genetic variability, correlation and path analysis of …
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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: [email protected]
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
91
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
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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