Correlation and Path Coefficient Analysis of Yield and

Research ArticleCorrelation and Path Coefficient Analysis of Yield and YieldComponents of Quality Protein Maize (Zea mays L) Hybrids atJimma Western Ethiopia

Jemal Aman 1 Kassahun Bantte2 Sentayehu Alamerew2 and Desta Berhe Sbhatu 3

1Ethiopian Sugar Corporation Research and Training Division Variety Development Research Directorate BiotechnologyResearch Team Wonji Research Center Wonji Ethiopia2College of Agriculture and Veterinary Medicine Jimma University Jimma Ethiopia3Mekelle Institute of Technology Mekelle University PO Box 1632 Mekelle Ethiopia

Correspondence should be addressed to Jemal Aman jemalbeshirgmailcom

Received 16 November 2019 Revised 16 March 2020 Accepted 25 March 2020 Published 8 June 2020

Academic Editor Cristina Patane

Copyright copy 2020 Jemal Aman et al )is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Maize is one of the most important staple food crops in many parts of Ethiopia However it is not used extensively due to its poornutritional quality and low productivity It lacks two essential amino acids namely lysine and tryptophan Knowledge of theinterrelationships of grain yield and its various causal (contributory) components is very helpful to improve the efficiency ofbreeding programs using appropriate selection indices )is article reports the findings of a study conducted to determine thenature of relationships of grain yield and its contributing components and to identify those components with significant effects onyield with the intention of using them as selection criteria using path coefficient analysis (PCA) )erefore PCA has shown thatyield per hectare had a significant and positive phenotypic correlation with plant height ear height number of kernels per rowand 100-grain weight Moreover PCA had a significant and positive genotypic correlation with days to 50 tasseling plant heightear height and 100-grain weight)e highest direct positive effect on yield per hectare was exhibited by ear height)e findings ofthis study showed that most genotypes are early maturing and are suitable for areas with short rainy seasons and prone to drought

1 Introduction

Zea mays L (Poaceae) is an important annual food crop ofthe world It is the source of primary staple food as well asprotein and calorie for millions of people in the world Maizeaccounts for about 15 to 56 of the total daily calories indiets of people in several developing countries in Africa andLatin America where animal protein is scarce and expensive[1] It is produced for food among low-income families inEthiopia and served in different dishes )ough severalhundred million people depend on maize its common(normal) variety lacks two essential amino acids namelylysine and tryptophan which are required in the biosyn-thesis of proteins [2] )erefore the discovery of the re-cessive allele of the opaque-2 maize gene was a significantbreakthrough in the alleviation of global protein deficiency

)e high level of lysine and tryptophan amino acids inthe maize endosperm protein is due to the presence of therecessive allele of the opaque-2 gene in the genome ofmutant maize [3] )is has created tremendous interest andenthusiasm in the scientific community for its potential indeveloping maize with superior protein quality Howeverthe gene was found to be closely associated with severalundesirable traits )e opaque-2 maize kernels were dull andchalky had 15 to 20 less grain weight and were moresusceptible to several diseases and insects [4] which led tothe loss of interest among scientists to work on it Afterseveral trials and systematic studies breeders succeeded infinding modifier genes that produce the desirable hardendosperm phenotype in materials containing the recessiveopaque-2 mutation )ese agronomically acceptable andnutritionally enhanced materials later came to be known as

HindawiInternational Journal of AgronomyVolume 2020 Article ID 9651537 7 pageshttpsdoiorg10115520209651537

quality protein maize (QPM) [2 5ndash7] QPM contains nearlytwice as much usable protein as other maize varieties grownin the tropics and yields 10 more grain than traditionalmaize varieties [2]

)e principal goal of maize breeding programs is todevelop new inbred and hybrid varieties that outperform theexisting varieties with respect to many traits In this pursuitspecial attention is given to grain yield as the most importantagronomic characteristic Grain yield is a complex quanti-tative trait affected by a number of factors )us theknowledge of interrelationships between grain yield and itscontributing components improves the efficiency ofbreeding programs through the use of appropriate selectionindices [8 9] Path coefficient analysis has been widely usedin crop breeding programs to determine the nature of re-lationships between grain yield and its contributing com-ponents and to identify the components with significanteffects on yield to be used as selection criteria Path analysisshows the direct and indirect effects of cause variables oneffect variables [10ndash12] According to this method thecorrelation coefficient between two traits is separated intothe components that measure the direct and indirect effects)is article reports the findings of a study that aimed atlooking into the phenotypic and genotypic correlationsbetween grain yield and other morphological traits andevaluating the direct and indirect effects of morphologicaltraits on grain yield

2 Materials and Methods

21 Description of the Study Area )e study was conductedbetween July and October 2009 at the Eladalle researchstation of the Jimma University College of Agriculture andVeterinary Medicine (alt 1722m lat 7deg33Prime0primeN and long36deg57Prime0primeE) )e mean maximum and minimum annualtemperatures of the Eladalle research station are 268degC and114degC respectively Likewise the mean maximum and theminimum annual relative humidity (RH) of the station are914 and 3992 respectively It also has a mean annualrainfall of 9515mm Its soil is reddish-brown clay soil withpH ranging from 507 to 60 [13]

