Advances in Plants & Agriculture Research

Research Article Open Access

Correlation and path coefficient studies of yield

and yield associated traits in bread wheat (Triticum
aestivum L.) Genotypes
Abstract Volume 6 Issue 5 - 2017

Sixty four wheat genotypes were tested in 8x8 simple lattice design at Ginchi, West
Birhanu Mecha,1 Sentayehu Alamerew,2
Shewa in 2012/13 cropping season,. The overall objective was to study the to assess
the association among yield and yield contributing traits and identify traits those have
Alemayehu Assefa,3 Ermias Assefa,2,5
the most direct and indirect effects on grain yield. Analysis of variance revealed that Dargicho Dutamo4
there was a significant difference among the sixty four genotypes for all the characters
1
Wachemo University, College of Agricultural Sciences, Ethiopia
2
Jimma University, College of Agriculture and Veterinary
studied. Grain yield had positive correlation with grain filling period, number of
Medicine, Ethiopia
productive tillers per plant, spike length, number of spikelets per spike, number of 3
Ethiopian Institute of Agricultural Research(EIAR), Ethiopia
kernels per spike, , 1000 kernel weight, biomass yield per plot, hectoliter weight 4Mizan-Tepi University, college of Agriculture and natural
and harvest index at both phenotypic and genotypic levels. Path coefficient analysis resources, Ethiopia
showed that biological yield, thousand kernel weight, harvest index and number of 5
Southern Agricultural Research Institute, Ethiopia
kernels per spike showed positive direct effect. Among these characters biological
yield, thousand kernel weight, harvest index and number of kernels per spike had Correspondence: Ermias Assefa, Southern Agricultural
positive correlation with grain yield in the process of selection much attention should Research Institute, Ethiopia, Email ethioerm99@gmil.com
be given to them as these characters are helpful for indirect selection.
Received: January 05, 2017 | Published: March 09, 2017

Introduction the relationships between yield and its components. To increase

the yield, study of direct and indirect effects of yield components
Wheat is a self-pollinating annual plant plays a major role among provides the basis for its successful breeding programme and hence
the few crop species being extensively grown as staple food sources the problem of yield increase can be more effectively tackled because
in the world Mollasadeghi et al.1 It was one of the first cereals to of performance of yield components and selection for closely related
be domesticated, and is thought to have originated in the ‘Fertile characters8 Although correlation estimates are helpful in determining
Crescent’ (includes parts of Jordan, Lebanon, Palestine, Syria, the components of complex trait such as yield, they do not provide
Southeastern Turkey, Iraq and Western Iran) around 11,000years an exact picture of the relative importance of direct and indirect
ago and it had reached to Ethiopia, India, Great Britain, Ireland and influences of each of the component characteristics of this trait. So far,
Spain before 5,000years ago Dubcovsky et al.2 Globally, wheat is the little information is generated about character associations between
leading source of cereal and vegetable protein in human food, having yield and yield contributing characters in these exotic bread wheat
higher protein content than either maize (corn) or rice, the other major genotypes in Ethiopia. Therefore, the objective of this study was to
cereals. In terms of total production tonnages used for food, it is assess the association among yield and yield contributing traits and
currently ahead of rice and maize as the main human food crop, after identify traits those have the most direct and indirect effects on grain
allowing for maize’s more extensive use in animal feeds Mollasadeghi yield.
et al.1 Grain yield in wheat is a complex character and is the product
of several contributing factors affecting yield directly or indirectly. Materials and methods
These factors influence grain production both directly and indirectly
and the breeder is naturally interested in investigating the extent Description of the study area
and type of association of such traits Zafarnaderi et al.3 Towards a The experiment was conducted at Ginchi, West Shewa in 2012/13
clear understanding of the type of plant traits, correlation and path cropping season. Ginchi Agricultural Research Sub Center is located
coefficient analysis are logical steps. Phenotypic and genotypic at an altitude of 2240meters above sea level, 84kilometers (kms) to
correlations within varieties are of value to indicate the degree to the West of Addis Ababa, and at a Latitude and Longitude of 09°03’N
which various characters are associated with economic productivity and 38°15’E, respectively. It is the center where the cereal crops
Mudasir et al.4 Correlation coefficient is an important statistical like Teff, barley and wheat are grown. The maximum and minimum
method, which can help wheat breeders in selection for higher yields. temperatures of the area are 24.72°C and 8.76°C, respectively,
Some of the researchers indicated the positive correlation between whereasthe mean annual rainfall is 1080.4mm. The major soil types
grain yield and yield component traits in wheat such as spikes number are black (Vertisol) and clay loam with pH of 6.4, which is heavy
per plant and grains number per spike Kashif et al.,5 straw yield and clay with 0.91-1.32% organic matter (HARC, Soil Analysis and Plant
1000kernel weight Akbar et al.,6 biological yield and harvest index.7 Physiology Team, 2012).
The grain yield and yield components of wheat are affected
Experimental materials
very much by the genotype and the environment. Therefore, as
new cultivars are being produced by breeding, the breeders study A total of sixty four bread wheat (Triticum aestivum L.) genotypes

