6(2): 166-171.

Shanthi, P. and Singh, J. (2001). Variability studies in induced mutants of Mahsuri

rice (Oryza sativa L.). Madras Agril. J. 88(10-12): 707-709.

Shashidhar, H.E., Pasha.F., Nanjunath, J., Vinlod, M.S. and Kanbar, A. (2005).

Correlation and path coefficient analysis in traditional cultivars and doubled

haploid lines of rain fed lowland Rice (Oryza sativa L.). J. Agric. 42(2): 156-

159.

112

Singh, A.K., Sharma, P. and Singh, P.K. (2013). Studies on genetic characteristic of

upland rice (Oryza sativa L.). Int. J. Agril. Env. Biot. 6(4): 515-520.

Singh, S.K., Singh, C.M. and Lal, G.M. (2011). Assessment of genetic variability for

yield and its component characters in rice (Oryza sativa L.). Res. Plant Biol.

1(4): 73-76.

Singh, R.K. and Omkar, S. (2006). Inheritance of scent in long spikelets rice

genotypes. Ann. Agril. Res. 27(2): 193-194.

Singh, B.D. (2001). Plant Breeding: Principles and methods. Kalyani Publishers, New

Delhi. pp. 896.

Singh, R.K., Gautam, P.L., Saxena, S. and Singh, S.(2000). Scented rice germplasm

conservation, evaluation and utilization. In: Aromatic Rice, Singh, R.K., U.S.

Singh and G.S. Khush (Eds.). Oxford and IBH Publishing, New Delhi, pp:

107-133.

Singh, R.K. and Chaudhary, B.D. (1985). Biometrical methods in quantitative genetic

analysis. Kalyani Publishers, New Delhi, India. pp.69-78.

Songsri, P., Joglloy, S., Kesmala, T., Vorasoot, N., Akkasaeng, C.P.A. and Holbrook,

C. (2008) . Heritability of drought resistance traits and correlation of drought

resistance and agronomic traits in peanut. Crop Sci., 48: 2245-2253.

Sravan, T., Rangare, N.R., Suresh, B. G.and Kumar, R. S. (2012). Genetic variability

and character association in rainfed upland rice (Oryza sativa .L). J. Rice

Res.,5( 1 & 2): 167-168

Subbaiah, P.V., Sekhar, M.R., Reddy, K.H.P. and Reddy, N.P.E. (2011). Variability

and genetic parameters for grain yield and its components and kernel quality

attributes in CMS based rice hybrids (Oryza sativa L.). Int. J. Appl. Biol.

Pharm. Tech. 2(3): 603-609.

Steel, R.G.and Torrie, J.H.(1984). Principles and procedure of statistics. pp .137-167.

Tang-YanLin, Wang-FuMin, Huang-JingFeng and Wang-XiuZhen. (2007). New

vegetation index and its application in estimating leaf area index of rice.

China Rice Sci. 14(3): 195-203.

113

Tripathi, S.N., Marker S., Pandey, P., Jaiswal, K.K. and.Tiwari, D.K. (2011).

Relationship between some morphological and physiological traits with grain

yield in bread wheat (Triticum aestivum Lem.). Trends App. Sci.Res., 6: 1037-

1045.

Tomar,J.B., Dabas, B.S., and Gautam, P.L. (2000). Genetic variability, correlation

coefficient and path analysis for quantitive characters under rainfed ecosystem

in the native landraces of rice. Indian J. Plant Genet. Res. 13(3): 239-246.

Tuwar, A.K., Singh, S.K., Sharma, A. and Bhati, P.K. (2013). Appraisal of genetic

varibility for yield and its component character in rice (Oryza sativa L.). Bio.

life. 1(3): 84-89.

Ullah, M. Z., Bashar, M. K., Bhuiyan, M. S. R., Khalequzzaman, M. and Hasan, M. J.

(2011). Interrelationship and cause-effect analysis among morpho-

physiological traits in biroin rice of Bangladesh. Int. J. Plant Breed. Genet. 5:

246-254.

UNDP. (1988). Land resources appraisal of Bangladesh for agricultural development.

Report 2: Agro-ecological Regions of Bangladesh, FAO, Rome. p. 212.

Verma, O.P., Santoshi, U.S. and Srivastava, H.K. (2002). Heterosis and inbreeding

depression for yield and certain physiological traits in hybrids involving

diverse ecotypes of rice (Oryza sativa L.). Indian J. Genet. 56(30): 267-278.

