Correlation and path coefficients analysis studies among yield and yield related traits of quality protein maize (QPM) inbred lines

Correlation and path coefficients analysis studies among yield and yield related traits of quality protein maize (QPM) inbred lines
IJPBCS
Correlation and path coefficients analysis studies among yield and yield related traits of quality protein maize (QPM) inbred lines Bullo Neda Tulu Department of Plant Science (Plant Breeding and Genetics courses), College of Veterinary Medicine and Agriculture, Salale Campus Addis Ababa University, P.O. Box 245, Fiche, Ethiopia Email: tulu0913479057@gmail.com, Tel.: 251-0913479057, Fax number: 251- 0111352910 The study was carried out to estimate correlation coefficients among grain yield and yield related traits and work out direct and indirect effects of yield-related traits on grain yield using path- coefficient analysis. Sixty-six F1 crosses and two standard checks were evaluated at Mechara, Ethiopia. The analysis of variance revealed that mean squares due to entries and crosses were highly significant (p<0.01) or significant (p<0.05) for most traits studied, indicating the existence of variability among the materials evaluated, which could be exploited for the improvement of respective traits. Grain yield showed positive and highly significant correlations with most traits at phenotypic and genotypic levels. Ear diameter and number of kernels per row exerted positive direct effect and also had positive association with grain yield. These traits could be used as a reliable indicator in indirect selection for higher grain yield since their direct effect and association with grain yield were positive at phenotypic and genotypic levels. Traits having strong relationship with grain yield can be used for indirect selection to improve grain yield because grain yield can be simultaneously improved along with the traits for which it showed strong relationship. Key Words: Correlation coefficients, Path coefficients, Quality Protein Maize INTRODUCTION
Maize is one of the most important food crops worldwide. It has the highest average yield per hectare and is the third after wheat and rice in area in the world. The global annual production of maize is about 844 million tons (FAOSTAT, 2010). Maize is cultivated in all of the major agro-ecological zones in Ethiopia. Therefore, considering its importance in terms of wide adaptation, total production and productivity, maize is one of the high priority crops to feed the increasing population of the country (Mosisa et al., 2002).
Millions of smallholder farmers in the major maize producing regions of Ethiopia consume maize as important staple food and derive their protein and calories requirements from it. But, normal maize varieties are deficient in two essential amino acids, lysine and tryptophan; as a result, they cannot provide quality protein and sustain acceptable growth and adequate health (Vasal, 2000). For that reason, introducing quality protein maize (QPM) varieties with high lysine and tryptophan content would substantially improve the protein status and greatly reduce the malnutrition problems of resource poor people that are dependent on maize as staple food (Leta et al., 2002). The potential contribution of QPM to improve human nutritional status has been also accorded worldwide attention highlighted with the award of the world food prize of 2000 to International Journal of Plant Breeding and Crop Science Vol. 1(2), pp. 006-017, October, 2014. © www.premierpublishers.org. ISSN: 2167-0449
Research Article

Tulu 006
scientists of the International Maize and Wheat improvement Center (CIMMYT) who undertook the research effort on QPM for more than 30 years (Vasal, 2000). To design appropriate breeding strategies for improvement in yield through selection, it would be desirable to conduct correlation and path coefficient analysis studies, which help to have better understanding of the relationship among yield and yield related traits (Mondal et al., 2011, Malek et al., 2014). Studies on genotypic and phenotypic correlations among characters of crop plants are useful in planning, evaluating and setting selection criteria for the desired characters in breeding program (Johanson et al., 1955b). Correlation measured by correlation coefficient is important in plant breeding because it measures the degree of association, genetic or non-genetic between two or more characters. If genetic association exists, selection for one trait will cause changes in other trait (Hallauer and Miranda, 1988). Genotypic correlations were also calculated to determine the genotypic proportion of the phenotypic correlation and there by strengthen the interpretation of the results. Genetic correlation may be attributed to pleiotropism and/or linkage disequilibrium (Hallauer and Miranda, 1988). Correlation coefficient analysis helps to determine the nature and degree of relationship between any two measurable characters. Characters that are not easily measured or which are largely influenced by the environment has low heritability ratio; hence, there is a need to examine the relationships among various characters. Therefore, knowledge of inter-character relationship is very important in plant breeding for indirect selection for characters that are not easily measured and for those that exhibit low heritability. Indirect selection will be effective if the heritability of the secondary trait is greater than that of the primary trait and if the indirect selection gives greater response to selection for traits than direct selection for the same trait, the genetic correlations are useful (Hallauer and Meranda, 1988). Correlation studies between characters have also been of great value in the determination of the most effective breeding procedures. As the number of independent character affecting a dependent character increases, there is bound to be some amount of interdependence (Ariyo et al., 1987). Thus, correlations may be insufficient to explain the associations in a manner that will enable one to decide on either a direct or an indirect selection strategy (Bhatt, 1973). Phenotypic and genotypic correlation coefficients can be further analyzed by path coefficient analysis, which involves the partitioning of the correlation coefficients into direct and indirect effects through alternate characters or path ways. Such analysis leads to the identification of important component traits useful in indirect selection for higher yield (Dewey and Lu, 1959). Estimation of simple correlation coefficient helps the breeders to determine the association between characters, but they do not provide the real picture of direct and indirect influence of each character associated with the other character. This weakness of correlation analysis can therefore be overcome by path coefficient analysis (Bhatt, 1973). Studies of correlation and path coefficient analysis would be very important to design appropriate breeding strategies for improvement in yield through selection to have better understanding of the relationship among yield-related traits (Dewey and Lu, 1959). Yield, being a complex trait, has been observed to be associated with a number of component characters. Grain yield in maize, specifically, is the result of a number of complex morphological and physiological traits. For full understanding of the complex relationships between grain yield and other characters, the computation of direct and indirect effects of these traits on grain yield is essential. Therefore, our objectives were to (i) Estimate correlation coefficients among grain yield and yield related traits and (ii) Work out direct and indirect effects of yield-related traits on grain yield using path- Coefficient analysis. MATERIAL AND METHODS
Description of the Study Area
The study was conducted at Mechara Agricultural Research Center during the main cropping season of 2012. The center is located in West Harerge Zone of Oromia Region at 434 km east of Addis Ababa, the capital of Ethiopia and 110 km south of Chiro town, the capital of West Hararghe zone. The center lies at 08036’North latitude and 40019’East longitude and at an altitude of about 1773 meter above sea level (m.a.s.l). The area receives an annual rainfall of 1294mm. The annual average minimum and maximum temperatures of Mechara are 18.10C and 33.10C, respectively (Mechara Agricultural Research Center, unpublished).
