Harnessing Combining Ability Estimates to Classify Newly Developed Maize Inbred Lines

Maize holds immense global importance as a staple food crop, providing essential nutrition to millions of people worldwide, particularly in developing regions like Africa and Latin America. Maize is not only crucial for human consumption but also serves as a vital ingredient in animal feed and various industrial products, including biofuels Shiferaw et al. 2011. In Egypt, maize is an indispensable crop, as it is not only a major food crop but also an essential animal feed, contributing to the livestock industry. Its high nutritional value makes it valuable for both human consumption and industrial purposes, such as the production of corn oil, fructose, and starch (Abd el Fatah et al. 2015).  Corn hybrids contribute to enhanced food security, agricultural sustainability, and farmer profitability worldwide. By selecting and utilizing elite inbred lines, breeders can develop high-performing corn hybrids that meet the demands of farmers and consumers. Line x tester analysis facilitates the estimation of general combining ability (GCA) and specific combining ability (SCA) effects, which aid in identifying superior inbred lines for developing high-yielding hybrids (Suhaisini et al. 2016; Ismail and El-Mouslhy, 2020; Patil et al. 2020; Ismail et al. 2022; Job and Igyuve, 2022; Subba et al. 2022). Heterotic groups play a crucial role in maize breeding by enabling the efficient utilization of genetic variation to produce high-yielding hybrids. These groups consist of genetically related genotypes that display similar hybrid performance when crossed with individuals from other genetically distinct germplasm groups (Vasal et al. 1992; Badu-Apraku et al. 2015; Fan et al. 2018; Tolley et al. 2021; Ismail et al. 2023b; and Mosa et al. 2025). In contrast, a heterotic pattern refers to a unique combination of two heterotic groups that exhibit exceptional heterosis and outstanding hybrid performance when crossed. Heterotic patterns facilitate the development of new hybrids with predictable levels of heterosis, thereby increasing crop yields and helping tackle pressing issues such as food security, climate change, and the rising global population. By leveraging heterosis, breeders can create hybrids with improved agronomic performance, disease resistance, and other beneficial traits, ultimately benefiting farmers and consumers alike. As the result, The present study was conducted with the following aims: (i) assess the combining ability effects of 18 white inbred lines for grain yield and yield related traits across three locations; (ii) classify the inbreds into heterotic groups using the SCA method and HSGCA method; (iii) compare the effectiveness of the SCA and HSGCA grouping methods in classifying the inbred lines; (iv) determine yield performance of the hybrids to identify the putative hybrids which may forward to seed production.

Genetic materials: A set of eighteen divergent inbreds developed under the Maize Research Department, Agricultural Research Center (ARC) at Sakha Agricultural Research Station, ARC, were crossed with two testers, viz., GZ-613 and SK-13, in line x tester fashion, Kempthorne (1957), during the 2022 season to generate 36 hybrids. The 36 hybrids, along with the check hybrid SC-10, were sown during 2023 at three locations, i.e., Sakha, Nubaria, and Sids Research stations. A randomised complete block design with three replications was adopted in each location. The plot size of the trials was a single row measuring 6.0 m in length, with inter- and intra-row spacings of 0.80 m and 0.25 m, respectively. Initial seeding was two kernels per hill, and later thinned to one plant per hill. The recommended agronomic package for each location was followed to maintain the stand.

Data collections: Data were recorded on days to 50% silking (days), Plant and Ear heights (cm), Ear length and diameter (cm), and Yield (kg) per plot adjusted at 15.5% grain moisture and converted to (ton/ha).

Data analysis: the combined analysis of recorded data was performed using the PROC GLM procedure in SAS software (SAS Institute Inc., 2008) according to Snedecor and Cochran (1989). The general and specific combining abilities were estimated following the line x tester analysis mating design proposed by Kempthorne (1957). The relative importance of GCA and SCA was investigated using the equation:

 modified from Baker (1978) by Hung and Holland (2012). Where 𝑘²_GCA is the variance of effects derived from GCA mean square, and 𝑘²_SCA is the variance of effects derived from SCA mean squares. The closer this ratio is to unity, the greater the predictability of a specific hybrid’s performance based solely on GCA. Pearson’s correlation coefficients and Correlogram were generated using the (ggcorrplot package) in R software.

