Selection Criteria for Improving Rapeseed Yield

Selection Criteria for Yield Improvement in Rapeseed (Brassica napus L.)
Selection Criteria for Yield Improvement in Rapeseed
(Brassica napus L.)
Maryam Ahmadzadeh1, Habib Allah Samizadeh2, Mohammad Reza Ahmadi3, Farid Soleymani4*,
Cássia Arantes de Lima5
1Guilan University, Rasht
2Faculty of agriculture, Guilan University, Rasht, Islamic Republic of Iran
3Seed and Plant Improvement Institute, Karaj, Islamic Republic of Iran
4,5Topazgene Research Company, Karaj, Islamic Republic of Iran
In this study, 19 lines from advanced inbreeding progenies, and Zarfam variety (as check), were
evaluated for phenotypic and genotypic variability, correlation, linear regression and path
coefficient of grain yield and other agronomic traits. Experiment was executed at the field of Seed
and Plant Improvement Institute (SPII), Karaj, Iran, during 2005-6. Genotypic and phenotypic
variances were highest for seed/plant followed by pods/branch and pods/plant. The presence of
the positive correlation coefficient of grain yield/plant with grain yield/unite area and oil yield
showed that grain yield/plant could be a criterion for the selection of the elite genotypes. Positive
and significant genotypic and phenotypic correlation was found between grain yield/plant and
number of pods/plant, number of pods/branch, biomass, angle of preliminary branches and
flowering duration. Based on the linear regression of grain yield/plant and other agronomic traits,
biomass, number of seeds/pods, first branch height of land surface, number of branches and
number of pods/branches, entered in regression relation, respectively. The path coefficient
analysis indicated the positive direct effect of grain yield/plant, angle of preliminary branches and
numbers of seeds/middle pods on grain yield/unite area. The results obtained from phenotypic
and genotypic correlation coefficient of grain yield/plant and other effective traits shown that
biomass and then number of pods per branches had a phenotypic and genotypic positive direct
effect on grain yield. Generally, traits such as biomass, first branch height of land surface and
number of branches which had high genotypic coefficients of variability, high heritability, high
degree of significant correlation coefficient and high direct effect on grain yield would be good
selection criteria to improve grain yield of rapeseeds.
Key words: Agronomic traits, Correlation, Linear regression, Path coefficient analysis, Rape seed
INTRODUCTION
The oilseed Brassica species (B. napus,B. rapaor B.
campestris and B. juncea) are now the third most important
source of edible vegetable oil in the word after soybean
and palm oil (Zhang and Zhou, 2006). Recently, grown
acreage and production of rapeseed is remarkably
increased. Due to the highly improvement of fatty acids
composition and meal quality of rapeseed, some countries
(Canada and Europe) started the expansion of that plant
production in some decades ago. Nowadays, rapeseed oil
is one of the most nutritionally desirable edible oils (Jiang,
2001).
It is known that yield per area in rapeseed is the product of
population density, the number of pods per plant, the
number of seeds per pod and the individual grain weight
(Diepenbrock, 2000). Studies show that because of
*Corresponding Author: Farid Soleymani, Topazgene
Research Company, Karaj, Islamic Republic of Iran.
Email: Faridsoleymani2012@gmail.com
Co-Authors 1
Email: ahmadzadeh2005@gmail.com;
2
Email: hsamizadeh@yahoo.com;
3
Email: mrahmadi@yahoo.com;
5
Email: arantes.cassia0@gmail.com
Research Article
Vol. 6(3), pp. 176-184, December, 2019. © www.premierpublishers.org. ISSN: 2326-3997
search Journal of Agricultural SciencesWorld Re

Ahmadzadeh et al. 177
remarkable effect of grain weight and pods/plant on yield,
these components can be good selection criteria in order
to improve seed yield of winter type rapeseeds (Ali et al.