Experimental Materials The experimental materials used for the current experiment consisted of a total of 68 entries (Table 1) which comprised of 66 F1 crosses obtained from 12 × 12 diallel crosses (excluding the reciprocal crosses and parents) of QPM inbred lines, and two standard checks; namely, BHQP542 and Melkassa6Q. Experimental Design and Field Management
The experiment was laid out in 4 x 17 (4 incomplete blocks in a replication and 17 plots in an incomplete block) alpha-lattice designs (Patterson and Williams, 1976) with two replications. Planting was done manually

Int. J. Plant Breeding Crop Sci. 007 Table 1. Sixty six diallel crosses of twelve QPM inbred lines and two standard checks
Serial No.
66 Diallel Crosses and two standard checks
1
[CML312/GQL5]-B-B-4-1-1-1 × [BO155W/CML395]-B-B-2-2-2-1
2
[CML312/GQL5]-B-B-4-1-1-1 × [CML202/CML181]-B-B-10-2-1-1
3
4
5
[CML312/GQL5]-B-B-4-1-1-1× [CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3
6
7
8
9
[CML312/GQL5]-B-B-4-1-1-1 × [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B- B-10-1-2-1
10
[CML312/GQL5]-B-B-4-1-1-1 × CML144
11
[CML312/GQL5]-B-B-4-1-1-1 × CML159
12
[BO155W/CML395]-B-B-2-2-2-1 × [CML202/CML181]-B-B-10-2-1-1
13
14
15
[BO155W/CML395]-B-B-2-2-2-1 × [CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3
16
17
Table 1: Continued
Serial No.
18
[BO155W/CML395]-B-B-2-2-2-1× [CML395/CML175]-B-B-5-1-1-1
19
[BO155W/CML395]-B-B-2-2-2-1× [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]- B-B-10-1-2-1
20
[BO155W/CML395]-B-B-2-2-2-1× CML144
21
[BO155W/CML395]-B-B-2-2-2-1× CML159
22
[CML202/CML181]-B-B-10-2-1-1× [CML216/CML182]-B-B-5-3-1-1
23
24
[CML202/CML181]-B-B-10-2-1-1× [CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3
25
26
[CML202/CML181]-B-B-10-2-1-1 × [CML395/CML182]-B-B-3-1-1-1
27
28
[CML202/CML181]-B-B-10-2-1-1× [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]- B-B-10-1-2-1
29
[CML202/CML181]-B-B-10-2-1-1× CML144
30
[CML202/CML181]-B-B-10-2-1-1× CML159
31
32
[CML216/CML182]-B-B-5-3-1-1× [CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3
33
34

Tulu 008 Table 1. Continued
Serial No.
35
36
[CML216/CML182]-B-B-5-3-1-1× [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B- B-10-1-2-1
37
[CML216/CML182]-B-B-5-3-1-1× CML144
38
[CML216/CML182]-B-B-5-3-1-1× CML159
39
[CML202/CML175]-B-B-1-4-2-3 × [CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3
40
41
42
43
[CML202/CML175]-B-B-1-4-2-3 × [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B- B-10-1-2-1
44
[CML202/CML175]-B-B-1-4-2-3 × CML144
45
[CML202/CML175]-B-B-1-4-2-3 × CML159
46
[CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3 × [CML387/CML182]-B-B-1-3-1-3
47
48
49
[CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3 × [CML182/[EV7992#/EV8449- SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B-B-10-1-2-1
50
[CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3 × CML144
51
[CML141/[MSRXPOOL9]C1F2-205-1(OSU23i)-5-3-X-X-1-B-B]-B-B-1-5-1-3 × CML159
Table 1. Continued
Serial No.
