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Combining abilities and genetic parameters for grain yield and some agronomic traits in maize (Zea mays L.)

Abstract

Background

The improvement of maize hybrids relies on significant genetic variability among the inbred lines used and the degree to which it is transferred to progeny. Understanding the genetic parameters and heritability of traits in every population is an essential precondition for selection program. This study was designed to estimate the magnitude of genetic parameters and heritability of traits in line × tester mating design. Fifteen yellow maize inbred lines derived from different sources were crossed with two lines as tester i.e., Gm-1001 and Gm-1002 in 2019 summer season. During 2020 season, the resulted 30 crosses along with check hybrid SC-168 were evaluated in a yield trial at two locations: Gemmeiza and Sids Agric. Res. Station.

Results

The mean squares among two locations were highly significant for all studied traits except KR−1. The L1 and L3 had the best general combiners for GY ard fed−1. In addition, the T1 as a tester was a GCA effects good combiner for earliness, shorter plants and lower ear placement traits. While T2 was good combiner for grain yield and some of attributes. Crosses L5 × T1 and L7 × T2 had positive and significant SCA effects for grain yield. The GCA variances were higher than SCA variances for all studied traits except for ED trait, indicating that additive genetic variance played the major role than non-additive genetic variance in the inheritance of these traits. Generally, phenotypic coefficient of variability was higher than genotypic coefficient of variability for all studied traits, suggesting that there was an influence by environments in the expression of these traits. Recorded heritability percentage in broad sense ranged from low for (ear position%, no. of ears/100 plants, ear diameter and grain yield) traits, medium for (days to 50% silking, plant height, ear height and no. of kernels/row) traits to moderate high for ear length trait.

Conclusion

Our investigation concluded that most of the studied lines exhibited highly general combiners and the superior crosses were as a result of a good × good combiner for most of yield components traits.

Background

Maize (Zea mays L.) is playing main role in human and livestock nutrition worldwide and is used for several industrial purposes such as oil and starch [11]. Internationally, maize is known as queen of cereals because of its highest genetic yield potential. The high yielding of maize hybrids influenced by inbred lines which had high general and specific combining abilities. Thus, maize breeders need to develop more maize inbred lines to produce more new hybrids. In breeding programs high yielding lines are often used as parents because the target is to increase grain yield. Line × tester mating design assesses a set of lines as a female by crossing each to a common tester as a male [21]. Combining ability is crucial for selecting appropriate parents for hybridization and identifying superior crosses in breeding programs [25]. Combining ability analysis is suitable to assess the potential inbred lines and helps to identify the nature of gene action involved in different quantitative traits. This mating design helps in defining the ability of the parental lines to inherit traits to the progeny and built on the basics of a factorial experiment however the line is considering as one factor and the tester as the other one. Simultaneously, it is useful in assessing several types of gene action and permits the inference of effects attributed to lines, testers and their interaction [17]. Progress of several crop development depends on the genetic variability and heritability present in the plant materials. The range of variability is measured by genotypic coefficient of variability (GCV) and phenotypic coefficient of variability (PCV) which offers information about relative extent of variation in altered traits. Hence, to have a thorough complete idea, it is necessary to an investigative valuation of yield components. Heritability gives the information on the magnitude of inheritance of quantitative traits [34]. Heritability is a heritable portion of phenotypic variance and helps the breeders to select out elite genotype from a diverse genetic component. GCV and PCV values were characterized as low when less than 10%, moderate 10–20% and high more than 20% [10] . While heritability was characterized as low when less than 40%, medium 40–59%, moderately high 60–79% and very high heritability 80% and above [31] .

The objectives of this study were to determine general and specific combining abilities effects, identify the superior crosses to improve the yielding ability in maize breeding programs, identify the nature of gene action and genetic variability for yield and yield components traits.

Methods

Plant materials and its sources

The plant materials of this investigation were composed of fifteen new yellow maize inbred lines derived from different sources at two Agricultural Research Stations; [Gemmeiza (Gm) and Sids (Sd)] namely L1, L2 to L15 along with two testers; Gm-1001 (T1) and Gm-1021 (T2).