22 Experimental Materials )e quality protein maize(QPM) hybrids used in this study were acquired fromCIMMYT )e hybrids include 43 three-way hybrids andtwo checks (one commercial and another local) Details ofmaterials are shown in Table 1

23 ExperimentalDesigns andProcedure )ematerials weresown in the alpha lattice (5times 9) with 5 plots per block withtwo replications in a 5 meters single row plot with thespacing of 075 meters between rows and 030 meters be-tween plants It may be argued that the number of repli-cations is small However the efficiency of alpha-latticedesign increases the precision of the experiment All agro-nomic practices including land preparation weeding andfertilization were applied to all plots as per the standardpractices for maize

24 Data Sources and Analyses Data for days to 50tasseling plant count and grain yield of the hybrids werecollected based on thewhole plot Likewise data for plant heightear height number of kernel-rows per ear and number ofkernels per row were taken based on five randomly selectedplants Finally 100-kernel weight was taken from compositeseeds of all the plants from the plots after removing the plants atthe ends of the rows )eir mean performances are given inTable 2 Phenotypic and genotypic correlations and path co-efficient analysis were analyzed using GENRES Version 701Phenotypic correlation (the observable correlation between twovariables that include genotypic and environmental compo-nents) and genotypic correlationwere computed usingGENRESusing the method described in Singh and Chaudhry [14] as

Table 1 List of QPM hybrids used in the study

Entry Name Origin1 CKH-08001 KB08B-0B20-122 CKH-08002 KB08B-0B20-21223 CKH-08003 KB08B-0B20-23244 CKH-08004 KB08B-0B20-27285 CKH-08005 KB08B-0B20-31326 CKH-08006 KB08B-0B20-33347 CKH-08007 KB08B-0B20-35368 CKH-08008 KB08B-0B20-39409 CKH-08009 KB08B-0B20-414210 CKH-08010 KB08B-0B20-454611 CKH-08011 KB08B-0B20-474812 CKH-08012 KB08B-0B20-495013 CKH-08013 KB08B-0B20-515214 CKH-08014 KB08B-0B20-555615 CKH-08015 KB08B-0B20-596016 CKH-08016 KB08B-0B20-616217 CKH-08017 KB08A-0A51-1218 CKH-08018 KB08A-0A51-3419 CKH-08019 KB08A-0A51-5620 CKH-08020 KB08A-0A51-91021 CKH-08021 KB08A-0A51-131422 CKH-08022 KB08A-0A51-151623 CKH-08023 KB08A-0A51-171824 CKH-08024 KB08A-0A51-192025 CKH-08025 KB08A-0A51-212226 CKH-08026 KB08A-0A51-293027 CKH-08027 KB08A-0A51-313228 CKH-08028 KB08A-0A51-353629 CKH-08029 KB08A-0A51-373830 CKH-08030 KB08A-0A51-434431 CKH-08031 KB08A-0A51-515232 CKH-08032 KB08A-0A51-535433 CKH-08033 KB08A-0A49-91034 CKH-08034 KB08A-0A49-171835 CKH-08035 KB08A-0A49-212236 CKH-08036 KB08A-0A49-232437 CKH-08037 KB08A-0A49-252638 CKH-08038 KB08A-0A49-414239 CKH-08039 KB08A-0A49-434440 CKH-08040 KB08A-0A49-272841 QPMHYB1 KB07B-0B37-1242 QPMHYB2 KB07B-0B35-1243 QPMHYB3 KB08B-0B20-717144 WH403 WS45 BH-660 ndash

2 International Journal of Agronomy

rp pcov x middot y

δ2px middot δ2py

1113969

rg gcov x middot y

δ2gx middot δ2gy

1113969

(1)

where rp and rg are phenotypic and genotypic correlationcoefficients respectively p cov xmiddoty and gcov x middot y are phe-notypic and genotypic covariances between variables x and yrespectively δ2px and δ2gx are phenotypic and genotypicvariances respectively for variable x and δ2py and δ2gy arephenotypic and genotypic variances respectively for variable y

Table 2 Mean performance of QPM hybrids evaluated at the College of Agriculture and Veterinary Medicine (Jimma University Ethiopia)for different characters

Entry Name TS PH EH PC NRE NKR HGWt Yield1 CKH-08001 8200ba 15680 5840l 1450cb 1400 2980 2405 2080h