2003). Many of researchers have also recorded high
correlation between number of branch and grain yield per
plant (Shabana et al, 1990 and Nasim et al, 1994, Zhang
and Zhou, 2006). In order to enlarge the seed yield, directly
and indirectly effects of yield in gradient bring about the
basis for its good-resulted breeding program and as a
consequence, the issue of yield increase can be more
effectively addressed on the main points of performance of
yield components and assortment for closely related traits
(Choudhry et al, 1986). Till now, lots of genetic parameters
in order to determine the selection criteria for yield
improvement in rapeseed have been assessed by various
studies (Ali et al. 2003, Akbar et al. 2003, Özer et al. 1999,
Momoh et al. 2004 and Golparvar and Karimi,
2012).Golparvar and Karimi (2012) showed that in early
generations, indirect selection by traits would have the
most direct effect on dependent variables. Those said
these traits usually determine by means of statistical
procedure like correlation, regression and path analysis.
Positive and remarkable connection between seed yield
and oil yield, plant height and 1000-seed weight have been
proved (Bagheri et al. 2008). Also, studies show that on oil
yield of rapeseed genotypes there is a positive and direct
effect of the seed yield, number of seed/plants, biological
yield, and 1000-seed weight (Farhudi et al. (2008). Akbar
et al. (2003) evaluated eighteen lines/varieties of B. juncea
L. for plant height, number of branches plant, number of
pods plant, 1000 grain weight and grain yield plant through
phenotypic coefficient of variation, genotypic CV,
correlations and path coefficient analysis. The result
showed that number of pods plant was strong parameter
followed by number of branches and plant height for grain
yield improvement. Pods per plant had highest GCV,
highly remarkable positive connection and maximum direct
cooperation for grain yield followed by some of branches
plant and plant height. In the experiment of Zhang and
Zhou (2006), the linear regression of grain yield per plant
and other agronomic traits represented that number of
pods per plant, number of seed per pod and 1000 grain
weight have linear relationship with grain yield per plant.
Present study was planned to investigation of phenotypic
and genotypic relationship of various parameters in 19
advanced lines with one cultivar as check to devise
suitable selection criteria for further breeding.
MATERIALS AND METHODS
In this research 19 selected lines from advanced
inbreeding progenies, and Zarfam cultivar (as check),
totally 20 lines and cultivar (Table 1), were evaluated.
Experiment was executed at the field of Seed and Plant
Improvement Institute (SPII), Karaj, Iran, during 2005-6.
The experimental design was a randomized complete
block design with three replications. Plots consisted of four
rows, each five-meter-long and spaced 30 cm apart.
Seeds were sown by hand on September 27 in 2005. The
experimental area was fertilized at a rate of 60 kg N/ ha
and 100 kg P2O5/ ha and K2O/ ha before sowing. Additional
60 kg N/ ha was applied just before flowering. The crops
were irrigated four times, mainly during flowering stage. In
growth during, to recording of traits, 5 normal plants were
selected randomly. Some of phenological and
morphological traits such as days to flowering, flowering
duration, number of branches, number of pods per plant,
number of seeds per top pods, number of seeds per middle
pods, number of seeds per bottom pods, number of seeds
per pod, pod length, growth duration, biomass, 1000 grain
weight and grain yield per plant and unit area (hectare)
were recorded. Seed oil content was estimated in
laboratory of oil Seeds Department of SPII, and then oil
yield was calculated through cross product of grain yield in
oil content.
Data thus collected were subjected to estimation of
variance, and then genetic parameters like genotypic and
phenotypic variances, genotypic and phenotypic
coefficients of variability (CV) and heritability were
analyzed for the traits showing significant difference,
genotypic and phenotypic variances and CV were
calculated based on the formula:
Table 1. Names of rapeseed lines
Raw Line name Raw Line name
1 Consul 11 Express
2 Hylite 12 Turner
3 RG-9908 13 Hyola 401
4 (Yanter × Tower) F4 14 Bristol
5 GK. Helena 15 Amber
6 Akamer 16 Hysin111 x PF 7045
7 Calibra 17 H. 42
8 Turner 18 Okapi
9 Pauc 906 19 Goliath
10 Talent 20 Zarfam
r
e
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et
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
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2 +
=
100=
X
v
cv
p
p
100=
X
v
cv
p
g
The phenotypic and genotypic correlation coefficients
were conducted, based on the formula:
vv
r
gjgi
gij
g
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voˆc
.