Heterotic group: To assign inbred lines into heterotic groups, the HSGCA method proposed by Fan et al., (2008) was used as follows: HSGCA = Cross mean Xij + Tester mean (Xi) = GCA + SCA where Xij is the mean yield of the cross between the ith tester and the jth line, Xj is the mean yield of the ith tester across jth lines. SCA heterotic group, according to Vasal et al. (1992), Inbreds producing hybrids with negative SCA effects when crossed with a tester were assigned to that tester’s heterotic group. Whereas, Inbreds formed hybrids with positive SCA effects, with all testers were considered 'not classified,' indicating an unknown heterotic group affiliation.

Analyses of variance of grain yield and yield-related traits of white hybrids across research environments

The combined ANOVA of the hybrids across three research locations showed highly significant mean squares (MS) for all measured traits for Loc, H, and Env × H (Table 1). Partitioning of the hybrids into components showed that line, tester, and their interaction line × tester were highly significant (P≤0.001) for all the examined traits, except tester MS for ear height.  The MS for line × Loc and tester × Loc was significant or highly significant for all the measured traits except the tester × Loc MS for DSK. The line × tester × Loc interaction was highly significant for DSK, EL, and yield.

Table 1. Mena squares from the combined ANOVA of DSK, PHT, EHT, EL, ED, and yield across the three locations evaluated in 2023.

* Significant at p < 0.05, ** significant at p < 0.01

DSK= Days to 50% silking (day), PHT= Plant height (cm), EHT= Ear height (cm), EL= Ear length (cm), ED= Ear diameter (cm), Yield= Grain yield (ton/ha).

Contributions of lines, testers, and Line × Tester in total variance

The results showed that the proportion of line (GCA line) was more than 50% of the total variance of DSK, PHT, EHT, and ED. Whereas, the tester proportion (GCA tester) was 71% and 59.64% of the total variance for EL and yield, respectively (Figure 1). The proportion of Line × Tester (SCA Line × Tester) reached 41% for the EHT trait. The sum of GCA line + GCA tester refers to the additive gene action, while SCA Line × Tester refers to the nonadditive gene action. Consequently, additive gene action was more important in the inheritance of all measured traits (Figure 2).

Figure 1. Percentage contribution of the total genotypic sum of squares of measured traits due to GCA-line, GCA-tester, and SCA line x tester

Figure 2. Proportion of additive (lower bar) and non-additive (upper bar) genetic variances for measured traits in line × tester

Mean Performance

The mean performance of 36 hybrids, along with the check hybrid SC-10, is shown in Table 2.  The two hybrids, H-1 and H-22, were significantly earlier than the check hybrid (66.9 days). Twenty-seven hybrids were significantly shorter than the check hybrid SC-10 (274.8 cm) for plant height. The tallest hybrid was H-3 (275.6 cm). Twenty-six of the 36 evaluated hybrids had the lowest ear height, significantly lower than that of the check hybrid. The best hybrids for plant and ear heights were H-22, H-27, and H-28. For ear length, thirteen hybrids significantly outperformed the check hybrid SC-10 (19.6 cm). Moreover, the highest ear length was observed in the hybrid H-26 (22.8 cm), followed by H-12 (22.7 cm). Ear diameter has ranged from 4.0 cm to 4.9 cm compared to the check hybrid (4.6 cm). The three hybrids, viz., H-4, H-3, and H-14, significantly surpassed the check hybrid for this trait. Four hybrids, i.e., H-32, H-34, H-18, and H-24, significantly out-yielded the check hybrid for the grain yield trait (Table 2).

Table. 2. Mean performance of 36 maize hybrids for DSK, PHT, EHT, EL, ED, and yield characters combined over three locations.

DSK= Days to 50% silking (day), PHT= Plant height (cm), EHT= Ear height (cm), EL= Ear length (cm), ED= Ear diameter (cm), Yield= Grain yield (ton/ha).

Combining ability

Relative importance of GCA effects over SCA effects

The assessment of the relative importance of GCA and SCA effects was expressed as the ratio of GCA effects to the total genetic effects, calculated as twice the GCA effects plus the SCA effects. The ratio, as long as it’s close to unity, results in greater predictability based on GCA alone (Baker 1978). The relative importance of GCA and SCA effects accounted for 83% of the total genetic effects on yield across the tested locations (Table 3). Similarly, they explained 93% for EL, 84% for ED, 66% for DSK, 69% for PHT, and 11% for EHT of the total genetic effects.

Table 3. General combining ability effects (ĝi) of 18 inbred lines and two testers for the studied traits across three locations.

