Correlations and pass coefficient analyses of yield and yield related traits in common bean genotypes (Phaseolus vulgaris L.) at Abaya and Yabello, Southern Ethiopia

Correlations and pass coefficient analyses ofyield and yield related traits in common bean genotypes (Phaseolus vulgaris L.) at Abaya and Yabello, Southern Ethiopia
IJPBCS
Correlations and pass coefficient analyses of yield and
yield related traits in common bean genotypes
(Phaseolus vulgaris L.) at Abaya and Yabello, Southern
Ethiopia
Ejigu Ejara1*, Wassu Mohammed2 and Berhanu Amsalu3
1Yabello Pastoral and Dry Land Agriculture Research Centre, Oromia Agricultural Research Institute, Yabello, Ethiopia.
2School of Plant Science, College of Agriculture, Haramaya University, Dire dawa, Ethiopia.
3Malkassa Agriculture Research Centre, Ethiopian agricultural Research Institute, Melkassa, Ethiopia.
Common bean is among the major crops grown in southern Ethiopia including Borana zone
where the majority of the farmers are Agro-pastoralist and produce the crop mainly for home
consumption. However, scarcity of varieties that fit to the environment is one of the major
production constraints. Therefore, this experiment wasconducted to evaluate 36 common bean
genotypes in triple lattice design to generate information on the association of yield and yield
related traits, and to determine the direct and indirect effects of yield related traits on grain yield.
Thousand seeds weight, seed number per plant, seed number per pod and number of primary
branches per plant showed positive and highly significant correlations with grain yield. Moreover,
thousand seed weight, seeds per plant and seeds per pods had high positive direct effects on
grain yield both at genotypic and phenotypic levels. This suggested the importance of
considering these traits during selection to improve grain yield in subsequent generations. In
contrast, the negative direct effects of days to flowering and maturity as well a s the negative
indirect effects of these traits via other traits on grain yield suggested the need to select
genotypes for early flowering and maturity for the study area.
Keywords: Direct effect, genotypic path coefficient analysis, indirect effects, phenotypic path coefficient analysis,
Residual effect
INTRODUCTION
Common bean is a diploid (2n = 2x = 22) and
predominantly self-crossing species although 3% or more
out crossing rate has also been observed (Ibarra-Perez et
al., 1997). It is the world’s second most important pulse
after soybean (Parades et al., 2009). Common bean is
regarded as “Grain of hope” as it is an important
component of subsistence agriculture and feeds about 300
million people in tropics and 100 million people in Africa
alone (Sofi et al., 2011).
For effective selection, information on characters’
association with yield and among themselves and the
extent of environmental influence on the expression of
these characters are necessary (Yağdı, 2009). Yield is the
principal factor for determining improvement of a crop. Like
other crops, seed yield in common bean (Phaseolus
vulgaris L.) is a quantitative character and influenced by a
number of yield contributing traits.
*Corresponding Author: Ejigu Ejara, Yabello Pastoral
and Dry Land Agriculture Research Centre, Oromia
Agricultural Research Institute, Yabello, Ethiopia. P.O.
Box: 85, Email: ehordofa@gmail.com, Fax:
+251464460663
International Journal of Plant Breeding and Crop Science
Vol. 4(2), pp. 215-224, June, 2017. © w w w .premierpublishers.org. ISSN: 2167-0449
ResearchArticle

Ejara et al. 216
The selection of desirable types should therefore be based
on yield as well as on other yield components. Information
on mutual association between yield and yield
components is necessary for efficient utilization of the
genetic stock in the crop improvement program (Nechifor
et al.,2011) To achieve significant progress in breeding
programs, it is essential to know the relationship between
seed yield and its components (Assady et al., 2005).
