Received: 25 March 2022 Revised: 3 January 2023 Accepted: 9 January 2023 DOI: 10.1111/pbr.13077 OR I G I N A L A R T I C L E Assessing the suitability of stress tolerant early-maturing maize (Zea mays) inbred lines for hybrid development using combining ability effects and DArTseq markers Samuel A. Adewale1,2 | Baffour Badu-Apraku1 | Richard O. Akinwale2 1International Institute of Tropical Agriculture, Ibadan, Nigeria Abstract 2Department of Crop Production and Identification of hybrids for commercialization is crucial for sustainable maize produc- Protection, Obafemi Awolowo University, Ile Ife, Nigeria tion in sub-Saharan Africa (SSA). One hundred and ninety test crosses, 10 tester  - tester crosses + 10 hybrid checks were evaluated across 11 environments, 2017 to Correspondence Baffour Badu-Apraku, International Institute of 2019. Inheritance of grain yield under Striga infestation, optimal and across environ- Tropical Agriculture, PMB 5320, Ibadan, Oyo ments was influenced by additive genetic action, but there was greater influence of State, Nigeria. Email: b.badu-apraku@cgiar.org nonadditive gene action under drought stress conditions. Nine, seven and two inbreds had significant and positive general combining ability (GCA) effects for grain Funding information Bill and Melinda Gates Foundation, yield under Striga-infested, optimal and drought stress environments, respectively, Grant/Award Number: OPP1134248; and would contribute high grain yield to their progenies. Heterotic grouping methods OPP1134248 based on specific and GCA, GCA effects of multiple traits and DArTseq markers clas- sified the inbreds into five, three and two heterotic groups, respectively, across research conditions. The DArTseq markers method that classified the inbred lines into two major heterotic groups and was one of the most efficient methods should be adopted for practical purposes in maize breeding programmes in SSA. Hybrids TZEI 7  TZdEI 352, TZEI 1238  TZEI 7 and TZEI 1252  TZEI 7 had outstanding grain yield under contrasting environments and should be tested on-farm for com- mercialization in SSA. K E YWORD S combining ability, drought, gene action, heterotic groups—DArTseq markers, Striga hermonthica 1 | INTRODUCTION including recurrent drought and low soil nitrogen (low-N). Yield reduc- tion attributable to Striga parasitism ranges from 20% to 80% depend- Maize, the most important staple food crop in sub-Saharan Africa (SSA), ing on the Striga seed bank in the soil, level of host plant resistance or has the highest yield potential in the savannas of the subregion. Maize tolerance, soil fertility status and environmental factors notably drought is grown primarily for its carbohydrate-rich grains and its high-energy (Atera & Itoh, 2011). Although, several methods including the use of content has made it very vital in human and animal diets (Badu-Apraku herbicides, hand pulling and high nitrogen fertilization are available for et al., 2010). Despite the great potential, maize production and produc- improving maize yield under Striga infestation, planting of Striga- tivity in the savannas of SSA is significantly limited by biotic factors resistant varieties is considered the most economically practicable and such as Striga hermonthica (Del.) Benth parasitism and abiotic factors sustainable strategy for combating the menace. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 The Authors. Plant Breeding published by Wiley-VCH GmbH. Plant Breed. 2023;142:223–237. wileyonlinelibrary.com/journal/pbr 223 224 ADEWALE ET AL. The occurrence of drought stress in the past two decades has et al., 2017), North Carolina Design II (Oyekunle & Badu-Apraku, 2013) intensified, undoubtedly due to global climate changes combined with and line  tester (Amegbor et al., 2017; Ertiro et al., 2017; Fan reduced soil fertility and water-holding capacity, as well as displace- et al., 2009). However, when several inbred lines as well as proven ment of maize to marginal areas by high-value crops (Bänzinger inbred testers (from previous studies) are available for hybrid develop- et al., 2000). Drought at the flowering and grain filling periods of ment, production of testcrosses becomes the most efficient alterna- maize are the most sensitive. During the past decade, an important tive approach for determining the combining abilities and heterotic strategy employed by maize scientists in enhancing maize production patterns of inbred lines (Agbaje et al., 2008). The genetic materials