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Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 129
aestivum L.) Genotypes

that include three standard checks and sixty one exotic bread wheat released cultivars Digelu, Alidoro and Meraro were used as a standard
accessions introduced from CIMMYT were included in this study checks. They were selected based on their agronomic performances
(Table 1). The accessions were obtained kindly from HARC. The three and suitability to the growing conditions.
Table 1 List of genotypes used in the study

Entry Pedigree Seed source Entry Pedigree Seed source

1 CIMMYTOB/2 CIMMYT 33 CIMMYTOB/65 CIMMYT
2 CIMMYTOB/7 CIMMYT 34 CIMMYTOB/66 CIMMYT
3 CIMMYTOB/14 CIMMYT 35 CIMMYTOB/67 CIMMYT
4 CIMMYTOB/22 CIMMYT 36 CIMMYTOB/68 CIMMYT
5 CIMMYTOB/23 CIMMYT 37 CIMMYTOB/70 CIMMYT
6 CIMMYTOB/24 CIMMYT 38 CIMMYTOB/71 CIMMYT
7 CIMMYTOB/25 CIMMYT 39 CIMMYTOB/75 CIMMYT
8 CIMMYTOB/27 CIMMYT 40 CIMMYTOB/76 CIMMYT
9 CIMMYTOB/29 CIMMYT 41 CIMMYTOB/77 CIMMYT
10 CIMMYTOB/32 CIMMYT 42 CIMMYTOB/78 CIMMYT
11 CIMMYTOB/33 CIMMYT 43 CIMMYTOB/79 CIMMYT
12 CIMMYTOB/35 CIMMYT 44 CIMMYTOB/80 CIMMYT
13 CIMMYTOB/39 CIMMYT 45 CIMMYTADT/1 CIMMYT
14 CIMMYTOB/40 CIMMYT 46 CIMMYTADT/2 CIMMYT
15 CIMMYTOB/41 CIMMYT 47 CIMMYTADT/3 CIMMYT
16 CIMMYTOB/44 CIMMYT 48 CIMMYTADT/4 CIMMYT
17 CIMMYTOB/45 CIMMYT 49 CIMMYTADT/5 CIMMYT
18 CIMMYTOB/48 CIMMYT 50 CIMMYTADT/6 CIMMYT
19 CIMMYTOB/49 CIMMYT 51 CIMMYTADT/7 CIMMYT
20 CIMMYTOB/50 CIMMYT 52 CIMMYTADT/8 CIMMYT
21 CIMMYTOB/51 CIMMYT 53 CIMMYTADT/9 CIMMYT
22 CIMMYTOB/52 CIMMYT 54 CIMMYTADT/11 CIMMYT
23 CIMMYTOB/53 CIMMYT 55 CIMMYTADT/13 CIMMYT
24 CIMMYTOB/54 CIMMYT 56 CIMMYTADT/15 CIMMYT
25 CIMMYTOB/57 CIMMYT 57 CIMMYTADT/16 CIMMYT
26 CIMMYTOB/58 CIMMYT 58 CIMMYTADT/17 CIMMYT
27 CIMMYTOB/59 CIMMYT 59 CIMMYTADT/19 CIMMYT
28 CIMMYTOB/60 CIMMYT 60 CIMMYTADT/20 CIMMYT
29 CIMMYTOB/61 CIMMYT 61 CIMMYTADT/21 CIMMYT
30 CIMMYTOB/62 CIMMYT 62 ALIDORO HARC
31 CIMMYTOB/63 CIMMYT 63 MERARO KARC
32 CIMMYTOB/64 CIMMYT 64 DIGELU KARC

Experimental design and trial management For data collection, the middle four rows were used (2m2 area). The
central four rows were harvested for grain yield and biomass yield
The experiment was carried out in 8x8 Simple Lattice Design at from each plot leaving boarder rows to avoid boarder effects. All other
random. The genotypes were grown under uniform rain fed conditions. agronomic practices were undertaken uniformly to the entire plot as
The plot size was six rows of 2.5m length with 0.2m row spacing recommended for wheat production in the area during the growing
i.e. 1.2mx2.5m =3m2 (standard plot size for variety trial). Planting season to raise a healthy crop.
was done by hand drilling on July 06, 2012. Seed rate was 150kg/ha
(45g/plot). Recommended fertilizer rate of 100/100kg/ha N/P2O5 in Description of data collected
the forms of Urea and DAP was applied to each plot in the shallow
furrow depths and mixed with soil at the same time during sowing. The data on the following attributes was collected on the basis of
the central four rows in each plot per replication.