52
53
54
[CML387/CML182]-B-B-1-3-1-3 × [CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]- B-B-10-1-2-1
55
[CML387/CML182]-B-B-1-3-1-3 × CML144
56
[CML387/CML182]-B-B-1-3-1-3 × CML159
57
58
59
[CML395/CML182]-B-B-3-1-1-1 × CML144
60
[CML395/CML182]-B-B-3-1-1-1 × CML159
61
62
[CML395/CML175]-B-B-5-1-1-1 × CML144
63
[CML395/CML175]-B-B-5-1-1-1 × CML159
64
[CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B-B-10-1-2-1× CML144
65
[CML182/[EV7992#/EV8449-SR]C1F2-334-1(OSU8i)-1-1-X-X-3-B-3-B-B]-B-B-10-1-2-1× CML159
66
CML144 × CML159
67
BHQP542
68
Melkassa6Q

Int. J. Plant Breeding Crop Sci. 009
by placing two seeds per hill, which were later thinned to one plant per hill. Each plot consisted of two rows of 4m length with spacing of 0.75m between rows and 0.25m between plants. Both rows were used to collect data on yield and other traits. At planting, 46kg P2O5 per ha and 18 kg N per ha were applied in a form of DAP and an additional of 46kg N per ha was applied 35 days after planting in a form of urea. Weed control and other crop management practices were applied following research recommendations. Data Collected Days to emergence was recorded as the number of days from planting to when 50% of the seedlings emerged above ground in each plot. The information was used to calculate days to anthesis, days to silking and days to maturity. Stand count after thinning is the number of well established plants per plot after thinning. Leaf rolling was recorded on 1-3 scale, where 1 = not rolled, 2 = moderately rolled and 3 = highly rolled. Days to anthesis was recorded as the number of days from emergence to when 50% of the plants in the plot shed pollen. Days to silking was recorded as the number of days from emergence to when 50% of the plants in the plot showed up 2-3cm long silk protrusion. Anthesis-silking interval was recorded as the difference between number of days to anthesis and silking. Days to maturity was recorded as the number of days from emergence to when 50% of the plants in a plot form black layer at the tip of the kernels on the ears. Number of ears per plant was recorded as the total number of ears harvested from a plot divided by the number of plants at harvest in that particular plot. Disease scores were recorded by visual observation of the diseased plant parts using 1-5 scale, where 1 = Resistance or no infection, 2 = moderately resistance or light infection, 3 = moderately susceptible or moderate infection, 4 = heavy or susceptible infection, 5 = very heavy or highly susceptible infection. Plant aspect is overall phenotypic appearance of the plant recorded on 1-5 scale; where, 1 = very good, 2 = good, 3 = fair, 4 = poor and 5 = very poor. Stand count at harvest is the number of plants per plot at harvest. Ear aspect is overall phenotypic appearance of all the ears harvested from a plot and expressed on 1-5 scale; where, 1 = very good, 2 = good, 3 = fair, 4 = poor and 5 = very poor. Shelling percentage was recorded as the ratio of shelled grain weight to unshelled cob weight (field weight) expressed in percent for each plot. Thousand kernel weight was taken at 12.5% moisture. Grain yield (kg/ha) is the total grain yield of each experimental plot adjusted to 12.5% moisture level was converted to ha basis. Plant height (cm) was measured as distance in cm from the soil surface to the base of tassel branching taken from 10 randomly selected plants and the measurement was made two weeks after pollen shedding was ceased. Ear height (cm) was measured as the distance in cm from the ground level to the upper most ears bearing node taken from 10 randomly selected plants. The measurement was made two weeks after pollen shedding was ceased. Ear length (cm) is the length from the base to the tip of the ear. Mean of 10 representative ears were used to represent a plot and measurements were taken just after harvest. Number of kernel rows per ear was recorded as the average number of kernel rows per ear of 10 randomly selected ears from each plot. Number of kernels per row was recorded as the average number of kernels per row of 10 randomly selected ears from each plot. Ear diameter (cm) was measured as the average diameter of 10 randomly selected ears from each experimental plot. Leaf area index is the average area in cm2 of five sampled leaves per plant in the plot calculated as the product of its length and width taken from each of the five sampled plants per plot then multiplied by the correction factor k ( k = 0.75). Number of nodes per plant is the average number of nodes per plant taken from five sampled plants per plot. Internode length (cm) is the average length of the internode that is immediately below the upper most ears taken from the five randomly sampled plants per plot.
Internode length (cm) is the average length of the internode that is immediately below the upper most ears

Tulu 010
taken from the five randomly sampled plants per plot. Stalk diameter (cm) is the average diameter in cm of the stalk immediately below the ear bearing nodes of five sampled plants per plot measured by a caliper.
Correlation coefficients analysis
Phenotypic, genotypic and environmental correlation coefficients were calculated using spar11 statistical package following the formula described by Miller et al. (1958).
The phenotypic and environmental correlation coefficients were tested for their significance by comparing the value of correlation coefficient with tabulated r-value at g-2 degree of freedom. However, the coefficients of correlation at genotypic level were tested for their significance using the formula described as indicated below (Robertson, 1959).