* Significant at p < 0.05, ** significant at p < 0.01

DSK= Days to 50% silking (day), PHT= Plant height (cm), EHT= Ear height (cm), EL= Ear length (cm), ED= Ear diameter (cm), Yield= Grain yield (ton/ha).

General combining ability

Significant negative GCA effects for DSK were observed for Inb-1, Inb-2, Inb-5, Inb-11, and Inb-14 (Table 3). The inbred lines, viz. Inb-6, Inb-10, Inb-11, and Inb-14 obtained significant desirable GCA effects for PHT. Similarly, the two parental inbred lines, Inb-11 and Inb-14, showed significant negative GCA effects for PHT. Six inbred lines, i.e., Inb-4, Inb-5, Inb-6, Inb-10, Inb-13, and Inb-14, had possessed significant positive GCA effects for EL. The inbreds for Inb-1, Inb-2, Inb-3, and Inb-5 exhibited significant positive GCA effects for ED. Significant positive GCA effects have been obtained by the inbred Inb-2, Inb-3, Inb-9, and Inb-15 for yield. The tester SK-13 showed significant desirable GCA effects for the measured traits, except for the EHT trait.

Specific combining ability

The hybrids H-1, H-3, H-7, H-9, H-26, H-34, and H-36 showed significant negative SCA effects on DSK (Table 4). Similarly, only one hybrid H-4 was identified for significant negative SCA effects for PHT. The desirable SCA effects for EHT were determined by the hybrids H-4, H-7, and H-17. For ear length, four hybrids, viz. H-3, H-5, H-24, and H-32 were overserved with significant positive SCA effects. H-5, H-14 and H-36 manifested significant positive SCA effects for ear diameter. Eight hybrids were identified with significant positive SCA effects on yield. The highest SCA effects on yield have been observed for H-32 (4.87**), followed by H-9 (3.98**), and then H-34 (3.77).          

Table 4. SCA effects of DSK, PHT, EHT, EL, ED, and yield for 36 hybrids formed from 2 testers and 18 females evaluated across three locations in 2023.

* Significant at p < 0.05, ** significant at p < 0.01

DSK= Days to 50% silking (day), PHT= Plant height (cm), EHT= Ear height (cm), EL= Ear length (cm), ED= Ear diameter (cm), Yield= Grain yield (ton/ha).

Correlations analysis between yield and yield-related traits

The correlation analysis showed that yield correlated significantly positively with EL (r = 0.73) and ED (r = 0.86) (Figure 3). Contrary to expectations, yield negatively correlated with DSK (r = −0.58). EHT correlated positively with PHT. Similarly, ED and El were positively correlated. Contrarily, DSK negatively correlated with EL and ED.

Figure 3. Correlation between yield and yield-related traits.

Heterotic groups

Heterotic group based on SCA and grain yield

Positive SCA effects indicate that lines are in opposite heterotic groups, whereas negative SCA effects indicate that the lines are in the same heterotic groups (Vasal et al. 1992). The inbred lines were classified into two groups. The inbreds Sk5003/12, Sk5003/16, Sk5003/17, and Sk5003/18 were placed in group A (Table 5). Whereas the inbred lines Sk5001/1, Sk5001/2, Sk5001/3, Sk5001/4, and Sk5001/5 were placed in group B. This method was unable to classify 9 Inbreds.

Table 5. Heterotic grouping based on specific combining ability and grain yield

Heterotic group based on specific and general combining ability method (HSGCA)

According to Fan et al. (2009), the inbreds showed negative HSGCA effects with the tester GZ-613 (HA), while those that showed positive HSGCA effects with the tester SK-13 (HB) were placed into the heterotic A group, and vice versa (Table 6). The inbred lines identified as having positive HSGCA effects for both testers were unclassified. Contrarily, the inbreds showed negative HSGCA effects for both testers; we kept the line in the heterotic group if its HSGCA had the smallest value (or largest negative value). In view of this, nine inbreds viz., Sk5003/6, Sk5003/7, Sk5003/9, Sk5003/12, Sk5003/13, Sk5003/14, Sk5003/16, Sk5003/17 and Sk5003/18 were placed in group A. Similarly, group B comprised six inbreds, viz., Sk5001/1, Sk5001/4, Sk5001/5, Sk5003/8, Sk5003/10, and Sk5003/11. Further, the inbreds Sk5001/2, Sk5001/3, and Sk5003/15 were unclassified because they showed positive HSGCA effects with both testers (figure 4).

Table 6. Estimates of heterotic groups based on specific and general combining ability method (HSGCA) for grain yield across the three locations.