Experimental sites and growing seasons

In 2019 season, the fifteen yellow maize inbred lines were crossed with the two testers in a line × tester mating design at Gemmeiza Agric. Res. Sta., National Maize Research Program to obtain 30 single crosses. During 2020 growing season, the resulted 30 crosses along with yellow check hybrid SC-168 were evaluated in a yield trial at Gemmeiza and Sids, Agricultural Research Stations.

Experimental design and its management

A randomized Complete Blocks Design (RCBD) with four replications was used at each location. Plot size was one row, 6 m long and 0.8 m a part. Seeds were planted in hills evenly spaced at 0.25 m along the row at the rate of two kernels hill1, which thinned to one plant hill1 after 21 days from planting date. The field trails were kept clean of weeds throughout the growing season, whereas all cultural practices for maize production were applied as recommended at the proper time.

Data recorded

The collected data were days to 50% silking (DTS day), plant height (PHT cm), ear height (EHT cm), ear position% (Epos %), no. of ears/100 plants (E100P), ear length (EL cm), ear diameter (ED cm), no. of kernels/row (KR1) and grain yield (GY ard fed1) adjusted to 15.5% moisture content, one ardab = 140 kg and one feddan = 4200 m2.

Statistical analysis

Data were analyzed using general linear model (GLM) procedures in SAS [29]. Means for all maize combinations adjusted for block effects through sites were analyzed according to Sendecore and Cochran [32]. Combining ability analysis was performed for traits that showed statistical differences among crosses. Kempthorne [18] was employed to determine general and specific combining abilities and their interaction effects with two locations.

Results

Analysis of variance

Analyses of variance across two locations for all studied traits are presented in Table 1. The results showed that the magnitude of mean squares among two locations were highly significant (P ≤ 0.001) for all studied traits except KR1. Moreover, highly significant differences were detected among crosses for all studied traits. Significant or highly significant mean squares were observed for lines (L), testers (T) and (L × T) interaction for all studied traits, except (T) for ED and L × T for EHT and Epos%. Significant or highly significant differences were observed between lines × location interaction for DTS, PHT, EHT and GY ard fed1, testers × location interaction for EL, ED and KR1. Meanwhile, Mean squares due to L × T × Loc interactions were significant for E100P and ED traits.

Table 1 Analysis of variances for nine traits combined across two locations

Mean performance

For all traits studied, the mean performance of the 30 crosses and one check hybrid SC-168 over two locations are presented in Table 2. Mean values of crosses for DTS ranged from 55.13 days for L1 × T2 to 61.63 days for L13 × T1. Furthermore, all crosses were significantly earlier than the check hybrid SC-168 (64.89 days). Regarding PHT, crosses ranged from 241.75 cm for cross L8 × T2 to 302.13 cm for cross L14 × T1. Four crosses; L8 × T2, L10 × T2, L7 × T2 and L15 × T2 were significantly shorter than the shortest check hybrid SC.168. For EHT, the lowest ear height was desirable trait for loading resistance. Thus, 9 out of 30 crosses had significantly lower ear height compared with the best check hybrid SC-168 (144.75 cm) and the crosses ranged from 106.39 cm for cross L11 × T2 to 164.00 cm for cross L14 × T1. For Epos%, 12 out of 30 crosses were significantly toward lower ear placement than the check hybrid SC-168 (54.32%). For E100p, mean values of crosses ranged from 96.20 for L9 × T1 to 117.90 for L11 × T2 and four crosses; L3 × T2, L5 × T2, L11 × T2 and L12 × T2 were significantly higher than the check hybrid SC-168. Concerning EL, nine crosses involving T1 as a tester (parent) had significantly compared with the best check hybrid SC-168. ED trait ranged from 4.31 cm for cross L15 × T2 to 5.05 cm for crosses L1 × T1 and L13 × T1 and most of crosses did not vary significantly from the check hybrid SC-168. For KR1, the crosses ranged from 31.20 for cross L13 × T2 to 39.18 for cross L2 × T1 and seven crosses differed significantly compared with the check hybrid. Concerning GY ard fed1, results revealed that the cross L5 × T2 had the highest grain yield (32.03) that was different significantly than the check hybrid followed by cross L1 × T2 (31.45 ard fed1). Furthermore, 14 crosses out of 30 crosses did not vary significantly compared with the check hybrid (27.53 ard fed1).

