







































 

 

 
8 

© 2024 Conscientia Beam. All Rights Reserved. 

Combining ability of maize (Zea mays l.) inbred lines adapted to sub-humid central highland 
of Ethiopia   

 

 

 Takele Mitiku1+ 
Gudeta Nepir2 
Demoz Negera3 

 

1Bako National Maize Research Center, Ethiopia. 
Email: takelemitku202@gmail.com  
2Ambo University Guder Compus, Ethiopia. 
Email: gudetangt@gmail.com  
3Ambo Agricultural Research Center, Ethiopia. 
Email: dworeti@gmail.com  

 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 26 March 2024 
Revised: 20 May 2024 
Accepted: 31 May 2024 
Published: 5 June 2024 
 

Keywords 
Additive gene type 
Conventional maize 
Dominant gene type 
General combiner 
Inbred line 
Recessive gene 
Specific combiner. 

 
The production of maize, a primary food crop in Africa, is hampered by a lack of high-

yielding cultivars, biotic and abiotic stressors. In order to create high-yielding maize 
varieties, choosing promising germ plasm that have good combining ability, and 
heterotic groups is so important. Estimating the GCA and SCA of grain yield and other 
yield-related traits of maize inbred lines was the goal of this work.  In 2019 using line 
by tester mating design, 26 inbred lines were crossed with two testers to produce 52 F1 
hybrids. The experiment was carried out in the Ambo and Kulumsa Agricultural 
Research Centres in the 2020 cropping season using an alpha lattice design with two 
replications. Analysis of variance revealed significant mean squares attributable to 
crosses and lines in each location and across locations for targeted traits. The finding of 
the line x tester ANOVA showed significant mean squares for lines, testers, and cross 
for grain yield, anthesis date, silking date, plant height, ear height, kernel row per ear, 
and number of ears per plant. This show inconsistency of inbred lines traits across 
testing sites. While crossings L1xT2, L17xT1, L8xT1, L10xT1, L14xT1, L20xT2, 
L24xT2, and L26xT1 were effective particular combiners, inbred L2, L4, L5, L7, L10, 
and L13 were strong general combiners for grain yield. The best particular combiners 
for plant and ear height traits were L11xT1, L17xT1, and L11xT1, L26xT2, while 
L14, L21, and L22 worked well for EPP and L14 for KPR with T1.  
 

Contribution/Originality: The originality of this study is to demonstrate potential genetic transfer of traits 

from parents to offspring. This finding contribute the improvement of hybrid parental line’s genetic variability so 

that enhance heterosis and yield potential. 

 

1. INTRODUCTION 

Maize (Zea mays L., 2n=20) is a significant cereal crop that is a member of the Maydeae tribe and the Poaceae 

grass family, Genus Zea, Species mays. It is the most frequently cultivated cereal grain in the world and the main 

staple meal in many poor nations. The world produced 1,124 MT of maize during the 2018/2019 cropping season, 

with the United States producing 366.287 MT, China 257.330 MT, Brazil 94.500 MT, and the European Union 

83.185 MT [1].  

In Africa, Egypt (5.45 t ha-1), South Africa (5.45 tha-1) and Ethiopia (3.74 t ha-1) are the top three maize 

producers in 2020/2021 cropping season. However, report revealed that, in Ethiopia, maize productivity is 4.18 tha-

Current Research in Agricultural Sciences 
2024 Vol. 11, No. 1, pp. 8-18 
ISSN(e): 2312-6418 
ISSN(p): 2313-3716 
DOI: 10.18488/cras.v11i1.3771 
© 2024 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 
 

 

 
 
 
 

https://orcid.org/0000-0002-8657-8879
mailto:takelemitku202@gmail.com
mailto:gudetangt@gmail.com
mailto:dworeti@gmail.com
https://www.doi.org/10.18488/cras.v11i1.3771


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9 

© 2024 Conscientia Beam. All Rights Reserved. 

1 which is relatively higher than report, though both reports indicate that maize productivity is still lower than the 

world maize average (5.65 t ha-1) in Ethiopia. 

With 10.56 million tonnes produced, maize is the most productive cereal crop in Ethiopia, followed by tef (5.51 

million tonnes) and wheat (5.78 million tonnes). Maize is a major crop for Ethiopian farmers' overall food security 

and economic development because 75% of all maize production is used by farming households [2]. In Ethiopia, 

maize is consumed by each person on an annual basis in amounts of 50 kilogrammes [3]. The most significant 

staple crop for the rural Ethiopian population in terms of caloric intake is maize [4].  According to CSA data, of the 

total grain crop area in Ethiopia for the 2019–20 main cropping season, 81.46% (10,478,218.03 hectares) were under 

cereals. The grain crop area was divided between teff, maize, sorghum, and wheat, which accounted for 24.11% (or 

roughly 3,101,177.38 hectares), 17.68% (or roughly 2,274,305.93 hectares), 14.21% (1,828,182.49 hectares), and 