2 CKH-08002 7900bndashg 17390 8480bndashk 1200bndashe 1300 3130 2715 3388cndashh

3 CKH-08003 7750bndashg 17420 6800ij 1250bndashe 1400 3030 2200 2603fndashh

4 CKH-08004 8050bndashd 18180 7980cndashk 1400bndashd 1480 2790 2725 2703fndashh

5 CKH-08005 7550cndashh 19810 9128bndashg 1500b 1420 3530 2650 4995bndashe

6 CKH-08006 7450fndashi 20630 9380bndashf 1400bndashd 1340 3220 2880 3500cndashh

7 CKH-08007 7350gndashi 20400 9680bndashd 1400bndashd 1680 3360 2285 3712cndashh

8 CKH-08008 7850bndashg 18810 8050cndashk 1500b 1480 3410 2825 3239cndashh

9 CKH-08009 7650bndashh 18550 8840bndashi 1250bndashe 1460 3360 2665 3986cndashh

10 CKH-08010 7900bndashg 17610 6810ij 1300bndashe 1425 3525 3395 3015endashh

11 CKH-08011 7850bndashg 17080 7020gndashj 1200bndashe 1420 3450 2725 2716fndashh

12 CKH-08012 7750bndashg 19280 8730bndashi 1250bndashe 1460 3650 2905 3936cndashh

13 CKH-08013 7600cndashh 18870 7750dndashj 1450bc 1360 3430 2955 3413cndashh

14 CKH-08014 7850bndashg 18790 8480bndashi 1300bndashe 1480 3030 2200 2417gh

15 CKH-08015 7700bndashg 19930 8820bndashi 1300bndashi 1420 3420 2820 3512cndashh

16 CKH-08016 7100hi 18420 7970cndashk 1450bc 1440 3280 2935 4223cndashh

17 CKH-08017 7600cndashh 18420 7730dndashj 1400bndashd 1480 3360 2875 3015endashh

18 CKH-08018 7800cndashg 17100 7080gndashj 1350bndashd 1540 2960 2405 2952endashh

19 CKH-08019 7000i 18930 8380bndashi 1300bndashe 1360 3410 2665 3076cndashh

20 CKH-08020 8000cndashf 19560 9480bndashe 1250bndashe 1520 3380 2705 4783bndashg

21 CKH-08021 7700bndashg 19270 7950cndashk 1450bc 1400 3380 2930 2777cndashh

22 CKH-08022 8100bndashd 18790 7940cndashk 1200bndashe 1500 3270 2600 3176cndashh

23 CKH-08023 7500cndashh 19670 9550bndashe 1400bndashe 1360 3450 3425 5318bc

24 CKH-08024 7650bndashh 20620 9990b 1250b 1440 3160 2820 4397bndashg

25 CKH-08025 7650bndashh 18780 8560bndashi 1500b 1460 3230 2750 2939endashh

26 CKH-08026 7650bndashh 19660 8940bndashh 1100bndashe 1480 2950 2340 3301cndashh

27 CKH-08027 7750bndashg 19940 8520bndashi 1450cb 1400 3470 2915 3787cndashh

28 CKH-08028 7600cndashh 19910 8830bndashi 1200bndashe 1400 3150 2485 3662cndashh

29 CKH-08029 8150andashc 18260 7620dndashj 1250bndashe 1440 3770 2665 3189cndashh

30 CKH-08030 8100bndashd 18470 7860dndashj 1250bndashe 1460 3540 2615 3787cndashh

31 CKH-08031 7750bndashg 18680 8120bndashi 1000e 1420 3400 2760 4771cndashg

32 CKH-08032 7700bndashg 19920 10150b 1350bndashd 1340 3190 3010 3588cndashh

33 CKH-08033 8050bndashd 16360 6830hndashj 1350bndashd 1360 3190 2630 2479gh

34 CKH-08034 7850bndashg 18800 8400bndashi 1300bndashe 1280 3230 2785 2641fndashh

35 CKH-08035 7550cndashh 20580 8210bndashi 1150cndashe 1360 3740 2800 3139cndashh

36 CKH-08036 7650bndashh 20260 9250bndashf 1450cb 1460 3090 2555 3338cndashh

37 CKH-08037 7800bndashg 17480 7350fndashj 1450cb 1480 2990 2330 2741endashh

38 CKH-08038 7450fndashi 19290 8810bndashi 1400bndashd 1560 3520 2990 5244bndashd

39 CKH-08039 7450fndashi 19150 7820bndashj 1250bndashe 1380 3340 2675 34384cndashh

40 CKH-08040 7350gndashi 20380 9580bndashd 1300cb 1420 3410 3300 6154b

41 QPMHYB1 7700bndashg 18730 7790dndashj 1400bndashd 1400 3230 2495 2603fndashh

52 QPMHYB2 7950bndashf 16870 7700dndashj 1400bndashd 1420 3430 2550 3064cndashh

43 QPMHYB3 7800cndashg 17750 7450endashj 1400bndashd 1440 3030 2890 2454gh

44 WH403 7750bndashg 18820 8390bndashi 1300bndashe 1440 3470 2845 4210bndashh

45 BH-660 8650a 26140 14720a 2400a mdash mdash 4290 10676a

Mean 7737 18967 8417 1352 1431 377 2764 3648CV () 307 862 1028 1216 678 970 1367 3026

LSD (005) 205 NS 1772 337 NS NS NS 2261NS nonsignificant TS days to 50 tasseling PH plant height EH ear height PC plant count NRE number of kernel-rows per ear NKR number of kernelsper row HGWt hundred-grain weight Means in the same column with the same letter are not statistically different at ple 005