=
vv
r
pjpi
pij
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In addition, effects of the agronomic traits on grain and oil
yield were analyzed using stepwise linear regression and
path coefficient technique was performed according to the
method suggested by Dewey and Lu (1959). Grain and oil
yield were kept as resultant variable and all other
component characters as causal variables (Ali et al. 2003).

World Res. J. Agric. Sci. 178
RESULTS AND DISCUSSION
Evaluation of phenotypic and genotypic variance and
CV
The genotypes differed significantly (P<0.01) for most of
the traits. Pod length, biomass, grain and oil yield per unit
area showed significant difference in 5% probability level
(Table 1). This indicates the presence of sufficient genetic
variability that could be exploited for initiation of a breeding
endeavor seeking to develop new high yielding rapeseed
genotypes. Phenotypic and genotypic variances were
calculated for traits that have significant difference among
genotypes (Table 2). Phenotypic and genotypic variances
for days to flowering, flowering duration, number of
branches, number of pods per branches, plant height,
1000 grain weight, growth during and grain yield per plant
were quantitatively identical. This denotes on exist of high
heritability in above traits. Thus, phenotypic selection of
genotypes based on these traits can be effective. High
different between phenotypic and genotypic variances for
number of pods per plant and biomass is indicating low
heritability for these traits. Phenotypic variances were
larger as compared to genotypic variances for all the traits
indicating the influence of environmental effect.
Phenotypic and genotypic coefficient of variances (PCV &
GCV) had highest amount for number of pods per plant.
This is in agreement with Ali et al. (2003) and Akbar et al.
(2003).
Analysis of phenotypic and genotypic correlation
According to Engqvist et al. (1993), knowledge of the
extent and type of relationship between agronomic traits in
oilseed rape is of relevance to plant breeders, in order to
avoid selection against an agronomical important trait
while performing early generation selection of another trait.
The phenotypic and genotypic correlation coefficients
between pairs of agronomic traits are summarized in Table
4. Genetic correlations larger than 1 are theoretically not
possible, therefore estimates exceeding 1 indicate a large
standard error. The presence of the positive phenotypic
and genotypic correlation coefficient between grain yield
per plant and per unit area and oil yield showed that grain
yield per plant could be represented grain yield per area
and could be a criterion for the selection of the superior
genotypes. Highly significant and positive phenotypic and
genotypic correlation was found between grain yield per
plant and number of pods per plant. Ali et al. (2003),
Shabana et al. (1990) and Akbar et al. (2003) also reported
the same results in rapeseed. Number of pods per plant
also showed highly significant correlation with number of
branches per plant. Similarly, biomass and angle of
preliminary branches had significant and positive
correlation with grain yield per plant. Flowering duration
was also significantly correlated with grain yield. This is in
agreement with Ali et al (2003). First branch height of land
surface had negative genotypic correlation, and number of
branches and number of seeds per middle pods had
positive genotypic correlation with grain yield per unit area.
Phenotypic and genotypic correlation of number of seed
per plant with grain yield per plant and unit area was
positive and significant.
