final_correlation and path coefficient analysis in advanced wheat (triticum aestivum l.) genotypes...
TRANSCRIPT
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1 INTRODUCTION
1.1 Background
Wheat is one of the major staple food crop of the world. Wheat is originated in
southwest Asia in the area known as the Fertile Crescent. Wheat is grown in more than
17% of the cultivable land and is consumed by nearly 40% of the global population (Goyal
& Prasad, 2010; Peng et al., 2011). Wheat fulfils about 21% of the total calorie and 20% of
the protein requirements of more than 4.5 billion population in developing countries
(Braun et al., 2010). In Nepal, wheat is cultivated in 754468 Hectare area with the
production of 1883133 tons and yield potential 2.49 tons/ha (MOAD, 2014). This yield is
far below than the most producing countries of the world and is not sufficient to fulfill the
demands of growing population. Increase in production of wheat is necessary to minimize
the prevalent yield gap and to provide food security in developing countries. For this, the
ways to sustain and increase wheat productivity is must. The major efforts of wheat
breeders have been directed towards improving its grain yield. Further research is essential
to ensure stable wheat production under the more difficult environment for area expansion.
For that, development of varieties which are high yielding and adaptable to wide range of
environment is needed.
Wheat grain yield is a function of many parameters which have interrelations
among themselves and affect the grain yield directly or indirectly. Therefore, direct
improvement of yield has not been possible through traditional breeding techniques.
Chibber et al. (2014) reported that the traits affecting and influencing yield needs to be
identified and selection has to be exerted on those characters which show a close
association with grain yield. In agronomic and breeding studies, correlation coefficients are
generally employed to determine the relation of grain yield and yield components. Anwar
et al. (2009), Bhutta et al. (2005) and Ali and Shakor (2012) also reported that estimation
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of the correlation between yield and its components alone is not sufficient to understand
the importance of each one of these component in determining the grain yield. Thus, a
simple but standardized partial regression coefficient is required to split the correlation
coefficients in to the magnitude of direct and indirect effects of the set of independent
variables on the dependent variables. Path coefficient analysis provides more information
among variables than do correlation coefficients since this analysis provides the direct
effects of specific yield components on yield and indirect effects via other yield
components (Garcia del Moral et al., 2003, Arshad et al., 2006). Choudhry et al.(1986) has
cited that study of correlation and direct and indirect effects of yield components provides
the basis for successful breeding plan.
The purpose of this study, therefore, was to estimate correlation between yields and
yield attributing traits as well as the direct and indirect effects of these component traits on
yield. The information so derived could be exploited in devising further breeding strategies
and selection procedures to develop new varieties of wheat capable of high productivity.
1.2 Objectives
1.2.1 Broad objective
To determine the correlation and path analysis of yield and yield contributing
characters in advanced wheat genotypes.
1.2.2 Specific objectives
i. To evaluate the genotypes for yield components and their performance.
ii. To study the nature and magnitude of association among yield traits.
iii. To study the direct and indirect effects of attributing traits on yield through path
analysis.
iv. To assess the suitability of these genotypes in a breeding plan.
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2 LITERATURE REVIEW
Wheat is accorded a premier place among cereals because of the vast acreage
devoted to its cultivation, high nutritive value and its association with some of the earliest
and most important civilizations of the world (Chibber, S., 2014). Although the crop is
most successful between the latitudes of 30° and 60°N and 27° and 40°S (Nuttonson,
1955), wheat can be grown beyond these limits, from within the Arctic Circle to higher
elevations near the equator. Wheat is grown in Nepal, India, Bangladesh, Pakistan and
other regions of south Asia.
2.1 Taxonomy
Wheat (Triticum aestivum L.), a self-pollinated cereal crop, belongs to the tribe
Triticeae and family Poaceae. Widely cultivated species of Triticum is mostly hexaploid
(2n=6x=42). Either domesticated emmer or durum wheat hybridized with yet another wild
diploid grass (Aegilops tauschii) are used to make the hexaploid wheats. Out of 50 wheat
genotypes used under this experiment, 49 advanced wheat genotypes from CIMMYT were
used. Details of these advanced wheat genotypes with their origin and selection history is
given in (Appendix 2). Gautam used as a check variety in this research is a released variety
of Nepal in 2004 having yield potential of 5 mt/ha and with maturity days of 105-115.
Gautam is recommended for irrigated, both normal and late sown condition of whole terai,
taar and foot hills (<5000 m), irrigated medium to high fertility condition of whole terai,
taar and low altitude (<1000m) (MOAD, 2014).
2.2 Status of wheat production and utilization in Nepal
Wheat is the third most important staple food crop both in terms of area and
production after rice and maize in Nepal. Wheat is becoming more important in Nepalese
economy. Wheat is grown in 754468 ha. of land with production of 1883133 metric tons
and productivity as 2496 Kg/ha (MOAD, 2014). It contributes 18.8% of the total cereal
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production in the country. The portion of wheat area under cultivation consists of about
6.95% in mountain, 35.87% in hills and 57.18 % in Terai (MOAD 2014). According to
MOAD 2014, areas under improved and local wheat are 94.95% and 5.05% in Terai,
86.38% and 13.62 % in Hills and 92.25 % and 7.75% in Mountains respectively. In
irrigated condition, areas under improved wheat in terai is 78.95% ,areas under improved
and local wheat are 43.77% and 0.44% in Hills and 45.4 % and 0.41% in Mountains,
respectively. There is great diversity observed in wheat in Nepal. Several exotic genotypes
are also introduced in Nepal from CIMMYT and USAID (NARC, 1997). There are 35
improved wheat cultivars and modern cultivars have covered 90% of the wheat area in
Nepal (Bhatta et al., 2000, Joshi et al., 2005).
2.3 Breeding strategy through selection in wheat
Selection, which is mainly based on phenotypic characters, is the major technique
used in a breeding program. Response to selection depends on many factors such as the
interrelationship of the characters. Plant breeders work with some components related to
yield in the selection programs and it is very important to determine relative importance of
such characters contributing to grain yield directly or indirectly. The choice of best parents
for improvement of wheat is of paramount importance in breeding program. Yield being a
polygenic character is highly influenced by the fluctuations in environment. Hence,
selection of plants based directly on economic yield would not be very reliable (Mahajan
et al., 2011). For effective selection, information on association of character with yield and
among themselves and the extent of environmental influence on the expression of these
characters are necessary (Yağdı, 2009).
Success in breeding and having fruitful varieties of agricultural products with a
higher quality depends on knowledge about grain yield controlling genetic characters and
its relation with grain yield components, also to phenologic traits (Jafari, A., 2001). Also
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correlation between important and non-important traits provides plant breeding experts
with a significant assistance in indirect selection of important traits (Qulipor et al., 2004).
When there is positive association of major yield characters, component breeding would be
very effective but when these characters are negatively associated, it would be difficult to
exercise simultaneous selection for them in developing a variety (Nemati et al., 2009).
Path coefficient and correlation analyses are used widely in many crop species by plant
breeders to define the nature of complex interrelationships among yield components and to
identify the sources of variation in yield and to introduce better traits for grain yield and
determine the best parents for breeding programs. Knowledge derived in this way can be
used to develop selection criteria to improve grain yield in relation to agricultural practices
(Finne et al., 2000; Samonte et al.,1998; Sinebo, 2002).
2.4 Yield and yield attributing traits
The challenging environment we are in, the use of genetic variability present
naturally is a key to success of any breeding program, thus extensive use of genetic
resources can do wonders in plant breeding research. Developing high yielding varieties is
the top most priority of a breeder. In wheat, high yield coupled with better quality is the
most desirable type for wheat growers. Thus, study of yield attributing characters like days
to flag leaf emergence, booting, heading, flag leaf senescence, grain filling duration, spike
length, peduncle length, number of grains per spike, thousand grain weight, biomass yield,
harvest index, chlorophyll content of leaves, grain yield etc. are of great importance for
breeding wheat cultivars with increased grain yield potential, enhanced water use
efficiency, heat tolerance, end use quality, and durable resistance to important diseases and
pests which can contribute to meeting at least half of the desired production increases. The
remaining half must come through better agronomic and soil management practices and
incentive policies.
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2.4.1 Flag leaf
In cereals, flag leaf makes up approximately 75% of the effective leaf area that
contributes to grain fill (Miller, 1999). Although lower leaves also supply assimilates to the
grain, detachment of flag leaf considerably influence the grain yield (Khaliq et al., 2008).
Thus flag leaf is primary source of assimilates for grain filling and grain yield due to its
short distance from spike and it also stays green for longer time than other leaves (Khaliq
et al., 2004). Khaliq et al.(2008) also reported that removal of flag leaf during active
growth and grain filling duration influence the yield contributing characters like grains per
spike, grain weight per spike and 1000 grain weight.
2.4.2 Flag leaf senescence
Senescence is the final stage in the life span of a leaf, and leads to death and
abscission. Senescence is defined as the gradual deterioration of its functions with age, as
leaves change color because chlorophyll is broken down, water content is reduced and
membranes break down (Hafsi et al., 2000). The genotypes with slow senescence showed
the highest grain yield under drought and a significant negative correlation is found
between chlorophyll content and average senescence. As the wheat crop approached
maturity, the older (lower) leaves began to senesce first, losing chlorophyll and
transferring carbohydrates and protein to developing kernels in the head. Visually this
process could be observed as a gradual change in canopy color from a dark green to a light
yellow-brown condition.
2.4.3 Chlorophyll content measured by SPAD meter reading
A portable field unit for chlorophyll content determination, Soil Plant Analysis
Development (SPAD), has been extensively used especially to control nitrogen nutrition in
several crops (Pelton et al., 1995). Moreover, SPAD values are correlated with diverse
photosynthetic parameters, such as foliar structure (Araus et al., 1997), photosynthetic rate
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and adsorption of photosynthetic active radiation by the canopy (Earl and Tollenaar, 1997).
The SPAD-502, Minolta, Japan measures the amount of chlorophyll in the leaf, which is
related to leaf greenness, by transmitting light from light emitting diodes (LED) through a
leaf at wavelengths of 650 and 940 nm. High chlorophyll content in leaves was considered
as a favorable trait in crop production (Teng et al., 2004).
2.5 Correlation Coefficients
The extent of genetic variation is most important in any crop improvement and
yield is a final product of any field crops (Singh et al., 1995). Some of the characters are
highly associated among themselves and with seed yield. The analysis of the relationships
among these characters and their associations with seed yield is essential to establish
selection criteria. Sokoto et al. ( 2012), Mohammadi et al. ( 2012), Ahmad et al. (2010)
mentioned that the correlation coefficient measures the mutual relationship between
various plant characters and determines the component characters on which selection can
be based for the improvement in yield as an associated complex character. Abderrahmane
et al. (2013) reported that total biomass and number of grains per spike are positively
correlated with grain yield. A previous study (Majumder et al., 2008) also reported that
grain yield per plant was positively correlated with grains per spike, harvest index, spike
length and 1000 grain weight. In a study aimed to know relationships between grain yield
and yield components in bread wheat under different water availability, Mohammadi et al.
(2012), also reported that grain yield was positively correlated with plant height, spike
length, days to physiological maturity and test weight.
Simple correlation coefficients revealed that the association between the grain
yields with days to maturity were positive but non-significant, positive and highly
significant with number of grains per spike and 1000- grains weight (Suleiman et al.,
2014). Thousand-grain weight, number of grains per spike, and plant height showed
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significant positive correlations with grain yield (Nasri et al., 2014). Galelcha and
Hanchinal, (2013) reported that days to maturity had significant positive correlation with
spike length and biological yield and grain yield. This positive relationship may be because
the crop enjoyed favorable environmental conditions during growing season and hence, the
more the crop stayed green, the better source-sink advantage in terms of grain filling. They
also reported that strong and positive correlation was observed between days to flowering
and days to maturity, but the correlation of days to flowering with grain yield was negative
and non-significant, genotypic as well as phenotypic correlation between grain yield and
number of grains per spike, total biomass per plant, harvest index and 1000 kernel weight
were highly significant. Several other studies also reported positive correlation of plant
height with grain yield (Mohammadi et al., 2012; Peymaninia et al., 2012; Sokoto et al.,
2012; Zafarnaderi et al., 2013) but in contrary to this, correlation between plant height and
yield was observed negative and highly significant at both genotypic and phenotypic level
which indicates that selection of short stature genotypes may be effective for better grain
yield (Khokhar et al., 2010). A study by Iftikhar et al. (2012) indicated that grain yield had
positive correlation with peduncle length, spike length, grains per spike and 1000-grain
weight, whereas, negative correlation with days to heading, plant height and tillers per
plant. Mohammad et al. (2006), Mohammadi et al. (2012), Tsegaye et al. (2012) and
Zafarnaderi et al. (2013) also reported negative relationship between days to flowering and
grain yield per plant in their studies in advanced wheat lines.
2.6 Path Coefficients
Path coefficient analysis concept was originally developed by Wright in 1921, but
the technique was first used for plant selection and improvement by Dewey and Lu in
1959. Selections based on simple correlation coefficients without regarding to interactions
among yield and yield components may mislead the breeders to reach their main breeding
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purposes (Garcı´a del Moral et al., 2003). In agriculture, path analyses have been used by
plant breeders to assist in identifying traits that are useful as selection criteria to improve
crop yield (Dewey and Lu, 1959; Milligan et al., 1990). Path analysis is a tool that is
available to the breeder for better understanding the causes involved in the associations
between traits and to partition the existing correlation into direct and indirect effects,
through a main variable (Lorencetti et al., 2006).
Path coefficient analysis measures the inter-association among yield components
for their direct and indirect effects on grain yield (Singh and Chaudhary, 1979). Yield is a
complex trait contributed by several components, therefore, we have to find out which
components contribute more to yield. The reason is that yield components are simple traits
with higher heritability than yield which makes it easier for improvement with the use of
path coefficient analysis (Farshadfar et al., 2012). The investigation of direct and indirect
effects of various characters on yield has major importance to increase the yielding
capacity of bread wheat. For this reason, many of the studies on correlation and path
analyses have been conducted in field crops. The aim of path coefficient analysis is to be
able to present an appropriate interpretation of correlation between variables, by creating
cause and effect models (Solymanzadeh et al., 2007). Path coefficient analysis divides the
correlation coefficients into direct and indirect effects (Garcia Del Moral et al., 2003).
If cause and effect relationship is well defined, it is possible to represent the whole
system of variables in the form of a diagram, known as path diagram. The advantage of
path diagram is that a set of simultaneous equations can be written directly from the
diagram and a solution of these equations provides information on direct and indirect effect
of these causal factors. A study on path analysis, Iftikhar et al. ( 2012) indicated that
1000-grain weight had the highest positive direct effect on yield followed by spike length
and days to heading while, plant height, grains per spike and peduncle length had negative
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direct effect on yield. So, these results suggested that traits such as spike length and 1000-
grain weight having positive correlation and direct effect on grain yield can be used as
suitable selection criteria to develop high yielding genotypes. Path coefficient analysis
revealed that plant height and days to heading, leaf area index and days to maturity had
negative direct effect on yield (Suleiman et al., 2014). Path analysis showed the most
significant and positive direct effect by harvest index on grain yield whereas harvest index
also showed positive indirect effect on grain yield (Nasri et al., 2014). Joshi et al. (2008)
reported that maturity days exerted the greatest influence directly upon yield. Path analysis
indicated that biomass, harvest index, days to flowering and plant height imparted
significant direct influence on grain yield (Gelalcha and Hanchinal, 2013). Tsegaye et al.
(2012) reported that biological yield and harvest index should be considered as selection
criteria in improving the grain yield as he found the direct contribution of those traits on
grain yield of durum wheat genotypes.