Table 2 Mean performances of 30 single crosses and check for all the studied traits combined across two locations

General combining ability effects

General combining ability (gi) effects for fifteen inbred lines and two testers for all studied traits across two locations are shown in Table 3. The parents L1, L4, L6, L7, L8 and L11 had negative significant or highly significant (desirable) toward earliness. With respect to PHT and EHT, the inbred lines L6, L7, L8 L10 and L11 showed negative and significant or highly significant GCA effects. For Epos%, the inbred lines L1, L11 and L15 had negative and significant GCA effects for lowest ear position. Concerning E100p, the lines L5 and L11 showed positive and highly significant GCA effects. For EL, six inbred lines (L2, L3, L5, L9, L12 and L14) exhibited positive and highly significant GCA effects. The inbred lines L1, L6, L8, L9 and L13 showed positive estimates of GCA effects for ear diameter. For KR1, L2, L3 and L5 expressed significant positively and seemed to be the best combiner for GCA effects. The best general combiners for GY ard fed1 were L1 and L3. These inbred lines which possessed high GCA effects for grain yield showed the same effect for one or more of the traits contributing to grain yield. Results showed that, T1 as a tester has good combiner GCA effects for earliness, shorter plants and lower ear placement traits. While T2 as a tester has good combiner GCA effects for grain yield and some of its attributes.

Table 3 General combining abilities effects for fifteen inbred lines and two testers for all the studied traits combined across two locations

Specific combining ability

Specific combining ability effects of 30 crosses for all studied traits combined across two locations are illustrated in Table 4. Results revealed that, crosses L7 × T2 and L9 × T2 showed the negative and significant SCA (desirable) effects for DTS toward earliness; L12 × T1 for PHT and EHT toward shorter plants. For ear position, L15 × T2 showed negative and significant SCA effects toward lower ear placement. Concerning E100p, cross L4 × T2 showed positive and significant SCA effect. Regarding to EL, five crosses out of 30 crosses exhibited positive and significant SCA effect and the cross L5 × T1 was the best one. The crosses L1 × T1 and L15 × T1 were the best combiners for ED. Four crosses; L1 × T2, L4 × T1, L7 × T2 and L12 × T2 had positive significantly SCA effect for KR1. The most desirable inter and intra allelic interactions were represented by crosses L5 × T1 and L7 × T2 for GY. In addition, that, these crosses showing desirable value of SCA effect.

Table 4 Specific combining abilities effects for fifteen single crosses for all the studied traits combined across two locations

Genetic parameters and their interactions with locations for all studied traits combined across two locations are shown in Table 5. Results revealed that the GCA variances were higher than SCA variances for all studied traits Except for ED trait. Furthermore, results reported that interaction of SCA × Loc was higher than GCA × Loc for all studied traits except DTS, El, KR1 and GY ard fed1.

Table 5 Genetic parameters and their interactions with locations for all the studied traits combined across two locations

Estimation of correlation coefficients between all studied traits across two locations are presented in Table 6. Results showed that EHT had highest positive and significant correlation with DTS and PHT (0.802 and 0.902), respectively. EL gave positive and significant correlation coefficients with DTS, PHT, EHT and Epos%. Grain yield has positive and significant correlations with E100p (0.703).

Table 6 Simple correlation coefficient between all the studied traits across two locations

Estimates of genetic variability; phenotypic and genotypic variances, phenotypic coefficient of variability (PCV) and genotypic coefficient of variability (GCV) and heritability in broad sense for grain yield and its attributes traits are presented in Table 7. Results showed that high phenotypic and genotypic variance values were recorded for PHT, EHT and E100p traits. Generally, phenotypic variance was higher than genotypic variance for all studied traits, suggesting that there was a minimal environment influence in the expression of these traits. PCV and GCV values differed and ranged rate for all studied traits; low PCV and GCA values were recorded for DTS, PHT, E100p and ED; moderate PCV values for EHT, Epos%, EL, KR−1 and GY traits and the GCV receded low values for the same traits. Heritability percentage in broad sense recorded ranged from low, medium to moderate high; low heritability values for Epos%, E100p, ED and GY traits; medium for DTS, PHT, EHT and KR−1 traits and moderately high heritability for EL trait.