International Journal of Agronomy 3

25 Path Coefficient Analysis Path coefficient analysis wascomputed as suggested by Dewey and Lu [15] by usinggenotypic correlation coefficients as

rij Pij + 1113944 rrkPkj (2)

where rij denotes the mutual association between the in-dependent character i (yield-related trait) and dependentcharacter j (grain yield) as measured by the genotypiccorrelation coefficients Pij refers to the components of directeffects of the independent character i on the dependentcharacter j as measured by the path coefficients and1113936 rik Pkj

refers to the summation of components of indirect effects ofa given independent character i on a given dependentcharacter j via all other independent characters k )econtribution of the remaining unknown characters ismeasured as the residual as given by

PR

1 minus 1113944 Pijrij1113872 1113873

1113969

(3)

3 Results and Discussion

31 Phenotypic and Genotypic Correlations Phenotypic andgenotypic correlations among the traits considered in thestudy are presented in Table 3 In the following the im-plications of the data generated by the analyses are provided

32 Phenotypic Correlation Study of the values of thephenotypic correlation coefficients indicated in Table 3 belowthe diagonal line shows that grain yield per hectare gave apositive phenotypic correlation with all traits except days to50 tasseling and plant count Grain yield has the higheststatistically significant correlations with plant height(rp 0697 ple 001) followed by number of kernels per row(rp 0626 ple 001) ear height (rp 0440 ple 005) and100-grain weight (rp 0436 ple 005) Some researchersreported similar observations eg [11 16ndash22] )e re-searchers observed that plant height ear aspect ear height earlength grains per row and 100-grain weight or grain yieldwere positively and significantly inter-correlated implyingthat hybrids with these traits possess high yield potential

)e highest positive phenotypic correlation was observedbetween plant height and ear height (rp 0788 ple 001)Similar observation was reported by several researchers[19ndash23] )ese observations indicate that improvements ineach of the traits would lead to overall improvements of thegenotypes Such correlations help in making reasonable de-cisions in selecting traits controlled by multiple genes Grainyield as a quantitative trait is polygenically controlled [24])ese findings imply that effective yield improvement de-pends on simultaneous improvements in all yield compo-nents In fact selection efforts based on grain yield alone areoften less effective and efficient [9] Selections need to bemade based on various traits of the crop at hand

33 Genotypic Correlation Observation of genotypic cor-relation coefficients shows that all traits examined in ourstudy have a positive correlation with yield per hectare

except plant count and number of kernel-rows per ear(Table 3) Traits that showed high genotypic correlationswith grain yield per hectare are plant height (rg 0873ple 001) ear height (rg 0698 ple 001) days to 50tasseling (rg 0585 ple 001) and 100-grain weight(rg 0506 ple 001) Similar findings were reported else-where [9 24 25] On the contrary significant and negativegenotypic correlation was observed between yield perhectare and number of kernel-rows per ear (rg 0744ple 001) )is finding is contrary to the findings of someresearchers [26 27] )e fact that the higher number ofkernel-rows per ear does not correlate with yield may implythat the kernels are smaller and lighter It is helpful to notethat the number of rows per ear and the number of grainsper row of the local check were not studied

In general genotypic correlations among traits affectinggrain yield explain true association as they exclude theenvironmental influences It can be suggested that im-provements in grain yield of maize can be accomplishedthrough selections based on these correlations Henceknowledge of associations between yield and its componenttraits as well as among the component traits themselves canpromote the efficiency of selection in maize breeding pro-grams In fact it is well established that correlation studiesbetween yield and yield components are pre-requisite inplanning effective breeding programs )e same is true withmaize breeders [27] Quantitative traits like grain yieldexpress themselves in close association with many othertraits Change in the expression of one trait is usually as-sociated with changes in the expression of many other traits)erefore the correlations obtained in the present study areuseful in the selection of traits having direct and significantcorrelation in improving grain yield

34 Path Coefficient Analyses Results of path coefficientanalysis of all other traits to grain yield per hectare are givenin Table 4 and Figure 1 )e results of path coefficientanalysis revealed that all the characters studied except plantheight plant count and number of kernel-rows per ear hadpositive direct effects on grain yield )e highest directpositive effect on yield per hectare was exhibited by earheight (06514) )is implies that higher ear height leads toincreased grain yield the genotypic correlation between earheight and grain yield (rg 0698 ple 001) is predomi-nately attributed to the direct effect (rg 0651 ple 001) ofear height on the grain yield per hectare (Figure 1) Manyresearch findings were in line with this finding [12 28 29]Similarly it is also in agreement with the findings of Asrar-ur-Rehman et al [23] and Bello et al [24] )ese researchersreported positive and significant direct effects of ear lengthand thousand-kernel weight on grain yield However thefinding of the present study contradicts with the findings ofRafiq et al [25]

Days to 50 tasseling has yielded the next highest anddirect effect on grain yield (0245) It is stated above that thegenotypic correlation between the traits is positive andstatistically significant (rg 0585 ple 001))e correlationexplains the true relationship between the two traits thus