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3 MATERIALS AND METHOD
3.1 Research site
The field experiment was conducted at the research field of Institute of Agriculture
and Animal Science (IAAS), Rampur Campus in the academic year of 2014-2015 from 22
November 2014 to 25 March 2015. It is located at 27°39′14″N latitude and 84°21′5″E
longitude with an elevation of 228 meters above mean sea level. The soil type is sandy
loam.
3.2 Climatic situation
The climatic data of research period at Rampur, Chitwan from November 2014 to
March 2015. Agro-meteorological data of wheat growing period were taken from
meteorological station of NMRP, Rampur, Chitwan. The climatic situation prevailed is
presented in the graph in Figure 1.
17-Nov
24-Nov
1-Dec
8-Dec
15-Dec
22-Dec
29-Dec
5-Jan
12-Jan
19-Jan
26-Jan
2-Feb
9-Feb
16-Feb
23-Feb
2-Mar
9-Mar
16-Mar
0
5
10
15
20
25
30
35
0102030405060708090100
MAX T MIN T RAINFALL RH
MONTHS
TE
MP
(°C
) A
ND
RA
INF
AL
L(m
m)
RE
LA
TIV
E H
UM
IDIT
Y(%
)
Figure 1: Climatic situation prevailed during research period
3.3 Plant materials
The plant materials used in this experiment were collected from CIMMYT and
Research stations of NARC. There were total of 50 wheat genotypes under study. Out of
which, 49 advanced wheat genotypes from CIMMYT and one local variety (Gautam) from
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NARC were included as plant material for the study. The complete list of all the genotypes
included in this study is presented in the Appendix 2.
3.4 Experimental layout
The experiment was laid out in alpha lattice design with two replications. There
were five blocks within a replication and ten genotypes within a block. There were 50 plots
per replication and the plot size was 4m. X 1.5m. = 6 m2 each and total number of plots
were 100. The fifty genotypes were allotted randomly in fifty plots of each replication. The
row spacing of each plot for wheat sowing was 25 cm. and there were six rows per plot.
The spacing between two plots was 50 cm and inter spacing between two replication was 1
m. The layout of the experimental field is presented in the Appendix 5. Where entry
numbers are the genotypes and respective genotypes were allotted in the plots in serpentine
motion from B1 to B5 which are presented in the Appendix 2.
3.5 Crop management
Land preparation was performed by ploughing two times with disc harrow followed
by leveling. Farm yard manure was applied at the rate of 15 tons/ha and chemical
fertilizers were applied at the rate of 120:60:60 Kg. NPK/ha. Sowing was done on
November 22, 2014 by hand in rows continuously. Full dose of Phosphorus and Potassium
and half dose of Nitrogen were applied at the time of sowing. Remaining one fourth dose
of Nitrogen was top dressed after first irrigation during CRI stage and second split of
Nitrogen was applied during booting stage at 65 DAS. The experiment was conducted
under rainfed condition but one irrigation was given at three weeks after sowing and
another at flowering stage was given for better crop establishment. Harvesting of the crop
was done on the basis of the physiological maturity of each genotypes from 18 March 2015
onwards.
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3.6 Data collection
Five plants were selected randomly for each observation in each treatment without
tagging. Data were collected for different quantitative agronomic characters as per the
genotype as follows:
3.6.1 Yield and Yield Attributing Traits
3.6.1.1 Days to Flag Leaf emergence (DFL)
Date in which 50% of the plants population in a plot had flag leaf emerged was
recorded as the days to flag leaf emergence.
3.6.1.2 Days to Booting (DB)
Date in which 50% of the plants population in a plot had booted were recorded as
days to booting.
3.6.1.3 Days to Heading (DH)
Date in which 50% of the plants population in a plot had come out of the flag leaf
and spike had been visible clearly was recorded as the days to heading.
3.6.1.4 Days to Anthesis (DA)
It was recorded as the date in which 50 % of plant population in a plot had exposed
their flowers out of the spikelet.
3.6.1.5 Flag Leaf Area (FLA)
Five plants were randomly selected from each plot and flag leaves were collected
from these selected plants to determine the flag leaf area in cm2 using the formula;
FLA = Length X Breadth X 0.74, where 0.74 is a constant value.
3.6.1.6 Chlorophyll Content and AUSRC
Self-calibrating Minolta Chlorophyll Meter (SPAD-502, Minolta, Japan) was used
to measure the amount of total chlorophyll content present in the flag leaf. After anthesis,
flag leaf of randomly selected five plants was used from each plot to determine
Chlorophyll content. Three readings of single flag leaf were made from three different
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parts of the flag leaf and total of 15 readings were made per plot and value was averaged
and recorded. Area under SPAD retread curve (AUSRC) was calculated using the formula;
AUSRC =
Where, Si represents the SPAD value recorded at various
3.6.1.7 Days to Flag Leaf Senescence (DFLS)
It was recorded as the date in which 50% of the flag leaves had lost their 90% green
color and turned yellow.
3.6.1.8 Days to maturity (DM)
It was recorded as the date in which glumes had lost their chlorophyll and turned
yellow in more than 90% of the spikes in a plot.
3.6.1.9 Plant height (PH)
The plant height was measured from the base of the plant to the tip of the apical
spikelet, excluding awns of the main tiller using meter scale. The measurement was
expressed in cm.
3.6.1.10 Spike Length (SL)
It was recorded as the length from the base of the spike to the top of the spike
ignoring awn length and expressed in cm.
3.6.1.11 Peduncle Length (PL)
It was recorded as the length from the last node of the wheat stem to the base of the
spike. It was expressed in cm.
3.6.1.12 Grain number per spike (GS)
Five spikes from five randomly selected plants were hand threshed to record the
number of grains per spike. Average value for each treatment was then calculated.
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3.6.1.13 Grain yield per spike (g)
Total grains of the main spikes used for recording grains per spike were weighed
using electronic balance to record average grains per spike.
3.6.1.14 Thousand grains weight (TGW)
After harvesting of the crop and drying, 500 seeds from each plot were counted and
weighed using an electronic balance. The value was then converted to thousand grain
weight and expressed in grams.
3.6.1.15 Biological Yield (BY)
It was recorded as the total weight of the wheat harvested along with their spikes
and weight was taken after 2 days of sun drying of wheat in field and weight was taken for
each plot. It was further converted in to Kg. per hectare.
Biological Yield per plot (g)BY (Kg./Ha. ) = ----------------------------------------- 10000 m2 1000 Plot size in m2
3.6.1.16 Grain Yield (GY)
It was taken as the weight of the wheat grains after threshing. It was converted in to
Kg. per hectare.
Grain Yield per plot (g)GY (Kg./Ha. ) = ---------------------------------------- 10000 m2
1000 Plot size in m2
3.6.1.17 Harvest Index (HI)
It is the ratio of the economic yield to the biological yield. The formula for HI in
this experiment was: HI = GY/BY
3.7 Statistical analysis
Data entry and processing was carried out using Microsoft Office Excel and Word
2013 software and mean and standard deviations for all quantitative traits were computed.
Analysis of variance (ANOVA), mean performance and DMRT was calculated by using R-
Studio (V. 0.99, 2015). Linear correlation was computed by using Microsoft Excel 2013
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and featured by SPSS 21 and path coefficient computation and scatterplots of each traits
were computed and displayed by using Microsoft Office Excel 2013. Histograms were
generated by using MINITAB 17.
3.8 Statistical techniques used for data analysis
3.8.1 Analysis of variance
The analysis of variance for different characters was carried out by using the mean
data for each location separately in order to partition the variability due to different
sources. The method given by Andreas et al. (2007) was followed.
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Table 1: Analysis of Variance (ANOVA) for Alpha Lattice Design
Sources of
Variations
Degree of
freedom (Df)
Sum of
Squares (Sum Sq)
Mean squares
(Mean Sq)
F value Pr(>F)
Replications r-1 SSr MSr MSr/MSe
Genotypes g-1 SSg MSg MSg/MSe
Blocks (within
replications)
rb-r SSb MSb MSb/MSe
Residual/Error rg-rb-t+1 SSe MSe
Total n-1 SSt
3.8.2 Correlation and path analysis
The intensity of linear relationship between two variables x and y, Karl Pearson’s
coefficient of correlation known as correlation coefficient rxy was used. It is given by:
rxy = Cov(x , y)
√ [V ( x )V ( y )]
Variance and covariance were computed by following formulae:
V(X) = 12!
¿2 −∑ (x¿)(x)n
¿] and V(y) = = 12! [∑ y2
−∑ ( y )( y )n
]
Cov (x,y) = 1n [∑ xy−
∑ (x )∑ ( y )n
¿
Significance of correlation coefficient was computed by t-value. For this we used
SPSS 21. The general model used for correlation coefficient in this study was given below.
[A] = r1y, r2y, ……,r16y = correlation coefficients of traits 1 to 16.
[B] = r12, r13, …….r116 …….. (i)
r23, r24, ……r216 ………. (ii)
,r1515 ……… r1516 ………. (xv)
r1616 ………………..(xvi)
These values of (i) to (xvi) are the correlation coefficients for each of the traits
among themselves in the form of matrix and [B] is correlation matrix whereas [B]-1 is
inverse matrix.
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[C]= P1y, P2y, ……….., P16y where, [C] = [B]-1 X [A]
For Path coefficient analysis, the phenotypic correlation coefficients were
decomposed into direct and indirect effects by path coefficient analysis. One of the
variables under study was considered as dependent variable (effect) affected by the
independent variables (causes). The path coefficient was calculated by applying the
following equations indicating the basic relationship between correlation and path
coefficients suggested by Dewey, D.R. and K.H. Lu (1959).
riy = Piy + ri1 P1y + ri2 P2y + ……..+ ri(i-1) Piy : where, i= 1,2,3,4……n
Where, n is the number of independent characters (causes); to denote coefficients
of correlation between causal factors 1 to I and dependent character y, to the coefficients of
correlation among all possible combinations of causal factors and to denote the direct
effects of character 1 to i on the character y. The indirect effect of ith variable through jth
variable on y-the dependent variable was computed as PIY × rji. The path coefficients were
calculated as follows:
P1y = ∑ B1i riy , P2y = ∑ B2i riy , P3y = ∑ B3i riy and so on.
The effect of residual factor (z) which measures the path coefficient from
extraneous variables not included in the path coefficient analysis was estimated as follows;
Pzy - √ ¿R2) , Where, R2 = Coefficient of multiple determinations.
3.8.3 Mean performance
On the basis of individual plant observations, the population mean for each
character was computed as follows:
X= 1n∑i=0
n
xi
X = population mean, Xi = individual value, n= number of observations
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3.8.4 Range
The minimum and maximum values on the basis of individual plant observations
were used to indicate the range of the given character.
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4 RESULT AND DISCUSSION
4.1 Results
4.1.1 Mean performance and analysis of variance
4.1.1.1 Days to flag leaf emergence (DFL)
The average of days to flag leaf emergence of 50 wheat genotypes was 60 days.
Analysis of variance (ANOVA) revealed highly significant difference among the
genotypes for this trait (Appendix 1(a)). Genotype 20 (BAJ#1*2/TINKIO#1), 21
(BAJ#1*2//ND643/2*W BLL1) and 38 (FRANCOLIN#1/CHONTE//FRNCLN) had
lowest DFL (51 days) whereas genotype 9 (KACHU//KIRITATI/2*TRCH) had highest
DFL (73 days) (Table 2).
Histogram showed that among 50 genotypes, 9 genotypes had less than 55 DFL, 14
genotypes had in between 55-60 DFL, 16 genotypes had in between 60-65 DFL, 10
genotypes had in between 65-70 DFL and 1 genotype had more than 70 DFL (Histogram-
A).
4.1.1.2 Days to booting (DB)
The average of days to booting of 50 wheat genotypes was 66 days. Analysis of
variance (ANOVA) revealed highly significant difference among the genotypes for this
trait (Appendix 1(b)). Genotype 21 (BAJ #1*2//ND643/2*WBLL1) had lowest DB (59
days) whereas genotype 24 (WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2* WBLL1/4
/WAXWI NG*2/KRONSTAD F2004) had highest DB (75 days) but the genotypes 24, 26,
44, 32, 27, 28, 6, 18, 33, 2, 36, 31, 23, 3, 41, 29 and 13 were statistically similar in their
mean performance for days to booting (Table 2).
Histogram depicted that among 50 genotypes, 4 genotypes had less than 61 DB, 15
genotypes had in between 61-65 DB, 14 genotypes had in between 65-69 DB, 13
21
Genotypes had in between 69-73 DB and 4 genotypes had more than 73 DB (Histogram-
B).
4.1.1.3 Days to heading (DH)
The average of days to heading of 50 wheat genotypes was 72 days. Analysis of
variance (ANOVA) revealed highly significant difference among the genotypes for this
trait (Appendix 1(c)). Genotype 36 (PAURAQ/4/WHEAR/KUKUNA/3/C80.1/3*
BATAVIA//2* WBLL1/5/PAURAQUE #1) had lowest DH (60 days) whereas genotype 9
(KACHU//KIRITATI/2*TRCH) had highest DH (82 days) but the genotypes 9, 24, 26, 32,
44, 18, 33, 27, 23, 31 and 28 were statistically similar in their mean performance for days
to heading (Table 2).
Histogram showed that among 50 genotypes, 1 genotypes had less than 63 DH, 11
genotypes had in between 63-68 DH, 15 genotypes had in between 68-73 DH, 18
Genotypes had in between 73-78 and 5 genotypes had more than 78 DH (Histogram-C).
4.1.1.4 Days to anthesis (DA)
The average of days to anthesis of 50 wheat genotypes was 79 days. Analysis of
variance (ANOVA) revealed highly significant difference among the genotypes for this
trait (Appendix 1(d)). Genotype 21 (BAJ #1*2//ND643/2*WBLL1) and 38 (FRANCOLIN
#1/CHONTE//FRNCLN) had lowest DA (72 days) whereas genotype 9
(KACHU//KIRITATI/2*TRCH) had highest DA (90 days) but the genotypes 9 and 24
(WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2*WBLL1/4/WAXWING*2/KRONSTAD
F2004) were statistically similar in their mean performance for days to anthesis (Table 2).
Histogram revealed that among 50 genotypes, 14 genotypes had less than 76 DA,
11 genotypes had in between 76-80 DA, 16 genotypes had in between 80-84 DA, 7
Genotypes had in between 84-88 DA and 2 genotypes had more than 88 DA (Histogram-
D).
22
4.1.1.5 Flag leaf area (FLA)
The average flag leaf area of 50 wheat genotypes was 93.32 cm2. Analysis of
variance (ANOVA) revealed highly significant difference among the genotypes for this
trait (Appendix 1(e)).Genotype 4 (BAJ #1) had lowest FLA (57.10 cm2) whereas genotype
1 (GAUTAM) had highest FLA (161.73 cm2) but the genotypes 1, 50, 48, 40, 21 and 42
were statistically similar in their mean performance for FLA (Table 2).
Among 50 genotypes, histogram showed that 13 genotypes had less than 75 FLA,
20 genotypes had in between 75-100 FLA, 12 genotypes had in between 100-125 FLA, 3
Genotypes had in between 125-150 FLA and 2 genotypes had more than 150 FLA
(Histogram-E).
4.1.1.6 Area under SPAD retreat curve at anthesis (AUSRC)
The average AUSRC of 50 wheat genotypes was 561.07. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (f)). Genotype 26 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/
KIRITATI/2*TRCH /6/BAJ#1) had lowest AUSRC (412.05) whereas genotype 16
(FRET2*2/4/SNI/TR AP#1/3/KAUZ*2/TRAP//KAUZ/5/2*FRNCLN) had highest
AUSRC (656.84) but the genotypes 16, 3, 6, 19, 11, 27, 9, 40, 2, 18, 13, 45, 43, 10 and 32
were statistically similar in their mean performance for AUSRC (Table 2).