Table 7 Genetic variability, genotypic coefficient and phenotypic coefficient of variability and heritability in broad sense for all studied traits of maize across two locations

Discussion

The magnitude of mean squares among two locations were highly significant (P ≤ 0.001) for all studied traits except KR1, indicating the existence of a clear variation between the two locations for these traits. These results agree with those reported by several researchers among of them; Mutimaamba et al. [24] and Mousa et al. [23]. Moreover, highly significant differences were detected among crosses for all studied traits, indicating that crosses had an extensive genetic diversity between themselves providing chance for selection among them. In literature, many works have found significant differences between crosses producing from maize, Aly [5], Darshan and Marker [9], Mutimaamba et al.[24], Mousa et al. [23] and Aly et al. [4]. Significant or highly significant mean squares were observed for lines (L), testers (T) and (L × T) interaction for all studied traits, except (T) for ED and L × T for EHT and Epos%. These results mean that extreme diversity exists among inbred lines and among testers and the inbred lines performed differently in their respective crosses depending on the type of testers used for these traits. These results agree with what was obtained by Abd El-Moula et al. [2], Aly and Khalil [6], Darshan and Marker [9] and Mousa et al.[23]. Significant or highly significant differences were observed between lines × location interaction for DTS, PHT, EHT and GY ard fed1, testers × location interaction for EL, ED and KR1. These results mean that lines and testers varied in their performance from location to another. Therefore, it would be useful to evaluate these lines and testers under multi- environmental conditions, especially for GY. Meanwhile, mean squares due to L × T × Loc interactions were significant for E100P and ED traits. Many studies reported that the interaction of line × tester × locations was highly significant for GY as Aly et al. [7] , Aly and Khalil [6], El-Gazzar et al. [12] and Gamea et al. [15].

Mean values of all crosses were significantly earlier than the check hybrid SC-168 (64.89 days). Four crosses; L8 × T2, L10 × T2, L7 × T2 and L15 × T2 were significantly shorter than the shortest check hybrid SC-168. In this respect, EL-Hosary and El-Gammal [13] and Kamara et al.[16] reported that the shortest plants in maize reduce lodging ratio and so increased yield potential. For EHT, the lowest ear height was desirable trait for loading resistance. Thus, 9 out of 30 crosses had significantly lower ear height compared with the best check hybrid SC-168. Concerning GY ard fed1, the cross L5 × T2 had the highest grain yield and was different significantly than the check hybrid followed by cross L1 × T2. Hence, these crosses can be used for improving maize grain yield [14] and [3].

High positive values of gi effects would be favorite for all studied traits except DTS, PHT, EHT and Epos% (desirable) where high negative would be useful from the plant breeder’s point of view. The parental lines L1, L4, L6, L7, L8 and L11 had desirable values toward earliness. With respect to PHT and EHT, the inbred lines L6, L7, L8 L10 and L11 were the best general combiners toward shorter plant and lower ear placement, implying the tendency of this line to reduce plant height, which is very important for development of genotypes resistant to loading [1] and [4]. The best general combiners for GY ard fed1 were L1 and L3. These inbred lines which possessed high GCA effects for grain yield showed the same effect for one or more of the traits contributing to grain yield, indicating that these lines could be considered as good combiners for improving grain yield which may contribute favorable alleles in the synthesis of new crosses.

The crosses L7 × T2 and L9 × T2 had desirable SCA effects for DTS toward earliness, L12 × T1 for PHT and EHT toward shorter plants. For ear position, L15 × T2 toward lower ear placement. Concerning E100p, cross L4 × T2 showed positive and significant SCA effect. Regarding to EL, the cross L5 × T1 was the best one. The crosses L1 × T1 and L15 × T1 were the best combiners for ED. Four crosses; L1 × T2, L4 × T1, L7 × T2 and L12 × T2 had positive significantly SCA effect for KR1. The most desirable inter and intra allelic interactions were represented by crosses L5 × T1 and L7 × T2 for GY.