13.91% (or roughly 1,789,372.23 hectares) of the total.  The total amount of maize produced in Ethiopia during the 

2018/19 crop season was 8.6 MT, with an average yield of 3.7 t ha-1 (FAOSTAT, 2018/19). Cereals made up 

88.52% (29,672,647,694 tonnes) of the grain yield production on an annual basis. In that order, the grain production 

was made up of 28.75% (9,635,734.5 tonnes) of maize, 17.11% (5,735,710.187 tonnes) of teff, 15.86% (5,315,270.328 

tonnes) of wheat, and 15.71% (5,265,580.059 tonnes) of sorghum [5].  

Low yields in Ethiopia's maize agroecology are caused by unimproved varieties combined with biotic problems 

like turcicum leaf blight, common leaf rust, stalk lodging, stalk borers, and storage pests, as well as abiotic stresses 

like frost, hailstorm, and low soil fertility [6].  

Future crop productivity is significantly at risk from climate change due to rising temperatures, changing 

rainfall patterns, and increased pest and disease pressures [7]. In the past forty years, drought and famine have 

both posed a danger to Ethiopia's maize production [8]. In comparison to the average yield per hectare for the 

world (5.2 t/ha) and that of industrialised nations (7.2 t/ha), Ethiopia's average national maize productivity is 

extremely low [9].  The most significant abiotic factors that have an impact on maize productivity are drought and 

low soil fertility [10-12]. 

It is absolutely necessary to improve germplasm and continue intensification to raise and stabilise yields in 

order to narrow production gaps in order to lessen these important issues Foley, et al. [13]. Legesse, et al. [14] 

also found that to adapt to the constantly shifting environmental conditions and growing population pressure, 

maize breeding programmes must incorporate genetically variable germplasm. 

Combining ability investigations show the kind and extent of different types of gene activity involved in the 

development of quantitative traits, which is helpful information for choosing ideal parents for successful 

hybridization programmes. Crop breeders typically employ combining ability analysis to choose parents with high 

general combining ability (GCA) and hybrids with high specific combining ability (SCA) effects. Combining ability 

analysis is a key technique for understanding gene activities. Understanding the genetic potential of a population 

and choosing the breeding strategy to be used in a particular population depend on knowledge of the nature and 

amount of gene action. 

 

2. MATERIALS AND METHODS 

2.1. Description of Experimental Site 

The main cropping season of 2020 was used to perform this study at the Kulumsa and Ambo Agriculture 

Research Centres in Ethiopia's central highland agroecology.  The Ambo Agriculture Research Centre is situated at 

an altitude of 2225 masl at 8 57 N latitude and 38 07 E longitude. The majority of the topsoil (0–30 cm) has a pH of 

7.8 and is a heavy clay soil type (vertisoils) [15]. The average minimum and maximum temperatures are 11.7 °C 

and 25.5 °C, respectively, with an average value of 18.6 °C. The long-term total annual rainfall is 1115 mm. At a 

height of 2200 masl, Kulumsa is situated at 8° 5' N latitude and 39° 10' E longitude. Luvisol/eutric nitosols, with 



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good drainage and pH, are the most common form of soil. The total long-term annual rainfall is 830 mm. The mean 

minimum and maximum temperatures are 10 oC and 23.2 oC, respectively with an average value of 16.6 oC. 

 

2.2. Experimental Materials 

Twenty-six maize inbred lines and two single cross testers (Table 1) were used in this study. The materials 

were developed at Ambo highland maize breeding program. The inbred lines were test crossed to two single cross 

testers following line × tester mating design as described by Kempthorne [16] during the main season of 2019 at 

Ambo Agricultural research center to generate 52 F1 hybrids. 

 

Table 1. List of lines and testers used in the experiment. 

S/N Coded parent lines Type of materials Generation Source 

1 CEL17289 Inbred lines S5 CIMMYT 
2 CEL17295 Inbred lines S5 CIMMYT 
3 CEL17298 Inbred lines S5 CIMMYT 
4 CEL17301 Inbred lines S5 CIMMYT 
5 CEL17310 Inbred lines S5 CIMMYT 
6 CEL17312 Inbred lines S5 CIMMYT 
7 CEL17314 Inbred lines S5 CIMMYT 
8 CEL17315 Inbred lines S5 CIMMYT 
9 CEL17316 Inbred lines S5 CIMMYT 
10 CEL17329 Inbred lines S5 CIMMYT 
11 CEL17330 Inbred lines S5 CIMMYT 
12 CEL17331 Inbred lines S5 CIMMYT 
13 CEL17333 Inbred lines S5 CIMMYT 
14 CEL17334 Inbred lines S5 CIMMYT 
15 CEL17335 Inbred lines S5 CIMMYT 
16 CEL17336 Inbred lines S5 CIMMYT 
17 CEL17351 Inbred lines S5 CIMMYT 
18 CEL17353 Inbred lines S5 CIMMYT 
19 CEL17357 Inbred lines S5 CIMMYT 
20 CEL17371 Inbred lines S5 CIMMYT 
21 CEL17372 Inbred lines S5 CIMMYT 
22 CEL17377 Inbred lines S5 CIMMYT 
23 CEL17378 Inbred lines S5 CIMMYT 
24 CEL17379 Inbred lines S5 CIMMYT 
25 CEL17380 Inbred lines S5 CIMMYT 
26 CEL17404 Inbred lines S5 CIMMYT 