Figure 4. HSGCA value of lines with tester GZ-613 (HA) and tester SK-13 (HB)

DISCUSSION

Analyses of variance of grain yield and yield-related traits

Genetic diversity is essential for making outstanding progress toward improving a trait in a selection program (Badu-Apraku et al. 2013). The significant mean squares (MS) observed for location reflect that locations are dissimilar and suggest the need for multi-environment evaluation of the hybrids. Numerous investigators had previously reported the same finding (Ismail et al. 2020b; Mutimaamba et al. 2020; Habiba et al. 2022; Abd-Elaziz et al. 2024; Ismail et al. 2024a). The hybrids MS were highly significant for all measured traits, indicating that inbreds are divergent and enabling a selection program to improve these traits. These findings are consistent with those reported by Badu-Apraku and Oyekunle 2012; Oyetunde et al. 2020; Adewale et al. 2023 and Nivethitha et al. 2023. The high-significant MS for the interaction H x Loc indicates that the expression of these traits would be inconsistent across test locations, highlighting the importance of identifying high-yielding, as well as stable, hybrids across environments (Amegbor et al., 2017). Lines, testers, and line × tester MS were identified as highly significant for all the examined traits except tester for EHT, indicating variation between inbreds and testers, and additive and non-additive effects were important in the inheritance of these traits. Additionally, the results indicated that inbreds are divergent and can be classified into heterotic groups. Consequently, superior inbreds could be identified for the improvement of maize hybrids (Akinwale et al. 2014; Badu-Apraku et al. 2015; Ruswandi et al. 2015; Ismail et al. 2023b; Tabu et al. 2023). The significance of line x Loc, tester x Loc, and line x tester x Loc indicated that the performance of these traits fluctuated from location to location and underscored that selection for improvement in these traits has to be carried out for specific environments (Badu-Apraku et al. 2013; El‐Gazzar et al. 2013; Ismail et al. 2020a).

Contributions of lines, testers and Line × Tester in total variance

The lines’ proportion of the total variance was high for DSK, PHT, EHT, and ED traits, indicating that selecting inbreds with high desirable GCA for these traits could be promising for improving them (Efendi et al. 2024). Similarly, the tester SK-13 could serve as a good combiner for improving the EL and yield traits since the tester proportion of the total variance for ED and yield was 71% and 59%, respectively. The proportion of line x testers was 41% for EHT, indicating the importance of heterosis for this trait. The dominance of GCA (GCA line + GCA tester) over the SCA (SCA line x tester) for all the studied traits implied that additive gene action was more important than non-additive gene action for all traits, and GCA was the main player accounting for the differences among the hybrids. These findings corroborated the results reported by Ismail et al. (2023b) and Tabu et al. (2023). The fact that additive genetic variance is the main contributor to measured traits indicates that General Combining Ability (GCA) can be a reliable indicator of hybrid performance. Therefore, testing with a single representative tester should be adequate for initial hybrid selections. Additionally, inbred lines that exhibit positive GCA effects for grain yield and other traits are likely to pass on these desirable characteristics to their offspring, making them valuable for a breeding program (Makumbi et al., 2011).

Mean Performance

The two hybrids, H-1 and H-22, were significantly earlier compared to the check hybrid (66.9 days). Thus, they could be exploited to develop early-maturity hybrids that can escape drought stressors. Most hybrids showed significantly shorter and lower ear placement than the check hybrid. Ismail et al. (2024b) reported that short-stature hybrids could be used to reduce lodging and increase plant density. Whereas the tallest hybrids could be targeted for silage. Therefore, the hybrid H-3 (275.6 cm) could be a promising silage hybrid. Conversely, the three hybrids, viz., H-22, H-27, and H-28, have been identified as ideal for increasing plant density and decreasing lodging. The hybrids outperformed the check hybrid for EL and ED traits, and these traits could be utilized in a breeding program for high-yielding hybrids, since they correlated with grain yield. Four hybrids, i.e., H-32, H-34, H-18, and H-24, had significantly out-yielded the check hybrid for the yield trait. Thus, these hybrids should be evaluated extensively in multilocation yield trials and promoted for commercialization in Egypt to improve food security (Habiba et al., 2022, Ismail et al., 2023a).