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
 Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 130
aestivum L.) Genotypes

i. Days to 50% heading (DH): The numbers of days from sowing to variance was done using Proc lattice and Proc GLM procedures of
50% of plants have started heading. SAS version 9.29 after testing the ANOVA assumptions.
ii. Days to 75% maturity (DM): The numbers of days from date of The Mathematical Model for Simple Lattice Design is:
sowing to a stage at which 75% of the plants have reached physio-
Yijr =µ + Ar + Gij + Bir + Bjr + eijr
logical maturity or 75% of the spikes on the plots turned golden
yellow color. Where Yijr= the value observed for the plot in the rth replication
containing the genotype Gij, μ=grand mean, Gij= genotype effect in the
iii. Grain filling period: The grain filling period in days was compu-
ith row & jth column, Ar=replication effect, Bir=ith block effect, Bjr=jth
ted by subtracting the number of days to heading from the number
block effect, eijr,= the plot residual effect*
of days to maturity.
iv. Thousand Kernels weight (TKW): The weight (g) of 1000 ker- Correlation coefficient (r)
nels from randomly sampled seeds per plot measured by using Estimation of correlation coefficients (r) was computed using
sensitive balance. It was the weight (gm) of 1000 kernel estimated GENRES Statistical Software Package (Pascal Intl Software Solutions,
by counting 1000 seeds randomly drawn from the grain yield of 1994) to study positively and negatively correlated characters with
each plot. yield and among themselves.
v. Grain yield per plot (GYP): The grain yield per plot was measu- Phenotypic correlation and genotypic correlation was computed
red in grams using sensitive balance after moisture of the seed is by the method described Singh and Chaundry (1985).
adjusted to 12.5%. Total dry weight of grains harvested from the
p cov x. y
middle four rows out of six rows was taken as grain yield per plot r =
p
and expressed as grams per plot. δ 2 px .δ 2 py
vi. Biomass yield per plot (BMYP): It was recorded by weighing the gcovx.y
total above ground yield harvested from the four central rows of rg =
each experimental plot at the time of harvest. δ 2gx.δ 2 gy

vii. Harvest index (%): It was estimated by dividing grain yield per Where,rp and rg are phenotypic and genotypic correlation
plot to biological yield per plot. It is ratio of grain yield to the abo- coefficients, respectively;pcovx.y and g covx.y are phenotypic and
ve ground biomass yield. genotypic, covariance between variables x and y, respectively; δ2px
viii. Hectoliter weight (HLW): It is grain weight of one hectoliter vo- and δ2gx are phenotypic and genotypic, variances for variable x;
lume random sample of wheat grain for each experimental plot and δ2py and δ2gy are phenotypic and genotypic variances for the
expressed by (kg/ha). variable y, respectively. The coefficients of correlation were tested
using ‘r’ tabulated value at n-2 degrees of freedom, at 5% and 1%
ix. Ten plants were randomly selected from the four central plots for probability level, where n is the number of treatments (accessions).
recording the following observations:
Path coefficient analysis
x. Plant height (cm): The average height (cm) of ten randomly taken
plants at the maturity time from the middle four rows of each plot The path coefficient analysis was carried out using GENRES
of the replication was measured from the ground level to the top of Statistical Software Package to study the direct and indirect
the spike excluding the awn. contributions of the traits to the associations. A measure of direct
and indirect effects of each character on grain yield was estimated
xi. Number of productive tillers per plant: The numbers of tillers using a standardized partial regression coefficient known as path
per plant bearing productive heads were counted at the time of har- coefficientanalysis, as suggested by Dewey et al.10 Thus, correlation
vest and average was recorded for the ten randomly taken plants coefficient of different characters with grain yield was partitioned into
from the middle four rows. direct and indirect effects adopting the following formul
xii. Spike length (cm): The average spike length of ten randomly
taken plants from the base of the main spike to the top of the last r=
iy
r1iP1 + r2 iP 2 + … + rIiP i + … + rniPn
spikelet excluding awns was recorded in centimeter from four cen- where riy is correlation of ith character with grain yield; r1iPi is
tral rows of each plot. indirect effects of ith character on grain yield through first character;
xiii. Number of spikelets per spike: Total numbers of spikelets on rni is correlation between nth character and ith character; n is number of
main spike of all ten plants from four central rows were counted at independent variables; Pi is direct effect of ith character on grain yield;
the time of maturity and average was recorded. Pn is direct effects of nth character on grain yield.

xiv. Number of kernels per spike (NKPS): Total number of grains Direct effect of different component characters on grain yield were
in the main spike were counted at the time of harvest from ten obtained by solving the following equations:
randomly taken plants and expressed as average and recorded from
four central rows of each plot. (=
r )
iy( Pi ) ( r ) ; and ( =
Pi )
ij (r ) − 1 (r P ) ij 1i i

Statistical Analysis where, (Pi) is matrix of direct effect; (rij) is matrix of correlation
coefficients among all the nth, component characters; (riy,) is matrix
Analysis of variance (ANOVA)
of correlation of all component characters with grain yield; (r1iPi) is
The data collected for each quantitative trait were subjected to indirect effect of ith character on seed yield through first character.
analysis of variance (ANOVA) for simple lattice design. Analysis of

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 131
aestivum L.) Genotypes

Results and discussion number of spikelets per spike, 1000 kernel weight, grain yield plot-
1
and hectoliter weight or test weight), significant at (p<0.05) for
Analysis of variance (ANOVA) the rest six characters; namely, days to 75% maturity, grain filling
Mean squares of the 13 characters from analysis of variance period, plant height, number of kernels spike-1, biomass yield and
(ANOVA) are presented in (Table 2). Highly significant differences harvest index. This result indicating that there is variability among the
among genotypes (P<0.01) were observed for seven characters (days genotypes studied and would respond positively to selection (Figure
to heading, number of productive tillers per plant, spike length, 1) (Figure 2).