Table 2. Analysis of variances of the 25 agronomic traits of rapeseed pure genotypes
Source of
variations
df Days to
flowering
flowering duration number of
branches
Plant
height
(cm)
number of
pods per
main stem
number of
pods per
branches
number of
pods per
plant
number of
seed per plant
Genotype 19 141.480**
34.05**
1.480**
257.06**
155.790**
4570.490**
4158.520**
1367536.375*
Block 2 0.617 ns
0.020 ns
0.580 ns
171.370 ns
25.950 ns
1828.470 ns
1062.38 ns
61758.208 ns
Error 38 0.336 0.210 0.438 75.250 56.870 1090.081 1268.660 612303.313
Coefficient of
variability %
0.324 4.630 10.510 6.650 14.340 24.639 19.292 13.830
Source of
variations
df pod length number of seeds per pod number of
seeds per
pod
Mean of pod
lengthTop Middle Bottom Top Middle Bottom
Genotype 19 0.497**
0.265*
0.217 ns
18.404 **
6.579*
8.502*
5.203**
0.265*
Block 2 1.160**
0.195 ns
0.237 ns
24.360**
3.603 ns
8.732 ns
5.329 ns
0.195 ns
Error 38 0.152 0.132 0.254 3.680 3.296 4.668 2.128 0.132
Coefficient of
variability %
8.791 6.040 9.720 15.600 7.307 11.872 7.925 6.040
Source of
variations
df first branch
height of
land surface
Angle of
preliminary
branches
Growth
during
Biomass 1000 grain
weight
Oil content grain yield/
plant
grain yield/
unit area
Genotype 19 265.960**
36.711*
26.346**
42.880*
0.632**
8.916 ns
4.370**
0.309*
Block 2 608.470**
171.620**
6.350 ns
48.80 ns
0.165 ns
4.651 ns
0.355 ns
0.274 ns
Error 38 60.958 18.828 6.420 18.502 0.146 5.776 0.878 0.161
Ns
,*,** : No significant, significant at 5% and 1% probability levels based on F test, respectively.

Analysis of linear regression of grain and oil yield with
other agronomic traits
In the present experiment the linear regression of oil yield
per unit area and other agronomic traits through stepwise
method was analyzed (Table 5). It was observed that grain
yield per unit area and 1000 grain weight had highest effect
on oil yield per unit area and significant level for regression
coefficient is high (α≤0.000). R2 is equivalent to %95. It is
showed that the greet part of existent variance in oil yield
per unit area was explained by this model. The coefficients
of the linear regression equation were estimated, and were
as follows: 21 064.0980.0232.0 XXY −+=
Table 3. Genetic parameters of traits in rapeseed pure genotypes
Days to
flowering
flowering
duration
number of
branches
first branch
height of land
surface
Plant height angle of
preliminary
branches
Genotypic variation 47.5 11.23 0.35 67.79 61.24 5.96
Phenotypic variation 47.16 11.29 0.49 87.59 85.96 12.24
Heritability (%) 99.8 99.5 71.4 77.4 71.2 58.7
Genotypic coefficient
of variability (%)
3.84 11.00 9.40 15.37 6.00 6.86
Phenotypic
coefficient of
variability (%)
3.84 11.03 11.12 17.47 7.10 9.84
number of pods
per main stem
number of pods
per branches
number of pods
per plant
number of seed
per pod
number of seeds
per middle pod
number of seed per
plant
Heritability (%) 54.5 72.8 62.8 47.7 43.9 55.2
of variability (%)
9.50 12.86 14.46 5.29 3.71 14.73
Phenotypic
coefficient of
variability (%)
9.50 24.05 28.14 7.66 5.66 19.82
Growth duration Biomass 1000 grain
weight
grain yield/ plant grain yield/ unit
area
Oil yield/ unit area
Heritability (%) 97.8 57.2 76.2 80.1 50.0 50.0
of variability (%)
1.03 9.25 12.14 15.52 10.52 11.07
Phenotypic
coefficient of
variability (%)
1.04 12.21 13.91 17.24 14.88 15.61
In addition, linear regression of grain yield per unit area
and other agronomic traits was also analyzed because
grain yield is depended to other traits (Table 6). It was
observed that grain yield per plant(X1), angle of preliminary
branches (X2) and number of seed per middle pods (X3)
had highest effect on grain yield per unit area. R2 is
equivalent to %71. The coefficients of the linear regression
equation were estimated, and were as follows:
321 298.0418.0555.0525.0 XXXY +++=
Seed yield per plant is an important target for oilseed
production (Zhang and Zhou, 2006). Since this trait is
depended to other traits, regression analysis was
separately done on it and biomass (X1), number of seeds
per pod (X2), first branch height of land surface (X3),
number of branches (X4) and number of pods per branches
(X5) entered to model, respectively (Table 7). R2 is
equivalent to %77. The coefficients of the linear regression
equation were estimated, and were as follows:
54
321
209.0279.0
322.0055.0766.0483.0
XX
XXXY
+−
−−+=
In the experiment of Golparvar and Karimi (2012)
biological yield and no. seeds/pod introduced as the best
indirect selection criteria for seed yield improvement in
early generations.