Combining ability

Relative importance of GCA effects over SCA effects

The author further underscored the GCA the effects' proportion of the total variation as a predictor of hybrid performance based solely on GCA. The importance of SCA diminishes as the ratio approaches 1. Consequently, the closer the ratio is to 1, the less important SCA is, and the Hybrid performance can be reliably predicted by averaging the parents' GCA values. Interestingly, the predictive ratio for all traits except EHT was greater than 0.65, which nearly ensures the preponderance of GCA over SCA in this set of lines and testers. Consequently, the GCA effect can be used to predict the general performance of the hybrids, so that assessments based on a single representative tester ought to be adequate for making initial choices among this group of hybrids. This finding concurs with that of Amegbor et al. (2023).

General combining ability

Significant positive GCA effects are desired for EL, ED, and yield, while significant negative GCA values are preferred for DSK, PHT, and EHT. Inbred parents with desirable GCA effects could serve as donor parents to improve the traits they confer. Accordingly, the tester SK-13 was identified as a good combiner for all measured traits except EHT, so that all the out-yielding hybrids involved SK-13 as the male parent. Additionally, this underscored our results that additive gene action was a key player in the inheritance of these traits. The inbreds Inb-1, Inb-2, Inb-5, Inb-11, and Inb-14 possessed significant negative GCA effects concerning DSK, indicating that inbreds having earliness alleles are transmitted to their progenies. Significant negative GCA effects were displayed by Inb-6, Inb-10, Inb-11, and Inb-14 for PHT. Similarly, the two inbreds, Inb-11 and Inb-14, showed the desirable negative GCA effects for EHT. The inbreds that displayed desirable GCA effects for PHT and EHT could be deployed as donor parents in hybridization programs to reduce plant height, which is preferred to minimize lodging and increase plant density, ultimately enhancing yield. Six and four inbreds were identified as good combiners for EL and ED, respectively; these inbreds highlight their potential in the improvement of the grain yield program. The inbreds Inb-2, Inb-3, Inb-9, and Inb-15 recorded the highest significant positive GCA values for grain yield, suggesting that they could be important sources of favourable alleles for enhancing grain yield potential. Notably, the inbreds that displayed significant GCA effects for the measured traits could be utilized in hybrid development, for inbred recycling, and as testers for evaluating newly developed inbred lines (Akinwale et al. 2014; Ertiro et al. 2017; Adewale et al. 2023).

Specific combining ability

The hybrids identified as having desirable SCA effects for the studied trait could be deployed in a breeding program. Eight hybrids were identified with significant positive SCA effects on yield. Notably, the two hybrids, H-32 and H-34, displayed significant positive SCA effects on yield and outyielded the check hybrid. So, Predictions of hybrid performance become largely deterministic based on parental GCA values, given their dominant influence over SCA.

Correlation analysis between yield and yield-related traits

As expected, yield correlated significantly positively with EL (r = 0.73) and ED (r = 0.86), emphasising that yield is controlled by several traits and that secondary traits should be considered when selecting for yield. So that EL and ED would be used as indirect selection for grain yield improvement. The results of this study are consistent with the findings of Amegbor et al. (2022 & 2023). Contradictory: yield negatively correlated with DSK (r = −0.54); that is, could be due to numerous complex factors, such as resource allocation, timing of reproduction, and environmental influences. This finding is in line with those obtained by Aman et al. (2020).

4.6 | Heterotic groups

Understanding heterotic patterns facilitates breeders to develop new hybrids with enhanced yield potential. By assigning maize inbred lines into various heterotic groups, breeders can select appropriate testers and improve the performance of newly created hybrids. As per Vasal et al. (1992), positive Specific Combining Ability (SCA) effects indicate that the lines belong to different heterotic groups, whereas negative SCA effects indicate that the lines belong to the same heterotic group. Accordingly, this method classified only 9 inbreds into two heterotic groups, but was unable to classify the remaining inbreds. On the other hand, the HSGCA method classified the inbreds into two groups, leaving only 3 unclassified. Interestingly, the inbreds were placed in the same group using both methods. However, the groups in the HSGCA methods contained more inbreds than those in the SCA method. Though the two inbred viz., Inb-2 and Inb-3, were placed in group A based on the SCA method, they were unclassified based on the HSGCA method. It is striking that the SCA method could classify 50% of inbreds, whereas the HSGCA method classified 15 out of 18 inbreds (83%). Thus, the HSGCA method is more effective than the SCA method. Annor et al. (2020) identified the HSGCA grouping method as the most effective for classifying early yellow tropical maize inbred lines across environmental conditions (Striga infestation, drought, and optimal). Hybrids can be developed by crossing inbreds from different groups (Elmyhun et al. 2020; Ismail et al. 2023b). A similar method was used by numerous investigators to classify inbreds into heterotic groups (Menkir et al. 2004; Akinwale et al. 2014; Oyetunde et al. 2020; Ismail et al. 2022).