Table 2 Mean square of the 13 characters from analysis of variance

Replication Genotype Intra Block Efficiency relative to

Characters CV (%)
(df=1) (df=63) Error (df=49) RCBD

Days to 50% heading (days) 8.51 86.99** 9.22 10.62 107.38

Days to 75% maturity (days) 2.53 37.75* 5.42 4.04 100.49

Grain filling period (days) 13.78 47.30* 5.58 10.16 100.72

Plant height (cm) 29.55 248.40* 22.48 11.94 101.24

Number of productive tillers per
0.018 0.68** 0.1563 10.58 105.37
plant
Spike length (cm) 0.713 1.0870** 0.1043 8.84 109.36

Number of spikelets per spike 3.3 2.5444** 0.2479 6.87 102.31

Number of kernels per spike 25.92 50.4459* 8.8261 16.2 111.15

1000 kernels weight (g) 0.131328 43.2103** 3.722 12.628 103.52

Biomass yield per plot (g) 22578 160197* 9604 19.5 115.26

Harvest index (%) 18.9036 37.2436* 6.1859 18.09 103.12

Hectoliter weight (kg/hL) 6.707 18.4252** 3.4176 13.75 116.27

Grain yield per plot(g) 1287.78 22864** 4066 2.07 120.9

*=significant at 5% probability level and **=highly significant at 1% probability level

CV, coefficient of variation, RCBD, randomized complete block design

Figure 2 Graphical presentation of genotypic correlation.

Figure 1 Graphical presentation of phenotypic correlation.

