Studies on genetic variability, correlation and path
analysis
in maize (Zea mays L.)
Parvaze Sofi and A.G. Rather
Sher-e-Kashmir
University
of Agricultural Sciences and Technology of Kashmir
The efficacy of selection process is greatly enhanced by using appropriate selection indices. The knowledge of the relationship among various traits affecting grain yield is imperative to arrive at potentially effective selection index. The present study was carried out to elucidate the various parameters of genetic variability, the nature of interrelationships among various traits affecting yield. Fifteen local and exotic maize inbred lines were crossed to three testers to develop 45 test cross progenies. The parents and crosses (63 entries in all) were evaluated in two diverse locations with three replications at each location using RBD. Data was recorded on 11 quantitative traits and subjected to statistical analysis for estimation of various variability parameters namely GCV, PCV, heritability and genetic advance besides computing genotypic correlation coefficients and path analysis to elucidate the direct and indirect effects of various traits studied. The analysis of variability parameters revealed presence of substantial variability for all traits. Grain yield, ear length, ear height, 100-seed weight and ear diameter had high GCV estimates, with high heritability. The genetic advance was higher for plant height, ear length, grain/row and grain yield. The genotypic correlation coefficient revealed that ear diameter, 100-seed weight, ear length, kernel rows/ear and grains/row had highest significant correlation with grain yield. The path analysis revealed that highest direct effect on grain yield was exhibited by 100-seed weight followed by grains/row, kernel rows/ear, ear length and ear diameter. Most of the traits exerted their positive indirect effects through 100-seed weight, kernel rows/ear and grains/row. The inferences of the results of present study and possible implications in maize breeding have been discussed.
Key words: Maize, GCV, PCV, Genetic advance, Direct effects,
Indirect effects
Grain yield is a complex trait conditioned by interaction of various growth and physiological process throughout the life cycle. The appropriate knowledge of such interrelationships between grain yield and its contributing components can significantly improve the efficiency of breeding programmes through the use of appropriate selection indices (Mohammadia et al., 2003). The nature of association between grain yield and its components determine the appropriate traits to be used in indirect selection for seeking improvement in grain yield. The correlation studies simplify measure the associations between yield and other traits while as path coefficient analysis permits the separation of correlation coefficient into direct effects (path coefficient) and indirect effects (effects exerted through other variables). It is basically a standardised partial regression analysis and deals with a closed system of variables that are linearly related. Such an information provides for realistic basis of allocation of appropriate weightage to various yield components. Wright (1921) was first to use this approach to organise the relationship between predictor variables and the response variable. A number of studies in maize have been conducted to elucidate the nature of association between yield and its components which identified traits like ear length, ear diameter, kernels/row, ears/plant, 100-seed weight and rows/ear as potential selection criteria in breeding programmes aiming at higher yield. (Debnath and Khan, 1991; Agrama, 1996; Mohan et al., 2002; Tollenaar et al., 2004). The present study was undertaken to elucidate such character association in local and CIMMYT inbred line crosses of maize in temperature valley of Kashmir.
The present study was carried out in 2004-05. the materials was generated by crossing 15 diverse white maize inbred lines (4 local and 11 exotic) to three phenotypically diverse testers (W3, W5 and W3 x W5) in a line x tester design, at winter maize Nursery, Amberpet, Hyderabad (India). The parental lines and test crosses were evaluated at two diverse locations of Kashmir valley namely Larnoo and Wadura representing distinct climatic zones. Each genotype was replicated thrice at each location in Randomised Block Design. Each entry was grown in two rows of 2 m length with row to row and plant to plant spacing maintained at 60 and 25 cm respectively. The recommended package of practices were followed to ensure a good crop. Data was recorded from 10 randomly selected competitive plants from each replication for 11 quantitative traits, the data was statistically analysed for correlation coefficients and path analysis as per the methods of Al-Jibouri et al. (1958) and Dewey and Lu (1959) respectively.
The success of plant breeding operations relies heavily an extent of genetic variability present in a crop species for a particular trait. In fact plant breeding uses selection for improving the architecture of a crop by management of available genetic variability (Asins, 2002). In the present study, the genotypic coefficient of variation (GCV) was less than its corresponding estimates of PCV for all traits indicating significant role of environment in the expression of these traits. Relatively higher estimates of GCV for grain yield/plant, ear length, ear height, 100-seed weight and ear diameter suggest that the selection can be effective for these traits. This is further established by the fact the most of the traits had high heritability estimates indicating thereby that there was preponderance of additive gene action. Genetic advance was higher for plant height, ear length, grain yield/plant and grains/row.