Testers 
1 CEL08008/CEL08047 Single cross tester S5 CIMMYT 
2 CEL08024/CML561 Single cross tester S5 CIMMYT 

 

2.3. Experimental Design and Agronomic Practices 

In an Alpha Lattice design, 52 F1 hybrids created through line-by-tester mating were repeated twice. The trial 

was carried out at the Agricultural Research Station in Ambo and Kulumsa. Each entry was planted in a single row 

plot of 4 metres long, with 0.75 metres between rows and 0.25 metres between plants. To achieve a density of 

53,333 maize plants per hectare, the experimental materials were manually planted with two seeds per hill, which 

were then thinned out to one plant/hill. DAP and UREA were used at the prescribed rates of 150 and 200 kg/ha, 

respectively.  

At planting time, a band application of diammonium phosphate (DAP), a phosphorous fertiliser, was made. At 

40 days and 70 days following planting, UREA was administered in two splits. Other agronomic management 

procedures were carried out in accordance with local recommendations.   

 

 



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3. RESULTS AND DISCUSSION 

3.1. Analysis of Variance 

Overall ANOVA result of the current investigation displayed in (Table 2). Analysis of variance was made on 

yield and yield-related traits of grain yield (GY), anthesis of date (AD),1000-kernel weight (TW), anthesis silking 

interval (ASI), silking date (SD), plant height (PH), and ear height (EH), number of ears per plant (EPP), number of 

kernel rows per ear (KRPE), number of kernels per row (KPR), ear length (EL) and ear diameter (ED) for each 

location and across the locations.  

 

Table 2. Analysis of variance for yield and yield related traits for testcross across location. 

Traits 
L 

df=1 
Re(L) 
df=2 

B(L*R) 
df=12 

Ent 
df=51 

Ent*L 
df=51 

Error 
df=90 Mean±SE(m) CV% R2 

GY 73.8*** 36.64*** 2.72*** 5.28** 2.85*** 0.948 7.44±0.97 13.08 0.88 
AD 68.08*** 15.64* 5.36 11.77** 4.14 5.02 97.02±2.24 2.31 0.7 
SD 10.17 5.7 6.29 12.65** 5.07 5.37 98.79±2.32 2.35 0.69 
ASI 12.62*** 4.64 1.13 1.96 1.28 1.73 1.76±1.32 74.55 0.63 
PH 510.9 1707*** 442*** 397.7** 212.5 156.78 225.4±12.52 5.56 0.76 
EH 3894*** 1611*** 302* 406.2** 229.8** 130.08 125.73±11.41 9.07 0.79 
EPP 0.93*** 0.2* 0.03 0.11*** 0.08* 0.05 1.39±0.22 15.51 0.73 
EL 0.81 19.16** 4.24 3.24 3.18 3.1 17.38±1.76 10.14 0.59 
ED 2.77 19.16 3.2* 7.13 6.41 5.13 46.68±2.26 4.86 0.63 
KRPE 1.56 2.4 1.77 1.18 1 0.99 12.61±0.99 7.3 0.6 
KPR 218.12*** 27.98 12.16 21.11* 10.49 14.35 36.66±3.8 10.33 0.62 
TSW 22.89 4429.93 3286.43 1707.25 1547.88 1862.03 350.51±43.15 12.31 0.53 

Note: L=Location, Re=Replication, Ent=Entry, SE= Standard error, CV= Coefficient of variation, R2=Coefficient of determination. *= significance 
at 0.1 level, **= significant at 0.05, and ***= significance at 0.01 level. 

 

This implies that there is sufficient diversity to select from among the genotypes that have been examined. 

Amiruzzaman, et al. [17]; Amare, et al. [18]; Ziggiju, et al. [19] and Tulu, et al. [20] are among the authors who 

have identified substantial genotype differences for yield-related metrics and grain yield of different sets of maize 

genotypes. The interaction between location and entrance (Location x entry) was significant (p 0.05) and highly 

significant (P<0.01) for grain yield, ear height, and ear per plant. However, non-significant interaction effects of 

(Location x entry) were detected for parameters such as the anthesis date, silking date, anthesis silking interval, 

plant height, ear length, ear diameter, kernel row per ear, kernel per row, and thousand seed weight Bayisa, et al. 