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 132
aestivum L.) Genotypes

Correlation of grain yield with other traits negative and significant association with number of spikelets per
spike (-0.31*) at phenotypic level. This trait revealed insignificant
Phenotypic (pr) and genotypic (gr) correlation between the various phenotypic association with the rest traits both positive and negative
characters are presented in Table 3. Grain yield had positive correlation directions. Plant height had significant and positive association with
with grain filling period (0.16ns, 0.4**), number of productive tillers number of spikelets spike-1(0.31*), days to 75% maturity (0.38**)
per plant (0.36**,0.49**), spike length (0.27*, 0.31*), number of and biomass yield plot-1 (0.36**). It showed insignificant phenotypic
spikelets per spike (0.251*,0.29*), number of kernels per spike positive and negative correlations with the rest characters. The
(0.326**,0.401**), 1000 kernel weight (0.264*,0.42**), biomass findings of Majumder et al.,18 contradicts the present results reported
yield per plot (0.74**,0.64**), hectoliter weight (0.21ns, 0.45**) positive correlation between positive association with number of
and harvest index (0.42**,0.327**) at both phenotypic and genotypic spikelets spike-1 (0.31*), grains per spike, days to maturity, and spike
levels. The works of Virk and Anand (1970) showed that wheat grain length (cm). The character number of productive tillers per plant
yield was positively correlated with spike length, biomass yield per showed highly significant and positive association with grain yield
plot and 1000 grain weight. Belay et al.11 and Aycecik et al.,12 reported per plot (0.36**) and significant and positive correlation with days
positive correlation of grain yield with number of grains per spike, to 50% heading (0.25*) at phenotypic level. This character showed
plant height and 1000 grain weight, which support the present studies. insignificant positive and negative phenotypic correlations with the
Ahmad et al. (2010), Akcura13 Ali et al.,7 and Peymaninia et al.,14 also rest characters. Earlier reports from Mohammad et al.,15 Khokhar et
reported strong positive correlation and direct effect of total biomass al. (2010) and Kumar et al.19 reported similar results. Spike length
and harvest index on grain yield. Generally, in those characters in revealed highly significant positive association with number of
which grain yield showed positive and significant correlation, there spikelets per spike-1 (0.37) and significant association with grain
were component interactions in which a gene conditioning an increase yield per plot (0.27) at phenotypic level. The character showed non-
in one character will also influence another character provided other significant positive and negative associations with the rest characters
conditions are kept constant (Figure 1) (Figure 2). at phenotypic level. Sokoto et al.20 and Abderrahmane et al.,21 reported
Days to heading showed negative association with grain yield highly significant positive correlation of this character with grain
plot-1 at both phenotypic and genotypic levels (rp= -0.18, rg=-0.36). yield. Number of spikelets spike-1 exhibited highly significant and
However, the associations were insignificant at phenotypic level. positive association with number of days to heading (0.59**) and days
Negative correlation indicated inverse relationship between earliness to maturity (0.42**) significant and positive association with plant
characters and grain yield that is desirable if stresses such as terminal height (0.31*) at phenotypic level. It had negative and significant
heat and drought are expected. This is in agreement with the findings association with 1000 kernels weight (-0.35**), grain filling period
Mohammad et al.,15 Tsegaye et al.,16 Zafarnaderi et al.3 and Gelalcha et (-0.31*), harvest index (-0.28*) and hectoliter weight (-0.36**) and
al.,17 who reported negative relationship between days to heading and positive and negative non significant correlation for the rest traits at
grain yield in their studies in advanced bread wheat lines. Plant height phenotypic level. Number of kernels spike-1 had highly significant and
had significant negative association with grain yield (rp=-0.26*, rg=- positive association with biomass yield per plot (0.69**) and non-
0.284*) at both phenotypic and genotypic levels in agreement with significant positive and negative correlation with the rest characters.
the findings of Mohammad et al. (2005) in ten candidate bread wheat Zarei et al (2013) reported highly significant and positive association
lines. Grain yield displayed negative and insignificant correlation number of kernels spike-1 with biomass yield similar results. Thousand
with days to maturity (rp=-0.1, rg=-0.16) at phenotypic and genotypic kernels weight had highly significant and positive associations with
levels. s18 reported similar negative and insignificant association grain filling period (0.46**) and hectoliter weight (0.38**) and it
between days to maturity and grain yield. showed negative significant association with days to 50% heading
(-0.6**), days to 75% maturity (-0.25*) and number of spikelets per
Correlation among characters spike (-0.35).
Phenotypic correlation (rp). The character, days to heading exerted The trait showed positive and negative non-significant phenotypic
significant and positive phenotypic correlation with days to maturity correlation with all the rest characters. Majumder et al.,18 found similar
(0.64**), number of productive tillers per plant (0.25*), number of results. On the contrary, Khokhar et al. (2010) observed thousand
spikelets per spike (0.59**) and biomass yield per plot (0.34**). kernels weight had highly significant and negative associations with
However, the same trait revealed negative and significant correlation days to 75% maturity.Biomass yield showed positive and significant
with grain filling period (-0.58**), thousand kernels weight (-0.6), correlation with days to heading (0.34**), and plant height (0.36**)
harvest index (-0.43**) and hectoliter weight (-0.28*) at phenotypic and non-significant association with the rest traits at phenotypic
level. It showed non significant association with the rest of the level irrespective of direction. The findings of Mohammad et al.15
characters. The works of Iftikhar et al. (2012) support these findings. contradicted these significant and positive associations of biomass
Significant and positive phenotypic correlation was observed for days yield with days to heading.
to 75% maturity with plant height (0.31*) and number of spikelets
per spike (0.42**) whereas positive non-significant phenotypic Harvest index exhibited significant negative phenotypic correlation
correlation with biomass yields (0.19ns). Days to maturity had negative with days to 50% heading (-0.43**), days to maturity (-0.37**) and
and significant phenotypic correlation with thousand kernel weight number of spikelets per spike (-0.28*) but showed insignificant
(-0.25*) and harvest index (-0.37**). However, the trait exhibited correlations with the rest of all characters in agreement with the results
insignificant phenotypic associations with the rest characters of Sajjad et al (2011) and Ashraf et al.22 Zarei et al. (2013) reported
irrespective of direction. harvest index had positive phenotypic correlation with number of
spikelets per spike that contradicted the present result. Harvest index
Grain filling period had highly significant and positive association has been recommended as a selection criterion for increasing yield of
with thousand kernel weight (0.46**) and hectoliter weight (0.34**) cereals. Because the high-yielding lines had increased biomass, while
and significant association with grain yield (0.28*); but it showed maintaining their mean grain weight and harvest index, the source