and productivity in SSA is the concurrent improvement of maize used in the present study were newly developed inbred lines with germplasm for resistance to Striga and tolerance to drought, instead improved levels of tolerance to drought stress and resistance to of selecting maize genotypes that are resistant or tolerant to either of Striga. the stresses. This is because these stresses involve a common adap- Heterotic grouping methods used by researchers influence greatly tive mechanism (Badu-Apraku, Fakorede, et al., 2011; Bänzinger the assignment of maize lines into a particular heterotic group. Several et al., 1999). In a study that compared the effects of drought and heterotic grouping methods including the specific combining ability S. hermonthica on maize under field conditions, Badu-Apraku et al. (SCA ) effects of grain yield, heterotic grouping based on the general (2004) reported grain yield reduction of 53% and 42% under drought combining ability (GCA) and SCA effects of grain yield (HSGCA), het- and Striga infestation, respectively. Therefore, in the Sudan, Southern erotic grouping based on the GCA effects of multiple traits and Northern Guinea savannas where intermittent drought occurs fre- (HGCAMT) and molecular markers have been used for grouping quently, introgression of new sources of favourable alleles for drought inbred lines (Badu-Apraku et al., 2013; Fan et al., 2008). Application of tolerance into cultivars that possess resistance to Striga is crucial, as molecular markers such as simple sequence repeats (SSR) and single both stresses often occur simultaneously. Now, farmers in Striga nucleotide polymorphism (SNP) in the heterotic grouping of inbred endemic agro-ecologies of West and Central Africa (WCA) are lines has been less efficient and reports have been contradictory demanding cultivars with both Striga resistance and tolerance to (Akinwale et al., 2014; Badu-Apraku, Fakorede, Talabi, et al., 2016; drought and are reluctant to adopt maize cultivars that are not Menkir et al., 2004). As a result of the inconsistencies, the Diversity adapted to both drought-prone and Striga endemic environments Array Technology sequencing (DArTseq) markers was selected for the (Annor et al., 2019). heterotic grouping of tropical early maize inbred lines in the present For a hybrid development programme to be commercially suc- study. The advent of next-generation sequencing has greatly facili- cessful, adequate information on the patterns of inheritance, combin- tated the development of a rapid SNP discovery method, known as ing ability and heterotic response among the available inbreds in the DArTseq™. The DArTseq™ was developed utilizing the DArT marker programme is crucial. Reports on mode of inheritance of grain yield platform in combination with next generation sequencing platforms and other agronomic traits under S. hermonthica infestation and (Cruz et al., 2013; Raman et al., 2014). The DArTseq approach, a vari- drought stress conditions have been contradictory (germplasm spe- ant of genotyping-by-sequencing, implements complexity reduction cific) especially in tropical maize germplasm. Some earlier researchers methods that effectively targets the genome fraction containing pre- reported that resistance to S. hermonthica is polygenically controlled dominantly active genes (Baloch et al., 2017). Several studies have and influenced by additive gene action (Akanvou et al., 1997; reported the potential of these markers in diversity and population Badu-Apraku, 2007; Kim, 1994). On the other hand, results of some structure assessment in many crops (Abbasov et al., 2020; Allan other studies have revealed nonadditive gene action as being more et al., 2020; Badu-Apraku et al., 2021). important (Kim, 1991; Sangaré et al., 2018). Similarly, Guei and The objectives of this study were to (i) assess the combining abil- Wassom (1992) and Badu-Apraku, Oyekunle, et al. (2011) found the ity effects of newly developed inbred lines for grain yield and other nonadditive gene action to be more important in regulating the agronomic traits under drought stress, Striga infestation and optimal inheritance of grain yield under drought stress. Contrarily, results of conditions; (ii) classify the inbreds into heterotic groups using the other studies have revealed the predominance of additive gene action DArTseq markers and their combining ability effects; (iii) assess the in controlling the inheritance of grain yield under drought stress efficiency