[21] and Dagne, et al. [22] discovered that this attribute performed consistently across experiments, which is in 

line with the conclusions of the present inquiry. Grain yield, anthesis date, silking date, plant height, ear height, and 

number of ears per plant are highly significant at (p<0.01) and kernel row per ear significant at (p<0.05) for 

genotype, whereas the remaining variables were not significant.  

 

 

 

 

 

 

 



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Table 3. Analyses of variance for line by tester for grain yield and yield related traits of 52 test crosses and 26 inbred lines evaluated in across location. 

SV Df GY AD SD ASI PH EH EPP EL ED KRPE KPR TSW 

Site 1 73.8*** 68.1*** 10.2ns 0.04*** 510.94* 3894.23*** 0.928*** 0.81 2.77 1.56 218.12*** 22.89 
Rep(Site) 2 36.64*** 15.64 5.70ns 0.007 1706.53*** 1610.55*** 0.20* 19.16*** 19.16* 2.40 27.98 4429.93 
Blk(rep) 7 11.15*** 13.45* 10.36ns 0.003 1410.94*** 837.44*** 0.023 11.57*** 10.22* 2.39* 21.31 3601.91 
Cross 51 5.28*** 12.00*** 12.52*** 0.003 397.75*** 406.16*** 0.11*** 3.24 7.13 1.19 21.11* 1707ns 
Line 25 5.48*** 22.08*** 23.66*** 0.004 457.69*** 277.66*** 0.11*** 2.53 7.04 0.89 23.27* 1028.37 
Tester 1 12.26*** 0.12*** 3.77ns 0.005 3706.17*** 8581.23*** 1.01*** 20.31*** 8.48 0.17 55.043* 190.38 
Line*tester 25 5.09*** 4.64 5.28ns 0.003 281.99*** 241.70*** 0.08* 2.90 7.98* 1.24 17.62 2207.49 
Site*line 25 2.92*** 4.76 4.12ns 0.003 246.34*** 182.38* 0.08* 3.18 7.33* 1.25 8.32 1418.21 
Site*tester 1 20.75*** 4.62 12.02ns 0.004 1173.25*** 1969.23*** 0.26* 5.24 22.23* 0.48 10.62 3336 
Site*cross 51 2.72*** 4.14 5.07 0.006 212.45 246.89*** 0.08*** 3.17 6.41 1.00 10.49 1547.89 
Site*line*tester 25 1.94*** 3.18 5.00ns 0.004 202.05*** 242.90*** 0.08* 3.16 4.91 0.83 14.12 870.51ns 
Error 95 0.42 4.44 5.13 0.003 100.4 99.67 0.046 17.38 4.51 0.99 13.56 1913.75 
Mean  7.44 97.02 98.79 1.23 225.35 125.73 1.39 2.62 46.57 12.61 36.66 350.51 
CV  8.70 2.17 2.29 4.21 4.45 7.4 15.41 9.32 4.56 7.91 10.05 12.48 
R2  0.95 0.72 0.69 0.63 0.83 0.83 0.72 0.64 0.66 0.58 0.62 0.49 
LSD  1.37 1.15 3.26 1.85 17.59 16.02 0.31 2.48 3.18 1.41 5.32 60.62 
Proportional contribution 
%L(GCA)  49.55 82.62 81.33 56.66 51.52 32.19 46.57 40.56 45.84 41.78 54 31.71 
%T(GCA)  4.43 0.02 0.52 2.43 16.7 39.79 37.22 13 2.21 0.32 5.11 0.23 
%LXT(SCA)  46.02 17.36 18.15 40.91 31.77 28.02 36.1 46.44 51.95 57.9 40.89 68.06 

Note: *= significance at 0.1 level and ***= significance at 0.01 level. 

 

 



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3.2. Combined Analysis for Combining Ability  

As a consequence of the combined analysis, highly significant mean squares resulting from crossings were 

found for GY, AD, SD, PH, EH, and EPP, as well as significant mean squares for KPR, indicating the potential of 

computing combining ability analysis and dividing into GCA and SCA. The findings also indicated the existence of 

statistically significant variations among GCA of lines for GY, AD, SD, PH, EH, EPP, and KPR. For GY, AD, PH, 

EH, EPP, EL, and KPR, highly significant mean squares of testers were also obtained (P <0.05). Similar results 

were seen across sites, with extremely significant mean squares resulting from SCA for GY, PH, EH, and EPP as 

well as ED (P 0.05) in Table 3. The current finding indicated the presence of a site x line interaction that was 

highly significant for GY and significant (P< 0.05) for EH, EPP, and ED. However, for AD, SD, ASI, EL, KRPE, 

KPR, and TSW, the interaction of S x L was shown to be non-significant. For GY, PH, and EH, the across-site 

interaction of Site x Tester (SxT) was very significant (P <0.01), while for EPP and ED, it was significant.  For EH 

and EPP, site x crosses also exhibited extremely significant interaction, but non-significant interaction for the 

majority of characteristics. 

