Molecular Plant Breeding 2016, Vol.7, No.19, 1
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9
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knowledge on selection index analysis. Selection indices are useful in understanding the extent of improvement
that can be used in breeding programmes to improve yield by combination of characters (Basavaraj and Sheriff,
1992). This method has been successfully followed by various researchers in various crops in Bangladesh such as
Habib et al.
(2007) in rice, Deb and Khaleque (2007) in chickpea, Ferdous (2010) in spring wheat, Akter et al.
(2013) in rice, Sarker et al. (2013) in chickpea and Bashar et al. (2015) in eggplant.
1 Results and Discussion
1.1 Character association
The genotypic and phenotypic correlation coefficients were analyzed and presented in Table 1. Fruits per plant,
fruit weight and 100 seed weight had significant and positive correlation with yield per plant both at genotypic
and phenotypic level. Hence yield per plant can be improved by selecting the genotypes with more number of
fruits per plant, fruit weight and 100 seed weight. The high correlation between fruits per plant and yield have
been reported by several workers
viz
., Pandit
et al. (2014),
Luitel et al. (2013), Ullah et al. (2011), Jabeen et al.
(2009), Islam and Singh (2009), Krishna
et al. (2007). Negative correlations were found for plant height, days to
first flowering, days to 50% flowering, days to fruit maturity and pedicel length with yield.
Table 1 Correlation between yield and yield components at genotypic and phenotypic level.
PB/P
SB/P
DFFL D50%F
DFM
FD(mm)
FL(cm)
FPL(cm)
FW(gm)
F/P
100SW
Y/P
PH
(cm)
r
g
0.072
0.27
0.094
0.11
-0.142
0.135
-0.24
-0.015
-0.062
-0.02
0.009
-0.128
r
p
0.054
0.089
0.096
0.124
-0.127
0.123
-0.225
-0.013
-0.046
-0.017
0.006
-0.105
PB/P
r
g
0.959** 0.033
-0.053
-0.22
-0.112
-0.317*
-0.225
-0.251
0.263
-0.208
0.082
r
p
0.553** -0.003
-0.089
-0.227
-0.089
-0.242
-0.161
-0.236
0.22
-0.167
0.06
SB/P
r
g
-0.027
-0.188
-0.219
-0.169
-0.066
-0.272
-0.027
0.281
-0.051
0.349*
r
p
-0.019
-0.149
-0.158
-0.139
-0.002
-0.182
-0.064
0.113
0.021
0.121
DFFL
r
g
0.868** 0.637**
-0.26
-0.212
-0.043
-0.536**
0.029
-0.433**
-0.299
r
p
0.826** 0.626**
-0.253
-0.196
-0.052
-0.507 **
0.023
-0.412**
-0.281
D50%
F
r
g
0.736**
-0.168
-0.374*
0.009
-0.522**
-0.157
-0.459**
-0.409**
r
p
0.718**
-0.167
-0.322 *
-0.016
-0.476 **
-0.138
-0.432 **
-0.362*
DFM
r
g
-0.14
-0.159
0.04
-0.539**
-0.236
-0.197
-0.480**
r
p
-0.152
-0.132
0.013
-0.487 **
-0.213
-0.167
-0.428**
FD
(mm)
r
g
-0.391*
-0.168
0.423**
-0.094
0.089
0.067
r
p
-0.347 **
-0.143
0.409 **
-0.107
0.076
0.053
FL
(cm)
r
g
0.474**
0.25
0.028
0.346*
0.342*
r
p
0.435**
0.22
0.033
0.328*
0.296*
FPL
(cm)
r
g
0.105
-0.272
0.125
-0.107
r
p
0.102
-0.225
0.118
-0.072
FW(g
m)
r
g
-0.187
0.458**
0.463**
r
p
-0.17
0.436**
0.466**
F/P
r
g
0.056
0.691**
r
p
0.059
0.698**
100SW
r
g
0.339*
r
p
0.323*
Note: PH= Plant height, PB/P= No. of primary branches/plant, SB/P= No. of secondary branches/plant, DFFL= Days to first
flowering, D50%F= Days to 50% flowering, DFM = Days to fruit maturity, FD= Fruit diameter, FL= Fruit length, FW= Fruit weight,
F/P= Fruits/plant, 100SW= 100 seed weight, Y/P= Yield/plant
**
= Significant at 1% level of probability,
*
= Significant at 5% level of probability
1.2 Path analysis
In the present study, path analysis was worked out to find out the direct and indirect effect of 12 traits on fruit
yield per plant (Table 2). Path analysis revealed that fruits per plant and fruit weight had high direct effect on yield