Path coefficient analysis
View of the table 8 and 9 revealed direct and indirect
contribution of the effective traits in development of oil
yield. It is observed that grain yield per unit area has
highest phenotypic and genotypic direct effect on oil yield.
Indirect effect of 1000 grain weight through grain yield per
unit area was higher from its direct effect. Thus selection
of genotypes based on 1000 grain weight can be caused
increase of grain yield, and consequently oil yield per unit
area. Özer et al. (1999) and Clarke and Simpson (1978)

Table 4. Genotypic and phenotypic correlation coefficients between different traits
Traits
flowering
duration
(1)
Number of
branches
(2)
Angle of
preliminary
branches(3)
First branch
height of
land
surface(4)
Plant
height
(5)
number of
pods per
branches
(6)
Number
of pods
per plant
(7)
Number
of seeds
top pod
(8)
Number of
seeds per
middle
pod (9)
number
of seeds
per pod
(10)
Number
of seeds
per plant
(11)
Biomass
(12)
1000
seed
weight
(13)
grain
yield
per plant
(14)
grain
yield per
unit area
(15)
oil yield
per unit
area
(16)
1 1 0.199 0.294 -0.146 0.053 0.394 0.349 -0.459 *
-0.559 **
0.452*
0.149 -0.393 -0.388 -0.457 *
-0.156 -0.177
2 0.241 1 0.574**
-0.407 0.209 0.764**
0.712 **
-0.218 -0.172 -0.083 0.675**
0.33 0.286 0.261 0.339 0.286
3 -0.431 0.900**
1 -0.278 0.129 0.652**
0.550*
-0.072 -0.018 -0.03 0.551 *
0.505 *
0.329 0.445 *
0.645 **
0.554 *
4 -0.175 -0.549*
-0.342 1 0.592**
-0.2 -0.172 -0.001 -0.207 -0.17 -0.133 -0.079 -0.362 -0.323 -0.285 -0.252
5 0.057 0.079 0.185 0.601**
1 0.466*
0.479*
-0.179 -0.146 -0.196 0.494*
0.42 -0.334 0.153 0.26 0.276
6 0.455*
0.860**
- -0.283 0.482*
1 0.959**
-0.358 -0.062 -0.178 0.873**
0.639**
0.254 0.561**
0.529*
0.454*
7 0.434 0.770**
0.975**
-0.292 0.464*
0.959**
1 -0.392 -0.131 -0.178 0.912**
0.607**
0.211 0.550*
0.463*
0.385
8 -0.519*
-0.394 -0.06 -0.036 -0.35 -0.544*
-0.635**
1 0.256 0.807**
-0.043 -0.006 -0.573 -0.021 -0.14 -0.102
9 -0.881**
0.188 0.064 -0.437 -0.34 -0.148 -0.407 0.237 1 0.548 0.088 -0.077 -0.035 -0.167 0.185 0.145
10 -0.669**
-0.277 -0.037 -0.482*
-0.537*
-0.391 -0.465*
0.985**
0.709**
1 0.205 0.143 -0.306 0.129 0.07 0.095
11 0.194 0.739**
- -0.299 0.496*
0.930**
0.895**
-0.268 -0.224 -0.01 1 0.685** 0.002 0.557*
0.484*
0.411
12 0.517*
0.218 0.929**
-0.27 0.303 0.696**
0.654**
-0.191 -0.292 -0.117 0.761**
1 0.02 0.815**
0.603**
0.576**
13 0.445*
0.324 0.465*
-0.447*
-0.524*
0.369 0.317 -0.803**
0.072 -0.582**
0.017 -0.065 1 0.043 0.261 0.274
14 0.502*
0.164 0.672**
-0.472*
0.028 0.538 0.493*
-0.073 -0.307 0.029 0.535*
0.938**
0.018 1 0.682**
0.645**
15 0.217 0.474*
- -0.813**
0.085 0.718**
0.652**
-0.336 0.621**
-0.176 0.744**
0.678**
0.275 0.895**
1 0.975**
16 0.24 0.335 - -0.760**
0.054 0.568**
0.454*
-0.275 0.398 -0.195 0.507*