But for GY, PH, and EH as well as significant (P<0.05) for EPP, the current investigation revealed a very 

significant interaction of site x line x tester. Site, line, and tester interactions were not significant for other 

attributes. This might mean that both sites are anticipating similar performance from those features. According to 

the most recent genetic investigation, for variables including GY, AD, SD, ASI, PH, EH, and EPP, the total of 

squares attributable to GCA was more than that owing to SCA. These findings suggest that the additive gene type 

contributes primarily, and that these features may be improved by selection. However, for features like KRPE, 

TSW, and ED, the sum of squares attributable to GCA was lower, indicating that these qualities are controlled by 

non-additive gene types and might be utilised through hybrid development. 

 

3.3. General Combining Ability Effect Across Locations 

Inbred lines L2, L4, L5, L7, L10, and L13 had significant and positive GCA estimates for GY. Given that 

inbred lines made effective general combiners for grain yield across locations, it is possible that they may be used to 

create synthetic varieties. For L1, L6, L12, L17, L21, L22, and L24, the GCA for GY was highly significant and 

significantly negative.  This demonstrates that the lines were inefficient general combiners, which implies that they 

failed to disperse additive genes in the intended manner.  L4, L5, and L18 were significant and negative for the 

anthesis date, but L4, L5, L11, L14, L18, and L19 were significant and positive for the silking date. The lines can be 

used in breeding programs to enhance hybrid early-maturing cultivars for drought-prone areas. For SD, L2, L3, L6, 

L7, L8, L9, L10, L12, L17, L22, L24, and L25 showed the similar results, whereas L3 and L12 showed positive and 

significant outcomes for AD. These lines were inadequate general combiners for the associated attributes. 

Numerous authors, including Shushay, et al. [23] and Yazachew, et al. [24] have found significant and both 

positive and negative lines GCA impacts.   

The plant's GCA falls from -12.38 cm (L16) to 17.33 cm (L24). Strong negative and positive GCA were found 

in the 26 lines L1, L16, L21, L22, and L9, making L23, L24, and L26 from those 26 lines good and terrible general 

combiners for this trait, respectively. The GCA effect for ear height runs from L21 to L12, 12.22cm, starting at -

10.96cm. Significantly negative GCA values were found for L18, L21, and L22, but significant positive GCA values 

were found for L12.  L12 was a bad general combiner for this trait since maize is sought after for its small ear 

height. Kamara, et al. [25]; Girma, et al. [26] and Ahmed, et al. [27] found substantial positive and negative GCA 

effects of inbred lines for plant and ear height by claiming that shorter plant height with lower ear placement was 

preferable. 

Inbred lines' GCA impacts on the number of ears per plant ranged from -0.23 (L11) to 0.17 (L26), with L3 and 

L11 having significantly negative GCA values and L18 and L26 having significantly positive GCA values. As a 

result, it was clear that L3 and L11 were ineffective general combiners for the qualities, however L18 and L26 



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performed well. The GCA estimates for L1, L5, L6, L7, L8, L9, L10, L11, L12, L15, and L26 in the case of KPR 

were significant and positive, demonstrating that these loci support additive genes that improve traits through 

selection. L2, L3, L13, L16, L17, L19, and L26 have negative and significant GCA estimations, which makes them 

poor general combiners for the attributes. The tester's negative and positive values were noted in regard to the 

GCA effect even though significant positive and negative results weren't attained. The following Table 4 represent 

GCA of inbred lines and two testers. 

 

Table 4. General combining ability of inbred lines and two testers across location. 

  SN Lines  GY AD SD PH EH EPP KPR 

1 L1 -0.63** -0.27 -0.49 -8.85* -5.74 0.11 2.24*** 
2 L2 0.69** 1.00 1.81*** -0.36 3.09 -0.03 -1.27*** 
3 L3 0.02 2.08** 2.06*** -0.40 -2.66 -0.21** -2.16*** 
4 L4 0.98*** -4.14*** -3.88** -0.74 4.52 -0.06 0.10 
5 L5 0.85*** -2.79** -2.84*** -6.40 -4.53 0.10 0.73* 
6 L6 -0.82*** 0.36 1.08*** 1.44 5.47 0.12 0.82* 
7 L7 1.08*** 1.47 1.56*** 0.01 6.09 0.12 1.23* 
8 L8 0.31 0.99 1.22*** -4.68 -7.46 -0.08 2.25*** 
9 L9 0.30 0.84 0.94*** 8.25* 6.17 0.20 0.20 