Table 3 Genotypic and phenotypic correlation coefficients among the traits

Traits TS PH EH PC NRE NKR HGWt YieldTS mdash ndash0490 ndash0176 ndash0120 ndash0204 ndash0347 0334lowast 0585lowastlowastPH ndash0274 mdash 0579lowastlowast 0412lowast ndash0218 0374lowast 0205 0873lowastlowastEH ndash0331 0786lowastlowast mdash ndash0094 0286 ndash0079lowast ndash0140 0698lowastlowastPC ndash0130 ndash0164 ndash0031 mdash 0683lowastlowast 0321 0789lowastlowast 0242NRE ndash0140 0146 ndash0074 ndash0483lowast mdash ndash0698 ndash0613lowastlowast ndash0744lowastlowastNKR ndash0151 0546lowastlowast 0481lowast ndash0088 0405lowast mdash 0719lowastlowast ndash0233HGWt ndash0363 0162 0210 0166 ndash0085 0280 mdash 0506lowastlowastYield ndash0244 0697lowastlowast 0448lowast ndash0040 0323 0626lowastlowast 0626lowastlowast mdashlowastStatistically significant correlation at ple 005 lowastlowaststatistically significant correlation at ple 001 TS days to 50 tasseling PH plant height EH ear height PCplant count NRE number of kernel-rows per ear NKR number of kernels per row HGWt hundred-grain weight

Table 4 Direct (boldface) and indirect effects of different traits on grain yield

Traits TS PH EH PC NRE NKR HGWt rg

TS ndash0023 ndash0013 0074 0330 0262 0113 0161 0585lowastlowastPH 0099 ndash0213 0930 0530 0396 0506 0594 0873lowastlowastEH 0070 ndash0372 0700 0570 0099 ndash0217 0095 0698lowastlowastPC 0212 ndash0367 0618 ndash0041 0110 ndash0230 0095 ndash0242NRE ndash0208 0308 ndash0526 0183 0205 0222 ndash0099 ndash0744lowastlowastNKR ndash0159 0306 ndash0580 0192 ndash0111 0176 ndash0078 0233HGWt 0055 ndash0104 0453 ndash0117 0120 ndash0057 0338 0506lowastlowast

Residual 0204 TS days to 50 tasseling PH plant height EH ear height PC plant count NRE number of kernel-rows per ear NKR number of kernels perrow HGWt hundred-grain weight

Residual effect 0204

0660

ndash0050ndash00200070

0270ndash0050

ndash0010ndash040002000020

ndash0070ndash0020ndash02100370 ndash0270

ndash00700060015002100100ndash0400

013702430122ndash02030651ndash04240245

PC NRE NRK HGWtEHPHTS

Grain yield

Figure 1 Average genotypic path coefficient diagram representing cause and effect relationships among quantitative traits and grain yield(TS days to 50 tasseling PH plant height EH ear height PC plant count NRE number of kernel-rows per ear NKR number of kernelsper row HGWt hundred-grain weight)


selection based on days to 50 tasseling would be effective)is is in contrast with the findings of Arsode et al [28] whoreported significant negative association of days to 50tasseling with grain yield per plant and plant height earlength ear girth number of kernel-rows per ear andnumber of kernels per row

Plant count showed negative direct effect on grain yield(minus0041) like its genotypic correlation with grain yield(minus0242) implying that there is true association of the twotraits However plant count yielded high positive indirecteffect on grain yield through ear height (0570) Likewisenumber of kernel-rows per ear and number of kernels perrow showed higher positive indirect effect on grain yieldthrough plant height )ese results are in line with thereports of different authors [10ndash12 30] )e negative andsignificant genotypic correlation between number ofkernel-rows per ear and grain yield (rg minus0744 ple 001)explains the true relationship of the traits Plant heightresulted in high and negative direct effect on grain yield(minus0424) Other researchers have reported similar findings[25 32] However we observed highest and significantgenotypic correlation between plant height and grain yield(rg 0873 ple 001) )is may be due to the indirecteffect of plant height on ear height )us we recommendthat selection based on plant height is made cautiouslyWe also observed that 100-grain weight had positive andsignificant direct effect on the grain yield as indicated inTable 4 (0338 rg 0506 ple 001) Other studies re-ported similar results [33 34] )e correlations and inter-correlations show that the seven causal traits (ie thecausal variables) explain much of the variability in grainyield In fact a residual effect of 0204 (Figure 1) impliesthat the causal traits explained about 796 of the vari-ability in the grain yield leaving 204 of the variabilityunexplained

4 Concluding Remarks

Genotypic correlation coefficients showed that all the traitsconsidered in our study have positive correlation with yieldper hectare except plant count and number of kernel-rowsper ear Plant height ear height days to 50 tasseling and100-grain weight showed high genotypic correlations withgrain yield per hectare Genotypic correlations among traitsaffecting grain yield explain the true association as theyexclude any environmental influences Hence it can beconcluded that plant height ear height days to 50tasseling and 100-grain weight are the best traits for se-lection to improve grain yield per hectare of the maizegenotypes tested in our study

Data Availability

Data used in preparing this manuscript is available from thecorresponding author on reasonable request

Conflicts of Interest

)e authors declare that they have no conflicts of interest

Acknowledgments

)e authors acknowledge the Rural Capacity BuildingProject of Oromia State of Ethiopia for funding the research

References

[1] B M Prasanna S K Vasal B Kassahun and N N SinghldquoQuality protein maize Review articlerdquo Current Sciencevol 81 no 10 pp 1308ndash1319 2001