The relative importance of GCA effects over SCA effects underscored the preponderance of GCA over SCA in this set of lines and testers, suggesting that additive gene action played a predominant role in the inheritance of the grain yield and all other measured traits. The effectiveness of the HSGCA method is better than the SCA method for classifying inbred. The inbreds Inb-2, Inb-3, Inb-9, and Inb-15 recorded the highest significant positive GCA values for grain yield, suggesting that they could be important sources of favourable alleles for enhancing grain yield potential. Four hybrids, i.e., H-32, H-34, H-18, and H-24, were identified as promising hybrids in the present study and should be evaluated extensively in multilocation yield trials and promoted for commercialization in Egypt to improve food security.

Abd-Elaziz, M.A.A., Darwish, M.M.B., Aboyousef, H.A., Afife, A.A.M., Ismail, M.R., and Hassan, N.A. 2024. Diallel Analysis of Seven Maize Inbred Lines for Different Characters across Locations. Journal of the Advances in Agricultural Researches, 29(1): 16-21. https://doi.org/10.21608/jalexu.2024.259048.1184

Abd El Fatah, H. Y., Mohamed, E. A., Hassan, M. B., and Mohamed, K. A. 2015. An economic analysis for maize market in Egypt. Middle East Journal of Agriculture Research, 4(4): 873–878. https://www.curresweb.com/mejar/mejar/2015/873-878.pdf

Adewale, S. A., Badu‐Apraku, B., and Akinwale, R. O. 2023. Assessing the suitability of stress tolerant early‐maturing maize (Zea mays L.) inbred lines for hybrid development using combining ability effects and DArTseq markers. Plant Breeding, 142(2): 223–237. https://doi.org/10.1111/pbr.13077

Akinwale, R. O., Badu-Apraku, B., Fakorede, M. A. B., and Vroh-Bi, I. 2014. Heterotic grouping of tropical early-maturing maize inbred lines based on combining ability in Striga-infested and Striga-free environments and the use of SSR markers for genotyping. Field Crops Research, 156: 48–62. https://doi.org/10.1016/j.fcr.2013.10.015

Aman, J., Bantte, K., Alamerew, S. and Sbhatu, D.B. 2020. Correlation and path coefficient analysis of yield and yield components of quality protein maize (Zea mays L.) hybrids at Jimma, western Ethiopia. International Journal of Agronomy, 2020: 1-7. https://doi.org/10.1155/2020/9651537

Amegbor, I.K., Abe, A., Adjebeng-Danquah, J., Adu, G.B. and Oteng-Frimpong, R. 2022. Agronomic performance and combining ability estimates of yellow maize inbred lines under adequate and deficit moisture conditions. Journal of Crop Science and Biotechnology , 25: 183-197. https://doi.org/10.1007/s12892-021-00122-4

Amegbor, I. K., Badu-Apraku, B., and Annor, B. 2017. Combining ability and heterotic patterns of extra-early maturing white maize inbreds with genes from Zea diploperennis under multiple environments. Euphytica, 213(24): 1–16. https://doi.org/10.1007/s10681-016-1823-y

Amegbor I.K, Darkwa K, Nelimor C, Manigben K.A, Adu G.B, Aboyadana P.A, Kusi F, Keteku A.K, Owusu E.Y, Ackah H, and Labuschagne M.T. 2023. Yield Performance and Genetic Analysis of Drought Tolerant Provitamin a Maize Under Drought and Rainfed Conditions. FARA Research Report, 7(48): 604-621. https://library.faraafrica.org/storage/2023/04/FRR-Vol-748604-621.pdf

Annor, B., Badu-Apraku, B., Nyadanu, D., Akromah, R., and Fakorede, M. A. B. 2020. Identifying heterotic groups and testers for hybrid development in early maturing yellow maize (Zea mays L.) for sub-Saharan Africa. Plant Breeding, 139: 708–716. https://doi.org/10.1111/pbr.12822

Badu-Apraku, B., Fakorede, M. A. B., Gedil, M., Talabi, A. O., Annor, B., Oyekunle, M., Akinwale, R. O., Fasanmade, T. Y., Akaogu, I. C., and Aderounmu, M. 2015. Heterotic responses of IITA and CIMMYT early white maize inbred lines under multiple stress environments. Euphytica, 206: 245–262. https://doi.org/10.1007/s10681-015-1506-0