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 133
aestivum L.) Genotypes

and sink were simultaneously increased in these lines. Hectoliter yield plot-1 showed positive significant genotypic correlation with days
weight exhibited highly significant positive association with grain to heading (0.4), days to maturity (0.49), plant height (0.84), number
filling period (0.34**) and thousand kernels weight (0.38**) whereas of productive tillers per plant, number of spikelets per spike (0.46) and
significant and negative association with days to heading (-0.28*) and number of kernels per spike (0.69) and negative significant genotypic
number of spikelets spike-1 (-0.36**) and insignificant association correlation with harvest index (-0.59) and non significant association
with the rest characters irrespective of direction at phenotypic level. with the rest traits at genotypic level irrespective of direction. Harvest
Demelash et al (2013) obtained similar results of highly significant index exhibited significant negative correlation with days to heading
positive correlation of hectoliter weight with thousand grain weights (-0.43) days to maturity (-0.44), number of spikelets per spike (-0.54)
but negatively correlated with days to heading and days to maturity and biomass yield plot-1 (-0.59) at genotypic level whereas it showed
in bread wheat. significant positive association with grain filling period (0.49**),
1000kernels weight (0.26*) and hectoliter weight (0.31*) at the
Genotypic correlation genotypic level. Hectoliter weight (Test weight) exhibited highly
The yield components exhibited varying trends of association significant positive association with grain filling period (0.50) and
among themselves. Days to 50% heading exerted significant positive thousand kernels weight (0.39) and significant positive association
association with days to maturity (0.42**), number of productive with harvest index (0.31*) at genotypic level. The trait revealed also
tillers per plant (0.35**), number of spikelets per spike (0.35**) highly significant negative genotypic correlation with days to heading
and biomass yield per plot (0.4**). However, the trait revealed (-0.34**) and number of spikelets spike-1 (-0.42**) while insignificant
negative but significant correlation with grain filling period (-0.58), association with the rests of characters at genotypic level with
thousand kernel weight (-0.6), harvest index (-0.43) and hectoliter irrespective of direction.
weight (-0.28) at genotypic level. It showed a non-significant positive
Path Coefficient Analysis
genotypic correlation with plant height (0.2), spike length (0.12),
and number of kernels per spike (0.18).23 Days to 75% maturity As correlation does not allow the partitioning of genotypic
showed significant and positive genotypic correlation with plant correlation coefficients into direct and indirect effects, they are
height (0.38) and number of spikelets per spike (0. 55). This trait had further analyzed by path coefficient analysis (Dewy and Lu, 1959)
highly significant and positive correlation with biomass yield (0.49) by using grain yield as a dependant variable. The genotypic direct
at genotypic level. Grain filling period had highly significant and and indirect effects of different characters on grain yield are presented
positive correlation with thousand kernel weight (0.76), harvest index in Table 2. In this study, genotypic path analysis manifested positive
(0.49), and hectoliter weight (0.5) whereas highly significant and direct effect on grain yield for all characters except plant height and
negative association was observed with number of productive tillers hectoliter weight. The highest positive direct effect on grain yield per
plant-1 (-0.38) and number of spikelets spike-1 (-0.57) at genotypic plot were exhibited by biological yield (1.14**) which had positive
level. Plant height had highly significant and positive association and significant correlation with grain yield.
with number of kernels spike-1(0.36), and biomass yield plot-1 (0.84)
The indirect effect via other characters was negligible or negative.
at genotypic level and showed negative and significant genotypic
Hence, the genotypic correlation with grain yield was largely due
correlation with harvest index. Number of productive tillers per plant
to the direct effect. The direct effect of number of kernels spike-1 on
had highly significant and positive genotypic correlation with biomass
grain yield was recorded to be positive with a value of 0.374 that
yield plot-1 (0.45**) and insignificant with all other traits irrespective
was equivalent to the correlation coefficient it had with grain yield.
of directions.
This suggests the correlation revealed the true relationship and direct
Spike length at genotypic level revealed highly significant selection through this character is effective. The high indirect positive
positive association with number of spikelets per spike-1 (0.44**) and effects of number of kernels per spike revealed via biomass yield
significant association with number of kernels spike-1(0.27) and 1000 (0.37) on grain yield, whereas moderate negative indirect effect via
kernels weight (0.25*). The character revealed insignificant positive number of productive tillers plant-1 (-0.124), thousand kernels weight
and negative associations with the rest characters at genotypic level. (-0.162), harvest index (-0.150) and test weight (-0.132) on grain yield.
Number of spikelets spike-1 exhibited significant association with The results revealed that number of kernels per spike may be used as
number of kernels spike-1 (0.32*) and highly significant association direct selection criteria in any breeding program designed to increase
with biomass yield per plot (0.46**) in positive direction at grain yield. The direct effect of 1000 kernels weight and harvest
genotypic level. It showed negative highly significant association index on grain yield was positive with value of 0.98, suggesting its
with 1000 kernels weight (-0.44**), harvest index (-0.54**) and importance in breeding program for developing wheat genotypes with
hectoliter weight (-0.42**) at genotypic level. It had positive and higher grain yield. The high positive indirect effects of 1000 kernels
negative insignificant correlation for the rest traits at genotypic level weight via harvest index (0.562) followed by grain filling period
irrespective of direction. (0.184) exhibited on grain yield. It showed negative indirect effect on
grain yield via days to heading and maturity, number of productive
Positive significant associations were observed for number of
tillers per plant, number of spikelets per spike, number of kernels per
kernels spike-1 with plant height (0.36**), spike length (0.27*),
spike and via hectoliter weight were recorded. These findings were
number of kernels spike-1 (0.32*) and biomass yield per plot (0.69**)
in accordance with those of Iftikhar et al. (2012). The direct effect
at genotypic level. The trait had negative significant association with
of harvest index on grain yield was positive and high (0.78) and it
1000 kernels weight (-0.25*) at genotypic level and insignificant with
showed high positive correlation coefficients. The indirect effects
the rest characters in positive and negative directions at genotypic
of these traits through other traits were mostly negative. Hence,
level. Thousand kernels weight had strong negative association with
the correlation coefficients of these traits with grain yield had been
days to heading (-0.6**), days to maturity (-0.39**), number of
largely due to the direct effect.
spikelets per spike (-0.44**) and number of kernels per spike (-0.25*)
and strong positive association with grain filling period (0.76), harvest Hectoliter weight had negative direct effect and positive
index (0.26*) and hectoliter weight (0.39) at genotypic level. Biomass correlation coefficient. Thus, the positive correlation coefficient was