10 L10 0.69** 0.23 0.57* -4.74 0.12 -0.12 1.69** 
11 L11 -0.06 -0.53 -0.62* -1.97 -4.20 -0.23** 0.84* 
12 L12 -0.55* 1.60* 1.85*** -6.75 12.22*** -0.10 1.45*** 
13 L13 0.88** 0.33 -0.37 6.79 2.87 0.12 -1.39*** 
14 L14 0.41 0.11 -0.75** -3.38 2.26 0.05 -0.29 
15 L15 -0.15 0.21 0.01 0.48 2.09 -0.09 1.84*** 
16 L16 0.07 0.60 -0.51 -12.38*** -2.12 0.08 -1.93*** 
17 L17 -1.26*** 1.35 0.91*** 4.85 0.44 -0.02 -2.02*** 
18 L18 0.26 -4.89*** -4.35*** -8.26 -10.18** 0.16* 0.58 
19 L19 0.10 -1.28 -1.64*** 5.91 -1.52 -0.02 -1.42*** 
20 L20 0.37 -0.39 0.02 5.43 4.32 -0.08 0.59 

21 L21 -0.68** 0.61 0.27 -7.56* -10.96*** -0.01 0.09 
22 L22 -2.69*** 1.24 2.18*** -7.69* -7.89* -0.10 -5.37*** 
23 L23 0.01 -0.40 -1.25*** 14.36*** 5.88 -0.04 -0.58 
24 L24 -0.94*** 1.21 0.89*** 17.33*** 4.37 0.07 -0.28 
25 L25 0.24 0.24 1.24*** -5.45 -4.86 -0.09 0.57 
26 L26 0.37 0.23 0.13 12.98*** 0.95 0.17* 1.48* 

 SE 0.23 0.74 0.25 3.54 3.53 0.08 0.35 
 T1 0.24 -0.03 -0.15 4.20 6.51 0.07 -0.52 
 T2 -0.25 0.03 0.15 -4.34 -6.60 -0.07 0.51 
 SE 0.27 0.03 0.15 4.06 5.60 0.07 0.51 

Note: Line, T=Tester, SE=Standard error, GY=Grain yield, AD=Anthesis days, SD=Silking date, PH=Plant height, EH=Ear height, 
EPP=Ear per plant KPR=Kernels per rows, TSW=Thousand seed weight. *= significance at 0.1 level, **= significant at 0.05, and ***= 
significance at 0.01 level. 

 

3.4. Specific Combining Ability of Across Location 

Specific combining ability across locations were listed in General combining ability across location value listed 

in Table 5. Grain yield had a favourable and significant across-location SCA effect in crosses like L1xT2, L14xT1, 

L17xT1, L8xT1, L10xT1, L14xT1, L17xT1, L20xT2, L24xT2, and L26xT1. This suggests that the two parents' 

genes significantly positively interacted and served as an effective combiner for this trait. These gene interactions 

cause heterosis to manifest, which can be used to create hybrid types. Cross L1xT1, L14xT2, L17xT2, L8xT2, 

L10xT2, L14xT2, L17xT2, L20xT1, L24xT1, and L26xT2 are subpar specific combiners for GY and showed 

negative significant SCA estimations. The results of Kamara, et al. [25]; Girma, et al. [26]; Ram, et al. [28] and 

Bullo and Dagne [29] others who observed substantial positive and negative SCA for grain yield, are consistent 

with the current conclusion. They proposed that SCA effects might aid in the selection of parental material for 



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© 2024 Conscientia Beam. All Rights Reserved. 

hybridization when high yielding particular combinations are sought. Jumbo and Carena [30] and Pswarayi and 

Vivek [31] revealed non-significant positive and negative SCA effects for grain yield, in contrast. 

Different crosses, including L10xT1 (-1.46), L10xT2 (1.46), L19xT1 (-1.48), and L19xT2 (1.48), demonstrated 

both positive and negative SCA estimates for AD, while L10xT1 (-1.66) and L10xT2 (1.65), respectively, 

demonstrated the highest levels of both negative and positive SCA estimates for SD. Good particular combiners for 

the two qualities in the desired direction were L10 and L19 with T1. While L10 and L19 with T2 may be used for 

late maturity in regions with sufficient rainfall, this cross may be utilised for early maturity in locations with 

insufficient rainfall. Numerous researchers have documented both significant positive and negative SCA effects for 

AD and SD, which is consistent with our conclusion [19, 32-34]. 