Path coefficients analysis
Path coefficient analysis was calculated using formula spar11 statistical package suggested by Dewey and Lu (1959) to determine direct and indirect effect of different variables on grain yield.
rij = pij + ΣrikPkj Statistical Analysis Analysis of variance (ANOVA) was carried out following the PROC MIXED procedure in SAS (SAS, 2003) to determine the differences among the genotypes. Genotypes were considered as a fixed effects while replications and blocks within replications where considered random.
RESULTS AND DISCUSSION
Analysis of Variance (ANOVA)
The analysis of variance revealed that mean squares due to entries were significant (P< 0.05) for internode length and ear diameter, and highly significant (P<0.01) for grain yield, days to silking, days to anthesis, plant height, ear height, number of nodes per plant, ear length, number of kernels per row, stalk diameter, days to maturity, thousand kernel weight and leaf area index (Table 2). The significant mean squares due to entries indicated the existence of variability among the materials evaluated, which could be exploited for the improvement of respective traits. Further partitioning of the sum of squares due to entries into that of crosses, checks and crosses versus checks indicated that mean squares due to crosses were either highly significant (P< 0.01) or significant (P<0.05) for most traits studied. In line with the current study, Vasal et al. (1993b) also found significant mean squares due to crosses for days to silking, plant height and grain yield in CIMMYT’s QPM germplasm. Checks showed non-significant effects for all traits studied except for number of nodes per plant and days to maturity that exhibited highly significant (P< 0.01) mean squares. Significant differences (P<0.01 or P<0.05) were observed for crosses versus checks for most studied traits.
Correlation analysis
The estimates of phenotypic associations for grain yield and yield-related traits are presented in Table 3. Grain yield showed positive and significant phenotypic associations with most studied traits. In line with the current study, Kebede (1989) found positive and significant correlations of grain yield with number of kernels per row, ear length and thousand kernel weights. Dass et al. (1990) also found positive and significant correlations of grain yield with ear diameter, number of kernels per row, thousand-kernel weight and plant height. Girma (1991) reported similar results with ear diameter, ear length and thousand kernel weights. Similar to the current study, Djordjevic and Ivanovic (1996) found positive and significant correlation of grain yield with plant height. Mandefro (1998) found positive and significant correlations of grain yield with plant height, ear height and number of kernels per row that agrees with this study. Pixley and Bjarnason (2002) also found positive and significant correlation of grain yield with ear height which accorded with the current study. Hadji (2004) also reported positive and significant associations of grain yield with ear height, plant height, ear length, ear diameter, number of kernels per row and thousand kernel weight. Similarly, Dagne (2008) also found positive and highly significant phenotypic correlations between grain yield and plant height, ear height, ear diameter, ear length, number of kernels per row and thousand kernel weight. Hence, the positive associations of the above mentioned traits with grain yield indicated that these traits are the most important ones to be considered for indirect selection to improve grain yield, because grain yield can be simultaneously improved with a trait for which it

Table 2. Mean squares for grain yield and yield related traits in 12 x 12 diallel crosses of QPM inbred lines and the two standard checks evaluated at Mechara in 2012
Genotype
df
GY
DS
DA
ASI
PH
EH
SCAT
NNPP
IL
EL
ED
LR
Entry
67
2081689.9**
30.06**
14.81**
6.9ns
475.0**
123.17**
8.65ns
0.96**
2.12*
3.95**
0.09*
0.09ns
Crosses
65
1898566.5*
27.65**
12.94**
7.02*
409.9**
99.49**
8.13ns
0.93**
1.83ns
4.04**
0.09**
0.078ns
Checks
1
979308.2ns
72.25ns
100ns
2.25ns
1225ns
156.3ns
10.24ns
1**
7.84ns
0.06ns
0.16ns
0.291ns
Crosses vs Checks
1
15087098.8**
144.12**
51.57**
2.04ns
3958.0**
1629.5**
40.86ns
2.6*
15.12**
2.02ns
0.12ns
0.669ns
Error
35
917044
11.65
5.14
4.35
56.41
28.65
7.04
0.42
1.24
1.63
0.04
0.001
CV (%)
15.19
4.58
3.19
59.36
4.2
8.28
6.03
5.85
6.13
7.44
3.84
5.02
Table 2. Continued
Genotypes
df
RPE
SDIA
SCAH
KPR
EA
DM
PA
TKW
SHP
EPP
LAI
DSCORE
Entry
67
1.73ns
0.54**
8.33ns
19.26**
0.34ns
6.00**
0.02ns
2351.99**
71.05ns
0.105ns
9767.07**
0.366ns
Crosses
65
1.77ns
0.54**
7.94ns
17.83**
0.33ns
2.67**
0.02ns
2381.43**
59.29ns
0.102ns