Badu-Apraku, B. and Oyekunle, M. 2012. Genetic analysis of grain yield and other traits of extra-early yellow maize inbreds and hybrid performance under contrasting environments. Field Crops Res 129: 99–110. https://doi.org/10.1016/j.fcr.2012.01.018

Badu-Apraku, B., Oyekunle, M., Fakorede, M. A. B., Vroh, I., Akinwale, R. O., and Aderounmu, M. 2013. Combining ability, heterotic patterns and genetic diversity of extra-early yellow inbreds under contrasting environments. Euphytica, 192: 413–433. https://doi.org/10.1007/s10681-013-0876-4

Baker, R.J. 1978. Issues in diallel analysis. Crop Sci 18: 535–536. https://doi.org/10.2135/cropsci1978.0011183X001800040001x

Efendi, R., Sebayang, A., Syahruddin, K., and Priyanto, S. B. 2024. Gene action and combining ability of erect and narrow leaves angles maize inbred lines. In AIP Conference Proceedings, 2957(1). AIP Publishing. https://doi.org/10.1063/5.0184508

El-Gazzar, I. A., El‐Ghonemy, M. A., and Mousa, S. Th. 2013. Evaluation of new inbred lines of white maize via line × tester analysis over three locations. Journal of Plant Production Mansoura University, 4(6): 897–906. https://doi.org/10.21608/jpp.2013.73303

Elmyhun, M., Liyew, C., Shita, A. and Andualem, M. 2020. Combining ability performance and heterotic grouping of maize (Zea mays) inbred lines in test cross formation in Western Amhara, North West Ethiopia. Cogent Food & Agriculture, 6: 1727625, 1-13. https://doi.org/10.1080/23311932.2020.1727625

Ertiro, B., Beyene, Y., Das, B., Mugo, S., Olsen, M., Oikeh, S., Juma, C., Labuschagne, M., and Prasanna, B. 2017. Combining ability and test-cross performance of drought-tolerant maize inbred lines under stress and non-stress environments in Kenya. Plant Breeding, 136(2): 197–205. https://doi.org/10.1111/pbr.12464

Fan, X.M., Bi, Y., Zhang, Y., Jeffers, D., Yin, X. and Kang, M. 2018. Improving breeding efficiency of a hybrid maize breeding program using a three heterotic‐group classification. Agronomy Journal 110(4): 1209-1216. 10.2134/agronj2017.05.0290

Fan, X.M.; Chen, H.M.; Tan, J.; Xu, C.X.; Zhang, Y.M.; Huang, Y.X. and Kang, M.S. 2008. A new maize heterotic pattern between temperate and tropical germplasm. Agronomy Journal 100(4): 917–923. 10.2134/agronj2007.0298

Fan, X. M., Zhang, M. Y., Yao, W. H., Chen, H. M., Tan, J., Xu, X. C., Han, X. L., Luo, L. M., and Kang, M. S. 2009. Classifying maize inbred lines into heterotic groups using a factorial mating design. Agronomy Journal, 101(9), 106–112. https://doi.org/10.2134/agronj2008.0217

Habiba, R. M. M., El-Diasty, M. Z., and Aly, R. S. H. 2022. Combining abilities and genetic parameters for grain yield and some agronomic traits in maize (Zea mays L.). Beni-Suef University Journal of Basic and Applied Sciences, 11(1): 108. https://doi.org/10.1186/s43088-022-00289-x

Hung, H.Y. and Holland, J.B. 2012. Diallel analysis of resistance to Fusarium ear rot and fumonisin contamination in maize. Crop Science 52(5): 2173-2181. https://doi.org/10.2135/cropsci2012.03.0154

Ismail, M.R., Aboyousef, H.A., Abd-Elaziz, M.A.A., Afifi, A.A.M. and Shalof, M.S. 2023a. Diallel analysis of maize inbred lines for estimating superiority and combining ability. African Crop Science Journal 31(4): 417– 425. https://doi.org/10.4314/acsj.v31i4.4

Ismail, M. R., Aboyousef, H. A., Mostafa, A. K., Afife, A. A. M., and Shalof, M. S. 2024a. Assessment of combining ability and mean performance of yield and its contributing traits in maize through line × tester analysis. Egyptian Journal of Plant Breeding, 28(1): 117–133. https://ejpb.journals.ekb.eg/article_366446.html