Correlation and path coefficient analysis could be used as
an important tool to bring information about appropriate
cause and effects relationship between yield and some
yield components (Khan et al., 2003). Although correlation
estimates are helpful in determining the components of
complex trait such as yield, they do not provide an exact
picture of the relative importance of direct and indirect
influences of each of the component characteristics of this
trait. Path coefficient analysis provides more effective
means of separating direct and indirect factors, permitting
a critical examination of the specific forces acting to
produce a given correlation and measuring the relative
importance of the causal factors. Correlation and pass co-
efficient analyses of traits studies have been also
conducted by considerable number of researchers on
common bean, for instance, Gonçalves et al. (2003),
Karasu and Oz (2010), Salehi et al. (2010), Dursun (2007),
Daniel et.al. (2015), Alamayehu, (2010), Barecha (2015),
Kassaye, (2006), Bhushan (2007) and etc. Most of the
studies on common bean correlation and path analyses of
traits were conducted in other parts of the country not in
Borena zone (southern Ethiopia) where moisture stress is
a major crop production problem and the agriculture
production is dominated by pastoralist and agro-
pastoralist. Moreover, information is lacking on the
potential of common bean genotypes in southern Ethiopia
in general and Abaya, and Yabello district of Borana zone
in particular. Hence the present study was undertaken with
the following objectives. (i) To determine associations
among yield and yield related traits in common bean
genotypes. (ii) To determine the direct and indirect effects
of yield related traits on grain yield of common bean
genotypes
MATHERIAL AND METHODS
The experiment was conducted at Yabello and Abaya
during 2015 cropping season. The two locations are the
research sites and sub- sites of Yabello Pastoral and
Dryland Agriculture Research Center, respectively (Table
1).
The experiment was laid out in 6 x 6 triple lattice design.
Each entry was planted in a plot having 6 rows of 4-meter
length. Four rows were harvested and two border rows
were left to exclude border effect. The row and plant
spacing was kept at 40 cm and 10 cm, respectively.
Individual plot size was 2.4 m x 4 m=9.6 m2 and 1m and
1.5m between replication and sub block, respectively.
Fertilizer was applied as nationally recommended for the
crop at the rates of 46 kg P2O5 and 18 kg N /ha (100kg/ha
DAP) at the time of planting.
Association of characters
Phenotypic and genotypic correlations between yield and
yield related traits were estimated using the method
described by Miller et al. (1958).
Phenotypic correlation coefficient (rpxy ) between character
x and y
yx
xy
xy
VpVp
Covp
rp 
Where: Covpxy = Phenotypic covariance between
character x and y
Vpx = Phenotypic variance for character x
Vpy = Phenotypic variance for character y
Genotypic correlation coefficient (rgxy) between character
x and y
yx
xy
xy
VgVg
Covg
rg 
Where: Covgxy = Genotypic covariance between
character x and y
Vgx = Genotypic variance for character x
Vgy = Genotypic variance for character y
The coefficient of correlations at phenotypic level was
tested for their significance by comparing the value of
correlation coefficient with tabulated r-value at g-2 degree
of freedom. However, the coefficient of correlations at
genotypic level was tested for their significance using the
formula described by Robertson (1959) indicated below:
Genotypic correlation coefficient was tested with the
following formula suggested by Robertson (1959).
xy
xy
SEg
rg
t
)(

the calculated ‘t’ value was compared with the tabulated ‘t’
value at g-2 degree of freedom at 5% level of significance,
where, g = number of genotypes
yx
xy
xy
hh
gr
SEg
.2
)1( 2


Where: SEgxy= Standard error of genotypic correlation
coefficient between character x and y
hx = Heritability value of character x
hy = Heritability value of character y

Int. J. Plant Breed. Crop Sci. 217
Table 1: Description of the study area
Variables Yabello Abaya
Soil type sandy Sandy clay loam
Altitude (m.a.s.l.) 1631 1442
Latitude 02o
88’006"N 06o
43’520"N
Longitude 038o
14’761"E 038o
25’425"E
Annual Temperature 0
C
Minimum 14.5 12.6
Maximum 26.3 29.9
Annual rainfall (mm)
Minimum 400 500
Maximum 700 1100
Table 2: Genotypic (above diagonal) and phenotypic (bellow diagonal) correlation coefficients of yield and yield related traits of 36
common bean genotypes tested at Abaya in 2015 cropping season
Variable FD MD PH (cm) NPB PL PPP SPP SPNT TSW(g) GY(t/ha)
FD 0.330* 0.319 -0.249 -0.362* 0.040 -0.361* -0.435** -0.638** -0.531**
MD 0.187 0.279 0.035 -0.550** 0.155 -0.363* -0.344* -0.522** -0.448**
PH(cm) 0.221* 0.150 0.060 -0.160 -0.270 0.052 -0.140 -0.124 -0.059
NPB -0.110 0.003 -0.174 0.059 0.018 0.391* 0.452** 0.369* 0.449**
PL(cm) -0.077 -0.321** -0.074 0.070 0.022 0.354* 0.375* 0.469** 0.374*
PPP -0.017 0.141 -0.245** 0.071 -0.051 -0.476** 0.045 -0.015 -0.007
SPP -0.229 -0.245** 0.063 0.283** 0.332** -0.504** 0.839** 0.718** 0.781**
SPNT -0.323** -0.225* -0.106 0.382** 0.294** 0.061 0.804** 0.829** 0.887**
TSW(g) -0.381** -0.408** -0.094 0.360** 0.436** -0.039 0.657** 0.748** 0.935**
GY(t/ha) -0.307** -0.336** -0.044 0.430** 0.349** -0.062** 0.731** 0.801** 0.915**
* & **, significant at P<0.05 and P<0.01, respectively, FD= days to flow ering, MD= days to 90% maturity, PH= plant height, NPB= number
of primary branch, PL= pod length, PPP= pod per plant, SPP= seed per pod, SPNT= seed per plant, TSW= thousand grain w eight, GY=
Grain yield per hectare at 10% moisture content.