Histogram amid 50 genotypes showed that 1 genotypes had less than 460 AUSRC,
8 genotypes had in between 460-510 AUSRC, 14 genotypes had in between 510-560
AUSRC, 18 genotypes had in between 560-610 AUSRC and 9 genotypes had more than
610 AUSRC (Histogram-F).
4.1.1.7 Days to flag leaf senescence (DFLS)
The average DFLS of 50 wheat genotypes was 114 days. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
23
(Appendix 1(g)). Genotype 26 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/
KIRITATI/2*TRC H/6/BAJ #1) had lowest DFLS (109 days) whereas genotype 9
(KACHU//KIRITATI/2*TRCH) had highest DFLS (120 days) but the genotypes 9, 24, 32,
39, 44, 1, 8, 33 and 7 were statistically similar in their mean performance for DFLS (Table
2).
Histogram amongst 50 genotypes showed that 2 genotypes had less than 110.5
DFLS, 19 genotypes had in between 110.5-113 DFLS, 13 genotypes had in between 113-
115.5 DFLS, 7 genotypes had in between 115.5-118 DFLS and 9 genotypes had more than
120.5 DFLS (Histogram-G).
4.1.1.8 Days to maturity (DM)
The average DM of 50 wheat genotypes was 121 days. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1(h)). Genotype 38 (FRANCOLIN#1/CHONTE//FRNCLN) had lowest DM
(115 days) whereas genotype 9 (KACHU//KIRITATI/2*TRCH) had highest DM (130
days) (Table 2).
Histogram revealed that among 50 genotypes, 8 genotypes had less than 118 DM,
12 genotypes had in between 118-121 DM, 21 genotypes had in between 121-124 DM, 8
genotypes had in between 124-127 DM and 1 genotypes had more than 127 DM
(Histogram-H)
4.1.1.9 Plant height (PH)
The average PH of 50 wheat genotypes was 103.04 cm. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (i)). Genotype 4 (BAJ #1) had lowest PH (87.78 cm.) whereas genotypes 29
(DANPHE/PAURAQUE #1//MUNAL #1) had the highest plant height (114.92 cm.) but
24
the genotypes 29, 46, 30, 27, 25, 22, 21, 50, 24, 35, 38, 44, 49, 28, 39, 23, 42 and 48 were
statistically similar in their mean performance for PH (Table 2).
Histogram revealed that among 50 genotypes, 3 genotypes had less than 95 cm PH,
10 genotypes had in between 95-100 cm PH, 19 genotypes had in between 100-105 cm
PH, 16 genotypes had in between 105-110 cm PH and 2 genotypes had more than 110 cm
PH (Histogram-I).
4.1.1.10 Spike length (SL)
The average SL of 50 wheat genotypes was 10.52 cm. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (j)). Genotype 4 (BAJ #1) had lowest SL (7.57 cm) whereas genotype 28
(KACHU*2/SUP152) had highest SL (12.67cm) but the genotypes 28, 40, 24, 36, 1, 17,
21, 48, 33, 30, 25, 27, 41 and 2 were statistically similar in their mean performance for
SL(Table 2).
Histogram revealed that among 50 genotypes, 3 genotypes had less than 9.3 cm SL,
16 genotypes had in between 9.3-10.1 cm SL, 13 genotypes had in between 10.1-10.9 cm
SL, 11 genotypes had in between 10.9-11.7 cm SL and 7 genotypes had more than 11.7 cm
SL (Histogram-J).
4.1.1.11 Peduncle length (PL)
The average PL of 50 wheat genotypes was 39.5 cm. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (k)). Genotype 41 (TAM200/PASTOR//TOBA97/3/FRNCLN/4/WHEAR//2*
PRL/2*PASTOR) (32.37 cm.), 32 (KIRITATI//HUW234+LR34/PRINIA /3/FRANCOLIN
# 1/4/BAJ#1) (32.31 cm.) had lowest PL whereas genotype 1 (Gautam) had highest PL
(45.26cm.) but the genotypes 1, 3, 43, 42, 23, 48, 46, 50, 49, 39, 10, 15, 20 and 35 were
statistically similar in their mean performance for PL (Table 2).
25
Histogram depicted that among 50 genotypes, 4 genotypes had less than 35 cm PL,
12 genotypes had in between 35-38 cm PL, 16 genotypes had in between 38-41 cm PL, 14
genotypes had in between 41-44 cm PL and 4 genotypes had more than 44 cm PL
(Histogram-K).
4.1.1.12 Grains per spike (GS)
The average GS of 50 wheat genotypes was 52. Analysis of variance (ANOVA)
revealed highly significant difference among the genotypes for this trait (Appendix 1 (l)).
Genotype 29 (DANPHE/PAURAQUE #1//MUNAL #1) and 30
(KIRITATI//2*PRL/2*PASTOR/3/CHONTE/5/PRL/2*PASTOR/4/CHOIX/STAR/3/HE1/
3*CNO79//2*SERI) had lowest GS (38 and 38 respectively) whereas genotype 10
(KIRITATI//HUW234+LR34/PRINIA/3/BAJ#1) had highest GS (75) but the genotypes
10, 4 and 3 were statistically similar in their mean performance for GS (Table 2).
Histogram amongst 50 genotypes showed 3 genotypes had less than 44 GS, 21
genotypes had in between 44-50 GS, 8 genotypes had in between 50-56 GS, 13 genotypes
had in between 56-62 GS and 5 genotypes had more than 62 GS (Histogram-L).
4.1.1.13 Thousand Grain weight (TGW)
The average TGW of 50 wheat genotypes was 30.32 g. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (m)). Genotype 28 (KACHU*2/SUP152) had lowest TGW (18.58 g) whereas
genotype 50 (SOKOLL/3/PASTOR//HXL7573/2*BAU/5/CROC_1/AE.SQUARROSA
(205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2) had highest TGW (39.17 g) but the
genotypes 50, 11, 12, 19, 4, 44, 17, 3, 6, 7, 48, 44, 1, 30, 49, 8, 21 and 46 were statistically
similar in their mean performance for TGW (Table 2).
Histogram revealed that among 50 genotypes, 2 genotypes had less than 21 g
TGW, 8 genotypes had in between 21-26 g TGW, 18 genotypes had in between 26-31 g
26
TGW, 17 genotypes had in between 31-36 g TGW and 5 genotypes had more than 36 g
TGW (Histogram-M).
4.1.1.14 Biological Yield (BY)
The average BY of 50 wheat genotypes was 13616 Kg/ha. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (n)). Genotype 26 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/
KIRITATI/2*TRCH /6/BAJ #1) had lowest BY (6742 Kg/ha) whereas genotype 44
(BAVIS/NAVJ07) had highest BY (17811 Kg/ha) but the genotypes 44, 32, 39, 17, 42, 1,
49 and 10 were statistically similar in their mean performance for BY (Table 2).
Histogram revealed that among 50 genotypes, 1 genotypes had less than 8000
Kg/ha BY, 1 genotypes had in between 8000-10000 Kg/ha BY, 5 genotypes had in
between 10000-12000 Kg/ha BY, 19 genotypes had in between 12000-14000 Kg/ha BY,
21 genotypes had in between 14000-16000 Kg/ha and 3 genotypes had BY more than
16000 Kg/ha (Histogram-N).
4.1.1.15 Harvest index (HI)
The average HI of 50 wheat genotypes was 0.41. Analysis of variance (ANOVA)
revealed highly significant difference among the genotypes for this trait (Appendix 1(o)).
Genotype 26 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KIRITATI/2* TRC
H/6/BAJ#1) had lowest HI (0.26) whereas genotype 20 (BAJ #1*2/TINKIO #1) had
highest HI (0.46) but the other genotypes except 37, 16, 28, 40, 42, 9, 10, 31, 45, 24 and
26 were statistically similar in their mean performance for HI (Table 2).
Histogram revealed that among 50 genotypes, 1 genotypes had less than 0.29 HI , 2
genotypes had in between 0.29-0.34 HI, 13 genotypes had in between 0.34-0.39 HI, 24
genotypes had in between 0.39-0.44 HI and 10 genotypes had HI more than 0.44
(Histogram-O).
27
4.1.1.16 Grain yield (GY)
The average GY of 50 wheat genotypes was 5505 Kg/ha. Analysis of variance
(ANOVA) revealed highly significant difference among the genotypes for this trait
(Appendix 1 (p)). Genotype 26 (FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/
TRAP//KAUZ/5 /KIR ITATI/2*TRCH/6/BAJ#1) had lowest GY (1686 Kg/ha) whereas
genotype 44 (BAVIS/NAVJ07) had highest GY (7259 Kg/ha) but the genotypes 44, 32,
39, 17, 27, 30, 34, 21, 33, 5, 12, 49, 18, 46, 50 and 23 were statistically similar in their
mean performance for GY (Table 2).
Histogram revealed that among 50 genotypes, 1 genotypes had less than 2000
Kg/ha GY, 1 genotypes had in between 3000-4000 Kg/ha GY, 9 genotypes had in between
4000-5000 Kg/ha GY, 26 genotypes had in between 5000-6000 Kg/ha GY and 11
genotypes had GY in between 6000-7000 Kg/ha whereas 2 genotypes had in more than
7000 kg/ha. (Histogram-P).
28
Histogram-A Histogram-B
29
Histogram-C Histogram-D
Histogram-E Histogram-F
Histogram-G Histogram-H
30
Histogram-I Histogram-J
Histogram-K Histogram-L
Histogram-M Histogram-N
31
Histogram-O Histogram-P
Figure 2: Histograms of sixteen traits of wheat genotypes
Histogram (A-H): A- Days to Flag Leaf emergence, B- Days to booting, C- Days to
heading, D- Days to anthesis, E- Flag leaf area, F- Area under SPAD retread curve, G-
Days to flag leaf senescence, H- Days to maturity.
Histograms (I-P): I- Plant height, J- Spike length, K- Peduncle length, L- Grains
per spike, M- Thousand grain weight, N- Biological yield, O- Harvest index, P- Grain
yield in kilograms per hectare.
32
Table 2: Mean performance of the 50 genotypes with their CV and Significance test values
Genotypes DFL DB DH DA FLA AUSRC DFLS DM
1 54.90 q-s 61.82 k-n 67.54 i-m 74.15 s-u 161.73 a 527.87 j-n 119.70 a-c 121.50 g-m
2 64.98 d-g 70.35 a-e 74.77 b-h 83.89 b-d 65.61 j-n 609.62 a-g 115.43 f-i 122.16 e-k
3 62.95 g-j 69.84 a-e 74.28 b-h 83.00 d-f 99.48 c-l 646.97 ab 116.41 d-h 121.14 h-n
4 55.38 qr 61.67 k-n 65.59 l-n 73.08 u-w 57.10 n 505.34 k-n 110.46 p-r 116.38 s-u
5 56.40 a-q 62.32 j-n 67.54 i-m 74.15 s-u 74.75 h-n 490.07 n 111.20 o-r 117.00 r-u
6 64.98 d-g 70.85 a-e 75.27 b-g 83.39 c-e 91.55 e-n 643.92 ab 115.43 f-i 121.16 h-n
7 57.95 n-p 63.34 f-n 69.28 f-m 79.00 j-m 88.90 e-n 550.20 f-n 118.41 a-e 121.64 g-m
8 59.38 l-n 65.17 e-m 68.59 h-m 78.08 l-o 100.49 c-k 546.12 g-n 118.96 a-d 121.38 g-n
9 72.98 a 61.00 l-n 82.30 a 89.58 a 64.14 j-n 620.73 a-f 120.41 a 130.27 a
10 60.48 k-m 66.00 d-l 70.30 e-m 80.58 g-j 112.38 c-h 593.96 a-i 111.41 n-r 120.77 i-n
11 57.95 n-p 64.84 e-m 70.28 e-m 78.50 k-n 71.09 i-n 629.65 a-d 111.41 n-r 117.64 p-t
12 53.97 r-t 58.99 mn 64.72 mn 72.08 vw 89.80 e-n 547.88 g-n 113.14 i-o 118.25 o-s
13 64.52 e-h 69.01 a-h 74.46 b-h 83.42 c-e 96.23 d-m 603.10 a-i 112.36 k-p 118.89 n-r
14 57.05 0-q 63.17 g-n 67.49 i-m 76.00 p-r 84.19 f-n 530.84 j-n 112.86 j-p 120.53 j-o
15 56.02 p-r 63.01 h-n 68.96 g-m 76.92 n-p 112.53 c-h 493.55 mn 112.36 k-p 119.89 k-p
16 63.05 g-j 68.17 b-j 72.99 d-j 81.50 f-h 94.49 d-n 656.84 a 114.86 f-l 124.03 b-f
17 55.47 qr 62.99 h-n 69.22 f-m 74.08 s-u 107.18 c-i 559.78 d-n 112.64 k-p 120.75 i-n
18 65.56 d-f 70.52 a-e 76.95 a-d 82.81 d-f 95.59 d-m 609.15 a-h 115.37 f-j 122.92 e-i
19 58.45 m-0 65.34 e-l 70.28 e-m 78.50 k-n 120.35 b-f 637.17 a-c 114.91 f-k 120.64 i-n
20 51.06 u 60.52 l-n 66.45 j-m 73.31 t-w 118.03 b-g 547.20 g-n 110.37 p-r 117.42 q-t
21 51.09 u 58.68 n 63.98 mn 71.89 w 130.10 a-d 567.87 c-l 111.37 n-r 117.55 p-t
33
22 61.59 i-l 66.02 d-l 72.95 d-j 80.70 g-i 81.93 g-n 545.03 g-n 111.89 m-q 120.95 h-n
23 63.98 f-h 69.85 a-e 75.77 a-f 83.39 c-e 78.32 h-n 566.57 c-l 112.93 i-p 121.16 h-n
24 69.06 b 75.02 a 79.95 ab 88.81 a 65.74 j-n 501.18 k-n 120.37 a 125.92 bc
25 60.01 l-n 65.99 d-l 71.72 d-l 79.96 h-k 71.97 i-n 531.53 j-n 111.16 o-r 119.28 m-r
26 68.98 b 73.35 ab 79.77 a-c 84.89 bc 69.33 i-n 412.05 o 108.93 r 122.16 e-k