Ismail, M. R., El-Hosary, A. A., and El-Badawy, M. El. M. 2020a. Comparisons among three different testers for the evaluation of new inbred lines in mays (Zea mays L.). Maydica 65(1). https://journals-crea.4science.it/index.php/maydica/article/view/2072

Ismail, M.R., El-Hosary, A.A. and El-Badawy, M. El. M. 2020b. Genetic analysis for some top crosses of yellow maize. Electronic Journal of Plant Breeding 11(4): 1099-1105. https://doi.org/10.37992/2020.1104.178

Ismail, M. R., and El-Mouslhy, T. T. 2020. Evaluation of new yellow maize (Zea mays) inbred lines using line × tester analysis. The Indian Journal of Agricultural Sciences, 90(3): 621-624. https://doi.org/10.56093/ijas.v90i3.101500

Ismail, M. R., Galal, Y. A., Kotp, M. S., and El-Shahed, H. M. 2022. Assessment of combining ability and heterotic groups of new white maize inbred lines. Egyptian Journal of Plant Breeding, 26(2): 267–278. https://ejpb.journals.ekb.eg/article_291742.html

Ismail, M. R., Mohamed, H. A. A., Abd-Elaziz, M. A. A., and Aboyousef, H. A.2024b. Combining ability of yellow maize (Zea mays L.) inbred lines for yield and agronomic traits. The Indian Journal of Agricultural Sciences, 94(2): 135–139. https://doi.org/10.56093/ijas.v94i2.138214

Ismail, M. R., Mostafa, A. K., Abd-Elaziz, M. A. A., Rizk, M. S., and El-Mouslhy, T. T. (2023b). Heterotic grouping of maize inbred lines using line x tester analysis. Electronic Journal of Plant Breeding, 14(4): 1293–1301. https://www.ejplantbreeding.org/index.php/EJPB/article/view/4968

Job, A., and Igyuve, T. M. 2022. Line x tester analysis of early maturing maize inbred lines for yield and secondary traits. International Journal of Agriculture and Biological Sciences, 6(3): 21–36. http://dx.doi.org/10.5281/zenodo.7035876

Kempthorne, O. 1957. An introduction to genetic statistics. John Wiley and Sons Inc. https://psycnet.apa.org/record/1958-01083-000

Makumbi, D., Betrán, J. F., Bänziger, M., and Ribaut, J. M. 2011. Combining ability, heterosis and genetic diversity in tropical maize (Zea mays L.) under stress and non-stress conditions. Euphytica, 180: 143–162. https://doi.org/10.1007/s10681-010-0334-5

Menkir A., Melake-Berhan A., Ingelbrecht I. and Adepoju, A. 2004. Grouping of tropical mid-altitude maize inbred line on the basis of yield data and molecular markers. Theor Appl Genet 108(8):1582–1590. https://doi.org/10.1007/s00122-004-1585-0

Mosa, H. E., Aboyousef, H. A., Ismail, M. R.; El-Shahed, H. M., Shosha, A. M. A. and Alsbeay, R. H. A.2025. Comparative Evaluation of Three Methods for Heterotic Grouping in Yellow Maize. Madras Agricultural Journal, 112 (10–12): 110–126. https://doi.org/10.29321/MAJ.10.SE1224

Mutimaamba, C., MacRobert, J., Cairns, J. E., Magorokosho, C., Ndhlela, T., Mukungurutse, C., Minnaar‐Ontong, A., and Labuschagne, M. 2020. Line × tester analysis of maize grain yield under acid and non‐acid soil conditions. Crop Science, 60(2): 991–1003. https://doi.org/10.1002/csc2.20009

Nivethitha, T., Ravikesavan, R., Vinodhana, N. K., & Senthil, N. 2023. Development and genetic evaluation of single cross super-sweet (shrunken 2) sweet corn hybrids (Zea mays L.) var. saccharata L.): A novel choice for commercial market. Electronic Journal of Plant Breeding, 14(2): 429–438. https://doi.org/10.37992/2023.1402.053

Oyetunde, O. A., Badu-Apraku, B., Ariyo, O. J., and Alake, C. O. 2020. Efficiencies of heterotic grouping methods for classifying early maturing maize inbred lines. Agronomy, 10(8), 1198. https://doi.org/10.3390/agronomy10081198

Patil, M. S., Motagi, B. N., and Kachapur, R. M. 2020. Heterosis and combining ability studies in maize (Zea mays L.) for drought tolerance, TLB disease resistance and productivity in northern dry tract of Karnataka. International Journal of Current Microbiology and Applied Sciences, 9