Vange, T. (2009). Biometrical studies on genetic diversity of some upland rice (Oryza

sativa L.). Acc. Nat. Sci. 7(1): 21-27.

Vishwakarma, D.N. Lalji, B., Maurya, D.M. and Maurya, K.N. (1989). Heriatbility

and genetic advance for yield and its components in rice. J. Agril. Res. 4(1):

37-39.

Wang-FuMin, Huang-JingFeng, Tang-YanLin and Wang-XiuZhen. (2007). New

vegetation index and its application in estimating leaf area index of rice.

China Rice Sci. 14(3): 195-203.

114

Wattoo, J.I., Khan, A.S., Ali, Z., Babar, M., Naeem, M., Amanullah, M. and Hussain,

N. (2010). Study of correlation among yield related traits path coefficient

analysis in rice (Oryza sativa L.). African J. Biol. 9(46): 7853-7856.

Yadav,S.K.,Paandey,P.,Kumar,B.and Suresh,B.G.(2011). Genetic architecture,

interrelation-ship and selection criteria for yield improvement in rice Pakistan

J. Biol. Sci. 14 (9): 540-545.

Yadav, R.K. (2010). Studies on genetic variability for some quantitative characters in

rice (Oryza sativa L.). Adv. Agril. Res. Int. 13: 205-207.

Yadav,S.K.,Suresh,B.G., Pandey, P. and Kumar, B. (2010). Assesment of genetic

variability, correlation and path association in rice (Oryza sativa L.). J.

Biol.Sci.18(0): 1-8.

Yadav, R.K. (1992). Genetic variability, correlation studies and their implication in

selection of high yielding genotypes of rice. Adv. Plant Sci. 5: 306-312.

Yaqoob, M., Nazir, H. and Rashid A. (2012). Assessment of genetic variability in rice

(oryza sativa l.) genotypes under rainfed conditions. Arid Zone Research

Institute, PARC, D. I. Khan, Pakistan. Agric. Res. 50:311-318 .

Yoshida, S.1983.Rice Welsh, J.R.(1990). Fundamentals of plant genetic and breeding.

John Wiley and Sons. New York.

Yoshida, S. (1983). Rice. In potential productivity of field crops under different

environments (Eds.WH Smith, SJ Banta).International Rice Research Institute,

Los Banos, Philippines ,pp. 103-127.

Yoshida, S. (1981). Fundamentals of rice crop science. International Rice Research

Institute, Los Banos Manilla, Phillippines. p. 269.

115

Zahid, A.M., Akhtar, M., Sabrar, M., Anwar, M. and Mushtaq, A. (2005).

Interrelationship among yield and economic traits in fine spikelet rice.

Proceedings of the Int. Seminar on Rice crop, October 2-3. Rice Research

Institute, Kala Sh

116

APPENDICES

Appendix I. Map showing the experimental site under the study

The experimental site under study

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Appendix II: Morphological, physical and chemical characteristics of initial soil

(0-15 cm depth) of the experimental site

A. Physical composition of the soil

Soil separates % Methods employed

Sand 36.90 Hydrometer method (Day, 1915)

Silt 26.40 Do

Clay 36.66 Do

Texture class Clay loam Do

B. Chemical composition of the soil

Sl. No. Soil characteristics Analytical data

1 Organic carbon (%) 0.82

2 Total N (kg/ha) 1790.00

3 Total S (ppm) 225.00

4 Total P (ppm) 840.00

5 Available N (kg/ha) 54.00

6 Available P (kg/ha) 69.00

7 Exchangeable K (kg/ha) 89.50

8 Available S (ppm) 16.00

9 pH (1:2.5 soil to water) 5.55

10 CEC 11.23

Source: Central library, Sher-e-Bangla Agricultural University, Dhaka.

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Appendix III. Monthly average Temperature, Relative Humidity and Total

Rainfall of the experimental site during the period from April,

2014 to July, 2014

Month Air temperature (ºc) Relative

humidity (%)

Rainfall (cm)

(total) Maximum Minimum

March,2014 31.50 23.30 40 0.04

April,2014 35.80 23.20 45 1.58

May,2014 35 24.20 58 2.63

June,2014 30.30 21.80 71.08 2.89

July,2014 33.45 25.50 65.43 4.55

Source: Bangladesh Meteorological Department (Climate & Weather Division),

Agargoan, Dhaka -1207