The calculated absolute t value was tested against the
tabulated t-value at g-2 degree of freedom for both
phenotypic and genotypic correlations. Environmental
correlation coefficients were tested at [(g-1) (r-1)-1)]
degree of freedom, where g is the number of genotypes.
Path coefficient analysis was worked out using the method
suggested by Dewey and Lu (1959) at phenotypic as well
as genotypic level to determine the direct and indirect
effect of yield related traits (yield components) on yield. For
this purpose, seed yield is used as dependent variable and
other characters were used as independent variables.
rij = Pij + Σrik Pkj
Where: rij= mutual association between the independent
(i) and the dependent character (j) as measured by the
correlation coefficient. Pij= component of direct effects of
independent character (i) on dependent character (j) as
measured by the path coefficient and, Σrikpkj= summation
of components of indirect effect of a given independent
character (i) on the given dependent character (j) via all
other independent characters (k).
The contribution of the remaining unknown factors
(effects) will be measured as the residual effect (RE) which
is calculated as:
, R2 = ∑ pijrij
RESULTS AND DISCUSSION
Genotypic and Phenotypic Correlation Coefficients
Correlation of yield with other traits
Grain yield had negative and highly significant (P<0.01)
correlation with days to flowering and days to maturity at
Abaya (Table 2) both at genotypic and phenotypic levels,
but it showed positive non-significant correlation at Yabello
(Table 3). This indicates that early maturing plants could
provide higher grain yield ha-1 than late maturing plants. It
is also suggesting that selection of genotypes for high
performance of these traits reduce grain yield. Bhushan
(2007) also reported negative and significant association
of grain yield with days to flowering and days to maturity
period in common bean. Alemayehu (2014) reported
negative association of grain yield with days to flowering
and days to maturity at both genotypic and phenotypic
level in common bean. Nchimbi and Mduruma, (2007),
Onder (2013), and Kasaye (2006) also reported negative
association of days to flowering with grain yield. in
contrast, Ahmed and Kamaluddin (2013) and Bagheri et
al. (2015) reported positive and significant association of
days to flowering with grain yield in common bean.
Over location correlation analysis indicated that grain yield
had positive and highly significant (P<0.01) genotypic

Ejara et al. 218
Table 3: Genotypic (above diagonal) and phenotypic (bellow diagonal) correlation coefficients of yield and yield related traits of
36 common bean genotypes tested at Yabello in 2015 cropping season
Variable FD MD PH(cm) NPB PL(cm) PPP SPP SPNT TSW(g) GY(t/ha)
FD 0.354* 0.460** -0.066 0.208 0.134 0.164 0.249 0.113 0.187
MD 0.289** 0.765** -0.050 -0.425** 0.389* -0.107 0.029 -0.098 0.082
PH(cm) 0.424** 0.670** -0.008 -0.204 0.422* -0.112 0.049 0.087 0.141
NPB -0.026 -0.015 0.054 -0.076 -0.024 0.073 0.065 0.111 0.173
PL(cm) 0.174 -0.340** -0.137 -0.014 -0.226 0.033 -0.08 0.038 -0.105
PPP 0.039 0.254** 0.249** 0.015 -0.171 -0.609** -0.23 -0.283 -0.173
SPP 0.152 -0.029 -0.065 0.095 0.051 -0.609** 0.900** 0.812** 0.802**
SPNT 0.195* 0.080 0.051 0.119 -0.053 -0.136 0.847** 0.871** 0.897**
TSW(g) 0.100 0.014 0.117 0.150 0.048 -0.194* 0.735** 0.823** 0.911**
GY(t/ha) 0.158 0.142 0.160 0.202* -0.059 -0.122 0.747** 0.860** 0.901**
Table 4: Genotypic (above diagonal) and phenotypic (bellow diagonal) correlation coefficients of yield and
yield related traits of 36 common bean genotypes tested across the tw o locations in 2015 cropping season
Variable PH (cm) NPB PPP SPP SPNT TSWT(g) GY (t/ha)
PH (cm) 0.012 0.21 -0.160 -0.098 -0.146 -0.075
NPB -0.330** 0.011 0.273 0.350* 0.352* 0.451**
PPP -0.041 0.073 -0.654** -0.338* -0.349* -0.273
SPP -0.185** 0.251** -0.527** 0.921** 0.804** 0.823**
Splnt -0.257** 0.337** -0.014 0.835** 0.845** 0.901**
TSWT(g) -0.234** 0.352** -0.079 0.705** 0.797** 0.909**
GY (t/ha) -0.264** 0.419** -0.054 0.747** 0.845** 0.915**