27 67.06 b-d 72.02 a-d 76.45 a-e 84.31 b-d 85.55 f-n 624.20 a-e 116.37 e-h 124.42 b-e
28 67.01 b-d 70.99 a-e 75.72 a-f 84.46 b-d 63.38 k-n 563.90 d-m 114.16 h-m 125.28 b-d
29 61.59 i-l 69.18 a-g 74.48 b-h 80.89 g-i 61.78 l-n 548.27 g-n 111.37 n-r 120.55 j-o
30 55.59 qr 63.02 h-n 68.95 g-m 75.20 q-s 94.65 d-m 500.06 l-n 110.89 o-r 117.95 p-t
31 64.47 e-h 69.99 a-e 75.72 a-f 83.08 d-f 58.79 mn 506.58 k-n 114.64 g-l 120.75 i-n
32 67.90 bc 72.82 a-c 78.04 a-d 85.15 b 67.14 j-n 589.47 a-j 120.20 a 126.00 b
33 63.43 f-i 70.47 a-e 76.49 a-e 83.23 de 91.59 e-n 570.70 c-k 118.93 a-d 123.62 c-g
34 53.01 s-u 61.99 k-n 65.22 l-n 73.46 t-w 85.28 f-n 570.20 c-l 111.66 m-q 120.78 h-n
35 52.51 tu 62.65 i-n 66.25 k-m 73.65 t-v 85.12 f-n 502.56 k-n 112.64 k-p 120.89 h-n
36 65.40 d-f 70.32 a-e 59.54 n 84.15 b-d 67.11 j-n 570.92 c-k 114.70 g-l 122.00 f-l
37 52.93 s-u 60.47 l-n 65.99 k-n 73.73 t-v 101.99 c-k 556.70 e-n 111.93 m-q 115.62 tu
38 51.01 u 60.99 l-n 64.72 mn 71.96 w 59.37 mn 511.05 k-n 109.66 qr 114.78 u
39 59.97 l-n 65.99 d-l 73.22 c-i 79.58 i-l 101.58 c-k 583.40 b-j 120.14 a 122.75 e-j
40 61.01 j-l 67.15 c-k 73.75 b-i 82.15 e-g 134.20 a-c 610.01 a-g 117.14 c-g 124.39 b-e
41 62.58 h-k 69.33 a-f 74.01 b-i 81.08 g-i 85.18 f-n 493.40 n 114.86 f-l 123.16 d-h
42 60.05 l-n 68.67 b-i 73.49 b-i 81.00 g-i 125.52 a-e 535.39 i-n 117.36 b-f 122.03 f-k
43 55.48 qr 66.00 d-l 69.80 f-m 76.58 o-q 119.51 b-g 595.71 a-j 113.91 h-n 121.77 f-l
44 66.48 c-e 73.00 a-c 77.80 a-d 85.08 b 102.75 c-j 583.81 b-j 119.91 ab 125.77 bc
45 61.08 j-l 68.33 b-j 73.01 d-j 81.08 g-i 95.84 d-m 597.18 a-j 112.36 l-p 119.66 l-q
34
46 60.59 k-m 66.18 d-l 72.48 d-k 77.39 n-p 100.29 c-l 564.37 d-l 111.87 m-q 122.05 f-k
47 56.05 p-r 66.17 d-l 69.49 f-m 77.50 m-p 90.35 e-n 538.02 h-n 112.86 j-p 117.53 q-t
48 54.01 r-t 63.65 f-n 68.75 g-m 76.15 p-r 136.22 a-c 552.44 f-n 114.14 h-m 119.89 k-p
49 53.01 s-u 61.65 k-n 69.25 f-m 74.65 r-t 96.72 c-m 551.04 f-n 114.64 g-l 120.89 h-n
50 55.09 q-s 61.68 k-n 68.98 g-m 75.39 q-s 153.05 ab 559.99 d-n 110.37 p-r 120.55 j-o
Mean 59.91 66.21 71.46 79.33 93.32 561.07 114.23 121.09
CV 1.90% 4.60% 4.50% 1.10% 21.20% 6.40% 1.10% 0.90%
F test *** *** *** *** *** *** *** ***
Genotypes PH SL PL GS TGW BY HI GY
1 98.52 j-s 11.90 a-e 45.26 a 51.49 g-p 34.23 a-g 15185.53 a-f 0.39 a-k 5801.74 c-i
2 100.43 h-q 11.29 a-k 38.78 h-o 62.38 c-f 30.28 c-l 14492.38 b-j 0.39 a-k 5673.76 c-j
3 101.40 g-p 9.28 qr 44.27 ab 70.66 a-c 35.62 a-d 13946.76 b-k 0.40 a-k 5588.23 c-j
4 87.78 t 7.57 s 37.27 m-p 71.94 ab 36.07 a-c 12001.48 h-m 0.45 ab 5429.38 c-k
5 94.22 o-t 9.80 m-r 37.36 m-p 55.79 e-m 31.03 b-k 14268.87 b-j 0.43 a-f 6205.07 a-g
6 101.68 f-o 10.99 b-n 36.98 m-p 60.78 d-g 35.35 a-e 13992.38 b-k 0.42 a-h 5921.26 b-i
7 97.85 l-s 9.58 o-r 39.82 e-m 54.76 e-m 34.62 a-f 13413.43 c-k 0.41 a-j 5450.73 c-k
8 93.08 q-t 8.92 rs 39.82 e-m 61.84 c-f 33.39 a-h 13834.81 b-k 0.38 a-k 5264.38 d-l
9 91.57 r-t 10.00 k-r 32.65 qr 47.79 l-t 21.98 p-s 11744.21 j-m 0.35 g-k 4096.99 l
10 101.42 g-p 10.40 h-r 42.65 a-g 74.69 a 29.34 e-l 15077.55 a-g 0.35 h-k 5141.99 d-l
11 94.40 n-t 9.83 l-r 39.12 g-o 55.46 e-m 37.06 ab 11830.10 i-m 0.46 a 5496.57 c-j
12 96.04 m-t 9.42 p-r 35.96 n-r 41.30 q-t 36.25 a-c 13680.82 b-k 0.45 a-d 6116.43 a-g
13 99.72 h-r 9.25 q-s 38.57 h-o 57.47 d-l 29.61 d-l 14675.38 b-h 0.40 a-j 5930.42 b-i
14 90.29 st 11.17 b-m 35.75 o-r 48.60 i-s 25.58 j-r 9239.50 mn 0.43 a-g 3980.85 l
15 98.07 l-s 10.40 i-r 42.27 a-h 41.67 p-t 26.98 i-q 14658.72 b-h 0.40 a-k 5838.76 c-i
35
16 98.09 l-s 9.92 k-r 39.25 f-o 41.70 p-t 25.18 k-r 12406.17 f-l 0.37 c-k 4604.19 i-l
17 103.54 d-m 11.87 a-f 41.21 b-k 54.60 f-n 35.87 a-c 15514.15 a-d 0.42 a-h 6602.27 a-c
18 102.25 f-o 9.70 n-r 40.08 d-m 56.79 d-l 23.15 m-s 14904.33 b-g 0.40 a-j 6034.29 a-h
19 93.00 q-t 8.93 rs 41.52 b-j 58.16 d-j 36.22 a-c 13196.76 d-l 0.44 a-f 5753.23 c-i
20 105.30 c-m 10.05 j-r 42.13 a-h 50.49 i-q 30.68 c-l 12362.66 g-l 0.46 a 5740.95 c-i
21 112.42 a-d 11.82 a-g 41.50 b-j 50.83 h-q 33.21 a-h 14201.78 b-k 0.44 a-e 6220.55 a-g
22 113.04 a-d 11.25 b-m 38.94 h-o 44.61 n-t 29.24 f-m 12899.94 d-l 0.45 a-d 5734.82 c-i
23 107.18 a-i 10.24 i-r 43.63 a-d 60.48 d-g 28.90 f-m 14242.38 b-j 0.42 a-i 5939.60 a-h
24 110.65 a-e 12.30 a-c 38.58 h-o 40.19 r-t 21.10 q-s 12654.33 e-l 0.32 kl 4063.45 l
25 113.22 a-c 11.62 a-i 37.47 l-p 47.42 l-t 30.27 c-l 14009.76 b-k 0.39 a-k 5431.96 c-k
26 93.38 p-t 9.64 o-r 36.03 n-q 45.08 n-t 30.56 c-l 6742.38 n 0.26 l 1686.26 m
27 113.30 ab 11.50 a-i 39.28 f-o 49.59 i-r 26.89 i-q 14570.99 b-i 0.44 a-e 6455.95 a-d
28 107.87 a-i 12.67 a 33.77 p-r 54.22 f-n 18.58 s 13626.43 b-k 0.37 d-k 5092.79 e-l
29 114.92 a 10.87 d-p 41.05 b-l 38.23 t 29.60 d-l 10401.78 lm 0.40 a-k 4130.55 kl
30 113.84 ab 11.70 a-i 40.59 c-m 37.81 t 33.65 a-h 14233.27 b-j 0.45 a-e 6367.32 a-e
31 97.44 m-s 9.32 qr 37.56 k-o 43.50 o-t 28.35 g-o 12764.15 d-l 0.34 i-k 4365.60 j-l
32 96.27 m-s 9.80 m-r 32.31 r 58.69 d-i 28.57 f-n 16327.20 ab 0.44 a-e 7176.74 ab
33 104.71 d-m 11.74 a-i 37.94 j-o 64.13 b-e 30.47 c-l 14036.25 b-k 0.44 a-e 6210.77 a-g
34 103.47 e-m 10.92 c-o 35.72 o-r 54.22 f-n 34.35 a-g 14509.76 b-j 0.43 a-f 6321.13 a-f
35 102.75 e-n 10.44 g-r 41.73 a-i 50.74 i-q 27.91 h-p 11928.27 h-m 0.41 a-i 4982.69 g-l
36 98.27 l-s 12.20 a-d 39.51 f-n 58.09 d-k 22.77 n-s 13518.87 b-k 0.38 a-k 5068.41 e-l
37 103.21 e-n 9.29 qr 37.19 m-p 48.13 l-t 31.54 b-j 14869.58 b-g 0.38 b-k 5596.61 c-j
38 108.97 a-g 10.02 j-r 38.42 i-o 39.72 r-t 32.89 b-i 12343.10 g-l 0.40 a-j 4982.79 g-l
36
39 107.24 a-i 10.77 e-p 42.71 a-g 58.30 d-i 30.71 c-l 16114.15 a-c 0.42 a-i 6693.93 a-c
40 106.05 b-l 12.44 ab 40.18 d-m 60.14 d-h 22.34 o-s 13711.60 b-k 0.37 e-k 4982.69 g-l
41 105.50 b-m 11.49 a-j 32.37 r 51.74 f-o 20.26 rs 11420.28 k-m 0.45 a-c 5147.95 d-l
42 107.14 a-j 10.17 i-r 44.10 a-c 50.00 i-q 29.66 d-l 15322.83 a-e 0.36 f-k 5456.69 c-k
43 103.92 d-m 9.80 m-r 44.10 a-c 65.79 b-d 31.25 b-k 13577.55 b-k 0.42 a-i 5701.16 c-i
44 108.57 a-g 10.45 f-q 40.15 d-m 54.59 f-n 36.00 a-c 17810.88 a 0.41 a-i 7259.49 a
45 109.75 a-f 9.79 m-r 39.77 e-m 49.64 i-r 32.97 b-h 14720.28 b-h 0.33 j-l 4781.28 h-l
46 114.92 a 10.77 d-p 42.95 a-f 43.83 o-t 33.11 a-h 14651.78 b-h 0.40 a-j 5945.55 a-h
47 99.04 i-r 9.37 qr 37.85 j-o 45.60 m-t 24.86 l-r 13072.83 d-l 0.38 a-k 4998.35 f-l
48 107.05 a-k 11.74 a-h 43.28 a-e 44.34 o-t 34.57 a-f 13511.60 b-k 0.42 a-h 5761.86 c-i
49 108.45 a-h 10.49 f-q 42.78 a-g 48.24 j-s 33.63 a-h 15094.93 a-g 0.40 a-k 6055.19 a-h
50 110.67 a-e 11.27 b-l 42.80 a-g 39.13 st 39.17 a 13485.11 c-k 0.44 a-e 5945.55 a-h
Mean 103.04 10.52 39.50 52.31 30.32 13616.00 0.40 5504.58
CV 3.50% 6% 4.70% 8.20% 10.10% 10.30% 10% 12%
F test *** *** *** *** *** *** ** ***
Means followed by the same letter(s) within a column are not significantly different from each other according to Duncan’s Multiple Range
Test. For significance test of each traits: *Means significance at 5% level, ** means significance at 1% level, *** means significance at 0.1%
level and without asterisk means non – significance at 5% level.
DFL=Days to Flag Leaf emergence, DB= Days to booting, DH= Days to heading, DA= Days to anthesis, FLA= Flag leaf area, AUSRC= Area
under SPAD retread curve at anthesis, DFLS= Days to flag leaf senescence, DM= Days to maturity, PH= Plant height, SL= Spike length, PL=
37
Peduncle length, GS= Grains per spike, TGW= Thousand grain weight, BY= Biological yield, HI= Harvest index, GY=Grain yield in
kilograms per hectare.
38
4.1.2 Correlation Coefficients
4.1.2.1 Scatterplots for yield attributing traits
The linear correlation between different fifteen grain yield attributing traits and
grain yield are represented in the scatterplots (Figure 3: a - o). The scatterplots give a
vague idea about the presence or absence of correlation and nature (positive or negative
correlation). The r value indicates the correlation coefficient between dependent and
independent variables, which is also called as Pearson’s Correlation Coefficient, presented
in graph at significance level 0.05 (*) and 0.01 (**) and R2 indicates the coefficient of
determination.
Figure 3: Scatterplots of fifteen traits of wheat genotypes with grain yield
45 50 55 60 65 70 750
2000
4000
6000
8000
f(x) = − 43.9635086759306 x + 8138.437124775R² = 0.0672372387261361
a. Scatterplot of Grain Yield vs Days to Flag Leaf emergence
Days to Flag Leaf emergence
Gra
in Y
ield
(Kg.
/ha.
)
55 60 65 70 75 800
2000
4000
6000
8000
f(x) = − 35.781386303356 x + 7873.6689071452R² = 0.0265412572911921
b. Scatterplot of Grain Yield vs Days to Booting
Days to Booting
Gra
in Y
ield
(Kg.
/ha.
)
55 60 65 70 75 80 850
2000
4000
6000
8000
f(x) = − 37.6372558477975 x + 8194.14162288361R² = 0.0380797326393918
c. Scatterplot of Grain Yield vs Days to Heading
Days to Heading
Gra
in Y
ield
(Kg.
/ha.
)
70 75 80 85 90 950
2000
4000
6000
8000
f(x) = − 52.134728757998 x + 9640.431352372R² = 0.0677414331488271
d. Scatterplot of Grain Yield vs Days to Anthesis
Days to Anthesis
Gra
in Y
ield
(Kg.
/ha.
)
45 65 85 105 125 145 165 1850
10002000300040005000600070008000
f(x) = 13.5136998255192 x + 4243.49296050245R² = 0.121310312608921
e. Scatterplot of Grain Yield vs Flag Leaf Area
Flag Leaf Area (cm2)
Gra
in Y
ield
(Kg.
/ha.
)
400 450 500 550 600 650 7000
10002000300040005000600070008000
f(x) = 7.1029981911891 x + 1519.2970218713R² = 0.138198840528674
f. Scatterplot of Grain Yield vs AUSRC
AUSRC
Gra
in Y
ield
(Kg.
/ha.
)
108 110 112 114 116 118 120 1220
2000
4000
6000
8000
f(x) = 53.3692593548904 x − 591.787176109126R² = 0.0339448605684549
g. Scatterplot of Grain Yield vs Days to Flag Leaf Senescence
Days to Flag Leaf Senescence
Gra
in Y
ield
(Kg.
/ha.
)
114 116 118 120 122 124 126 128 130 1320
2000
4000
6000
8000
f(x) = − 24.14578030756 x + 8428.395857443R² = 0.00569294359745998
h. Scatterplot of Grain Yield vs Days to Maturity
Days to Maturity
Gra
in Y
ield
(Kg.
/ha.
)
85 90 95 100 105 110 115 1200
2000
4000
6000
8000
f(x) = 77.3127538005322 x − 2461.56820609924R² = 0.151756280337793
i. Scatterplot of Grain Yield vs Plant Height
Plant Height (cm.)
Gra
in Y
ield
(Kg.
/ha.
)
8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 130
2000
4000
6000
8000
f(x) = 78.35811382122 x + 4679.942530145R² = 0.00595323186969699
j. Scatterplot of Grain Yield vs Spike Length
Spike Length (cm.)
Gra
in Y
ield
(Kg.
/ha.
)
30 32 34 36 38 40 42 44 460
10002000300040005000600070008000
f(x) = 68.4938526373029 x + 2799.07614082653R² = 0.0555665693999534
k. Scatterplot of Grain Yield vs Peduncle Length
Peduncle Length (cm.)
Gra
in Y
ield
(Kg.
/ha.
)
35 40 45 50 55 60 65 700
10002000300040005000600070008000
f(x) = 48.397739461107 x + 2972.9943642684R² = 0.130106250685795
l. Scatterplot of Grain Yield vs Grains per Spike
Grains per Spike
Gra
in Y
ield
(Kg.
/ha.