7.02*
0.321ns
Checks
1
0.25ns
0.44ns
6.25ns
64ns
0.25ns
169**
0ns
238.39ns
226.25ns
0.145ns
8527.60ns
1.78ns
Crosses vs Check
1
0.29ns
0.54*
36.15*
67.43**
1.28*
59.46**
0.002ns
2551.78ns
679.88ns
0.26ns
645409.47**
1.873ns
Error
35
1.11
0.12
6.83
5.5
0.23
0.9
0.01
708.43
35.62
0.01
1815.44
0.24
CV (%)
7.71
5.54
9.12
6.93
17.4
0.7
5.75
9.09
12.32
5.08
8.36
8.02
**, * significant at 0.01 and 0.05, respectively, ns = non-significant; ASI = anthesis-silking interval; DA = days to anthesis; df = degree of freedom; DM = days to maturity; DS = days to silking; DSCORE= = disease score; EA = ear aspect; ED = ear diameter; EH = ear height; EL = ear length; EPP = number of ears per plant; GY= grain yield; IL = internode length; KPR = number of kernels per row; LAI=leaf area index; LR = leaf rolling; NNPP = number of nodes per plant; PA = plant aspect; PH = plant height; RPE = number of kernel rows per ear; SCAH = stand count at harvest; SCAT = stand count after thinning; SDIA = stalk diameter; SHP = shelling percentage; TKW = thousand kernel weight
Table 3. Estimates of correlation coefficients at phenotypic (below diagonal) and genotypic (above diagonal) levels among 15 traits in diallel crosses of QPM inbred lines
Traits
GY
DS
DA
ASI
PH
EH
NNPP
IL
EL
ED
SDIA
KPR
DM
TKW
LAI
GY
-0.07
-0.13
0.09
0.59**
0.42**
0.34**
0.47**
0.50**
0.32**
0.39**
0.57**
0.14
0.18
0.20
DS
-0.29**
0.94**
0.75**
0.44**
0.43**
0.08
0.37**
0.08
-0.08
0.44**
0.00
0.16
-0.11
0.61**
DA
-0.27**
0.92**
0.49**
0.36**
0.39**
0.10
0.30*
0.02
-0.03
0.38**
-0.01
0.14
-0.13
0.58**
ASI
-0.18*
0.73**
0.44**
0.46**
0.34**
0.08
0.39**
0.18
0.04
0.43**
0.03
0.16
0.02
0.43**
PH
0.58**
0.24**
0.22*
0.21*
0.74**
0.39**
0.68**
0.50**
0.11
0.61**
0.57**
0.21
0.09
0.43**
EH
0.46**
0.17**
0.19*
0.06
0.74**
0.40**
0.59**
0.46**
-0.03
0.48**
0.28*
0.23
0.21
0.31**
NNPP
0.23**
0.04
0.06
0.03
0.30**
0.29**
0.06
0.25*
-0.01
0.20
0.23
0.11
0.25*
0.15

Tulu 012 IL
0.37**
0.27**
0.22*
0.26**
0.63**
0.53**
0.10
0.32**
-0.16
0.28*
0.32**
0.16
-0.02
0.23
EL
0.45**
-0.02
-0.03
0.03
0.43**
0.41**
0.20*
0.30**
0.10
0.46**
0.70**
0.12
-0.11
0.25*
ED
0.36**
-0.15
-0.16
-0.07
0.16
0.04
0.01
0.01
0.16
0.27*
0.14
0.03
0.25*
0.22
SDIA
0.36**
0.24**
0.21*
0.23**
0.53**
0.44**
0.16
0.24**
0.37**
0.20*
0.41**
0.08
-0.04
0.58**
KPR
0.55**
-0.14
-0.09
-0.14
0.50**
0.30**
0.19*
0.27**
0.72**
0.20*
0.33**
0.14
-0.2
0.23
DM
0.09
0.09
0.07
0.10
0.17*
0.24**
0.01
0.12
0.09
-0.01
0.14
0.09
0.41**
-0.31**
TKW
0.24**
-0.18*
-0.18*
-0.08
0.17
0.29**
0.16
0.04
0.00
0.22*
0.09
-0.05
0.49**
-0.28*
LAI
0.20*
0.37**
0.40**
0.19*
0.38**
0.28**
0.11
0.19*
0.26**
0.20*
0.43**
0.24**
-0.42**
-0.23**
Table 4. Estimates of correlation coefficients at environmental level among 15 traits in diallel crosses of QPM inbred lines
Traits
GY
DS
DA
ASI
PH
EH
NNPP
IL
EL
ED
SDIA
KPR
DM
TKW
LAI
DS
-0.78**
DA
-0.61**
0.89**
ASI
-0.67**
0.71**
0.35**
PH
0.62**
-0.54**
-0.39**
-0.54**
EH
0.59**
-0.58**
-0.44**
-0.59**
0.80**
NNPP
0.04
-0.04
-0.02
-0.05
0.05
0.01
IL
0.13
0.01
-0.01
0.00
0.48**
0.36**
0.19
EL
0.33**
-0.24*
-0.14
-0.24*
0.20
0.29*
0.11
0.24*
ED
0.46**
-0.32**
-0.24*
-0.30*
0.38**
0.24*
0.05
0.19
0.31*
SDIA
0.31**
-0.31**
-0.29*
-0.21
0.20
0.30*
0.09
0.11
0.13
0.02
KPR
0.52**
-0.44**
-0.28*
-0.44**
0.29*
0.37**
0.11
0.15
0.80**
0.35**
0.15
DM
0.01
-0.01
-0.04
0.02
0.12
0.28*
-0.12
0.04
0.04
-0.09
0.26*
0.01
TKW
0.36**
-0.32**
-0.30*
-0.23
0.45**
0.52**
-0.01
0.17
0.21
0.16
0.40**
0.22
0.61**
LAI
0.2
-0.23
-0.11
-0.29*
0.20
0.17
0.03
0.06
0.26*
0.14
-0.01
0.27
-0.63
-0.15
ASI = anthesis-silking interval; DA = days to anthesis; DM = days to maturity; DS = days to silking; ED = ear diameter; EH = ear height; EL = ear length; GY = grain yield; IL = internode length; KPR = number of kernels per row; LAI = leaf area index; NNPP = number of nodes per plant; PH = plant height; SDIA = stalk diameter; TKW = thousand kernel weight

showed strong relationship. On the other hand, grain yield showed negative and significant correlations with days to silking (rp = -0.30**), days to anthesis (rp = - 0.27**) and anthesis-silking interval (rp = -0.18*), indicating genotypes with longer anthesis-silking interval and earlier in maturity period would result in reduced grain yield. In line with this study, Betran et al. (2003d) observed negative and significant phenotypic correlation between grain yield and days to anthesis. Similarly, Hadji (2004) reported negative and significant association between grain yield and days to silking. In addition, grain yield showed non-significant phenotypic correlation with days to maturity; indicating that selection for increased level of these traits may not bring significant change in grain yield. In contrast, Dagne (2008) found positive and significant phenotypic correlations between grain yield and days to silking and days to anthesis. Hadji (2004) also found positive and significant correlation of grain yield with days to maturity. In contrast to findings of the current study, Dass et al. (1990) found positive and significant correlation of grain yield with days to maturity. Djordjevic and Ivanovic (1996) found non-significant correlation of grain yield with ear height. Mandefro (1998) found positive and significant correlation of grain yield with days to silking and days to maturity.