L11xT1, L17xT1 and L11xT1, L26xT2 are negative and significant for grain plant height for the PH and EH 

crosses, respectively. These crosses are effective general combiners for these features since the short plant is desired 

to prevent lodging. The positive and substantial SCA values for the PH cross L11xT2, L17xT2, the EH cross 

L11xT2, and L26xT2 are poor specific combiners. This suggests that the combination was controlled by non-

additive gene activation.  

Because of this, heterosis breeding may be used to enhance the qualities. In their tests on the combining ability 

and heterotic orientation of mid-altitude sub-humid tropical maize inbred lines for grain yield and associated 

variables. L14xT1, L21xT1, and L22xT1 were crosses with substantial positive SCA effects for EPP traits, whereas 

L14xT2, L21xT2, and L22xT2 were crosses with significant negative SCA effects. L14xT1 (3.09) and L14xT2 (-

3.09) for KPR cross with contrasting SCA values. For EPP, L14, L21, and L22 worked well as specifiec combiners, 

as did L14 for KPR with T1. These features could be enhanced by heterosis breeding to produce more kernels and 

ears per row. The following Table. 5 represent SCA of inbred lines with their two testers.  

 

Table 5. Specific combining ability of crosses across locations. 

SN Genotype GY AD SD PH EH EPP KPR 

1 L1XT1 -1.36*** 0.78 1.15 2.44 -1.06 0.02 2.15 
2 L1XT2 1.37*** -0.78 -1.16 -2.30 1.16 -0.02 -2.15 
3 L2XT1 0.13 0.02 -0.16 -0.96 6.42 0.00 0.90 
4 L2XT2 -0.12 -0.02 0.16 1.10 -6.32 0.00 -0.89 
5 L3XT1 0.19 0.40 1.13 -8.95 -2.98 0.02 -0.22 
6 L3XT2 -0.17 -0.40 -1.13 9.09 3.08 -0.02 0.22 
7 L4XT1 -0.21 -0.84 -0.40 -1.85 -1.56 -0.05 -1.50 
8 L4XT2 0.22 0.83 0.39 1.99 1.65 0.05 1.50 
9 L5XT1 0.02 1.02 0.68 3.37 1.14 -0.03 1.64 
10 L5XT2 -0.01 -1.02 -0.68 -3.23 -1.04 0.03 -1.64 
11 L6XT1 -0.24 -0.08 0.36 -6.16 -3.33 0.10 0.54 
12 L6XT2 0.25 0.08 -0.36 6.30 3.43 -0.10 -0.54 
13 L7XT1 -0.50 0.75 0.40 0.85 -3.32 -0.04 0.90 
14 L7XT2 0.51 -0.75 -0.40 -0.71 3.42 0.04 -0.89 
15 L8XT1 0.74** -0.23 -0.61 4.52 -0.38 0.01 0.91 
16 L8XT2 -0.74** 0.23 0.61 -4.38 0.48 -0.01 -0.90 

17 L9XT1 0.12 -0.36 -0.18 6.31 4.52 0.16 -0.10 
18 L9XT2 -0.11 0.36 0.18 -6.17 -4.43 -0.16 0.10 
19 L10XT1 0.76** -1.46 -1.66 7.70 6.44 0.05 1.14 
20 L10XT2 -0.76** 1.46 1.65 -7.56 -6.35 -0.05 -1.14 
21 L11XT1 -0.28 0.03 -0.30 -13.69** -11.80* -0.02 0.77 
22 L11XT2 0.29 -0.03 0.29 13.69** 11.80* 0.02 -0.77 
23 L12XT1 0.45 -1.35 -0.93 -6.92 -8.56 0.01 -1.34 

 

 

 

 



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© 2024 Conscientia Beam. All Rights Reserved. 

Table 5. Continued. 