)
15 20 25 30 35 40 450
10002000300040005000600070008000
f(x) = 73.9153575622559 x + 3263.58356749533R² = 0.148670527177652
m. Scatterplot of Grain Yield vs Thousand Grains Weight
Thousand Grains Weight (g.)
Gra
in Y
ield
(Kg.
/ha.
)
5000
7000
9000
1100
013
000
1500
017
000
1900
00
10002000300040005000600070008000
f(x) = 0.4314293520956 x − 369.75873813358R² = 0.69878573124469
n. Scatterplot of Grain Yield vs Biolog-ical Yield
Biological Yield (Kg./ha.)
Gra
in Y
ield
(Kg.
/ha.
)
0.2 0.25 0.3 0.35 0.4 0.45 0.50
10002000300040005000600070008000
f(x) = 15784.1516114533 x − 871.939918155142R² = 0.490339185774851
o. Scatterplot of Grain Yield vs Harvest Index
Harvest Index
Gra
in Y
ield
(Kg.
/ha.
)
39
4.1.2.2 Estimated Correlation Coefficients
Correlation coefficients represent the associations of different dependent and
independent variables. Correlation coefficient analysis using grain yield as dependent
variable and days to flag leaf emergence, days to booting, days to heading, days to
anthesis, flag leaf area, area under SPAD retread curve at anthesis, days to flag leaf
senescence, plant height, spike length, peduncle length, grains per spike, thousand grain
weight, biological yield and harvest index as independent variables is presented in Table 3.
4.1.2.2.1 Days to flag leaf emergence vs grain yield
Days to flag leaf emergence had non-significant but negative correlation with grain
yield. Days to Flag leaf emergence had highly significant and positive correlation with
days to anthesis followed by days to heading, days to booting, days to maturity, days to
flag leaf senescence. It had highly significant negative correlation with 1000 grain weight
followed by HI, FLA and peduncle length. It had non-significant positive correlation with
spike length followed by grains per spike, plant height and non-significant negative
correlation with biological yield.
4.1.2.2.2 Days to booting vs grain yield
Days to booting had non-significant and negative correlation with grain yield. It
had highly significant and positive correlation with days to anthesis followed by days to
heading, days to maturity and days to flag leaf senescence. It exhibited highly significant
negative correlation with 1000 grain weight followed by HI. It had significant negative
correlation with flag leaf area. It had non-significant positive correlation with spike length,
plant height, grains per spike, AUSRC at anthesis and BY and non-significant negative
correlation with peduncle length.
40
4.1.2.2.3 Days to heading vs grain yield
It showed non-significant and negative correlation with grain yield. It exhibited
highly significant and positive correlation with days to anthesis followed by days to
maturity, and days to flag leaf senescence. It had highly significant negative correlation
with harvest index. It showed significant but negative correlation with thousand grain
weight. It exhibited non-significant positive correlation with AUSRC at anthesis followed
by plant height, spike length and grains per spike. It revealed non-significant negative
correlation with FLA followed by peduncle length and biological yield. Narwal et al.
(1999) and Ismail (2001) found similar results in their study that days to heading showed
non-significant negative association with yield at both genotypic and phenotypic levels.
4.1.2.2.4 Days to anthesis vs grain yield
It exhibited non-significant but negative correlation with grain yield. It had highly
significant positive correlation with days to maturity followed by days to flag leaf
senescence. It showed highly significant negative correlation with 1000 grain weight
followed by HI and FLA. It showed significant positive correlation with AUSRC at
anthesis followed by spike length. It indicated significant negative correlation with
peduncle length. It had non-significant positive correlation with plant height and grain per
spike but non-significant negative correlation with BY. Mohammadi et al. (2012), Tsegaye
et al. (2012) and Zafarnaderi et al. (2013) reported negative relationship between days to
flowering and grain yield per plant in their studies in advanced wheat lines. A study also
showed strong and positive correlation was observed between days to flowering and days
to maturity, but the correlation of days to flowering with grain yield was negative and non-
significant (Gelalcha & Hanchinal, 2013).
41
4.1.2.2.5 Flag leaf area vs grain yield
FLA exhibited significant positive correlation with grain yield. It had highly
significant positive correlation with peduncle length followed by thousand grains weight
and BY. It showed significant positive correlation with plant height. It showed non-
significant positive correlation with spike length followed by AUSRC at anthesis, harvest
index, days to flag leaf senescence and grains per spike. It had non-significant negative
correlation with days to maturity. Flag leaf area exhibited positive and significant
genotypic correlation with grain yield (Khaliq et al., 2004).
4.1.2.2.6 Area under SPAD retread curve (AUSRC) at anthesis vs grain yield
AUSRC had showed highly significant positive correlation with grain yield. It also
showed highly significant positive association with GS followed by BY. It had significant
positive correlation with days to flag leaf senescence followed by plant height and days to
maturity. It had non-significant positive correlation with HI followed by SL, PL and TGW.
4.1.2.2.7 Days to flag leaf senescence vs grain yield
Days to flag leaf senescence showed non-significant and positive correlation with
grain yield. It had highly significant and positive correlation with DM followed spike
length, plant height and BY. It showed non-significant positive correlation with grains per
spike and non-significant negative correlation with thousand grains weight followed by HI
and peduncle length.
4.1.2.2.8 Days to maturity vs grain yield
Grain yield had non-significant negative correlation with DM. DM showed highly
significant and positive correlation with spike length. It showed highly significant negative
correlation with 1000 grain weight. It exhibited significant but negative correlation with
HI. It had non-significant positive correlation with plant height followed by BY and grains
per spike whereas it had non-significant negative correlation with peduncle length.
42
4.1.2.2.9 Plant height vs grain yield
Grain yield exhibited highly significant and positive correlation with plant
height.PH had highly significant positive correlation with BY followed by peduncle length.
It had non-significant but positive correlation with spike length followed by thousand grain
weight, grains per spike and harvest index. A study by Khan et al. (2013) also found
positive and significant correlation for plant height with grain yield.
4.1.2.2.10 Spike length vs grain yield
Spike length showed positive correlation with grain yield. It had significant
negative correlation with 1000 grain weight but non-significant positive correlation with
grains per spike followed by peduncle length and BY. It had non-significant negative
correlation with HI. Khaliq et al, 2004 also found the highest positive correlation between
spike length and grain yield.
4.1.2.2.11 Peduncle Length vs grain yield
It indicated positive correlation with grain yield. It showed highly significant
positive correlation with 1000 grain weight and significant positive correlation with
biological yield. It had positive correlation with grains per spike followed by HI. Peduncle
length exhibited positive and significant genotypic correlation with grain yield (Khaliq et
al., 2004).
4.1.2.2.12 Grains per spike vs grain yield
It indicated significant positive correlation with grain yield. It showed significant
positive correlation with BY. It showed positive correlation with HI and negative
correlation with 1000 grain weight. Nasri et al. (2014) also showed significant positive
correlation of grains per spike with grain yield.
43
4.1.2.2.13 Thousand grain weight vs grain yield
It indicated highly significant positive correlation with grain yield. It exhibited
significant positive correlation with HI and positive correlation with BY. Nashri et al.
(2014) and Khan et al. (2013) also reported significant positive association between grain
yield and 1000 grain weight.
4.1.2.2.14 Biological yield vs grain yield
It exhibited highest highly significant positive correlation with grain yield. It
showed positive association with HI. Nasri et al. (2014) and Gelalcha & Hanchinal (2013)
also reported significant and positive correlation of BY with grain yield.
4.1.2.2.15 Harvest index vs grain yield
It indicated highly significant positive correlation with grain yield. Nasri et al.
(2014) in his finding showed that HI had significant positive correlation with grain yield.
44
Table 3: Correlation coefficients of fifteen traits for grain yield in advanced wheat genotypes
DFL DB DH DA FLA
AUSR
C DFLS DM PH SL PL GS TGW BY HI
G
Y
DFL 1
DB 0.824** 1
DH 0.835** 0.716** 1
DA 0.969** 0.844** 0.841** 1
FLA -0.433** -0.341* -0.263 -0.371** 1
AUSR
C0.248 0.162 0.197 0.295* 0.164 1
DFLS 0.500** 0.395** 0.473** 0.533** 0.093 0.328* 1
DM 0.753** 0.551** 0.702** 0.751** -0.093 0.289* 0.735** 1
PH 0.065 0.230 0.099 0.150 0.282* 0.301* 0.410** 0.110 1
SL 0.238 0.240 0.086 0.294* 0.248 0.161 0.437** 0.440** 0.271 1
PL -0.380** -0.161 -0.230 -0.279* 0.644** 0.116 -0.038 -0.218 0.544** 0.145 1
GS 0.070 0.226 0.084 0.120 0.089 0.409** 0.001 0.030 0.145 0.149 0.137 1
TGW-0.514** -0.405** -0.349* -0.533** 0.374** 0.073 -0.236 -0.506** 0.248 -0.299* 0.394**
-
0.1121
BY -0.052 0.025 -0.030 -0.024 0.371** 0.401** 0.372** 0.103 0.558** 0.145 0.307* 0.323* 0.226 1
HI -0.437** -0.372** -0.376** -0.459** 0.148 0.191 -0.133 -0.293* 0.005 -0.012 0.042 0.239 0.348* 0.213 1
GY -0.259 -0.163 -0.195 -0.260 0.348* 0.372** 0.184 -0.075 0.390** 0.077 0.236 0.361* 0.386** 0.836** 0.700** 1
*Means significance at 5% level, ** means significance at 1% level, without asterisk means non – significance at 5% level. DFL=Days to Flag Leaf emergence, DB= Days to booting, DH= Days to heading, DA= Days to anthesis, FLA= Flag leaf area, AUSRC= Area under SPAD
45
retread curve at anthesis, DFLS= Days to flag leaf senescence, DM= Days to maturity, PH= Plant height, SL= Spike length, PL= Peduncle length, GS= Grains per spike, TGW= Thousand grain weight, BY= Biological yield, HI= Harvest index, GY=Grain yield in kilograms per hectare.
46
4.1.3 Path Analysis
Path coefficient analysis has been found to give more specific information on the
direct and indirect influence of each of the component characters upon grain yield. Path
coefficient analysis using grain yield as dependent variable and days to flag leaf
emergence, days to booting, days to heading, days to anthesis, flag leaf area, area under
SPAD retreat curve at anthesis, days to flag leaf senescence, plant height, spike length,
peduncle length, grains per spike, thousand grain weight, biological yield and harvest
index as independent variables is presented in Table 4.
4.1.3.1 Direct effects on grain yield
The highest (0.30-0.99) positive direct effect on grain were exhibited by biological
yield (0.737) followed by harvest index (0.555). In most of the previous studies also
showed the similar result that biological yield and harvest index had positive direct effect
on the grain yield (Singh and Diwivedi, 2002; Leilah and Al-Khateeb, 2005; Ali and
Shakor, 2012, Fellahi, 2013, Gelalcha and Hanchinal, 2013 ). So these are the primary
selection criteria for the improving grain yield in these wheat genotypes under our study.
The positive direct effect on grain yield was also exhibited by thousand grains weight
(0.072) followed by days to flag leaf emergence (0.063), days to maturity (0.054), days to
booting (0.043), days to heading (0.032), flag leaf area (0.018) and grains per spike
(0.010). The positive direct effect of TGW, DM on yield was also reported by Aydin et al.
(2010), Mohammadi M. et al. (2012). Shamsi et al. (2011) also showed that the most
important yield component on grain yield was 1000 grain weight. While days to anthesis
followed by AUSRC at anthesis, days to flag leaf senescence, plant height, spike length
and peduncle length had negative direct effect on grain yield with value -0.072, -0.044, -
0.037, -0.028, -0.006 and -0.006 respectively.
47
4.1.3.2 Indirect effects on grain yield
4.1.3.2.1 Days to flag leaf emergence vs grain yield
Days to flag leaf emergence exhibited positive direct effect on grain yield with
value 0.063. It also showed moderate (0.20-0.29) indirect negative effect on grain yield
via harvest index (-0.242). It had positive indirect effect on grain yield via days to booting,
days to heading, days to maturity, PL,GS while negative indirect effect via DA, FLA,
AUSRC, DFLS, PH, SL, TGW, BY and HI.
4.1.3.2.2 Days to booting vs grain yield
Days to booting exhibited negligible direct positive effect on grain yield (0.043)
while days to booting showed moderate negative indirect effect on grain yield via harvest
index (-0.206). It also exhibited positive indirect effect on grain yield via days to flag leaf
emergence followed by biological yield, days to heading, days to maturity, peduncle length
and grains per spike. While it had negative indirect effect via HI, days to anthesis, TGW,
days to flag leaf senescence, FLA, PH, AUSRC at anthesis and spike length.
4.1.3.2.3 Days to heading vs grain yield
Days to heading had positive direct effect (0.032) on grain yield. It also exhibited
moderate negative indirect effect on grain yield via harvest index (-0.209). It also had
positive indirect effect on grain yield via DFL, DB, DM, PL, GS while negative indirect
effect via DA, FLA AUSRC, DFLS, PH, SL, TGW and BY.
4.1.3.2.4 Days to anthesis vs grain yield
Days to anthesis exhibited negative direct effect (-0.072) on grain yield. It also
showed moderate negative indirect effect on grain yield via harvest index (-0.255). It had
positive indirect effect via DFL, DB, DH, DM, PL and GS while FLA, AUSRC, DFLS,
PH SL, TGW and BY also showed negative indirect effect on grain yield.
48
4.1.3.2.5 Flag leaf area vs grain yield
Flag leaf area exhibited positive direct effect (0.018) on grain yield. It also had
moderate positive indirect effect on grain yield via biological yield (0.274). FLA had
positive indirect effect on grain yield via DA, GS, TGW and HI while negative indirect
effect via DFL, DB, DH, AUSRC, DFLS, DM, PH, SL, PL.
4.1.3.2.6 AUSRC at anthesis vs grain yield
AUSRC at anthesis (SPAD chlorophyll) had negative direct effect (-0.044) and had
moderate positive indirect effect via biological yield (0.296) while low positive indirect
effect via harvest index (0.106) on grain yield. AUSRC at anthesis showed positive
indirect effect on grain yield via DFL, DB, DH, FLA, DM, GS and TGW while negative
indirect effect on grain yield via DA, DFLS, PH, SL and PL.
4.1.3.2.7 Days to flag leaf senescence vs grain yield
Days to flag leaf senescence exhibited negative direct effect (-0.037) and moderate
positive indirect effect on grain yield via biological yield (0.274). DFLS had positive
indirect effect on grain yield via DFL, DB, DH, FLA, DM while negative indirect effect on
grain yield via DA, AUSRC, PH, SL, TGW and HI.
4.1.3.2.8 Days to maturity vs grain yield
Days to maturity showed positive direct effect (0.054) and low (0.10-0.19) negative
indirect effect on grain yield via harvest index (-0.163). DM exhibited positive indirect
effect on grain yield via DFL, DB, DH, PL and BY while negative indirect effect on grain
yield through DA, FLA, AUSRC, DFLS,PH, SL and TGW.
4.1.3.2.9 Plant height vs grain yield
Plant height unveiled negative direct effect (-0.028) and had high (0.30-0.99)
positive indirect effect on grain yield via biomass yield (0.412). PH showed positive
49
indirect effect on grain yield via DFL, DB, DH, FLA, DM, GS, TGW and HI while
negative indirect effect on grain yield via DA, AUSRC, DFLS, SL and PL.
4.1.3.2.10 Spike length vs grain yield
Spike length revealed negative direct effect (-0.006) and low positive indirect effect
on grain yield via biological yield (0.107). SL had positive indirect effect on grain yield via
DFL, DB, DH, FLA, DM and GS while negative indirect effect through DA, AUSRC,
DFLS, PH, PL, TGW and HI. Mohammad et al. (2012) and Iftikhar et al., (2012) also
reported the direct effect of spike length on grain yield was negative. Naqvi (2012)
reported direct effect of spike length on grain yield was lowest and non-significant.