[2] S K Vasal ldquoQuality protein maize overcoming the hurdlesrdquoin Food Systems for Improved Human Nutrition LinkingAgriculture Nutrition and Productivity P K Kataki andS C Babu Eds pp 193ndash227 Food Products Press an Imprintof the Haworth Press Inc Philadelphia PA USA 2002

[3] B C Gibbon and B A Larkins ldquoMolecular genetic ap-proaches to developing quality protein maizerdquo Trends inGenetics vol 21 no 4 pp 227ndash233 2005

[4] M D Ignjatovic G Stankovic K Markovic V Lazic-Jancicand M Denic ldquoQuality protein maize (QPM)rdquo Genetikavol 40 no 30 pp 205ndash214 2008

[5] K Dreher M Morris M Khairallah J M Ribaut S Pandeyand G Srinivasan ldquoIs marker-assisted selection cost-effectivecompared to conventional plant breeding methods )e caseof quality protein maizerdquo in Proceedings of the 4th AnnualConference of the International Consortium on AgriculturalBiotech Research the Economics of Agricultural BiotechnologyRavello Italy August 2000

[6] M Bjarnason and S K Vasal ldquoBreeding of quality proteinmaizerdquo Plant Breeding Review vol 9 pp 181ndash216 1992

[7] S K Vasal E Villegas C Y Tang J Werder and M ReadldquoCombined use of two genetic systems in the developmentand improvement of quality protein maizerdquo Kulturpflanzevol 32 pp 171ndash185 1984

[8] N Belay ldquoGenetic variability heritability correlation andpath coefficient analysis for grain yield and yield componentin maize (Zea mays L) hybridsrdquoAdvances in Crop Science andTechnology vol 6 p 399 2018

[9] S A Mohammadi B M Prasanna and N N Singh ldquoSe-quential path model for determining interrelationshipsamong grain yield and related characters in maizerdquo CropScience vol 43 no 5 pp 1690ndash1697 2003

[10] J Tadesse and T Leta ldquoAssociation and path coefficientanalysis among grain yield and related traits in Ethiopianmaize (Zea mays L) inbred linesrdquo African Journal of PlantScience vol 13 no 9 pp 264ndash272 2019

[11] M K Shengu ldquoPath coefficient analysis of early maturingmaize (Zea mays) inbred lines in central rift valley ofEthiopiardquo Plant vol 5 no 3 pp 47ndash50 2017

[12] M Muneeb S Muhammad H Ghazanfar and M YasirldquoCorrelation and path analysis of grain yield components inexotic maize (Zea mays L) hybridsrdquo International Journal ofSciences Basic and Applied Research (IJSBAR) vol 12 no 1pp 22ndash27 2013

[13] BPEDORS [Bureau of Planning and Economic Developmentof Oromia Regional State] Physical and Socioeconomic Profileof 180 Districts of Oromia Region Oromia State PhysicalPlanning Development Finfinne Ethiopia 2000

[14] R K Singh and B D Chaudhry Biometrical Methods inQuantitative Genetic Analysis Kalyani Publishers New DelhiIndia 1985

[15] D R Dewey and K H Lu ldquoA correlation and path-coefficientanalysis of components of crested wheatgrass seed produc-tionrdquo Agronomy Journal vol 51 no 9 pp 515ndash518 1959


[16] A Izzam H Rehman A Sohail S Ali H Manzoor andQ Hussain ldquoGenetic variability and correlation studies formorphological and yield traits in maize (Zea mays L)rdquo Pureand Applied Biology vol 6 no 4 2017

[17] G Singh R Kumar and Jasmine ldquoGenetic parameters andcharacter association study for yield traits in maize (Zea maysL)rdquo Journal of Pharmacognosy and Phytochemistry vol 6no 5 pp 808ndash813 2017

[18] G B Adu R Akromah M S Abdulai R Akromah et alldquoTrait association for improved grain yield of extra-earlymaturing maize hybrids evaluated in the forest and transi-tional zones of Ghanardquo Australian Journal of Crop Sciencevol 10 no 8 pp 1127ndash1135 2016

[19] M Filipovic M Babic N Delic G Bekavac and V BabicldquoDetermination relevant breeding criteria by the path andfactor analysis in maizerdquo Genetika vol 46 no 1 pp 49ndash582014

[20] M A B Fakorede A Oluwaranti B Badu-Apraku andA Menkir ldquoTrait association of maize varieties in contrastingseasons in the rainforest of southwest Nigeriardquo African CropScience Conference Proceedings vol 10 pp 541ndash544 2011

[21] S K Khavari Khorasani M Kh E Zandipour andA Heidarian ldquoMultivariate analysis of agronomic traits ofnew corn hybrids (Zea mays L)rdquo International Journal ofAgriScience vol 1 no 6 pp 314ndash322 2011

[22] B M Ashofteh B A SiahSar S Khavari M GolbashyN N Nejad and A Alizade ldquoEffects of genotype by envi-ronment interactions on morphological traits yield and yieldcomponents of new grain corn (Zea mays L) varietiesrdquoAgroecology vol 2 no 1 pp 151ndash161 2010