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 134
aestivum L.) Genotypes

largely due to its respective indirect effects. Plant height had negative flowering and days to maturity with grain yield was negative. The
significant correlation with grain yield (Table 3). Its direct effect on direct effect of days to heading on grain yield, on the other hand, is
grain yield was also negative and smaller than its correlation value positive (Table 4). Number of productive tillers per plant was positive
(Table 4) indicating indirect influence of the trait via other component strongly correlated with grain yield while the magnitude of the direct
characters. Its high magnitude of indirect effect through biomass yield effect is also positive and less than that of correlation coefficient
per plot supports this idea. This result is in agreement with Ahmed et indicating importance of other traits via which productive tillers per
al. (2003), who reported negative correlation and direct effect of plant plant contributed to grain yield (Tables 1) (Table 2). The significantly
height with grain yield. Aycicek and Yildirim (2006) also pointed that high magnitude of its indirect effect through total biomass per plot
plant height had negative direct effect on grain yield. Days to heading supports this idea. Spike length was positive strongly correlated (0.41)
and days to maturity had positive direct effects on grain yield. Kumar24 with grain yield while the magnitude of the direct effect (0.204) was
reported similar results in rice cultivars. The correlation coefficient also positive and less than that of correlation coefficient indicating
they had with grain yield was negative. This negative relationship importance of other traits via which spike length contributed to grain
between early flowering and maturity accompanied with sufficient yield (Tables 1) (Table 2). The indirect positive effects of spike length
grain filling period characters and grain yield is desirable if stresses via showed moderate indirect positive effect through thousand kernels
conditions such as terminal heat and drought are expected during weight (0.169) and biomass yield (0.16) and revealed high negative
growing season. This suggestion can be justified by earlier report of indirect effect via harvest index (-0.30) on grain yield. Similar findings
Gelalcha and Hanchinal (2013) in which correlation between days to were reported by Narwal et al. (1999) and Mohsin et al. (2009).

Table 3 Estimates of phenotypic (below diagonal bold) and genotypic (above diagonal not bold) correlation coefficients among yield and yield components in
64 bread wheat genotypes tested at Ginchi (2012/2013)

Character DH DM GFP PH NPTP SL NSPS NKPS TKW BMY HI

DH 1 .42** -.58** .2ns .35** .12ns .68** .18ns -.6** .40** -.43**
DM 0.64** 1 -.33** .38** .16ns .15 ns .55** .12ns -.39** 0.49** -.44**
GFP -0.58** .15 ns 1 .11ns -.38** -.04ns -.57** -.21ns .76** -0.14ns 0.49**
PH 0.18 ns .31* .08 ns 1 .22ns .03ns .32* .36** -.09 ns 0.84** -.39**
NPTP 0.25* .23 ns -.05 ns .15 ns 1 -.11ns .08 ns .13 ns -.23 ns 0.45** 0.004ns
SL 0.06 ns .16 ns .06 ns .02 ns -.11ns 1.0 .44** .27* .25* 0.18ns -.21ns
NSPS 0.59** .42** -.31* .31* .07ns .37** 1 .32* -.44** 0.46** -.54**
NKPS 0.09 ns .20 ns .09 ns .19 ns .13ns .14 ns .18 ns 1 -.25* 0.69** -.16ns
TKW -0.60** -.25* .46** -.08ns -.14 ns .18 ns -.35** -.08 ns 1.0 -0.18ns 0.26*
BMY 0.34 ** .19 ns .12 ns .36** .23ns .07 ns .16 ns .19 ns -.019ns 1 -.59**
HI -0.43** -.37** .16 ns -.21ns -.05ns -.13ns -.28* .02 ns .22 ns -0.19 ns 1.0
HLW -0.28* .04 ns .34** .10 ns -.04ns -.06ns -.36** .07 ns .38** .042ns 0.24ns
GY -0.18 ns -0.1 ns .28 * -.26* .36 ** .27* .251* .326** .264* .74** 0.42**

P<0.05=0.25 & P<0.01=0.325 for df=n-2, where n is the number of genotypes, DH, days to heading; DM, days to maturity; GFP, grain filling period; PH, plant height
(cm); NPTPP, no. of productive tiller plant-1 and; SL, spike length (cm), NSPS, No. Of spikelets spike-1; NKPS, no. of kernels spike-1; TKW, 1000 kernel weight (g);
GY, grain yield plot-1; BMY, biomass yield plot-1; HI, harvest index and HLW, hectoliter weight.
Table 4 Estimate of direct effect (bold face and diagonal) and indirect effects (off diagonal) at genotypic level in 64 bread wheat genotypes tested at Ginchi
(2012/13).