0.597**
0.304 0.805**
0.988**
1
Table 5. Analysis of linear regression of oil yield and other agronomic traits
0.973R
0.946R2
0.940Adjusted R2
Sig.FFMSdfSSVariable
0.000150.2920.22530.450Regression
0.001170.025Residual
Standardized coefficientsUnstandardized coefficients
Model R2
Partial R2Sig.tBetaS.E.BBVariable
0.9420.9420.00017.2810.9800.0280.481Grain yield per unit area
0.9920.0060.274-1.129-0.0640.0220.0251000 grain weight
--0.725-0.358-0.088-0.031Constant

Table 6. Analysis of linear regression of grain yield per unit area and other agronomic traits
0.841R
0.707R2
0.653Adjusted R2
0.00012.9000.46531.395Regression
Model R2
0.4700.4700.0023.7130.5550.0400.148Grain yield per plant
0.6200.4020.0122.8230.4180.0130.036Angle of preliminary branches
0.7070.0420.0442.1830.2980.0320.069number of seeds per middle pod
--0.055-2.072-0.920-1.906Constant
Table 7. Analysis of linear regression of grain yield per plant and other agronomic traits
0.841R
0.707R2
0.653Adjusted R2
0.0009.1864.275521.376Regression
Model R2
0.6670.6680.0013.9740.7660.0620.245Biomass
0.6810.0010.721-0.364-0.0550.143-0.052number of seeds per pod
0.7120.1030.041-2.248-0.3220.019-0.042First branch height of land surface
0.7330.0710.2781.129-0.2790.423-0.478number of branches
0.7660.2980.4940.7020.2090.0100.007number of pods per branches
--0.1721.439-3.2014.606Constant
also introduced 1000 grain weight as a criterion for oilseed
rape yield improvement. Therefore, selection for
increasing grain and oil yield through these traits might be
more successful.
In the next stage, grain yield per unit area was kept as
depended variable and other traits (except oil yield and
1000 grain weight) as independent variables. The obtained
results from partitioned of phenotypic and genotypic
correlation coefficient between causal variables and grain
yield per unit area as resultant variable to direct and
indirect effect has been showed in table 10 and 11. This
analysis is followed by below results:
• Grain yield per plant
Grain yield per plant had maximum phenotypic and
genotypic positive direct effect on grain yield per unit area.
Viewing the indirect effect of these traits, it can be revealed
that large part of phenotypic correlation and moiety of
genotypic correlation between grain yield per plant and
grain yield per unit area were explained by direct effect of
grain yield per plant. Thus, with breeding this trait, grain
yield per unit area is also improved. Since grain yield per
plant is depended to other traits, recognition of cause
variables for its improvement is essential. Therefore
biomass, first branch height of land surface, number of
branches, number of pods per branches and number of
seeds per pod was identified as causal variables for grain
yield per plant (table 12 and 13).