At genotypic level grain yield showed positive and highly significant correlations with most of the traits studied (Table 3). Similarly, Mandefro (1998) indicated positive and significant associations of grain yield with plant height, ear height and number of kernels per row. Dass et al. (1990) also found positive and significant associations of grain yield with ear diameter, number of kernels per row and plant height. Djordjevic and Ivanovic (1996) found similar result of strong correlation between grain yield and plant height. In line with the current study, Hadji (2004) reported positive and highly significant genotypic associations of grain yield with plant height, ear length and number of kernels per row. In contrast to this study, Mandefro (1998) reported positive and significant correlations of grain yield with days to anthesis, days to silking and days to maturity. Dass et al. (1990) also reported positive and significant associations of grain yield with days to anthesis, thousand kernel weight and days to maturity. In addition to phenotypic and genotypic correlations discussed above, environmental correlation was computed to estimate the magnitude of environmental effects for a pair of traits (Table 4). In this study, positive and highly significant environmental correlations were observed between grain yield and plant height, ear height, ear length, ear diameter, stalk diameter, number of kernels per row and thousand kernel weight; days to silking was positively and significantly correlated with days to anthesis and anthesis-silking interval. Positive and highly significant environmental correlation is observed between days to anthesis and anthesis-silking interval. Plant height is positively and significantly correlated with ear height, internode length, ear diameter, thousand kernel weight and number of kernels per row. Ear height had positive and highly significant correlation with internode length, ear length, ear diameter, number of kernels per row, thousand kernel weight, stalk diameter and days to maturity. Ear length showed positive and significant environmental correlation with internode length, number of kernels per row, ear diameter and leaf area index while ear diameter had positive and highly significant environmental correlation with number of kernels per row. Stalk diameter showed strong correlation with thousand kernel weight and days to maturity. Similarly, highly significant and positive correlation was observed between number of kernels per row and leaf area index. Days to maturity showed strong positive correlation with thousand kernel weight. In line with the current study, Hadji (2004) reported positive and significant environmental correlations of grain yield with plant height, ear height, ear length, ear diameter and number of kernels per row. Similarly, Mandefro (1998) reported significant and positive environmental correlation between days to anthesis and silking; plant height and ear height; thousand kernel weight and grain yield; plant height and grain yield. Such positive and significant environmental correlations between paired traits indicated that any environment which favors the expression of one trait also favors the expression of the other trait. In contrary, grain yield showed negative and highly significant environmental correlations with days to silking, days to anthesis, anthesis-silking interval while days to silking correlated negatively with plant height, ear height, ear diameter, stalk diameter, number of kernels per row and thousand kernel weight. Days to anthesis showed significantly negative correlations with plant height, ear height, stalk diameter, number of kernels per row and thousand kernel weights. Negative and highly significant environmental correlations were observed between anthesis-silking interval and plant height, ear height, ear length, ear diameter and number of kernels per row. Leaf area index in turn showed strong negative correlations with days to maturity. These strong correlation shows that some environmental variables which increase the expression of one trait could decrease the expression of another trait.