SN Genotype GY AD SD PH EH EPP KPR 

24 L12XT2 -0.44 1.34 0.92 7.06 8.65 -0.01 1.34 
25 L13XT1 -0.54 0.38 0.55 1.96 1.92 -0.10 -1.20 
26 L13XT2 0.55 -0.39 -0.55 -1.82 -1.82 0.10 1.21 
27 L14XT1 1.90*** -0.60 -0.42 1.53 3.63 0.23* -3.09 
28 L14XT2 -1.90*** 0.60 0.41 -1.39 -3.53 -0.23* 3.09 
29 L15XT1 -0.35 -0.24 -1.07 -2.37 -2.22 -0.09 0.76 
30 L15XT2 0.36 0.24 1.07 2.51 2.32 0.09 -0.75 
31 L16XT1 -0.02 0.92 0.74 -0.25 -8.01 -0.06 -2.50 
32 L16XT2 0.03 -0.93 -0.75 0.39 8.11 0.06 2.51 
33 L17XT1 1.46*** 0.65 1.36 10.06* 6.36 0.03 1.36 
34 L17XT2 -1.45*** -0.65 -1.36 -10.06* -6.26 -0.03 -1.36 
35 L18XT1 -0.22 0.65 0.75 5.21 4.12 -0.04 0.23 
36 L18XT2 0.24 -0.65 -0.76 -5.07 -4.03 0.04 -0.23 
37 L19XT1 0.10 -1.48 -0.96 -6.45 -6.45 -0.01 0.28 
38 L19XT2 -0.09 1.48 0.96 6.59 6.55 0.01 -0.28 
39 L20XT1 0.76* 0.14 0.86 1.39 2.29 0.01 -1.99 
40 L20XT2 -0.76* -0.15 -0.86 -1.25 -2.19 -0.01 1.99 
41 L21XT1 -0.46 -0.60 -1.05 3.40 3.52 0.25* 1.77 
42 L21XT2 0.46 0.60 1.05 -3.26 -3.42 -0.25* -1.77 
43 L22XT1 -1.50*** 1.28 0.30 -0.65 -0.54 -0.20 -2.74 
44 L22XT2 1.50*** -1.28 -0.30 0.79 0.64 0.20 2.74 
45 L23XT1 -0.30 0.65 0.41 -6.27 -4.89 0.08 1.88 
46 L23XT2 0.31 -0.65 -0.41 6.41 4.98 -0.08 -1.88 
47 L24XT1 -1.36*** -0.23 -0.71 1.45 3.42 -0.19 -0.63 
48 L24XT2 1.36*** 0.23 0.70 -1.31 -3.32 0.19 0.63 
49 L25XT1 -0.28 0.27 0.30 -0.87 0.44 -0.06 -0.49 
50 L25XT2 0.29 -0.27 -0.31 1.01 -0.34 0.06 0.50 
51 L26XT1 1.12*** -0.48 -0.59 6.88 12.06* -0.09 0.61 
52 L26XT2 -1.12*** 0.48 0.58 -6.75 -12.06* 0.09 -0.60 

 SE 0.32 1.05 1.13 5.00 4.98 0.11 1.83 
Note: Line, T=Tester, SE=Standard Error, GY=Grain yield, AD=Anthesis days, SD=Silking date, PH=Plant 

height, EH=Ear height, EPP=Ear per plant KPR=Kernels per rows, TSW=Thousand kernels weight. *= 
significance at 0.1 level, **= significant at 0.05, and ***= significance at 0.01 level. 

 

4. CONCLUSION  

The goal of the current study was to conduct the GCA and SCA of traditional highland maize inbred lines. In 

2019–2020, two single cross testers were used to cross 26 inbred lines using the LxT mating pattern. As a result, 

52 maize single cross hybrids were tested in Ambo and Kulumsa using an alpha lattice design and two replications. 

The majority of the examined traits had considerable genotype-to-genotype variation, according to analysis of 

variance.  For the majority of the examined qualities, testers found highly significant variations between inbred 

lines and lines, indicating the involvement of additive and non-additive gene activity in regulating the inheritance of 

the traits. Thus, the current study implies that most qualities can be improved by selection. The current study 

highlighted crosses L14x T1 (10.13 t/ha), L26 x T1, L10xT1, L7x T1), (L7 xT2), and L20 x T1 for additional 

multi-location evaluation based on the grain yield performance at two locations. Additionally, lines L2, L4, L5, L7, 

L10, and L10 were found to be effective positive general combiners that could aid in the creation of synthetic 

varieties. Genes were responsible for the inheritance of yield, and features related to yield were greatly influenced 

by the environment, according to significant GCA and SCA mean sum of square in various environments. 

Programmes for the production of maize varieties may use inbred lines with favourable GCA effects for desired 

features.  Good general combiner lines L4, L5, L7, L10, and L13 were used to select for the trait and increase grain 

production. Crosses including L1xT2, L14xT1, L17xT1, L8xT1, L10xT1, L14xT1, L17xT1, L20xT2, L24xT2, 

and L26xT1 had notable favourable impacts on grain yield. They were regulated by non-additive action and could 

be increased by hybrid breeding. L3, L4, L4, L11, L14, L18, L19, and L23 were effective general combiners for the 

silking date and the anthesis date, respectively, so that the inheritance was governed by additive gene effect and 

could be enhanced through selection. 

 



Current Research in Agricultural Sciences, 2024, 11(1): 8-18 

 

 
17 

© 2024 Conscientia Beam. All Rights Reserved. 

Funding: This study received no specific financial support.    
Institutional Review Board Statement: Not applicable. 
Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key 
aspects of the investigation have been omitted, and that any differences from the study as planned have been 
clarified. This study followed all writing ethics. 
Competing Interests: The authors declare that they have no competing interests. 
Authors’ Contributions: All authors contributed equally to the conception and design of the study. All 
authors have read and agreed to the published version of the manuscript. 

 

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