1

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

4

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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66

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