* & **, significant at P<0.05 and P<0.01, respectively, FD=days to flow ering, MD= days to maturity, PH (cm)=
plant height in centimetre, NPB= number of primary branch, PL (cm)= pod length in centimetre, PPP= pods per
plant, SPP= seeds per pod, SPNT= seeds per plant, TSW (g)= thousand seed w eight in gram, GY= Grain yield
per hectare in ton per hectar
correlation with seed per pod, seed per plant thousand
seed weight and number of primary branches that ranged
from rg= 0.45 to 0.91 (Table 4). Therefore, any
improvement of these traits would result in a substantial
increment on grain yield. These results are substantiated
with those of Cokkizgin (2013) and Karasu and Oz (2010)
who reported positive and highly significant genotypic
correlation of grain yield with seed per pod, seeds per plant
and thousand seed weight in common bean.
Grain yield showed positive and highly significant (P<0.01)
phenotypic association with number of primary branches,
seeds per pod, seeds per plant and thousand seeds weight
that ranged from rp= 0. 419 to 0.915 (Table 4). The perusal
of the correlation coefficient results suggested that these
traits should be given prime importance due to their
contribution to grain yield they had. Therefore, the positive
association of grain yield with these traits suggested the
possibility of simultaneously improving grain yield through
indirect selection of these traits. Other authors also
reported positive and significant phenotypic association of
grain yield with seeds per plant (Cokkizgin, 2013), seeds
per pod (Roy, et al 2006), thousand seeds weight (Karasu,
2010) and number of brunches per main stem (kulaz and
Ciftci, 2013). Grain yield showed negative and significant
phenotypic association with plant height. This suggested
that selection of tall genotypes for high performance of
these traits reduce grain yield. Hossein et al., (2012) and
(Kulaz Ciftci, 2013) also reported negative phenotypic and
genotypic correlations of grain yield with plant height in
common bean.
Grain yield showed positive and highly significant (P ≤
0.01) environmental correlation with all crop growth traits
and yield components except plant height were it showed
significantly negative correlation. This showed that
environment factor that favour yield related traits also
favours grain yield performance. Carlos et al. (2014)
suggested that environmental correlations between
descriptors with differences in magnitude and sign, in
relation to the respective genotypic correlation, revealed
that the environment favoured one character over another
and that the genetic and environmental causes of variation
have different physiological mechanisms, defaulting the
indirect selection.
Correlation coefficient among other traits
Positive and highly significantly (P< 0.01) genotypic and
phenotypic correlations were observed between days to
flowering and plant height at Yabello indicating that
genotypes taking longer to flower also has taller plant
height. Bhushan (2007) also reported positive and strong
association of days to flowering with plant height. Days to

Table 5. Phenotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off
diagonal) of yield and yield related traits in 36 common bean genotypes tested at Yabello in 2015 cropping season
FD MD PH(cm) NPB PL(cm) PPP SPP SPNT TSW(g) rp
FD 0.014 0.021 -0.003 -0.002 -0.009 0.009 0.066 -0.002 0.063 0.158
MD 0.004 0.072 -0.005 -0.001 0.017 0.060 -0.012 -0.001 0.009 0.142
PH(cm) 0.006 0.048 -0.008 0.004 0.007 0.059 -0.028 0.000 0.073 0.160
NPB 0.000 -0.001 0.000 0.065 0.001 0.003 0.041 -0.001 0.094 0.202*
PL(cm) 0.003 0.024 0.001 -0.001 -0.049 -0.040 0.022 0.000 0.030 -0.059
PPP 0.001 0.018 -0.002 0.001 0.008 0.236 -0.264 0.001 -0.121 -0.122
SPP 0.002 -0.002 0.000 0.006 -0.002 -0.144 0.433 -0.007 0.460 0.747**
SPNT 0.003 0.006 0.000 0.008 0.003 -0.032 0.367 -0.008 0.515 0.860**
TSW(g) 0.001 0.001 -0.001 0.010 -0.002 -0.046 0.319 -0.006 0.625 0.901**
Residual effect = 0.477
flowering and days to maturity had negative and strong (P<
0.01) associated with seeds per plant and thousand seed
weight at both genotypic and phenotypic level at Abaya.