4.1.3.2.11 Peduncle length vs grain yield
Peduncle length demonstrated negative direct effect (-0.006) and moderate positive
indirect effect via biological yield (0.227). PL exhibited positive indirect effect on grain
yield through DA, FLA, DFLS, GS, TGW and HI while negative indirect effect on grain
yield was shown through DFL, DB, DH, AUSRC, DM, PH and SL. The path analysis
indicated that peduncle length had negative direct effect on grain yield (Khan et al., 2010,
Iftikhar et al., 2012).
4.1.3.2.12 Grains per spike vs grain yield
Grains per spike exhibited positive direct effect (0.010) and moderate positive
indirect effect on grain yield via biological yield (0.238) and low positive indirect effect
via HI (0.133). GS had positive indirect effect on grain yield via DFL, DB, DH, FLA and
DM while negative indirect effect on grain yield via DA, AUSRC, PH, SL, PL and TGW.
Khan et al. (2013) also reported grains/spike had direct positive effect but in low
magnitude.
50
4.1.3.2.13 Thousand grain weight vs grain yield
Thousand grain weight showed direct positive effect (0.072) and low positive
indirect effect on grain yield via biological yield (0.166) and harvest index (0.193). TGW
exhibited positive indirect effect on grain yield through DA, FLA, DFLS and SL. While
negative indirect effect on grain yield through DFL, DB, DH, AUSRC, DM, PH, PL and
GS. Zafarnaderi, (2013) also reported direct effect of the 1000 grain weight on grain yield
was positive and low.
4.1.3.2.14 Biological yield vs grain yield
The highest (0.30-0.99) positive direct effect on grain per plant were exhibited by
biological yield (0.737). Ali and Shakor, 2012, Fellahi, 2013, Gelalcha and Hanchinal,
2013 also found similar result. BY showed indirect positive effect on grain yield via HI
(0.118), DB, DA, FLA, DM, GS and TGW while negative indirect effect via DFL, DH,
AUSRC, DFLS, PH, SL and PL.
4.1.3.2.15 Harvest index vs grain yield
The high positive direct effect of harvest index (0.555) on grain yield was exhibited
which was similar with the finding of Ali & Shakor, 2012, Fellahi, 2013, Gelalcha and
Hanchinal, 2013, Nasri et al., 2014. HI exhibited positive indirect effect on grain yield
through BY (0.157), DA, FLA, DFLS, TGW while negative indirect effect via DFL, DB,
DH, AUSRC and DM.
51
Table 4: Path Analysis Matrix of direct and indirect effects of fifteen traits on grain yield of advanced wheat genotypes
DFL DB DH DA FLA AUSRC DFLS DM PH SL PL GS TGW BY HIDFL 0.063 0.052 0.053 0.061 -0.027 0.016 0.032 0.048 0.004 0.015 -0.024 0.004 -0.032 -0.003 -0.028DB 0.036 0.043 0.031 0.037 -0.015 0.007 0.017 0.024 0.010 0.010 -0.007 0.010 -0.018 0.001 -0.016DH 0.027 0.023 0.032 0.027 -0.008 0.006 0.015 0.022 0.003 0.003 -0.007 0.003 -0.011 -0.001 -0.012DA -0.070 -0.061 -0.061 -0.072 0.027 -0.021 -0.038 -0.054 -0.011 -0.021 0.020 -0.009 0.038 0.002 0.033FLA -0.008 -0.006 -0.005 -0.007 0.018 0.003 0.002 -0.002 0.005 0.004 0.011 0.002 0.007 0.007 0.003AUSRC -0.011 -0.007 -0.009 -0.013 -0.007 -0.044 -0.014 -0.013 -0.013 -0.007 -0.005 -0.018 -0.003 -0.017 -0.008DFLS -0.019 -0.015 -0.018 -0.020 -0.003 -0.012 -0.037 -0.027 -0.015 -0.016 0.001 0.000 0.009 -0.014 0.005DM 0.041 0.030 0.038 0.041 -0.005 0.016 0.040 0.054 0.006 0.024 -0.012 0.002 -0.027 0.006 -0.016PH -0.002 -0.006 -0.003 -0.004 -0.008 -0.008 -0.012 -0.003 -0.028 -0.008 -0.015 -0.004 -0.007 -0.016 0.000SL -0.001 -0.002 -0.001 -0.002 -0.002 -0.001 -0.003 -0.003 -0.002 -0.006 -0.001 -0.001 0.002 -0.001 0.000PL 0.002 0.001 0.001 0.002 -0.004 -0.001 0.000 0.001 -0.003 -0.001 -0.006 -0.001 -0.002 -0.002 0.000GS 0.001 0.002 0.001 0.001 0.001 0.004 0.000 0.000 0.001 0.001 0.001 0.010 -0.001 0.003 0.002TGW -0.037 -0.029 -0.025 -0.038 0.027 0.005 -0.017 -0.037 0.018 -0.022 0.028 -0.008 0.072 0.016 0.025BY -0.039 0.018 -0.022 -0.018 0.274 0.296 0.274 0.076 0.412 0.107 0.227 0.238 0.166 0.737 0.157HI -0.242 -0.206 -0.209 -0.255 0.082 0.106 -0.074 -0.163 0.003 -0.007 0.023 0.133 0.193 0.118 0.555Correlation -0.259 -0.163 -0.195 -0.260 0.348* 0.372** 0.184 -0.075 0.390** 0.077 0.236 0.361* 0.386** 0.836** 0.700**
Residual effect: 0.0081. Underlined numbers are positive direct effects (bold face), double underlined numbers are high in magnitude. Values in the off diagonal or
columns show indirect effects on grain yield. DFL=Days to Flag Leaf emergence, DB= Days to booting, DH= Days to heading, DA= Days to anthesis, FLA= Flag leaf
area, AUSRC= Area under SPAD retread curve at anthesis, DFLS= Days to flag leaf senescence, DM= Days to maturity, PH= Plant height, SL= Spike length, PL=
Peduncle length, GS= Grains per spike, TGW= Thousand grain weight, BY= Biological yield, HI= Harvest index, GY=Grain yield in kilogram per hectare. High = 0.30-
0.99, Moderate = 0.20-0.29, Low = 0.10-0.19
52
4.2 Discussion
4.2.1 Mean performance and ANOVA analysis
There were significant differences among the genotypes for all characters reported
here in because of diverse genetic background of advanced wheat genotypes used in this
experiment (Appendix 2). The mean performance of these genotypes of wheat under our
study with their CV, DMRT and significance test values are represented in the Table 2 and
ANOVA for all the traits are presented in Appendix 1.
4.2.2 Correlation and Path Analysis
In agronomic and breeding studies, correlation coefficients are generally done to
determine the relation of grain yield and yield components. However, correlation
coefficients mostly bring forth the interrelations of independent components. It is
reasonable to know whether any yield components has a direct or indirect effect on grain
yield, hence, selection studies can be carried out successfully. Path coefficient analysis
depicts whether the association of grain yield with its component characters is due to the
direct effects of the component characters on grain yield or is a consequence of its indirect
effect through some other traits. Thus, study of correlation and direct and indirect effects
of yield components provides the basis for successful breeding plan.
In the present research, for BY and HI, highly significant and positive correlation
was observed with grain yield with values 0.836** and 0.700** respectively and the direct
effects were also positive and highest with values 0.737 and 0.555 respectively (Table 3
and Table 4). This suggests that there was little or no indirect effects of these traits on
grain yield and whatever relationship existed with grain yield was direct. Singh and
Chaudhary (1979) suggested that if the correlation coefficient between a causal factor and
the effect is almost equal to its direct effect, the correlation explains the true relationship
and the direct selection through these traits is effective. Therefore, these traits (BY and HI)
could be used as selection criteria for improving wheat grain yield. Fellahi et al., (2013),
53
Gelalcha and Hanchinal (2013), Tsegaye et al. (2012) also suggested the similar result.
This implies that selection of wheat genotypes on the basis of biomass yield and harvest
index would be beneficial for increasing wheat grain yield.
The correlation coefficient of plant height (0.390**), thousand grains weight
(0.386**) and AUSRC at anthesis (0.372**) were also observed to be highly significant
and positive with grain yield. The direct effects of these traits with values -0.028, 0.072, -
0.044 respectively indicated the negligible effect on grain yield (Table 3 and Table 4).
Similar result for 1000 grain weight were also found by Suleiman et al., 2014 and negative
direct effect of plant height on grain yield was also reported by Iftikhar et al., 2012 and
Suleiman et al., 2014. This indicates that indirect effect seems to be the cause of high
correlation showing that indirect positive effect through BY and HI on grain yield are the
possible cause of positive correlation and negative direct effects are because of the
negative indirect effects of the other traits, so these traits are to be considered
simultaneously for the selection of wheat genotypes. These findings also tell that higher
the plant height, thousand grain weight, AUSRC increases the grain yield by increasing
biomass yield and harvest index. So, while selection of the genotypes for higher grain yield
through these traits BY and HI should also be considered simultaneously in selection.
Grains per spike (0.361*) exhibited significant positive association with grain yield
and also showed positive direct effect on grain yield with value 0.010 which is negligible.
This indicated that the positive and significant correlation of GS is due to the moderate
positive indirect effect of the GS on grain yield through BY (0.238) and low positive
indirect effect via HI (0.133). GS had positive and significant correlation with grain yield
was also reported by Gelalcha and Hanchinal, 2013. Flag leaf area (0.348*) depicted
significant and positive correlation and negligible direct effect (0.018) with grain yield but
moderate positive indirect effect on grain yield via BY (0.274). This indicates that casual
factor BY should be considered in selection if the selection is to be made through flag leaf
54
area. This also indicates that higher the flag leaf area higher will be the grain yield.
Suleiman et al., 2014 also revealed that leaf area index had negative direct effect on yield.
DFLS, SL and PL also showed positive correlation and negative direct effect on
grain yield. It indicates that DFLS and PL contribute to grain yield indirectly moderately
via BY and SL contribute with low indirect effect via BY. Hence indicating while selection
for breeding, indirect casual factor BY and other positively contributing factors should be
considered if selection is made through DFLS, SL and PL. Negative direct effect of
peduncle length on grain yield was reported by Iftikhar et al., 2012. DFL, DB, DH and DM
exhibited negative correlation with grain yield but negligible positive direct effect on grain
yield. The negative correlation is due to the moderate negative indirect contribution of the
DFL, DB and DH on grain yield via HI (-0.242. -0.206, -0.209 respectively) and that of
DM via low indirect effect of HI (-0.163). Days to anthesis have negative correlation and
negative direct effect on grain yield. The negative correlation is due to the negative indirect
effect of DA on grain yield via HI (-0.255) and other negatively indirectly contributing
factors. These traits indicating relatively non-significant correlation and negligible direct or
indirect effect on grain yield are of relatively poor importance in selection breeding for
increasing grain yield in these advanced wheat genotypes.
Therefore, while selection of the wheat genotypes for increasing grain yield, the
yield attributing traits which shows significant correlation and exhibit positive direct and
indirect effect with considerable magnitude on grain yield are to be considered in selection
and are of importance in breeding strategies.
55
5 SUMMARY AND CONCLUSION
The study was carried to determine the selection criteria for plant breeders using
correlation and path coefficient analyses in advanced wheat genotypes. Partitioning
correlations into direct and indirect effects by path coefficient analysis enhances the
information derived from the correlation coefficients. Relationship between yield and its
component characters were computed and the causes of such relations were further
analyzed. The path coefficient analysis appeared to provide a clue to the contribution of
various components of yield to overall grain yield of the genotypes under study.
A set of fifty genotypes of wheat ( Triticum aestivum L.) including Gautam was
grown to study correlation and path analysis of the yield and yield attributing traits in these
genotypes. Genotypes were studied in alpha lattice design with two replications in the
research field of Institute of Agriculture and Animal Science, Rampur Campus during the
winter season of 2014/2015. Data of yield and yield attributing traits were recorded for
days to flag leaf emergence (DFL), days to booting (DB), days to heading (DH), days to
anthesis (DA), flag leaf area (FLA), Area under SPAD retread curve (AUSRC), days to
flag leaf senescence (DFLS), days to maturity (DM), plant height (PH), spike length (SL),
peduncle length (PL), grains per spike (GS), thousand grain weight (TGW), biological
yield (BY), harvest index (HI) and grain yield (GY). The ANOVA result revealed highly
significant difference among these genotypes for all these traits.
Simple correlation coefficients revealed that the association of grain yield with
biological yield followed by harvest index, plant height, thousand grain weight and
AUSRC at anthesis were positive and highly significant (at 1% level of significance). The
positive and significant (at 5% level of significance) association of grains per spike
followed by flag leaf area with grain yield was also found. This indicates that these traits
56
were yield determinative traits as revealed by correlation analysis and hence, selection for
these traits bring improvement in grain yield of wheat. Whereas, peduncle length followed
by days to flag leaf senescence, spike length exhibited positive but non-significant
correlation with grain yield. This indicates that these three traits also have importance in
breeding of wheat and the genotypes with longer reproductive phase and longer spike
length and peduncle length should be selected for wheat improvement. While, days to
anthesis followed by days to flag leaf emergence, days to heading, days to booting and
days to maturity also showed negative correlation with grain yield indicating that the early
maturing genotypes with longer reproductive phase are better for obtaining high grain
yield. Biological yield, flag leaf area, AUSRC at anthesis, days to flag leaf senescence and
plant height showed highly significant and positive inter se association. Similarly, harvest
index, days to flag leaf emergence, days to booting, days to heading and days to anthesis
showed highly significant negative inter se association.
Besides, path analysis explains the positive and direct effect of biological yield
followed by harvest index, thousand grain weight, days to flag leaf emergence, days to
maturity, days to booting, days to heading, flag leaf area and grains per spike on grain
yield. This indicates that while selection, these traits must be considered to improve the
grain yield of the wheat genotypes. Whereas, days to anthesis, AUSRC at anthesis, days to
flag leaf senescence, plant height, spike length and peduncle length exhibited negative
direct effect on the grain yield. Thus, for increasing the grain yield through selection for
these traits, the indirect positive yield attributing traits for these traits must be considered
simultaneously in selection breeding.
In the present research, BY and HI had highly significant and positive correlation
with grain yield and the direct effects were also positive and highest. This suggests that
there was little or no indirect effects of these traits on grain yield and the correlation
57
explained the true relationship and the direct selection of the genotypes through these traits
is effective. Therefore, selection through BY and HI is a prerequisite for improving grain
yield in wheat. On the other hand, thousand grain weight also have highly significant
correlation and positive direct effect on the grain yield. Grains per spike and flag leaf area
also exhibited significant positive association and direct positive effect whereas plant
height and AUSRC at anthesis showed highly significant positive correlation but negative
direct effect on grain yield with high to moderate indirect effect on grain yield through
biological yield on grain yield. This reveals that these associations are true and the
selection through these traits simultaneously for grain yield improvement is effective. Plant
height has highest positive indirect effect on grain yield via biological yield whereas flag
leaf area, grains per spike, AUSRC at anthesis, days to flag leaf senescence and peduncle
length showed moderate indirect effect on grain weight via biological yield and thousand
grain weight exhibited low indirect effect on grain yield via biological yield and harvest
index. Similarly, days to flag leaf emergence, days to booting, days to heading and days to
anthesis depicted moderate negative indirect effect on grain yield via harvest index. This
indicates biological yield and harvest index are prerequisite in the selection of wheat
genotypes for improving grain yield.