For most studied traits, the genotypic correlation coefficients were greater than the phenotypic correlation coefficients and the ratio of the two correlations (genotypic correlation to phenotypic correlation) were greater than the corresponding environmental correlation coefficients. This suggests that the apparent associations might be largely due to genetic causes and the environment played minimum role in the associations among the traits. The significant positive correlations between grain yield and other various yield-related traits indicate that improvement of these traits can contribute positively towards improved grain yield; and hence, can be considered when indirectly selecting for high grain yield in the process of breeding maize.

Tulu 014
Table 5. Phenotypic path analysis of the direct (bold) and indirect effects of 13 traits on grain yield in diallel crosses of QPM inbred lines
Traits
DS
DA
ASI
PH
EH
NNPP
IL
EL
ED
SDIA
KPR
TKW
LAI
rp
DS
-0.87
-0.10
-0.03
0.13
0.01
0.00
0.02
0.01
0.05
0.02
-0.03
-0.03
0.05
-0.29**
DA
-0.80
0.36
0.07
0.05
0.01
0.00
0.01
0.00
-0.02
0.02
-0.02
-0.03
0.05
-0.27**
ASI
-0.64
0.16
0.17
0.05
0.00
0.00
0.01
0.00
-0.01
0.02
-0.03
-0.01
0.03
-0.18*
PH
-0.21
0.08
0.04
0.22
0.02
0.00
0.03
0.01
0.02
0.05
0.09
0.02
0.05
0.58**
EH
-0.15
0.07
0.06
0.74
0.03
0.00
0.03
0.01
0.01
0.04
0.06
0.04
0.04
0.46**
NNPP
-0.03
0.02
0.01
0.07
0.01
0.00
0.01
0.00
0.00
0.02
0.04
0.03
0.01
0.23**
IL
-0.23
0.08
0.04
0.14
0.02
0.00
0.05
0.01
0.00
0.02
0.05
0.01
0.03
0.37**
EL
0.02
-0.01
0.01
0.09
0.01
0.00
0.02
0.02
0.02
0.04
0.14
0.00
0.03
0.45**
ED
0.13
-0.06
-0.01
0.03
0.00
0.00
0.00
0.00
0.15
0.02
0.04
0.03
0.03
0.36**
SDIA
-0.21
0.08
0.04
0.12
0.01
0.00
0.01
0.01
0.03
0.10
0.06
0.01
0.06
0.36**
KPR
0.12
-0.03
-0.02
0.11
0.01
0.00
0.01
0.02
0.03
0.03
0.19
-0.01
0.03
0.55**
TKW
0.16
-0.06
-0.01
0.04
0.01
0.00
0.00
0.00
0.03
0.01
-0.01
0.15
-0.03
0.24**
LAI
-0.31
0.15
0.03
0.08
0.01
0.00
0.01
0.01
0.03
0.04
0.05
-0.03
0.13
0.20*
ASI = anthesis-silking interval; DA = days to anthesis; DS = days to silking; ED = ear diameter; EH = ear height; EL = ear length; GY = grain yield; IL = internode length; KPR = number of kernels per row; LAI = leaf area index; NNPP = number of nodes per plant; PH = plant height; SDIA = stalk diameter; TKW = thousand kernel weight

Table 61. Genotypic path analysis of the direct (bold) and indirect effects of 8 traits on grain yield in diallel crosses of QPM inbred lines
Traits
PH
EH
NNPP
IL
EL
ED
SDIA
KPR
rg
PH
-2.17
4.85
-0.91
-1.58
-0.99
0.00
-1.38
2.34
0.59**
EH
-1.79
5.89
-1.16
-1.39
-0.82
0.00
-1.22
2.34
0.42**
NNPP
-1.01
3.51
-1.95
0.12
-0.43
-0.01
-0.40
0.54
0.34**
IL
-1.60
3.80
0.11
-2.15
-0.46
-0.02
-0.72
1.33
0.47**
EL
-1.51
3.41
-0.59
-0.70
-1.42
0.00
-1.46
1.86
0.50**
ED
-0.05
-0.6
0.29
0.43
0.02
0.09
-0.84
0.18
0.32**
SDIA
-1.46
3.48
-0.38
-0.75
-1.01
0.04
-2.06
1.71
0.39**
KPR
-1.76
1.55
-0.36
-0.84
-0.91
0.00
-1.22
2.89
0.57**
ED = ear diameter; EH = ear height; EL = ear length; IL = internode length; KPR = number of kernels per row; NNPP = number of nodes per plant; PH = plant height; SDIA = stalk diameter
Phenotypic path coefficient analysis
The phenotypic direct and indirect effects of yield-related traits on grain yield are presented in Table 5. Plant height, ear height, number of nodes per plant, internode length, ear length, ear diameter, stalk diameter, number of kernels per row, thousand kernel weight and leaf area index exerted positive direct effects on grain yield. These traits also showed positive and significant correlations with grain yield. Among these, plant height, number of kernels per row, thousand kernel weights, ear diameter and leaf area index exerted favorable direct effects on grain yield, indicating that improvement of these traits would contribute to increase grain yield keeping other variables constant. In line with the current study, Hadji (2004) found positive direct effects of ear height, ear length, ear diameter, number of kernels per row and thousand kernel weights on grain yield and also positive and significant correlations of these traits with grain yield.