Bhushan (2007) also reported similar results in common
bean.
Pod length had positive and highly significant phenotypic
correlation with seeds per pod, seeds per plant and
thousand seed weight at Abaya. In contrary to this, pod
length had negative and highly significant (P< 0.01)
phenotypic correlation with days to maturity which agree
with the result reported by Roy et al (2006). Genotypic
association of pod length with thousand seed weight was
positive and highly significant (P< 0.01) but significant (P<
0.05) with seeds per pod and seeds per plant at Abaya.
Pod length had positive phenotypic correlation with
flowering date, seeds per pods and thousand seed weight
at Yabello though the association is not significant. The
results of genotypic correlation analyses at Yabello
indicated that the association of pods length with days to
maturity was negative and highly significant but negative
and not significant with plant height, number of primary
branches, pods per plants and seed per plants (Table 3).
Roy et al (2006) also reported negative phenotypic
association of pod length with pod per plant, seed per pod,
days to flowering and plant height in common bean.
Phenotypic and genotypic correlation coefficients for
combined analysis across two locations are presented in
Table 4. The results indicated that number of seeds per
pod was strongly (P< 0.01) and positively correlated with
seed per plant both at phenotypic (rp=0.835) and
genotypic (rg=0.921) level. Number of seeds per pod was
also exhibited positive and significant phenotypic and
genotypic correlation with thousand seed weight and
number of primary branches. Alemayehu (2014) and
Cokkizgin (2013) also reported negative phenotypic and
genotypic correlations of number of seeds per pod with
pods per plant and plant height, respectively.
Number of seeds per plant was positively and strongly (P<
0.01) correlated with thousand seed weight, number of
primary branches and seeds per pod both at genotypic and
phenotypic levels, but negative and strong associated with
plant height were observed at phenotypic level. Cokkizgin
(2013) also reported similar results in common bean.
Path Coefficient Analysis
Association of character determined by correlation
coefficients may not be sufficient to indicate the
contribution of traits to yield. Therefore, it is necessary to
understand the relative importance of direct and indirect
effects of each trait on yield. Association study with path
coefficient analysis gives detailed information on the
causal factors’ as direct and indirect effect through others,
on target trait (Adefris et al. 2000). Such analysis leads to
identification of important component traits useful in
indirect selection for complex traits like yield.
Phenotypic path coefficient analysis
The phenotypic direct and indirect effects of different
characters on seed yield are presented in Table 5, 6 and
9. Days to flowering and days to maturity exerted positive
phenotypic direct effect on grain yield at Yabello, but these
traits exerted negative indirect effect on grain yield through
plant height, number of primary branch, pod length and
seed per plant (Table 5). At Abaya, days to flowering
exerted positive direct effect on grain yield, but negative
indirect effect of days to flowering on grain yield was
exerted through days to maturity, number of primary
branches, pods per plant, seeds per pod and thousand
seeds weight (Table 6). The total contribution of these two
traits was positive but non-significant at Yabello but
negative and significant at Abaya. Kassaye (2006)
reported positive direct effect of days to flowering and days
to maturity on grain yield. Roy et al. (2006) and Kulaz and
Ciftci, (2013) also reported similar results in common
bean. The direct contribution of pods length was negative
at both Locations both at phenotypic and genotypic level
and had positive and significant correlation with grain yield
at Abaya but negative and non-significant at Yabello
(Table 5 and 6). This result is in agreement with the result
reported by Salehi (2010) and Dursun (2007).