of the grouping methods in classifying the inbred lines; (Badu-Apraku et al., 2004; Edmeades et al., 1999; Meseka (iv) determine yield performance and stability of the hybrids across et al., 2013). This calls for more studies to confirm the mode of gene stress and optimal environments. action controlling the inheritance of grain yield of newly developed inbred lines under the contrasting stress conditions. Information on the combining abilities and heterotic groups of 2 | MATERIALS AND METHODS these inbreds would be very useful to breeding programmes in the tropics, as it would facilitate efficient planning of crosses to develop 2.1 | Genetic materials outstanding high-yielding hybrids for stress and nonstress environ- ments. Adequate information on hybrid performance under contrast- Thirty-eight early-maturing white maize (Zea mays L.) inbred lines ing environmental stresses can be obtained using mating designs such selected based on their responses to Striga infestation and drought as diallel (Akinwale et al., 2014; Badu-Apraku et al., 2015; Konate stress plus five inbred testers were utilized for this study (Tables S1 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 225 and S2). The inbred lines were developed from bi-parental crosses and K2O per hectare at two weeks after planting with additional involving a broad-based Striga resistant population, TZE Comp 5W 60 kg N ha1 applied as top dressing at four weeks after planting. In STR C7 and six drought tolerant inbred lines (TZEI 56, TZEI 31, TZEI the Striga-infested fields, weeds were removed manually leaving the 2, TZEI 87, TZEI 65 and TZEI 18) (Adewale et al., 2020). Striga plants. The Striga-free trials were kept weed-free using primex- tra and gramoxone at the rate of 5 L/ha each of atrazine and paraquat as pre- and post-emergence herbicides, respectively, and subse- 2.2 | Generation of testcrosses and field quently, by hand weeding. phenotyping The 38 inbred lines were crossed to five inbred testers (TZEI 7, TZEI 2.3 | Field data collection 18, TZEI 19, TZEI 31 and TZdEI 352) to produce 190 testcrosses using the line  tester design. The five elite inbred testers were considered Data were collected on plot basis for measured traits under the three as males while the 38 early inbred lines were regarded as females. In research conditions (drought stress, Striga and optimal) on days to addition, the five inbred testers were intermated in a half-diallel mat- anthesis and silking, anthesis–silking interval (ASI), plant and ear ing design to produce 10 tester  tester hybrids. The 190 testcrosses, heights, number of ears per plant, root, and stalk lodging. Data on 10 tester  tester hybrids + 10 hybrid checks (including commercial plant aspect were collected only under drought stress and optimal checks ENT 3  TZEI 65 commercialized in Nigeria, Ghana and Mali, conditions. Stay-green characteristic (leaf death score) was scored for TZE-W Pop DT STR C4  TZEI 7 commercialized in Ghana and TZEI the drought-stressed plots at 70 days after planting on a scale of 1–9, 60  TZEI 86 commercialized in Nigeria and Ghana) constituted the where 1 = 0%–10% dead leaf area (almost all leaves green), hybrid trial. The experimental design was 14  15 alpha lattice design 9 = 90%–100% dead leaf area (all leaves virtually dead). Under the with two replicates. The experimental units were single-row plots, artificial Striga infested environments, data on Striga emergence 3 m long, with within row spacing and intra-row spacing of 0.75 and counts and host plant damage severity were collected twice, at eight 0.4 m, respectively. Evaluation of the trial was carried out at four and 10 WAP (Akinwale et al., 2014; Kim, 1991). Grain yield (kg/ha) experimental sites in Nigeria (Table S3), namely, Mokwa (Striga- under drought was estimated from shelled grain weight per plot infested and Striga-free conditions) and Kubwa (Striga-infested) during adjusted to 15% moisture content. Conversely, in the Striga-infested the 2017 and 2018 growing seasons, managed drought stress at and Striga-free environments, grain yield (kg/ha) was estimated by Ikenne during the 2017/2018 and 2018/2019 dry seasons as well as assuming 80% (800 g grain/kg ear weight) shelling percentage of the well-watered (rainfed) conditions at Ikenne during the growing sea- de-husked ears per plot and adjusting to 15% moisture content. sons of 2017 and 2018. Additionally, the trials were evaluated under rainfed conditions at Kadawa, a natural terminal