Character DH DM GFP PH NPTPP SL NSPS NKPS TKW BMY HI HLW Rg

DH 0.108 0.134 -0.103 0.032 -0.063 -0.016 0.075 0.03 -0.187 0.21 -0.48 0.103 -0.36**
DM 0.22 0.116 -0.182 0.059 -0.029 -0.021 0.058 0.019 -0.11 0.38 -0.36 0.009 -0.16ns
GFP -0.421 -0.095 0.247 0.016 0.069 0.006 -0.061 -0.035 0.21 -0.31 0.83 -0.152 0.40**
PH 0.103 0.108 0.025 -0.116 -0.039 -0.005 0.035 0.061 -0.024 0.9 -0.77 -0.033 -.284*
NPTPP 0.109 0.045 -0.094 -0.034 0.238 0.015 0.009 0.022 -0.263 0.405 0.008 0.032 0.4.92**
SL 0.059 0.043 0.01 0.005 -0.02 0.204 0.05 0.046 0.169 0.16 -0.3 0.021 0.401**
NSPS 0.354 0.156 -0.14 0.05 -0.015 -0.061 0.102 0.054 -0.12 0.286 -0.41 0.134 0.290*
NKPS 0.092 0.033 0.052 0.057 -0.124 0.038 0.034 0.374 -0.162 0.37 -0.15 -0.132 0.42**
TKW -0.353 -0.113 0.184 -0.014 -0.262 -0.035 -0.148 -0.14 0.98 -0.033 0.562 -0.112 0.58**
BMY 0.101 0.102 -0.072 0.15 -0.086 -0.008 0.029 0.016 -0.009 1.14 -0.801 -0.008 0.64**

Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum Copyright:
©2017 Mecha et al. 135
aestivum L.) Genotypes

Table Continued..
Character DH DM GFP PH NPTPP SL NSPS NKPS TKW BMY HI HLW Rg
HI -0.185 -0.131 0.144 -0.06 -0.001 0.03 -0.059 -0.004 0.079 -0.23 0.78 -0.096 0.327**
HLW -0.166 -0.008 0.117 0.016 0.018 0.009 -0.045 0.017 0.1 0.027 0.602 -0.21 0.45**

DH, days to heading; DM, days to maturity; GFP, grain filling period; PH, plant height (cm); NPTPP, no. of productive tiller plant-1 and; SL, spike length (cm), NSPS,
No. Of spikelets spike-1; NKPS, no. of kernels spike-1; TKW, 1000 kernel weight (g); GY, grain yield plot-1; BMY, biomass yield plot-1; HI, harvest index and HLW,
hectoliter weight.

Number of spikelets per spike had positive direct effect. The on grain yield as a dependant variable showed that biological yield
indirect effects via other characters were mostly positive and had the highest positive direct effect. The correlation coefficient with
negligible. Therefore, its positive correlation coefficient with grain grain yield was also positive and significant. Thousand kernel weight,
yield was mainly due to their direct effect. Harvest index had positive harvest index and number of kernels per spike also showed positive
direct effect. Its indirect effects through other characters were mostly direct effect. The correlation coefficients were also positive and
negative and negligible. Thus, its positive correlation coefficient with significant. Since biological yield, thousand kernel weight, harvest
grain yield was mainly due to its direct effect. Grain filling period index and number of kernels per spike had positive correlation with
had positive direct effect. The indirect effect of this trait via other grain yield in the process of selection much attention should be given
traits was mostly negative and negligible. Thus, the correlation to them as these characters are helpful for indirect selection.31–36
coefficient of this trait with grain yield was due to the direct effect.
Generally, characters that exerted positive direct effect and positive Acknowledgements
and significant correlation coefficient with grain yield were known to The authors would like to thank Jimma University College
affect grain yield in the favorable direction and needs much attention of Agriculture and Veterinary Medicine (JUCAVM) and Holeta
during the process of selection cases. Path coefficient analysis for Agricultural Research Center (HARC) for their financial support.
traits like biomass yield per plot, thousand kernels weight, harvest
index and number of kernels per spike showed that the highest positive Conflict of interest
direct effects towards grain yield and strong and positive correlations
with grain yield (Tables 8 and 9). The respective indirect effects The author declares no conflict of interest.
of these characters via other characters were either negligible or
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Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226
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©2017 Mecha et al. 136
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Citation: Mecha B, Alamerew S, Assefa A, et al. Correlation and path coefficient studies of yield and yield associated traits in bread wheat (Triticum aestivum L.)
Genotypes. Adv Plants Agric Res. 2017;6(5):128‒136. DOI: 10.15406/apar.2017.06.00226