Ejara et al. 220
Table 6. Phenotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off diagonal)
of yield and yield related traits in 36 common bean genotypes tested at Abaya in 2015 cropping season
FD MD PH(cm) NPB PL(cm) PPP SPP SPNT TSW(g) pr
FD 0.036 -0.005 0.008 -0.010 0.007 -0.004 -0.116 0.053 -0.276 -0.307**
MD 0.007 -0.025 0.006 0.000 0.027 0.033 -0.124 0.037 -0.296 -0.336**
PH(cm) 0.008 -0.004 0.037 -0.015 0.006 -0.058 0.032 0.017 -0.068 -0.044
NPB -0.004 0.000 -0.007 0.087 -0.006 0.017 0.144 -0.062 0.261 0.430**
PL(cm) -0.003 0.008 -0.003 0.006 -0.085 -0.012 0.169 -0.048 0.316 0.349**
PPP -0.001 -0.004 -0.009 0.006 0.004 0.236 -0.256 -0.010 -0.029 -0.062
SPP -0.008 0.006 0.002 0.025 -0.028 -0.119 0.508 -0.131 0.476 0.731**
SPNT -0.012 0.006 -0.004 0.033 -0.025 0.014 0.409 -0.163 0.542 0.801**
TSW(g) -0.014 0.010 -0.004 0.031 -0.037 -0.009 0.334 -0.122 0.724 0.915**
Residual effect=0.449
* & **, significant and highly significant at P<0.05 and P<0.01, respectively. FD=days to 50% flowering, MD=days to 90%
maturity, PH (cm) = plant height in centimetre, NPB= number of primary branch, PL= pod length, PPP= pod per plant,
SPP= seed per pod, SPNT= seed per plant, TSW (g) = thousand seed w eight in gram
The combined path coefficient analysis at phenotypic level
revealed that high positive direct effect on grain yield was
exerted by thousand seed weight (0.635), number of
seeds per pod (0.43) and pod number per plant (0.215)
(Table 10). Trait with high positive direct effects implies
that these characters are the major contributors for the
improvement of grain yield at phenotypic level given that
the relations of other traits are kept constant. Increasing
one of the characters that had positive direct effect with
seed yield will result in increased seed yield. These traits
are also showed positive and highly significant correlation
with grain yield except number of pod per plant which
showed negative correlation with grain yield. Negative
correlation of pods per plant with grain yield might be due
to high negative indirect effect of pods per plant via seeds
per pod (-0.226) and pods per plant via thousand seeds
weight. The presence of positive direct effect of thousand
seeds weight, seeds number per pod and pods number
per plant on common bean was reported by many
researchers (Kulaz and Ciftci, 2013; Hossein et al., 2012;
Dursun, 2007 and Roy et al. 2006).
Seeds per plant had high negative direct effect on grain
yield at phenotypic level (-0.048) but positive and highly
correlated with grain yield (rp= 0.845). This negative direct
effect of seed per plant was minimized through high
positive indirect effect of seed per plant via thousand seed
weight (0.506) and seeds per plant via seed per pod
(0.359). Plant height also showed negative direct effect (-
0.011) on grain yield and negatively and significantly
correlated (rp=-0.264) with grain yield. In agreement with
this finding, Cokkiznk (2013) reported negative direct
effect of seed per plant and plant height. Karasu and Oz
(2010) reported negative phenotypic direct effect of seed
per plant.
High positive indirect effect on grain yield was exerted by
seeds number per plant through thousand seeds weight
(0.506); seeds per pod through thousand seeds weight
(0.447) and seeds per plant through seeds number per pod
(0.359). High negative phenotypic indirect effect on grain
yield was exerted by pods number per plant through seeds
per pod (-0.226) followed by plant height through thousand
seeds weight. Kulaz and Ciftci, (2013) also reported
negative phenotypic indirect effect of plant height via
thousand seeds weight and pods number per plant via
seeds number per pods in common bean.
Genotypic path coefficient analysis
The path analysis revealed positive genotypic direct effect
of days to flowering and days to 90% maturity on grain
yield (0.014 and 0.072, respectively) at Yabello, but both
traits exerted negative indirect effect via plant height,
number of primary branches, and seeds number per plant
which consequently reduced the correlation of these traits
with grain yield (Table 7). At Abaya, days to flowering
exerted positive direct effect on grain yield, but negative
indirect effect on grain yield via days to maturity, number
of primary brunches, seeds per pod and thousand seeds
weight (Table 8). The total genotypic contribution of these
traits was negative and significant at Abaya indicating the
direct contribution of these traits on grain yield was offset
by their indirect effects via other traits. Ahmed and
Kamaluddin (2013), Roy et al. (2006) and Kulaz and Ciftci,
(2013), Onder et al. (2013) also reported similar results in
common bean.
The path coefficient analysis at genotypic level on the
basis of combined analysis for those traits that exhibited
homogenous error variances revealed that the maximum
positive direct effect on grain yield was exerted by
thousand seeds weight (0.513) followed by number of
seeds per plant (0.256) and number of seeds per pod
(0.242) (Table 9). These three traits had high positive and
significant correlations with grain yield at genotypic level.