drought-prone envi- ronment during the 2018 growing season. However, terminal drought 2.4 | DNA extraction, DArT markers genotyping was not achieved, and this test environment was considered as an and quality control optimal environment. The managed drought stress experiment at Ikenne was carried out as described by Adebayo et al. (2014) and Fresh leaf samples were collected from the 38 inbred lines and five Badu-Apraku, Fakorede, Gedil, et al. (2016). The Striga fields at testers (one leaf per plant, 8–10 seedlings per genotype) at Mokwa and Kubwa were artificially infested with Striga by injecting two weeks after planting. The leaves were bulked and lyophilized ethylene gas into the soil at about two weeks before planting to before DNA extraction. Genomic DNA samples were isolated from induce suicidal germination of existing Striga seeds in the soil. The the freeze-dried leaf tissues using the DArT protocol for genomic artificial Striga infestation procedure proposed by the IITA Maize DNA extraction available online (www.diversityarrays.com/files/ Improvement Programme was followed (Kim, 1991). Detailed descrip- DArT_DNA_isolation.pdf). The quality of genomic DNA was checked tion of the trial management under artificial Striga infestation at by agarose gel electrophoresis and quantity was estimated using Kubwa and Mokwa has been described by Badu-Apraku et al. (2020). Nanodrop ND-2000 spectrophotometer (Thermo Scientific, Wilming- In all experiments, three maize seeds were sown in the same hole ton, DE, USA). The extracted DNA were sent to Integrated Genomic and seedlings thinned to two plants per stand at two weeks after Service and Support (IGSS), Nairobi-Kenya for SNP genotyping using emergence to obtain a population density of 66 666 plants per hect- the high-throughput DArTseq technology (Raman et al., 2014). Reads are. Fertilizer application on the Striga-infested maize plots was and tags found in the resulting sequences were aligned to the Z. mays delayed until about 25 days after planting (DAP) during which 20– L. reference genome, version AGPV3 (B73 Ref-Gen v4 assembly) (Jiao 30 kg N ha1, 30 kg P ha1 and 30 kg K ha1 were applied as 15– et al., 2017), giving a raw dataset of 47 440 DArTseq markers. DArT- 15–15 NPK depending on the fertility status of the soil. The reduced seq markers with missing rate greater than 10%, heterozygosity more rate of fertilizer application was necessary because Striga emergence than 20%, minor allele frequency (MAF) less than 0.05 as well as those decreases at high N rate (Badu-Apraku et al., 2020; Kim, 1991). Com- with unknown, and duplicate chromosome positions were eliminated, pound fertilizer (15–15–15 NPK) was applied to the Striga-free trials resulting in 7224 DArTseq markers distributed across the 10 chromo- at Ile-Ife, Ikenne, Mokwa and Kadawa at the rate of 60 kg N, P2O5 somes, which were employed for the phylogenetic analysis. 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 226 ADEWALE ET AL.    2.5 | Data analysis HPWGC LYBG TNBGC 100 þ  C TNWGC100 2 Analysis of variance (ANOVA) was carried out on plot means for all data collected under each (drought, Striga-infested, and optimal grow- where HPWGC = number of highly productive between-group ing conditions) and across 11 research environments using PROC crosses, TNBGC = total number of between-group crosses, GLM and RANDOM statement with test option, all implemented in LYBGC = number of low-yielding within-group crosses, and Statistical Analysis System (SAS Institute, 2011). The location–year TNWGC = total number of within-group crosses. In order to identify combinations, replicates and incomplete blocks within replicate the best performing hybrids across stress environments (Striga and effects were considered as random factors whereas the genotype was drought), the multiple-trait selection index (MI) was estimated as considered as a fixed effect in the combined ANOVA. Without the described by Badu-Apraku, Yallou, et al. (2017). The top 15 Striga and checks, GCA effects of the inbreds, and SCA effects of the crosses as drought tolerant/resistant and the five most susceptible as well as five well as their mean squares under each and across research conditions checks were selected for genotype main effect plus for grain yield and other agronomic traits were estimated for the test- genotype  environment interaction (GGE) biplot analysis to break- crosses following the line  tester analysis mating design proposed by down the G  E