Genotypic relationships among traits affecting grain yield elucidate true association as they exclude the environmental influences. In the present study the traits studied were positively correlated with grain yield (Table Ð1). The highest significant positive correlation with grain yield was shown by ear diameter, followed by 100-seed weight, ear days to 50% silking and days to pollen shed has significant negative correlation with grain yield. Similar results have been reported in maize by Mohan et al. (2002), Vasic et al. (2001), Mohammadia et al. (2004), Neto and Miranda (2001).
The path coefficient analysis (Table-3) revealed that most of the traits has positive direct effect on grain yield. The highest direct effect on grain yield was exhibited by 100-seed weight followed by grams/row. Kernel rows/ear, ear length and ear diameter. Days to pollen shed, days to 50 per cent silking and ear height showed negative direct effect on grain yield even though ear height had positive correlation with grain yield. These traits also shared positive indirect effects on grain yield through other yield traits such as ear length and ear diameter. Ear diameter had highest indirect effect on grain yield through grains rows (0.362) followed by ear height (0.316) through rows/ear. In fact bulk of indirect effect on grain yield was exerted by the traits studied through these two traits, Similar results in maize have been reported by Wang et al. (1998); Vasic et al. (2001) Broccoli and Burak (2000), Abdmishani et al. (2004) and Mohammadia et al. (2003). Thus in light of the results obtained in the present study, it can be suggested that the traits such as grains/row, 100-seed weight, Kernel rows/ear, ear length and ear diameter should be used as target traits for improvement of grain yield in maize. Thus it can be emphasised that the ideal plant type should have higher values of the traits described above, whereas, the traits showing negative effects on grain yield should be selected for lower values such as plant height and ear height. In fact Vasic et al. (2001) used various indices of selection for improvement of grain yield, and were able to show that even with a simple selection for improvement or grain yield, and were able to show that even with a simple selection index involving
The conventional path analysis or one carried out in present study suffers from the limitation of non-independence of predictor variables often leading to high multicollinearity. In fact Samonte et al. (1998) proposed a sequential path analysis which is based on minimising multicolinearity due to complex interaction of yield component traits, and delineates the importance of predictor variables into various orders based on their direct effects. Thus multiple regression based path analysis can be improved by stepwise regression analysis by sequentially removing the non-significant predictor variables from analysis. Besides, more and more traits can be included in the path analysis in order to reduce the residual effects.
Abdmishani, C., Vaezi, S., Samadi, Y.B. and Ghannadha, M. (2004). correlation and path analysis of grain yield and its components in maize. Maize Genetics Conference Abstract 46(1) : 1-2.
Agrama, H.S (1996). Sequential path analysis of grain yield and its components in maize. Plant Breeding. 115 : 343-346.
Al-Jibouri, H., Miller, P. and Robinson, H.F. (1958). Genetic and environmental variances in upland cotton cross of inter specific origin. Agronomy J. 50 : 633-637.
Debnath, S.C. and Khan, M.A. (1991). Genotypic variation, conariation and path coefficient analysis in maize. Pakistan J. of Sci. and 2nd. Res. 34 : 391-394.
Dewey, D. and Lu, K.H. (1959). A correlation and path coefficient analysis of components of crested wheat grass seed production. Agron. J. 51 : 515-518.
Mohammadia, S.A., Prasanna, B.M and Singh, N.N. (2003). Sequential path model for determining interrelationship among grain yield and related characters in maize. Crop Sci. 43 : 1690-1697.
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Neto, A.L. and Miranda, J.B. (2001). Genetic correlation between traits in the ESALQ-PB1 maize population divergently selected for tassel size and ear height. Scientia Agricola. 58(1) : 9016-9018.