This high genotypic positive direct effect associated with
strong positive and highly significant (P < 0.01) correlation
with grain yield indicates an increase in thousand grain
weight, seeds per plant and seeds per pod will increases
grain yield. Similar result was reported by Datt (2011) and

Table 7: Genotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off
diagonal) of yield and yield related traits in 36 common bean genotypes tested at Yabello in 2015 cropping
season.
FD MD PH(cm) NPB PL(cm) PPP SPP SPNT TSW(g) rg
FD 0.031 0.061 -0.059 -0.005 -0.010 0.034 0.074 -0.017 0.079 0.187
MD 0.011 0.172 -0.098 -0.004 0.021 0.099 -0.048 -0.002 -0.068 0.082
PH(cm) 0.014 0.132 -0.129 -0.001 0.010 0.108 -0.050 -0.003 0.061 0.141
NPB -0.002 -0.009 0.001 0.079 0.004 -0.006 0.033 -0.005 0.077 0.173
PL(cm) 0.006 -0.073 0.026 -0.006 -0.048 -0.058 0.015 0.006 0.027 -0.105
PPP 0.004 0.067 -0.054 -0.002 0.011 0.256 -0.274 0.017 -0.197 -0.173
SPP 0.005 -0.018 0.014 0.006 -0.002 -0.156 0.450 -0.063 0.565 0.802**
SPNT 0.008 0.005 -0.006 0.005 0.004 -0.061 0.405 -0.069 0.606 0.897**
TSW(g) 0.004 -0.017 -0.011 0.009 -0.002 -0.072 0.365 -0.060 0.696 0.911**
Table 8: Genotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off
diagonal) ofyield and yield related traits in 36 common beangenotypes tested atAbaya in 2015 cropping season.
FD MD PH(cm) NPB PL(cm) PPP SPP SPNT TSW(g) rg
FD -0.022 -0.031 0.020 -0.012 0.050 0.014 -0.235 0.089 -0.403 -0.531**
MD -0.007 -0.094 0.017 0.002 0.075 0.055 -0.237 0.070 -0.330 -0.448**
PH(cm) -0.007 -0.026 0.062 0.003 0.022 -0.096 0.034 0.029 -0.078 -0.059
NPB 0.005 -0.003 0.004 0.048 -0.008 0.006 0.255 -0.092 0.233 0.449**
PL(cm) 0.008 0.052 -0.010 0.003 -0.137 0.008 0.231 -0.076 0.297 0.374*
PPP -0.001 -0.015 -0.017 0.001 -0.003 0.357 -0.311 -0.009 -0.009 -0.007
SPP 0.008 0.034 0.003 0.019 -0.049 -0.170 0.653 -0.171 0.454 0.781**
SPNT 0.010 0.032 -0.009 0.022 -0.051 0.016 0.547 -0.204 0.524 0.887**
TSW(g) 0.014 0.049 -0.008 0.018 -0.064 -0.005 0.469 -0.169 0.632 0.935**
Residual effect =0.347
* & **, significant and highly significant at P<0.05 and P<0.01, respectively. FD= days to 50% flow ering, MD= days to 90%
maturity, PH (cm) = plant height in centimetre, NPB= number of primary branch, PL= pod length, PPP= pod per plant, SPP=
seed per pod, SPNT= seed per plant, TSW (g) = thousand seed w eight in gram
Table 9. Genotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off diagonal) of
yield and yield related traits in 36 common bean genotypes tested across the tw o locations in 2015 cropping season
PH(cm) NPB PPP SPP SPNT TSW(g) Rg
PH(cm) 0.033 0.001 0.030 -0.039 -0.025 -0.075 -0.075
NPB 0.000 0.113 0.002 0.066 0.090 0.181 0.451**
PPP 0.007 0.001 0.143 -0.158 -0.086 -0.179 -0.273
SPP -0.005 0.031 -0.094 0.242 0.236 0.413 0.823**
Splnt -0.003 0.039 -0.048 0.223 0.256 0.434 0.901**
TSW(g) -0.005 0.040 -0.050 0.195 0.216 0.513 0.909**
Table 10. Phenotypic path coefficient analysis (direct effects underlined diagonal and indirect effects are off diagonal) of yield
and yield related traits in 36 common bean genotypes tested across the tw o locations in 2015 cropping season.