[23] S Asrar-ur-Rehman U Saleem and G M Subhani ldquoCor-relation and path coefficient analysis in maize (Zea mays L)rdquoJournal of Agricultural Science vol 45 no 3 pp 177ndash1832007

[24] O B Bello S Y Abdulmaliq M S Afolabi and S A IgeldquoCorrelation and path coefficient analysis of yield and ag-ronomic characters among open pollinated maize varietiesand their F1 hybrids in a diallel crossrdquo African Journal ofBiotechnology vol 9 no 18 pp 2633ndash2639 2010

[25] C M Rafiq M Rafique A Hussain andM Altaf ldquoStudies onheritability correlation and path analysis in maize (Zea maysL)rdquo Journal of Agricultural Research vol 48 no 1 pp 35ndash382010

[26] S E Sadek M A Ahmed and H M Abd El-GhaneyldquoCorrelation and path coefficient analysis in five parentsinbred lines and their six white maize (Zea mays L) singlecrosses developed and grown in Egyptrdquo Journal of AppliedScience Research vol 2 no 3 pp 159ndash167 2006

[27] A A Wannows H K Azzam and S A Al-Ahmad ldquoGeneticvariances heritability correlation and path coefficient anal-ysis in yellow maize crosses (Zea mays L)rdquo Agriculture andBiology Journal of North America vol 1 no 4 pp 630ndash6372010

[28] P Arsode M Krishna N Sunil and S Vani Sree ldquoCorre-lation studies for grain yield and its components in hybrids ofquality protein maize (Zea mays l)rdquo Be Journal of ResearchPJTSAU vol 46 no 2-3 pp 85ndash88 2018

[29] R Kumar R B Dubey K D Ameta R Kunwar R Vermaand P Bisen ldquoCorrelation and path coefficient analysis foryield contributing and quality traits in quality protein maize(Zea mays L)rdquo International Journal of Current Microbiologyand Applied Sciences vol 6 no 10 pp 2139ndash2146 2017

[30] H Kinfe G Alemayehu L Wolde and Y Tsehaye ldquoCor-relation and path coefficient analysis of grain yield and yield

related traits in maize (Zea mays L) hybrids at BakoEthiopiardquo Journal of Biology Agriculture and Healthcarevol 5 no 15 pp 44ndash53 2015

[31] D O Olawamide and L S Fayeun ldquoCorrelation and pathcoefficient analysis for yield and yield components in latematuring pro-vitamin A synthetic maize (Zea mays L)breeding linesrdquo Journal of Experimental Agriculture Inter-national vol 42 no 1 pp 64ndash72 2020

[32] D K Parh M A Hossain and M J Uddin ldquoCorrelation andpath coefficient analysis in open pollinated maize (Zea maysL)rdquo Bangladesh Journal of Agriculture vol 11 pp 11ndash141986

[33] G Beulah S Marker and D Rajasekhar ldquoAssessment ofquantitative genetic variability and character association inmaize (Zea mays L)rdquo Journal of Pharmacogency and Phy-tochemistry vol 7 no 1 pp 2813ndash2816 2017

[34] D S Jakhar R Singh and A Kumar ldquoStudies on path co-efficient analysis in maize (Zea mays L) for grain yield and itsattributesrdquo International Journal of Current Microbiology andApplied Science vol 6 no 4 pp 2851ndash2856 2017


quality protein maize (QPM) [2 5ndash7] QPM contains nearlytwice as much usable protein as other maize varieties grownin the tropics and yields 10 more grain than traditionalmaize varieties [2]

)e principal goal of maize breeding programs is todevelop new inbred and hybrid varieties that outperform theexisting varieties with respect to many traits In this pursuitspecial attention is given to grain yield as the most importantagronomic characteristic Grain yield is a complex quanti-tative trait affected by a number of factors )us theknowledge of interrelationships between grain yield and itscontributing components improves the efficiency ofbreeding programs through the use of appropriate selectionindices [8 9] Path coefficient analysis has been widely usedin crop breeding programs to determine the nature of re-lationships between grain yield and its contributing com-ponents and to identify the components with significanteffects on yield to be used as selection criteria Path analysisshows the direct and indirect effects of cause variables oneffect variables [10ndash12] According to this method thecorrelation coefficient between two traits is separated intothe components that measure the direct and indirect effects)is article reports the findings of a study that aimed atlooking into the phenotypic and genotypic correlationsbetween grain yield and other morphological traits andevaluating the direct and indirect effects of morphologicaltraits on grain yield

2 Materials and Methods

21 Description of the Study Area )e study was conductedbetween July and October 2009 at the Eladalle researchstation of the Jimma University College of Agriculture andVeterinary Medicine (alt 1722m lat 7deg33Prime0primeN and long36deg57Prime0primeE) )e mean maximum and minimum annualtemperatures of the Eladalle research station are 268degC and114degC respectively Likewise the mean maximum and theminimum annual relative humidity (RH) of the station are914 and 3992 respectively It also has a mean annualrainfall of 9515mm Its soil is reddish-brown clay soil withpH ranging from 507 to 60 [13]