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Table 1: Mean,
range, GCV, PCV, heritability and genetic advance for grain yield and component traits in maize
Trait
|
Mean |
Range |
GCV
(%) |
PCV
(%) |
Heritability
(%) |
Genetic
advance @ 5% selection intensity |
|
Days to pollen shed |
85.119 |
76.103-90.312 |
15.206 |
16.278 |
68.763 |
7.569 |
|
Days to 50% silking |
-90.119 |
80.440 Ð 101.774 |
14.769 |
15.889 |
65.574 |
7.160 |
|
Days to 75% husk browning |
-141.050 |
132.457 Ð 156.348 |
15.347 |
15.884 |
82.600 |
14.121 |
|
Plant height (cm) |
-198.330 |
148.433 Ð 240.883 |
17.976 |
20.788 |
64.661 |
24.095 |
|
Ear height (cm) |
95.875 |
69.333 Ð 137.666 |
12.229 |
26.598 |
64.280 |
17.795 |
|
Ear length (cm) |
15.340 |
12.140 Ð 20.318 |
12.171 |
26.004 |
67.840 |
22.925 |
|
Ear diameter (cm) |
4.122 |
3.240 Ð 5.238 |
19.384 |
22.404 |
67.230 |
10.603 |
|
100-seed weight |
27.384 |
22.833 Ð 34.816 |
19.935 |
23.581 |
63.520 |
14.094 |
|
Kernel rows/ear |
-14.429 |
12.473 Ð 17.314 |
15.103 |
17.334 |
48.430 |
11.055 |
|
Grain/row |
62.213 |
28.200 Ð 43.166 |
15.398 |
18.093 |
44.490 |
22.609 |
|
Grain yield/plant |
62.213 |
32.117 Ð 85.166 |
34.773 |
39.098 |
72.480 |
22.526 |
Trait
|
DPS |
DS |
DHB |
PH |
EH |
EL |
ED |
100
SW |
KRE |
GR |
GYP |
DPS
|
1.00 |
0.997** |
0.856** |
-0.286 |
-0.563** |
-0.072 |
-0.162 |
-0.002 |
-0.351* |
-0.572** |
-0.315* |
|
DS |
|
1.00 |
0.850** |
-0.280 |
-0.527** |
-0.066 |
-0.172 |
-0.022 |
-0.346* |
-0.562** |
-0.328* |
|
DHB |
|
|
1.00 |
-0.049 |
-0.481* |
0.235 |
0.147 |
0.228 |
-0.072 |
-0.232 |
-0.081 |
|
PH |
|
|
|
1.00 |
0.679** |
0.500** |
0.674** |
0.560** |
0.639** |
0.891** |
0.522** |
|
EH |
|
|
|
|
1.00 |
0.253 |
0.262 |
0.152 |
0.442* |
0.621** |
0.236 |
|
EL |
|
|
|
|
|
1.00 |
0.133 |
0.588** |
0.806** |
0.549** |
0.801 |
|
ED |
|
|
|
|
|
|
1.00 |
0.952** |
0.878** |
0.621** |
0.867** |
|
100
SW |
|
|
|
|
|
|
|
1.00 |
0.788** |
0.330* |
0.839 |
|
KRE |
|
|
|
|
|
|
|
|
1.00 |
0.636** |
0.790** |
|
GR |
|
|
|
|
|
|
|
|
|
1.00 |
0.699** |
|
GYP |
|
|
|
|
|
|
|
|
|
|
1.00 |
|
Trait |
DPS |
DS |
DHB |
PH |
EH |
EL |
ED |
100
SW |
KRE |
GR |
Correlation
with grain yield |
DPS
|
-0.091 |
0.035 |
0.041 |
-0.316 |
-0.066 |
0.036 |
0.002 |
0.110 |
-0.177 |
0.108 |
-0.315* |
|
DS |
-0.258 |
-0.064 |
0.318 |
-0.148 |
-0.316 |
0.048 |
0.029 |
0.091 |
-0.216 |
0.189 |
-0.328* |
|
DHB |
-0.039 |
0.024 |
-0.027 |
-0.073 |
-0.098 |
0.034 |
0.043 |
0.103 |
-0.069 |
0.037 |
-0.081 |
|
PH |
-0.187 |
-0.211 |
0.180 |
0.183 |
0.074 |
0.073 |
0.027 |
0.061 |
0.236 |
0.178 |
0.522** |
|
EH |
-0.148 |
0.013 |
0.029 |
0.005 |
-0.061 |
0.133 |
0.064 |
-0.197 |
0.316 |
0.081 |
0.236 |
|
EL |
-0.063 |
0.116 |
0.113 |
-0.139 |
-0.087 |
0.278 |
0.024 |
0.128 |
0.139 |
0.283 |
0.801** |
|
ED |
-0.098 |
0.020 |
0.075 |
-0.092 |
-0.109 |
0.045 |
0.189 |
0.210 |
0.264 |
0.362 |
0.867** |
|
100
SW |
0.188 |
-0.266 |
0.096 |
-0.167 |
-0.122 |
0.078 |
0.189 |
0.705 |
0.047 |
0.088 |
0.839** |
|
KRE |
-0.153 |
0.137 |
0.103 |
-0.103 |
-0.182 |
0.048 |
0.031 |
0.165 |
0.569 |
0.177 |
0.790** |
|
GR |
0.077 |
-0.049 |
-0.054 |
-0.0880 |
0.034 |
0.074 |
0.039 |
0.044 |
0 |