PH(cm) NPB PPP SPP SPNT TSW(g) Rp
PH(cm) -0.011 -0.028 -0.009 -0.079 0.012 -0.149 -0.264**
NPB 0.004 0.084 0.016 0.108 -0.016 0.224 0.419**
PPP 0.000 0.006 0.215 -0.226 0.001 -0.050 -0.054
SPP 0.002 0.021 -0.113 0.430 -0.040 0.447 0.747**
Splnt 0.003 0.028 -0.003 0.359 -0.048 0.506 0.845**
TSW(g) 0.003 0.030 -0.017 0.303 -0.038 0.635 0.915**
Residual effect =0.458
* & **, significant and highly significant at P<0.05 and P<0.01, respectively PH (cm) = plant height in centimetre, NPB= number of primary
branch, PPP= pod per plant, SPP= seed per pod, SPNT= seed per plant, TSW (g) = thousand seed w eight in gram.
Karasu and Oz (2010), Raffi and Nath (2004) and Roy et.al
(2006).
On the other hand, seed number per plant exerted high
positive genotypic indirect effect on grain yield via
thousand seed weight (0.434) followed by seed per pod
via thousand seed weight. The results of this study are in
agreement with the result reported by Karasu and Oz
(2010) in common bean. High negative indirect effect was
exerted by pod per plant through thousand seeds weight.

Ejara et al. 222
Pod per plant had positive genotypic direct effects on grain
yield, but the genotypic correlation of this trait with yield
was negative. These may be due to the negative indirect
effects of this trait through seeds per pod, seed per plant
and thousand seeds weight. Similar results were reported
by Onder et al. (2013) in common bean.
In the present study, the residual effect was high both at
phenotypic (45.82%) and genotypic (51.3%) levels for the
seven traits path analysis. The path analysis conducted for
each locations considering 10 traits also showed high both
at phenotypic (47.7%= Yabello and 44.9%= Abaya) and
genotypic (41.6% = Yabello and 34.7%= Abaya). These
results showed that the traits included in this study did not
account for all the variability observed on grain yield.
According to Sengupta and Kataria (1971), this residual
effect towards seed yield in the present study might be due
to other characters or environmental factors or sampling
errors or the combinations.
CONCLUSION
Positive and highly significant correlation of thousand seed
weight, seed number per plant, seed numbers per pods
and number of primary branches showed positive and
significant correlations with grain yield at both phenotypic
and genotypic levels. These traits also had strong positive
direct effects on grain yield. Therefore, Selection for high
performance of these traits could also result in increasing
grain yield and those traits could be taken as selection
criteria in common bean improvement.
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Accepted 19 April, 2017
Citation: Ejara E, Mohammed W, Amsalu B (2017).
Correlations and pass coefficient analyses of yield and
yield related traits in common bean genotypes (Phaseolus
vulgaris L.) at Abaya and Yabello, Southern Ethiopia.
International Journal of Plant Breeding and Crop Science
4(2): 215-224.
Copyright: © 2017 Ejara et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

Ejara et al. 224
APPENDIX TABLE 1
Appendix Table 1 Description of the new common bean entries and released varieties
S.No. Genotype Pedigree Source
1 ALB58 SER 16 x G35346 CIAT
16 G21212 - CIAT
17 BFS 27 SBCZ16257-33/-MC-2P-MQ-1D-MC CIAT
18 BFS 320 - CIAT
19 BFS 34 SBCF16231-002/-MC-8P-MQ-4D-MC CIAT
27 SX b 412 BM14524-16/-MQ-MQ-25C-MC-MC-2 CIAT
Appendix Table 2. list of released varieties used as a check
Variety
Year of
release
Yield ton /ha
Recommendedaltitude
(masl)
Days to
maturity Breeding centerOn station
Farmers
field
30 NASIR 2003 2.3 2.03 1200-1800 86-88 MARC
31 ROBA-1 1990 2.0-2.4 1.9-2.1 1400-1800 75-95 MARC
32 Awash 1 1989 2.0-2.4 1.8-2.1 1400-1800 90 MARC
33 Awash Melka
1999 2.5 2.0-2.3 1400-1900 88-95
MARC
34 Awash 2
2013 2.8-3.1 1.8-2.2 1300-1700 85-90
MARC
35 Mexican-142
1973 2.1 1.3 1400-1800 95-100
MARC
36 Chorie
2006 2.3 1.9 1300-1950 87-109
MARC
Source: MARC

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Correlations and pass coefficient analyses of yield and yield related traits in common bean genotypes (Phaseolus vulgaris L.) at Abaya and Yabello, Southern Ethiopia