22 Experimental Materials )e quality protein maize(QPM) hybrids used in this study were acquired fromCIMMYT )e hybrids include 43 three-way hybrids andtwo checks (one commercial and another local) Details ofmaterials are shown in Table 1

23 ExperimentalDesigns andProcedure )ematerials weresown in the alpha lattice (5times 9) with 5 plots per block withtwo replications in a 5 meters single row plot with thespacing of 075 meters between rows and 030 meters be-tween plants It may be argued that the number of repli-cations is small However the efficiency of alpha-latticedesign increases the precision of the experiment All agro-nomic practices including land preparation weeding andfertilization were applied to all plots as per the standardpractices for maize

24 Data Sources and Analyses Data for days to 50tasseling plant count and grain yield of the hybrids werecollected based on thewhole plot Likewise data for plant heightear height number of kernel-rows per ear and number ofkernels per row were taken based on five randomly selectedplants Finally 100-kernel weight was taken from compositeseeds of all the plants from the plots after removing the plants atthe ends of the rows )eir mean performances are given inTable 2 Phenotypic and genotypic correlations and path co-efficient analysis were analyzed using GENRES Version 701Phenotypic correlation (the observable correlation between twovariables that include genotypic and environmental compo-nents) and genotypic correlationwere computed usingGENRESusing the method described in Singh and Chaudhry [14] as

Table 1 List of QPM hybrids used in the study

Entry Name Origin1 CKH-08001 KB08B-0B20-122 CKH-08002 KB08B-0B20-21223 CKH-08003 KB08B-0B20-23244 CKH-08004 KB08B-0B20-27285 CKH-08005 KB08B-0B20-31326 CKH-08006 KB08B-0B20-33347 CKH-08007 KB08B-0B20-35368 CKH-08008 KB08B-0B20-39409 CKH-08009 KB08B-0B20-414210 CKH-08010 KB08B-0B20-454611 CKH-08011 KB08B-0B20-474812 CKH-08012 KB08B-0B20-495013 CKH-08013 KB08B-0B20-515214 CKH-08014 KB08B-0B20-555615 CKH-08015 KB08B-0B20-596016 CKH-08016 KB08B-0B20-616217 CKH-08017 KB08A-0A51-1218 CKH-08018 KB08A-0A51-3419 CKH-08019 KB08A-0A51-5620 CKH-08020 KB08A-0A51-91021 CKH-08021 KB08A-0A51-131422 CKH-08022 KB08A-0A51-151623 CKH-08023 KB08A-0A51-171824 CKH-08024 KB08A-0A51-192025 CKH-08025 KB08A-0A51-212226 CKH-08026 KB08A-0A51-293027 CKH-08027 KB08A-0A51-313228 CKH-08028 KB08A-0A51-353629 CKH-08029 KB08A-0A51-373830 CKH-08030 KB08A-0A51-434431 CKH-08031 KB08A-0A51-515232 CKH-08032 KB08A-0A51-535433 CKH-08033 KB08A-0A49-91034 CKH-08034 KB08A-0A49-171835 CKH-08035 KB08A-0A49-212236 CKH-08036 KB08A-0A49-232437 CKH-08037 KB08A-0A49-252638 CKH-08038 KB08A-0A49-414239 CKH-08039 KB08A-0A49-434440 CKH-08040 KB08A-0A49-272841 QPMHYB1 KB07B-0B37-1242 QPMHYB2 KB07B-0B35-1243 QPMHYB3 KB08B-0B20-717144 WH403 WS45 BH-660 ndash


rp pcov x middot y

δ2px middot δ2py

1113969

rg gcov x middot y

δ2gx middot δ2gy

1113969

(1)
















































Mean 7737 18967 8417 1352 1431 377 2764 3648CV () 307 862 1028 1216 678 970 1367 3026







PR

1 minus 1113944 Pijrij1113872 1113873

1113969

(3)


















0660


0270ndash0050

ndash0010ndash040002000020


ndash00700060015002100100ndash0400

013702430122ndash02030651ndash04240245


Grain yield







Data Availability




Acknowledgments


References






































rp pcov x middot y

δ2px middot δ2py

1113969

rg gcov x middot y

δ2gx middot δ2gy

1113969

(1)
















































Mean 7737 18967 8417 1352 1431 377 2764 3648CV () 307 862 1028 1216 678 970 1367 3026







PR

1 minus 1113944 Pijrij1113872 1113873

1113969

(3)


















0660


0270ndash0050

ndash0010ndash040002000020


ndash00700060015002100100ndash0400

013702430122ndash02030651ndash04240245


Grain yield







Data Availability




Acknowledgments


References










































PR

1 minus 1113944 Pijrij1113872 1113873

1113969

(3)


















0660


0270ndash0050

ndash0010ndash040002000020


ndash00700060015002100100ndash0400

013702430122ndash02030651ndash04240245


Grain yield







Data Availability




Acknowledgments


References













































0660


0270ndash0050

ndash0010ndash040002000020


ndash00700060015002100100ndash0400

013702430122ndash02030651ndash04240245


Grain yield







Data Availability




Acknowledgments


References










































Data Availability




Acknowledgments


References



























































Documents

Correlation and Path Coefficient Analysis of Yield and