Biomass had phenotypic and genotypic direct positive
contribution toward grain yield which was strengthened by
indirect positive effect of number of pod per branches and
number of branches. However number of seeds per pod
and first branch height of land surface slightly reduced it
due to negative effect.
Table 8. Analysis of phenotypic direct and indirect effect
of two traits on oil yield in rapeseed pure genotypes
1000 grain
weight
Grain yield per
unit area
Traits
-0.006**0.969Grain yield per
unit area
0.0210.2531000 grain weight
UP=0.232
Table 9. Analysis of genotypic direct and indirect effect
of two traits on oil yield in rapeseed pure genotypes
1000 grain
weight
Grain yield per
unit area
Traits
0.014**0.975Grain yield per
unit area
0.0500.2681000 grain weight
UG=0.144

Table 10. Analysis of phenotypic direct and indirect effect
of three traits on grain yield per unit area in rapeseed pure
genotypes
number of
seeds per
middle pod
Angle of
preliminary
branches
Grain
yield per
plant (g)
Traits
-0.0470.180**0.549Grain yield per
plant (g)
-0.005*0.4050.244Angle of
preliminary
branches
*
0.284-0.007-0.092number of
seeds per
middle pod
UP=0.558
Table 11. Analysis of genotypic direct and indirect effect
of three traits on grain yield per unit area in rapeseed
pure genotypes
number of seeds
per middle pod
Grain yield per
plant (g)
Traits
-0.244**1.028Grain yield per
plant (g)
**
0.793-0.316*number of seeds
per middle pod
UG=0.412
*
,**
: significant at 5% and 1% probability levels,
respectively. Numerics on diagonal are direct effect.
In the present experiment, first branch height of land
surface had phenotypic and genotypic negative and
significant direct contribution on grain yield. Its genotypic
indirect effect through number of branches was negative
and significant. Thus, it was known as an effective trait on
grain yield. Generally, the lower first branch height of land
surface brings the higher grain yield per unit area. With
regard to negative correlation between forenamed trait and
number of branches, it can be said that if the first branch
height of land surface is low, a greater number of branches
will generate per plant and number of pods per branches
and per plant will be increase. Hereupon grain yield per
plant will be also increase. Al-barzinjy et al. (2003) with
comparison of growth, pod distribution and canopy
structure of old and new cultivars of oilseed rape (B. napus
L.), observed that highest yield is related to a cultivar
characterizing by short plants, bearing more branches that
started branching earlier and on a low position on the stem.
They express in addition to stem and pod development,
number of branches and leaf area after anthesis were
important characteristics for yield improvement.
Number of branches had optimum phenotypic and
genotypic negative direct contribution grain yield and its
genotypic indirect effect was negative and significant. But
sum of its phenotypic and genotypic indirect effect through
other traits was positive and higher from its direct effect.
Therefore, it seems that correlation between number of
branches and grain yield is originated from its indirect
effect through traits such as biomass, number of pods per
branches and first branch height of land surface. In fact,
increase of number of branches lead to increase of number
of pods per plant and biomass, and decrease of first
branch height of land surface, hereupon, grain yield per
plant will be increase. Number of branches was introduced
by Akbar et al. (2003) as one of the causal variables for
grain yield per plant. Number of pods per branches had
low phenotypic and genotypic direct contribution on grain
yield. This trait in simple correlation study was positive and
considerable significant associated with grain yield. The
usefulness of path coefficient analysis is apparent.
Number of seeds per pod had lowest phenotypic and
genotypic direct contribution on grain yield. Özer et al.
(1999), expressed number of seeds per pod should be de-
emphasized in the selection phenomenon. Thurling (1974)
also reported faint and nonsignificant relation between
number of seeds per pod and grain yield, whereas, in the
experiment of Shabana et al. (1990), number of pods per
branches and number of seeds per pod as well as biomass
were known as effective traits on grain yield per plant.