58
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APPENDICES
Appendix 1: Analysis of Variance (ANOVA)
ANOVA for fifteen traits and their respective means with coefficient of variation:
1(a). Analysis of Variance Table for Days to Flag Leaf emergence (DFL)
Sources of Variations D
f
Sum Sq Mean Sq F value Pr(>F)
Replications 1 1.69 1.69 1.2763 0.2652
Genotypes.unadj 49 2916.69 59.524 44.953 <2e-16 ***
Blocks/Replications 8 9.52 1.19 0.8987 0.5266
Residual 41 54.29 1.324
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 1.9 %
DFL Means: 60 days
1(b). Analysis of Variance for Days to Booting (DB)
Sources of
Variations
Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 26.01 26.01 2.8543 0.09872 .
Genotypes.unadj 49 1738.09 35.471 3.8926 9.67E-06 ***
Blocks/Replications 8 80.88 10.11 1.1095 0.37701
Residual 41 373.61 9.112
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 4.6 %
DB Means: 66 days
1(c). Analysis of Variance for Days to Heading (DH)
Sources of
Variations
Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 1 1 0.0963 0.7579
Genotypes.unadj 49 2253.8 45.997 4.43 1.71E-06 ***
Blocks/Replications 8 94.3 11.787 1.1353 0.361
Residual 41 425.7 10.383
67
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 4.5 %
DH Means: 72 days
1(d). Analysis of Variance for Days to Anthesis (DA)
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 2.89 2.89 3.9248 0.05431 .
Genotypes.unadj 49 2089.61 42.645 57.9148 < 2e-16 **
*
Blocks/Replications 8 1.42 0.178 0.2411 0.98042
Residual 41 30.19 0.736
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 1.1 %
DA Means: 79 days
1(e). Analysis of Variance for Flag Leaf Area (FLA)
Sources of Variations Df Sum Sq Mean Sq F
value
Pr(>F)
Replications 1 5 4.92 0.0126 0.91134
Genotypes.unadj 49 55695 1136.63 2.9002 0.000336 **
*
Blocks/Replications 8 2080 259.94 0.6633 0.720229
Residual 41 16069 391.92
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 21.2 %
FLA Means: 93.32 cm2
1(f). Analysis of Variance for AUSRC
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 326 326.2 0.255 0.6163
Genotypes.unadj 49 229660 4686.9 3.6647 2.09E-
05
**
*
Blocks/Replications 8 8526 1065.7 0.8333 0.5788
68
Residual 41 52436 1278.9
Significance Codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 6.4 %
AUSRC Means: 561.07
1(g). Analysis of Variance for Days to Flag Leaf Senescence (DFLS)
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 68.89 68.89 40.9406 1.17E-
07
**
*
Genotypes.unadj 49 999.21 20.392 12.1188 1.74E-
13
**
*
Blocks/Replications 8 8.62 1.078 0.6403 0.7392
Residual 41 68.99 1.683
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 1.1 %
DFLS Means: 114 days
1 (h). Analysis of Variance for Days to Maturity
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 12.25 12.25 9.7241 0.003321 **
Genotypes.unadj 49 818.69 16.708 13.2629 3.47E-14 **
*
Blocks/Replications 8 15.6 1.95 1.5479 0.170932
Residual 41 51.65 1.2598
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 0.9 %
DM Means: 121 days
1(i). Analysis of Variance for Plant Height (PH)
Sources of Variations D Sum Sq Mean Sq F value Pr(>F)
69
f
Replications 1 374.81 374.81 28.7151 3.52E-06 **
*
Genotypes.unadj 49 2128.68 43.44 3.3282 6.86E-05 **
*
Blocks/Replications 8 351.71 43.96 3.3682 0.004675 **
Residual 41 535.16 13.05
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 3.5 %
PH Means: 103.04 cm.
1(j). Analysis of Variance for Spike Length (SL)
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 2.89 2.89 7.3174 0.009902 **
Genotypes.unadj 49 81.292 1.65903 4.2006 3.53E-06 **
*
Blocks/Replications 8 9.707 1.21338 3.0722 0.00834 **
Residual 41 16.193 0.39495
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 6 %
SL Means: 10.52 cm.
1(k). Analysis of Variance for Peduncle Length (PL)
Sources of Variations Df Sum
Sq
Mean Sq F value Pr(>F)
Replications 1 0.46 0.4624 0.1351 0.7151
Genotypes.unadj 49 993.06 20.2665 5.9204 2.54E-
08
**
*
Blocks/Replications 8 23.33 2.9159 0.8518 0.5638
Residual 41 140.35 3.4232
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
70
Coefficient of variation: 4.7 %
PL Means: 39.5 cm.
1(l). Analysis of Variance for Grains per Spike (GS)
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications 1 2.6 2.56 0.1383 0.71194
Genotypes.unadj 49 4657.1 95.042 5.1327 2.13E-07 **
*
Blocks/Replications 8 446.2 55.77 3.0119 0.009394 **
Residual 41 759.2 18.517
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 8.2 %
GS Means: 52 grains
1(m). Analysis of Variance for Thousand Grain Weight (TGW)
Sources of Variations Df Sum
Sq
Mean Sq F value Pr(>F)
Replications 1 36.54 36.542 3.9256 0.05429 .
Genotypes.unadj 49 2281.5 46.561 5.0019 3.09E-
07
**
*
Blocks/Replications 8 62.77 7.847 0.8429 0.57091
Residual 41 381.65 9.309
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 10.1 %
TGW Means: 30.32 g.
1(n). Analysis of Variance for Biological Yield (BY)
Sources of Variations Df Sum Sq Mean Sq F value Pr(>F)
Replications1
3426151
1
3426151
1 17.4778 0.000149
**
*
Genotypes.unadj 49 3.15E+08 6423826 3.277 8.26E-05 **
71
*
Blocks/Replications8
1516631
1 1895789 0.9671 0.474651
Residual41
8037190
0 1960290
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 10.3 %
BY Means: 13616 Kg./Ha.
1(o). Analysis of Variance for Harvest Index (HI)
Sources of Variations Df Sum Sq Mean Sq F
value
Pr(>F)
Replications 1 0.005863 0.005863 3.5742 0.06576
6
.
Genotypes. unadj 49 0.165013 0.003368 2.0529 0.00985
6
**
Blocks/Replications 8 0.011678 0.00146 0.8898 0.53352
3
Residual 41 0.067257 0.00164
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 10 %
HI Means: 0.404
1(p). Analysis of Variance for Grain Yield (GY)
Sources of VariationsDf Sum Sq Mean Sq
F
value Pr(>F)
Replications 1 2008361 2008361 4.5947 0.03805 *
Genotypes.unadj49 83842665 1711075 3.9146
8.98E-
06
**
*
Blocks/Replications 8 3218136 402267 0.9203 0.50987
Residual 41 17921307 437105
72
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Coefficient of variation: 12 %
GY Means: 5505 Kg./Ha.
73
Appendix 2: List of the genotypes under studyGenotype
EntryCross Name Origin
1 GAUTAM \\NEPAL
2 KACHU #1 MXI13-14\MULTTESTIGOS\5
3 QUAIU #1 MXI13-14\MULTTESTIGOS\10
4 BAJ #1 MXI13-14\MULTTESTIGOS\13
5 FRANCOLIN #1 MXI13-14\MULTTESTIGOS\14
6 KACHU/BECARD//WBLL1*2/BRAMBLING MXI13-14\M35ES22SAWHT\4
7 QUAIU #1/SUP152 MXI13-14\M35ES22SAWHT\6
8 QUAIU #1/SUP152 MXI13-14\M35ES22SAWHT\7
9 KACHU//KIRITATI/2*TRCH MXI13-14\M35ES22SAWHT\12
10 KIRITATI//HUW234+LR34/PRINIA/3/BAJ #1 MXI13-14\M35ES22SAWHT\16
11 ND643/2*WBLL1//VILLA JUAREZ F2009 MXI13-14\M35ES22SAWHT\20
12 SUP152/FRNCLN MXI13-14\M35ES22SAWHT\29
13 BAJ #1/SUP152 MXI13-14\M35ES22SAWHT\36
14 WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2*WBLL1/5/PRL/2*PASTOR/4/CHOIX/STAR/3/
HE1/3*CNO79//2*SERI
MXI13-14\M35ES22SAWHT\42
15 CROC_1/AE.SQUARROSA (205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2/5/2*DANPHE #1 MXI13-14\M35ES22SAWHT\55
16 FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/2*FRNCLN MXI13-14\M35ES22SAWHT\56
17 BAJ #1/3/2*HUW234+LR34/PRINIA//PFAU/WEAVER MXI13-14\M35ES22SAWHT\64
18 KISKADEE #1*2//KIRITATI/2*TRCH MXI13-14\M35ES22SAWHT\66
19 MUTUS*2/HARIL #1 MXI13-14\M35ES22SAWHT\75
20 BAJ #1*2/TINKIO #1 MXI13-14\M35ES22SAWHT\86
74
21 BAJ #1*2//ND643/2*WBLL1 MXI13-14\M35ES22SAWHT\87
22 WBLL1*2/BRAMBLING*2//BAVIS MXI13-14\M35ES22SAWHT\90
23 PRL/2*PASTOR//WHEAR/SOKOLL MXI13-14\M35ES22SAWHT\97
24 WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2*WBLL1/4/WAXWING*2/KRONSTAD F2004 MXI13-14\M35ES22SAWHT\98
25 WHEAR/KIRITATI/3/C80.1/3*BATAVIA//2*WBLL1/4/BECARD MXI13-14\M35ES22SAWHT\111
26 FRET2*2/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/KIRITATI/2*TRCH/6/BAJ #1 MXI13-14\M35ES22SAWHT\124
27 FRET2*2/BRAMBLING//KIRITATI/2*TRCH/3/FRET2/TUKURU//FRET2 MXI13-14\M35ES22SAWHT\125
28 KACHU*2/SUP152 MXI13-14\M35ES22SAWHT\128
29 DANPHE/PAURAQUE #1//MUNAL #1 MXI13-14\M35ES22SAWHT\131
30
KIRITATI//2*PRL/2*PASTOR/3/CHONTE/5/PRL/2*PASTOR/4/CHOIX/STAR/3/
HE1/3*CNO79//2*SERI MXI13-14\M35ES22SAWHT\132
31
KIRITATI//HUW234+LR34/PRINIA/3/CHONTE/5/PRL/2*PASTOR/4/CHOIX/STAR/3/
HE1/3*CNO79//2*SERI MXI13-14\M35ES22SAWHT\133
32 KIRITATI//HUW234+LR34/PRINIA/3/FRANCOLIN #1/4/BAJ #1 MXI13-14\M35ES22SAWHT\134
33 MUTUS//KIRITATI/2*TRCH/3/WHEAR/KRONSTAD F2004 MXI13-14\M35ES22SAWHT\136
34 ND643/2*WBLL1//2*KACHU MXI13-14\M35ES22SAWHT\137
35 PAURAQ/5/KIRITATI/4/2*SERI.1B*2/3/KAUZ*2/BOW//KAUZ/6/PAURAQUE #1 MXI13-14\M35ES22SAWHT\147
36 PAURAQ/4/WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2*WBLL1/5/PAURAQUE #1 MXI13-14\M35ES22SAWHT\151
37 FRANCOLIN #1*2//ND643/2*WBLL1 MXI13-14\M35ES22SAWHT\161
38 FRANCOLIN #1/CHONTE//FRNCLN MXI13-14\M35ES22SAWHT\162
39 BAJ #1*2/KISKADEE #1 MXI13-14\M35ES22SAWHT\164
40 WHEAR/KUKUNA/3/C80.1/3*BATAVIA//2*WBLL1*2/4/KIRITATI/2*TRCH MXI13-14\M35ES22SAWHT\168
41 TAM200/PASTOR//TOBA97/3/FRNCLN/4/WHEAR//2*PRL/2*PASTOR MXI13-14\M35ES22SAWHT\173
75
42
TOB/ERA//TOB/CNO67/3/PLO/4/VEE#5/5/KAUZ/6/FRET2/7/VORB/8/MILAN/KAUZ//DHARWAR
DRY/3/BAV92 MXI13-14\M35ES22SAWHT\174
43
FALCIN/AE.SQUARROSA (312)/3/THB/CEP7780//SHA4/LIRA/4/FRET2/5/DANPHE #1/11/CROC_1/AE.