Even though days to anthesis and anthesis-silking interval associated negatively and significantly with grain yield, these traits exerted positive direct effects on grain yield. The negative associations of days to anthesis and anthesis-silking interval with grain yield was attributed to the negative indirect effects of these traits through days to silking, ear diameter, number of kernels per row and thousand kernel weight. Under these circumstances, a restricted simultaneous selection model is to be followed, i.e. restrictions are to be imposed to nullify the undesirable indirect effects through days to silking, ear diameter, number of kernels per row and thousand kernel weight in order to make use of the direct effects (Singh and Kakar, 1977). Number of nodes per plant exerted negligible direct effects on grain yield but correlated positively and significantly with grain yield. Here, the indirect effects seem to be cause of correlation. In such situations the indirect causal factors are to be considered simultaneously for selection (Singh and Chaudary, 1977).
Genotypic path coefficient analysis
The genotypic direct and indirect effects of yield-related traits on grain yield are presented in Table 6. Plant height and ear length exerted negative direct effects on grain yield but exhibited positive correlation with grain yield due to their positive indirect effects through ear height and number of kernels per row. In contrast to the current study, Djordjevic and Ivanovic (1996) reported that plant height exerted positive direct effect and positive association with grain yield. Number of nodes per plant exerted negative direct effects but exhibited positive correlation with grain yield. The positive association of this trait with grain yield was due to its positive indirect effects through ear height, internode length and number of kernels per row. According to Singh and Chaudhary (1977), whenever a character had positive association and high positive indirect effects but negative direct effect, emphasis should be given to the indirect effects and thus, indirect causal factors are to be considered simultaneously for selection in this case. On the other hand, ear height, ear diameter and number of kernels per row exerted positive direct effect and also had positive association with grain yield. These traits could be used as a reliable indicator in indirect selection for higher grain yield since their direct effect and association with grain yield were positive. Djordjevic and Ivanovic (1996) reported similar results on the strong association of ear height with grain yield.
Ear height exerted the highest positive direct effects and also exhibited positive correlation with grain yield, implying that this trait can be used as a good indicator of increased grain yield in indirect selection. Contrary to this study, Hadji (2004) found ear height exerting the highest negative direct effect, even though it exhibited positive

Tulu 016
correlation with grain yield due to its positive indirect effects through number of kernels per row and ear diameter. Ear diameter exerted small positive direct effects on grain yield and correlated positively with grain yield and also exhibited positive indirect effects through number of nodes per plant, internode length, ear length and number of kernels per row. Since its direct effects on grain yield was very small, the indirect effects seem to be the cause of strong correlation. Thus, the indirect causal factors are to be considered simultaneously for selection in both cases (Singh and Chaudhary, 1977). CONCLUSIONS Analysis of variance indicated that mean squares due to entries and crosses were significant for most studied traits indicating the existence of high level of variability and the possibility of selection among the hybrids for grain yield and agronomic traits of interest. Significant differences were recorded for crosses versus checks for most traits studied; indicating the existence of variations between the mean performances of crosses and checks. Grain yield showed positive and significant phenotypic and genotypic associations with most traits studied. Traits having strong relationship with grain yield can be used for indirect selection to improve grain yield because grain yield can be simultaneously improved along with the traits for which it showed strong relationship. Similarly, grain yield was positively and significantly correlated with many other yield-related traits at environmental level, showing that any environment that favored the expression of grain yield also favored the expression of associated traits and vice versa. The phenotypic path coefficient analysis showed that plant height, ear height, number of nodes per plant, internode length, ear length, ear diameter, stalk diameter, number of kernels per row, thousand kernel weight and leaf area index exerted positive direct effects on grain yield and also correlated positively and significantly with grain yield. As these traits influenced grain yield favorably, selection for those traits will lead to the improvement of grain yield. At genotypic level ear height, ear diameter and number of kernels per row exerted positive direct effects and also had positive association with grain yield, indicating the possibility to increase grain yield through improving these traits. The significant positive correlations between grain yield and other various yield-related traits indicate that improvement of these traits can contribute positively towards improved grain yield; and hence, can be considered when indirectly selecting for high grain yield in the process of breeding maize. Whenever a character had positive association and high positive indirect effects but negative direct effects, emphasis should be given to the indirect effects and thus, indirect causal factors are to be considered simultaneously for selection. ACKNOWLEDGMENT I wish to thank Dr. Dagne Wegary for his genuine, consistent professional assistance and encouragement, valuable suggestion, comments, critical remarks and moral support throughout the study period which made the completion of this study smooth and successful. I really appreciate the willingness of Mr. Bulti Tesso in supporting me with the provision of statistical software for path coefficient analysis.
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Accepted 19 September, 2014.
Citation: Tulu BN (2014). Correlation and path
coefficients analysis studies among yield and yield
related traits of quality protein maize (QPM) inbred lines.
International Journal of Plant Breeding and Crop Science,
1(2): 006-017.
Copyright: © 2014 Tulu BN. 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.

Correlation and path coefficients analysis studies among yield and yield related traits of quality protein maize (QPM) inbred lines

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Correlation and path coefficients analysis studies among yield and yield related traits of quality protein maize (QPM) inbred lines