The GCA variances were higher than SCA variances for all studied traits except for ED trait, indicating that additive genetic variance played the major role than non-additive genetic variance in the inheritance of these traits. Similar results were reported by Tessema et al. [36], Mosa [20], Mousa et al. [23] and Rachman et al. [26]. Furthermore, the SCA × Loc interaction was higher than GCA × Loc for all studied traits except DTS, El, KR-1 and GY ard fed1, indicating that additive gene action was more stable than non-additive gene action under different environmental conditions for these traits. These results are in accordance with those obtained by Mousa and Aly [22], El-Gazzar et al. [12] , Mousa et al. [23] and Aly et al. [4].

The high PCV and GCV variance values were recorded for PHT, EHT and E100p traits. Generally, phenotypic variance was higher than genotypic variance for all studied traits, suggesting that there was a minimal environmental influence on the expression of these traits. These results are in harmony with the results by Langade et al. [19], Sesay et al. [30] , Wedwessen and Zeleke [37] , Tesfaye et al. [35] and Aly et al.[4] . PCV and GCV values differed and ranged rate for all studied traits,low PCV and GCA values were recorded for DTS, PHT, E100p and ED, moderate PCV values for EHT, Epos%, EL, KR1 and GY traits and the GCV receded low values for the same traits. Similar results were obtained by Reddy and Jabeen [27] for DTS, Sesay et al. [30] for EHT, EL and GY traits. Heritability percentage in broad sense were low for Epos%, E100p, ED and GY traits,medium for DTS, PHT, EHT and KR1 traits and moderately for EL trait. Similar results were obtained by Sesay et al. [30] , Sravanti et al. [33], Bartaula et al. [8] and Aly et al. [4].

Conclusion

Finally, it could be concluded that most of studied lines exhibited highly general combiners and the superior crosses were as a results of a good × good combiner for most of yield components traits. Thus, the results suggested that the production of F1 hybrids at commercial level could be dependent on the combination between the good combiner lines.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

DTS:

Days to 50% silking (days)

PHT:

Plant height (cm.)

EHT:

Ear height (cm)

Epos%:

Ear position %

E100P:

Ears/100plant

El:

Ear length (cm)

ED:

Ear diameter (cm)

KR 1 :

No. of kernels row

GY:

Grain yield ard fed1

GCA:

General combining ability

SCA:

Specific combining ability

PCV:

Phenotypic coefficient of variability

GCV:

Genotypic coefficient of variability

References

  1. Abd El-Azeem MEM, Aly RSH, El Sayed WM, Hassan NA (2021) Combining ability and gene action using 10 X 10 diallel crosses of ten maize inbred lines (Zea mays L.). Plant Prod Mansoura Univ 12(11):1205–1211. https://doi.org/10.21608/jpp.2021.105615.1073

    Article  Google Scholar 

  2. Abd El-Moula MA, Abd El-Aal AMM (2009) Evaluation of some new yellow maize inbred lines via top cross analysis. Egypt J of Appl Sci 24(12A):148–166

    Google Scholar 

  3. Abdel-Moneam MA, Sultan MS, Abido WAE, Hadházy A, Sadek SA, Shalof MS (2020) Investigation of combining ability and superiority percentages for yield and some related traits in yellow maize using line × tester analysis. Acta Agrar Debr 1:4. https://doi.org/10.34101/actaagrar/1/4144

    Article  Google Scholar 

  4. Aly RSH, Abd El-Azeem MEM, Abd El-Mottalb AA, El Sayed WM (2022) Genetic variability, combining ability, gene action and superiority for new white maize inbred lines (Zea mays L.). J Plant Prod Sci Suez Canal Univ 11(1):1–10. https://doi.org/10.21608/jpps.2022.234792

    Article  Google Scholar 

  5. Aly RSH (2013) Relationship between combining ability of grain yield and yield components for some newly yellow maize inbred lines via line × tester analysis. Alex J Agric Res 58(2):115–124

    Google Scholar 

  6. Aly RSH, Khalil MAG (2013) Combining ability for sixteen promising white maize inbred lines for grain yield and yield component traits. Egypt J Plant Breed 17(3):143–160

    Article  Google Scholar 

  7. Aly RSH, Metwali EMR, Mousa SThM (2011) Combining ability of maize (Zea mays L.) inbred lines for grin yield and some agronomic traits using topcross mating design. Global J Mol Sci 6(1):01–08