SQUARROSA (213)//PGO/10/ATTILA*2/9/KT/BAGE//FN/U/3/BZA/4/TRM/5/ALDAN/6/SERI/7/VEE
#10/8/OPATA MXI13-14\M35ES22SAWHT\175
44 BAVIS/NAVJ07 MXI13-14\M35ES22SAWHT\177
45
CROC_1/AE.SQUARROSA (213)//PGO/10/ATTILA*2/9/KT/BAGE//FN/U/3/BZA/4/TRM/5
/ALDAN/6/SERI/7/VEE#10/8/OPATA/11/ATTILA*2/PBW65 MXI13-14\M35ES22SAWHT\178
46 W15.92/4/PASTOR//HXL7573/2*BAU/3/WBLL1/5/DANPHE #1 MXI13-14\M35ES22SAWHT\180
47
BAVIS/3/ATTILA/BAV92//PASTOR/5/CROC_1/AE.SQUARROSA
(205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2 MXI13-14\M35ES22SAWHT\183
48 BABAX/LR42//BABAX/3/ER2000/4/PAURAQUE #1 MXI13-14\M35ES22SAWHT\186
49 VEE/MJI//2*TUI/3/PASTOR/4/BERKUT/5/BAVIS MXI13-14\M35ES22SAWHT\188
50
SOKOLL/3/PASTOR//HXL7573/2*BAU/5/CROC_1/AE.SQUARROSA
(205)//BORL95/3/PRL/SARA//TSI/VEE#5/4/FRET2 MXI13-14\M35ES22SAWHT\200
76
Appendix 3: Direct and indirect effects of yield attributing traits on grain yield of
wheat genotypes
3(a). Direct and indirect effects of Days to Flag Leaf emergence (DFL) on Grain Yield
Direct effect of Days to Flag Leaf emergence (DFL) on Grain Yield 0.063
Indirect effect via Days to Booting (DB) 0.036
Indirect effect via Days to Heading (DH) 0.027
Indirect effect via Days to Anthesis (DA) -
0.070
Indirect effect via Flag Leaf Area (FLA) -
0.008
Indirect effect via SPAD chlorophyll (AUSRC) -
0.011
Indirect effect via Days to Flag Leaf Senescence (DFLS) -
0.019
Indirect effect via Days to Maturity (DM) 0.041
Indirect effect via Plant Height (PH) -
0.002
Indirect effect via Spike Length (SL) -
0.001
Indirect effect via Peduncle Length (PL) 0.002
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) -
0.037
Indirect effect via Biomass Yield (BY) -
0.039
Indirect effect via Harvest Index (HI) -
0.242
Total Effect on Grain Yield -
0.259
3(b). Direct and indirect effects of Days to Booting (DB) on Grain Yield
Direct effect of Days to Booting (DB) on Grain Yield 0.043
Indirect effect via Days to Flag Leaf emergence (DFL) 0.052
Indirect effect via Days to Heading (DH) 0.023
Indirect effect via Days to Anthesis (DA) -0.061
Indirect effect via Flag Leaf Area (FLA) -0.006
Indirect effect via SPAD chlorophyll (AUSRC) -0.007
77
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.015
Indirect effect via Days to Maturity (DM) 0.030
Indirect effect via Plant Height (PH) -0.006
Indirect effect via Spike Length (SL) -0.002
Indirect effect via Peduncle Length (PL) 0.001
Indirect effect via Grains per Spike (GS) 0.002
Indirect effect via Thousand Grain Weight (TGW) -0.029
Indirect effect via Biomass Yield (BY) 0.018
Indirect effect via Harvest Index (HI) -0.206
Total Effect on Grain Yield -0.163
3(c). Direct and indirect effects of Days to Heading (DH) on Grain Yield
Direct effect of Days to Heading (DH) on Grain Yield 0.032
Indirect effect via Days to Flag Leaf emergence (DFL) 0.053
Indirect effect via Days to Booting (DB) 0.031
Indirect effect via Days to Anthesis (DA) -0.061
Indirect effect via Flag Leaf Area (FLA) -0.005
Indirect effect via SPAD chlorophyll (AUSRC) -0.009
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.018
Indirect effect via Days to Maturity (DM) 0.038
Indirect effect via Plant Height (PH) -0.003
Indirect effect via Spike Length (SL) -0.001
Indirect effect via Peduncle Length (PL) 0.001
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) -0.025
Indirect effect via Biomass Yield (BY) -0.022
Indirect effect via Harvest Index (HI) -0.209
Total Effect on Grain Yield -0.195
3(d). Direct and indirect effects of Days to Anthesis (DA) on Grain Yield
Direct effect of Days to Anthesis (DA) on Grain Yield -0.072
Indirect effect via Days to Flag Leaf emergence (DFL) 0.061
Indirect effect via Days to Booting (DB) 0.037
Indirect effect via Days to Heading (DH) 0.027
Indirect effect via Flag Leaf Area (FLA) -0.007
Indirect effect via SPAD chlorophyll (AUSRC) -0.013
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.020
78
Indirect effect via Days to Maturity (DM) 0.041
Indirect effect via Plant Height (PH) -0.004
Indirect effect via Spike Length (SL) -0.002
Indirect effect via Peduncle Length (PL) 0.002
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) -0.038
Indirect effect via Biomass Yield (BY) -0.018
Indirect effect via Harvest Index (HI) -0.255
Total Effect on Grain Yield -0.260
3(e). Direct and indirect effects of Flag Leaf Area (FLA) on Grain Yield
Direct effect of Flag Leaf Area (FLA) on Grain Yield 0.018
Indirect effect via Days to Flag Leaf emergence (DFL) -
0.027
Indirect effect via Days to Booting (DB) -
0.015
Indirect effect via Days to Heading (DH) -
0.008
Indirect effect via Days to Anthesis (DA) 0.027
Indirect effect via SPAD chlorophyll (AUSRC) -
0.007
Indirect effect via Days to Flag Leaf Senescence (DFLS) -
0.003
Indirect effect via Days to Maturity (DM) -
0.005
Indirect effect via Plant Height (PH) -
0.008
Indirect effect via Spike Length (SL) -
0.002
Indirect effect via Peduncle Length (PL) -
0.004
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) 0.027
Indirect effect via Biomass Yield (BY) 0.274
79
Indirect effect via Harvest Index (HI) 0.082
Total Effect on Grain Yield 0.348*
3(f). Direct and indirect effects of AUSRC at anthesis (AUSRC) on Grain Yield
Direct effect of AUSRC at anthesis on Grain Yield -0.044
Indirect effect via Days to Flag Leaf emergence (DFL) 0.016
Indirect effect via Days to Booting (DB) 0.007
Indirect effect via Days to Heading (DH) 0.006
Indirect effect via Days to Anthesis (DA) -0.021
Indirect effect via Flag Leaf Area (FLA) 0.003
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.012
Indirect effect via Days to Maturity (DM) 0.016
Indirect effect via Plant Height (PH) -0.008
Indirect effect via Spike Length (SL) -0.001
Indirect effect via Peduncle Length (PL) -0.001
Indirect effect via Grains per Spike (GS) 0.004
Indirect effect via Thousand Grain Weight (TGW) 0.005
Indirect effect via Biomass Yield (BY) 0.296
Indirect effect via Harvest Index (HI) 0.106
Total Effect on Grain Yield 0.372**
3(g). Direct and indirect effects of Days to Flag Leaf senescence (DFLS) on Grain Yield
Direct effect of Days to Flag Leaf senescence (DFLS) on Grain Yield -0.037
Indirect effect via Days to Flag Leaf emergence (DFL) 0.032
Indirect effect via Days to Booting (DB) 0.017
Indirect effect via Days to Heading (DH) 0.015
Indirect effect via Days to Anthesis (DA) -0.038
Indirect effect via Flag Leaf Area (FLA) 0.002
Indirect effect via SPAD chlorophyll (AUSRC) -0.014
Indirect effect via Days to Maturity (DM) 0.040
Indirect effect via Plant Height (PH) -0.012
Indirect effect via Spike Length (SL) -0.003
Indirect effect via Peduncle Length (PL) 0.000
Indirect effect via Grains per Spike (GS) 0.000
80
Indirect effect via Thousand Grain Weight (TGW) -0.017
Indirect effect via Biomass Yield (BY) 0.274
Indirect effect via Harvest Index (HI) -0.074
Total Effect on Grain Yield 0.184
3(h). Direct and indirect effects of Days to Maturity (DM) on Grain Yield
Direct effect of Days to Maturity (DM) on Grain Yield 0.054
Indirect effect via Days to Flag Leaf emergence (DFL) 0.048
Indirect effect via Days to Booting (DB) 0.024
Indirect effect via Days to Heading (DH) 0.022
Indirect effect via Days to Anthesis (DA) -
0.054
Indirect effect via Flag Leaf Area (FLA) -
0.002
Indirect effect via SPAD chlorophyll (AUSRC) -
0.013
Indirect effect via Days to Flag Leaf Senescence (DFLS) -
0.027
Indirect effect via Plant Height (PH) -
0.003
Indirect effect via Spike Length (SL) -
0.003
Indirect effect via Peduncle Length (PL) 0.001
Indirect effect via Grains per Spike (GS) 0.000
Indirect effect via Thousand Grain Weight (TGW) -
0.037
Indirect effect via Biomass Yield (BY) 0.076
Indirect effect via Harvest Index (HI) -
0.163
Total Effect on Grain Yield -
0.075
3(i). Direct and indirect effects of Plant Height (PH) on Grain Yield
Direct effect of Plant Height (PH) on Grain Yield -0.028
81
Indirect effect via Days to Flag Leaf emergence (DFL) 0.004
Indirect effect via Days to Booting (DB) 0.010
Indirect effect via Days to Heading (DH) 0.003
Indirect effect via Days to Anthesis (DA) -0.011
Indirect effect via Flag Leaf Area (FLA) 0.005
Indirect effect via SPAD chlorophyll (AUSRC) -0.013
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.015
Indirect effect via Days to Maturity (DM) 0.006
Indirect effect via Spike Length (SL) -0.002
Indirect effect via Peduncle Length (PL) -0.003
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) 0.018
Indirect effect via Biomass Yield (BY) 0.412
Indirect effect via Harvest Index (HI) 0.003
Total Effect on Grain Yield 0.390**
3(j). Direct and indirect effects of Spike Length (SL) on Grain Yield
Direct effect of Spike Length (SL) on Grain Yield -0.006
Indirect effect via Days to Flag Leaf emergence (DFL) 0.015
Indirect effect via Days to Booting (DB) 0.010
Indirect effect via Days to Heading (DH) 0.003
Indirect effect via Days to Anthesis (DA) -0.021
Indirect effect via Flag Leaf Area (FLA) 0.004
Indirect effect via SPAD chlorophyll (AUSRC) -0.007
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.016
Indirect effect via Days to Maturity (DM) 0.024
Indirect effect via Plant Height (PH) -0.008
Indirect effect via Peduncle Length (PL) -0.001
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) -0.022
Indirect effect via Biomass Yield (BY) 0.107
Indirect effect via Harvest Index (HI) -0.007
Total Effect on Grain Yield 0.077
82
3(k). Direct and indirect effects of Peduncle Length (PL) on Grain Yield
Direct effect of Peduncle Length (PL) on Grain Yield -
0.006
Indirect effect via Days to Flag Leaf emergence (DFL) -
0.024
Indirect effect via Days to Booting (DB) -
0.007
Indirect effect via Days to Heading (DH) -
0.007
Indirect effect via Days to Anthesis (DA) 0.020
Indirect effect via Flag Leaf Area (FLA) 0.011
Indirect effect via SPAD chlorophyll (AUSRC) -
0.005
Indirect effect via Days to Flag Leaf Senescence (DFLS) 0.001
Indirect effect via Days to Maturity (DM) -
0.012
Indirect effect via Plant Height (PH) -
0.015
Indirect effect via Spike Length (SL) -
0.001
Indirect effect via Grains per Spike (GS) 0.001
Indirect effect via Thousand Grain Weight (TGW) 0.028
Indirect effect via Biomass Yield (BY) 0.227
Indirect effect via Harvest Index (HI) 0.023
Total Effect on Grain Yield 0.236
3(l). Direct and indirect effects of Grains per Spike (GS) on Grain Yield
Direct effect of Grains per Spike (GS) on Grain Yield 0.010
Indirect effect via Days to Flag Leaf emergence (DFL) 0.004
Indirect effect via Days to Booting (DB) 0.010
Indirect effect via Days to Heading (DH) 0.003
Indirect effect via Days to Anthesis (DA) -
0.009
Indirect effect via Flag Leaf Area (FLA) 0.002
Indirect effect via SPAD chlorophyll (AUSRC) -
0.018
Indirect effect via Days to Flag Leaf Senescence (DFLS) 0.000
Indirect effect via Days to Maturity (DM) 0.002
83
Indirect effect via Plant Height (PH) -
0.004
Indirect effect via Spike Length (SL) -
0.001
Indirect effect via Peduncle Length (PL) -
0.001
Indirect effect via Thousand Grain Weight (TGW) -
0.008
Indirect effect via Biomass Yield (BY) 0.238
Indirect effect via Harvest Index (HI) 0.133
Total Effect on Grain Yield 0.361*
3(m). Direct and indirect effects of Thousand Grains Weight (TGW) on Grain Yield
Direct effect of Thousand Grains Weight (TGW) on Grain Yield 0.072
Indirect effect via Days to Flag Leaf emergence (DFL) -0.032
Indirect effect via Days to Booting (DB) -0.018
Indirect effect via Days to Heading (DH) -0.011
Indirect effect via Days to Anthesis (DA) 0.038
Indirect effect via Flag Leaf Area (FLA) 0.007
Indirect effect via SPAD chlorophyll (AUSRC) -0.003
Indirect effect via Days to Flag Leaf Senescence (DFLS) 0.009
Indirect effect via Days to Maturity (DM) -0.027
Indirect effect via Plant Height (PH) -0.007
Indirect effect via Spike Length (SL) 0.002
Indirect effect via Peduncle Length (PL) -0.002
Indirect effect via Grains per Spike (GS) -0.001
Indirect effect via Biomass Yield (BY) 0.166
Indirect effect via Harvest Index (HI) 0.193
Total Effect on Grain Yield 0.386**
3(n). Direct and indirect effects of Biological Yield (BY) on Grain Yield
Direct effect of Biological Yield (BY) on Grain Yield 0.737
Indirect effect via Days to Flag Leaf emergence (DFL) -0.003
Indirect effect via Days to Booting (DB) 0.001
84
Indirect effect via Days to Heading (DH) -0.001
Indirect effect via Days to Anthesis (DA) 0.002
Indirect effect via Flag Leaf Area (FLA) 0.007
Indirect effect via SPAD chlorophyll (AUSRC) -0.017
Indirect effect via Days to Flag Leaf Senescence (DFLS) -0.014
Indirect effect via Days to Maturity (DM) 0.006
Indirect effect via Plant Height (PH) -0.016
Indirect effect via Spike Length (SL) -0.001
Indirect effect via Peduncle Length (PL) -0.002
Indirect effect via Grains per Spike (GS) 0.003
Indirect effect via Thousand Grain Weight (TGW) 0.016
Indirect effect via Harvest Index (HI) 0.118
Total Effect on Grain Yield 0.836**
3(o). Direct and indirect effects of Harvest Index (HI) on Grain Yield
Direct effect of Harvest Index (HI) on Grain Yield 0.555
Indirect effect via Days to Flag Leaf emergence (DFL) -0.028
Indirect effect via Days to Booting (DB) -0.016
Indirect effect via Days to Heading (DH) -0.012
Indirect effect via Days to Anthesis (DA) 0.033
Indirect effect via Flag Leaf Area (FLA) 0.003
Indirect effect via SPAD chlorophyll (AUSRC) -0.008
Indirect effect via Days to Flag Leaf Senescence (DFLS) 0.005
Indirect effect via Days to Maturity (DM) -0.016
Indirect effect via Plant Height (PH) 0.000
Indirect effect via Spike Length (SL) 0.000
Indirect effect via Peduncle Length (PL) 0.000
Indirect effect via Grains per Spike (GS) 0.002
Indirect effect via Thousand Grain Weight (TGW) 0.025
Indirect effect via Biomass Yield (BY) 0.157
Total Effect on Grain Yield 0.700**
85
Appendix 4. Climatic data during the wheat growing period obtained from the
nearby meteorological station of NMRP, Chitwan, 2014/2015
Date of observation Temperature(0C) RH (%) Avg. Rainfall (mm)Max. Min.
November 17 29.8 14.1 88 0.55November 24 26.3 13.7 89 0.23December 1 24.2 13.4 91 0.00December 8 20.8 13.75 91 0.00December 15 21 13 82 0.00December 22 22 9.95 89 0.00December 29 22.5 9.25 89 0.00January 5 22 15.25 91 2.00January 12 16.3 11.1 90 0.00January 19 17 10.2 89 0.00January 26 24 9.8 90 0.00February 2 22 6 90 0.00February 9 20.4 11.5 90 0.00February 16 25 9.5 90 1.86February 23 28 17.5 88 0.00March 2 21.5 16.2 82 5.50March 9 30.6 20.95 77 1.40March 16 27.5 20.8 71 0.00
86
Appendix 5: Experimental layout of the research plots in alpha lattice design
87
BIOGRAPHICAL SKETCH
The author was born on 7th March 1992 A.D (2048/11/24) in Khairahani,
Chitwan, Nepal as an eldest daughter of Mr. Bala Krishna Sharma and Mrs. Parbati
Kumari Sharma. She accomplished her School Leaving Certificate (SLC) from Hillbird
Higher Secondary School, Bharatpur. She received her higher secondary degree in
Science (10+2) from Orchid Science College, Chitwan. She further continued her study
in Institute of Agriculture and Animal Science, Rampur Campus, Rampur, Chitwan,
Nepal in 2011 A.D. and got an opportunity to pursue B.Sc. Ag. Degree majoring
Undergraduate Practicum Assessment course in Plant Breeding. She is an honest and
diligent person with strong purpose. She was engaged in different biological and social
research activities and has attained some profession related trainings and workshops
during her study. She has shown good leadership being involved in some organizations.
She aspires to serve the nation through her excellence and hard toil.
Anupama Sharma
88
The author was born as an eldest son of Mr. Bir Bahadur Ayer and Mrs. Tulasi
Devi Ayer on 2048-11-20 B.S. (3rd March 1992 A.D.) at Dadeldhura, Nepal. He had
completed his School Leaving Certificate from Shree Sahashraling Higher Secondary
School, Chamada, Dadeldhura. He had received his higher secondary degree (10+2) in
science at Radiant Higher Secondary School at Mahendranagar, Kanchanpur. He further
continued his study in Institute of Agriculture and Animal Science in Rampur Campus,
Rampur, Chitwan and got an opportunity to pursue Bachelor of Science in Agriculture
Degree majoring Undergraduate Practicum Assessment course in Plant Breeding. He is
calm, hardworking with strong determination and perseverance. He has been engaged in
various biological and social research activities and has attained different agriculture
related trainings, seminars and workshops during his course of study. He has
coordinated a season long Integrated Pest Management Farmers’ Field School at IAAS.
In addition to these, he has conducted a research on carrot on the topic “Effect of Soil
Conditioner Application on Carrot Growth and Changes in Soil Productivity” under soil
science at IAAS. Being involved in social organizations, he has revealed a good
leadership. He wishes to contribute further to the agriculture sector in his country.
Dipendra Kumar Ayer