• Angle of preliminary branches
Angle of preliminary branches after grain yield per plant
had maximum phenotypic positive direct effect on grain
yield per unit area. Amount of its phenotypic indirect effect
through grain yield per plant and number of seeds per
middle pods was low. It seems that whatever the angle of
preliminary branches is more; distribution of solar radiation
improves on upper branches having the main role on
production of pod and grain yield (Clark, 1979), therefore
the branches may be producemore number of pods.
Norton et al. (1991) reported better distribution of solar
radiation enhances durability of pod and grain on branch.
Since genotypic correlation between forenamed trait and
grain yield per unit area was greater of 1, therefore it didn’t
enter in genotypic path analysis. This can be arisen from
environment or plant density effects on angle of
preliminary branches. Because this trait is measurable in
early stages of growth, can be a good physiological and
morphological criterion for yield improvement. It should be
noted that for achievement to desirable effect of angle of
preliminary branches on grain yield, plant density and
other environmental condition must be optimized.
• Number of seeds per middle pods
Phenotypic and genotypic direct contribution related to
number of seeds per middle pods is equal to 0.284 and
0.718, respectively. High difference between phenotypic
and genotypic direct effect of this trait can be derived from
influence of environment. As it can be observed from
Table 3, considerable difference exists between
phenotypic and genotypic variation for number of seeds
per middle pods, and thereupon its heritability is low than
other traits. Phenotypic and genotypic indirect effects of
this trait through grain yield per plant and angle of
preliminary branches is low.

Table 12. Analysis of phenotypic direct and indirect effect of five traits on grain yield per plant in rapeseed pure genotypes
number of seeds
per pod
number of pods
per branches
number of
branches
First branch height
of land surface
BiomassTraits
-0.0010.191-0.1140.028**0.711Biomass
0.001-0.060-0.140*-0.348-0.056number of seeds per pod
0.0010.229*-0.3450.1420.235number of branches
0.0010.229-0.2640.0700.454*number of pods per branches
-0.007-0.0530.0290.0590.101number of seeds per pod
UP=0.481
Table 13. Analysis of genotypic direct and indirect effect of five traits on grain yield per plant in rapeseed pure genotypes
number of
seeds per pod
number of pods
per branches
number of
branches
First branch height of
land surface
BiomassTraits
0.0280.106-0.1230.171**1.011Biomass
0.117-0.043-0.310***-0.633-0.273First branch height of land
surface
0.0670.132**-0.5650.348*0.220number of branches
0.0950.153-0.486*0.1790.703**number of pods per branches
-0.243-0.0600.1590.305*-0.118number of seeds per pod
UG=0.231
*
,**
: significant at 5% and 1% probability levels, respectively. Numerics on diagonal are direct effect.
CONCLUSION
Generally, path coefficient analysis confirmed the findings
of the correlation analysis, but also provided additional
information on component interrelationships which would
not have been obtained from an examination of correlation
coefficients. Based on present results, traits such as
biomass, first branch height of land surface, number of
branches, angle of preliminary branches, 1000 grain
weight and number of pods per branches would be good
criteria for selection of fertile genotypes that in this among,
biomass, first branch height of land surface and number of
branches which had high genotypic coefficients of
variability, high heritability, high degree of significant
correlation coefficient, high regression coefficient (positive
or negative) and high direct effect on grain yield would be
very effective and excellent tools for improving seed yield
potential to hereby cultivars with high grain and oil yield
have been obtained, whereas, number of seeds per pod
would not be the same.
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Accepted 20 December 2019
Citation: Ahmadzadeh M, Samizadeh HA, Ahmadi MR,
Soleymani F, de Lima CA (2019). Selection Criteria for
Yield Improvement in Rapeseed (Brassica napus L.).
World Research Journal of Agricultural Sciences, 6(3):
176-184.
Copyright: © 2019 Ahmadzadeh 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.

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