    Google Scholar 

  8. Bartaula S, Panthi U, Timilsena K, Acharya SS, Shrestha J (2019) Variability, heritability, and genetic advance of maize (Zea mays L.) genotypes. Res Agric Livest Fish 6(2):163–169. https://doi.org/10.3329/ralf.v6i2.42962

    Article  Google Scholar 

  9. Darshan SS, Marker S (2019) Heterosis and combining ability for grain yield and its component characters in quality protein maize (Zea mays L.) hybrids. Electron J Plant Breed 10(1):111–118. https://doi.org/10.5958/0975-928X.2019.00013.9

    Article  Google Scholar 

  10. Deshmukh SNN, Basu MS, Reddy PS (1986) Genetic variability, character association and path coefficients of quantitative traits in Virginia Bunch varieties of Groundnut. Indian J Agric Sci 56:816–821

    Google Scholar 

  11. Eisele TG, Lazzari D, da Silva TA, Pinto RJB, Matsuzaki RA, Maioli MFOSD, do Ameral Junior AT (2021) Combining ability and genetic divergence among tropical maize inbred lines using SSR markers. Acta Sci Agron 43:il.53317. https://doi.org/10.4025/actasciagron.v43i1.53317

    Article  Google Scholar 

  12. El-Gazzar IA, El-Ghonemy MA, Mousa STh (2013) Evaluation of new inbred lines of white maize via line × tester analysis over three locations. J Plant Prod Mansoura Univ 4:897–906

    Google Scholar 

  13. El-Hosary AAA, El-Gammaal AA (2013) Utilization of line × tester model for evaluating the combining ability of some new white maize inbred lines. Egypt J Plant Breed 17(1):79–72

    Article  Google Scholar 

  14. El-Hosary AAA, Motawea MH, El Gammal AA (2018) Combining ability for yield and some of its attributes in maize across two locations. Egypt J Plant Breed 22(3):625–640

    Google Scholar 

  15. Gamea HAA, Farg SA, Aboyousef HA (2019) combining ability of some white maize inbred lines for grain yield and some other traits. J Plant Prod Mansoura Univ 10(12):1059–1063. https://doi.org/10.21608/JPP.2019.71550

    Article  Google Scholar 

  16. Kamara MM, El-Degwy IS, Koyama H (2014) Estimation combining ability of some maize inbred lines using line × tester mating design under two nitrogen levels. Aus J Crop Sci 8(9):1336–1342

    Google Scholar 

  17. Kaushik LS, Singh DP, Paroda RS (1984) Line × tester analysis for fixed effect model in cotton (Gossypium hirsutum L.). Theor Appl Genet 68(6):487–491. https://doi.org/10.1007/BF00284994

    CAS  Article  PubMed  Google Scholar 

  18. Kempthorne O (1957) An introduction to genetic statistical. John Wiley and Sons Inc, New York

    Google Scholar 

  19. Langade DM, Shahi JP, Srivastava K, Singh A, Agarwal VK, Sharma A (2013) Appraisal of genetic variability and seasonal interaction for yield and quality traits in maize (Zea mays L.). Plant Gene and Trait 4(18):95–103. https://doi.org/10.5376/pgt.2013.04.0018

    Article  Google Scholar 

  20. Mosa HE (2010) Estimation of combining ability of maize inbred lines using top cross mating design. J Agric Res Kafr El-Sheikh Univ 36(1):1–15

    Google Scholar 

  21. Motamedi M, Choukan R, Hervan E, Bihamta MR, Kajouri FD (2014) Investigation of genetic control for yield and related traits in maize (Zea mays L.) lines derived from temperate and sub-tropical germplasm. Int J Biosci 5(12):123–129

    Article  Google Scholar 

  22. Mousa SThM, Aly RSH (2012) Estimation of combining ability effects of new white maize inbred lines (Zea mays L.) via line × tester analysis. Egypt J Agric Res 90(4):77–90

    Google Scholar 

  23. Mousa STHM, Mohamed HAA, Aly RSH, Darwish HA (2021) Combining ability of white maize inbred lines via line X tester analysis. J Plant Prod Mansoura Univ 12(2):109–113. https://doi.org/10.21608/jpp.2021.154325

    Article  Google Scholar 

  24. Mutimaamba C, MacRobert J, Cairns JE, Magorokosho C, Ndhlela TH, Mukungurutse C, Minnaar-Ontong A, Labuschagne M (2020) Line × tester analysis of maize grain yield under acid and non-acid soil conditions. Crop Sci. https://doi.org/10.1002/csc2.2000

    Article  PubMed  PubMed Central  Google Scholar 

  25. Oyekunle M, Badu Apraku B, Hearne S, Franco J (2015) Genetic diversity of tropical early maturing maize inbred lines and their performance in hybrid combinations under drought and optimum growing conditions. Field Crop Res 170:55–65

    Article  Google Scholar 

  26. Rachman F, Oemaningtyas T, Wirnas D, Reflinur R (2022) Estimation of genetic parameters and heterosis through line × tester crosses of national sorghum varieties and local Indonesian cultivars. Biodiversitas 23(3):1588–1597. https://doi.org/10.13057/biodiv/d230349

    Article  Google Scholar 

  27. Reddy VR, Jabeen F (2016) Narrow sense heritability, correlation and path analysis in maize (Zea mays L.). SABRAO J Breed Genet 48(2):120–126

    Google Scholar 

  28. Robinson HF, Comstock RE, Harvey PH (1955) Genetic variances in open pollinated varieties of corn. Genetics 40:45–60. https://doi.org/10.1093/genetics/40.1.45

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. SAS Institute Inc. Statistical Analysis System / STAT user’s guide, version 9.2. SAS Institute Inc. Cary, NC, USA; 2008

  30. Sesay S, Ojo D, Ariyo OJ, Meseka S (2016) Genetic variability, heritability and genetic advance studies in top-cross and three-way cross maize (Zea mays L.) hybrids. Maydica Electron Publ 61(2):1–7

    Google Scholar 

  31. Singh BD (2001) Plant breeding: principles and methods. Kalyani Publishers, New Delhi, India

    Google Scholar 

  32. Snedecor GW, Cochran WG (1968) Statistical methods, 6th edn. Iowa State College Press, Ames, IA

    Google Scholar 

  33. Sravanti K, Swarnalatha ID, Sudarshan MR, Supriya K (2017) Evaluation of maize genotypes (Zea mays L.) for variability, heritability and genetic advance. Int J Curr Microbiol Appl Sci 6(10):2227–2232. https://doi.org/10.20546/ijcmas.2017.610.263

    Article  Google Scholar 

  34. Sumathi P, Madineni S, Veerabad Hiran P (2010) Genetic variability for different biometercial traits in pearl millet genotypes [Pennisetum glaucum (L) R.Br.]. Electron J Plant Breed 1(14):437–440

    Google Scholar 

  35. Tesfaye D, Abakema D, Habte E (2021) Genetic variability, heritability and genetic advance estimation of highland adapted maize (Zea mays L.) genotypes in Ethiopia. J Curr Opin Crop Sci 2(2):184–191

    Google Scholar 

  36. Tessema T, Sentayehu A, Temesgen M, Dagne W (2014) Test cross mean performance and combining ability study of elite lowland maize (Zea mays L.) inbred lines at Melkassa, Ethiopia. Adv Crop Sci Technol 2:140. https://doi.org/10.4172/2329-8863.1000140

    CAS  Article  Google Scholar 

  37. Wedwessen T, Zeleke H (2020) Genetic variability, heritability and genetic advance estimates in maize (Zea mays L.) inbred lines. Inter J Novel Res Life Sci 7(2):10–16

    Google Scholar 

Download references

Acknowledgements

The work was carried out and supported by the Maize Research Program, FCRI, ARC, Egypt and the Genetic department, faculty of Agriculture, Mansoura University.

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Conceptualization and methodology, RSHA, RSHA; application of the field trial at two location, RSHA, RMMH; made the statistical analysis of data, RMMH, MZ, RSHA; writing—original draft preparation, RMMH; review and editing, RMMH, MZ, RSHA; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Rehab Mohamed Mohamed Habiba.

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

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  • DOI: https://doi.org/10.1186/s43088-022-00289-x

Keywords

  • Maize
  • Line × tester
  • Combining ability
  • Gene action and genetic parameters