interactions into its component parts (Yan, 2001), Singh and Chaudhary (1985). The source of variation for testers was using the genotype  environment analysis with R for Windows partitioned into orthogonal contrasts to estimate the significance of (GEAR) software (Pacheco et al., 2016). The ‘mean versus stability contrasts among testers. The testers TZEI 31, TZEI 19, TZEI 18, TZEI view’ was used to identify the most promising hybrids with high and 7 and TZdEI 352 were designated as 1, 2, 3, 4 and 5, respectively. The stable yield across stress and optimal environments. relative contributions of GCA (GCAline + GCAtester) and SCA effects were computed for grain yield and other measured traits as the pro- portion of the GCA component to the total genetic sum of squares for 3 | RESULTS each trait (Annor et al., 2019). The larger the percentage of the sum of squares of a trait due to GCA/SCA, the greater the predictability of 3.1 | Analyses of variance of grain yield and other the trait based on GCA or SCA. traits of early-maturing white hybrids across research The 38 inbred lines were assigned to heterotic groups in indi- environments vidual and across research environments by employing the HGCAMT method (Badu-Apraku et al., 2013), HSGCA method (Fan The combined ANOVA of the hybrids across 11 research environ- et al., 2008) and genetic distance (GD) based on DArT-SNP markers. ments showed highly significant mean squares for grain yield and Ward's minimum variance cluster analysis based on the Euclidean other measured traits for Env, G and G  Env (Table 1). The combined distance generated from the three methods was used to assign the ANOVA of the testcrosses under Striga infestation, drought and opti- 38 inbreds into heterotic groups under each environment and mal research conditions are presented in Tables S4 and S5. Partition- across the 11 test environments using SAS version 9.3 (SAS ing of the overall variation of genotypes into lines (GCAline), testers Institute, 2011). For the heterotic grouping based on the DArTseq- (GCAtester) and line  tester (SCAline  tester) components revealed sig- SNP markers, the pair-wise GD estimates among the inbred lines nificant variation for nearly all the measured traits (Table 1). The were computed using PowerMarker version 3.25 (Liu & Env  GCAline, Env  GCAtester and E  SCAline  tester interaction Muse, 2005). To compare the effectiveness of the three heterotic mean squares revealed significant effects for majority of the studied grouping approaches, the 190 testcross hybrids were ranked from traits across research environments. Orthogonal comparisons the best performing to the least performing, taking into consider- between Testers 1, 2, 3, and 4, 5 were significantly different for all ation their grain yield under drought, Striga, optimal and across measured traits except ear height (Table 1). Comparisons of the differ- research environments (Fan et al., 2009). This involved the division ences between Tester s1 and 2, 3 and Testers 2 and 3 were signifi- of the total number of hybrids for each heterotic grouping proce- cantly different for grain yield and a few other traits. However, dure into two main groups that is, intergroup and intragroup crosses comparison between Testers 4 and 5 was not significantly different based on the classification of the inbred lines into the same or dif- for most measured traits including grain yield. ferent heterotic groups by each grouping method. These two groups were thereafter classified into high-yielding hybrids (Yield Group 1 with average grain yield among the first 63); moderately 3.2 | Relative contributions of general and specific high-yielding hybrids (Yield Group 2 with average grain yield combining ability effects of the inbred lines between the 64th and the 126th) and low-yielding hybrids (Yield Group 3 with average grain yield between the 127th and the The proportion of GCA (GCAline + GCAtester) sum of squares to the 190th). The best grouping method was detected based on the total genetic effects was consistently higher for all assayed traits breeding efficiency described by Fan et al. (2009) and adapted by under Striga infestation and across research conditions except for Badu-Apraku, Fakorede, Talabi, et al. (2016). The breeding effi- grain yield and majority of the traits under optimal conditions ciency was estimated as follows: (Figure 1). Contributions of SCA sum of squares were larger for grain 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 227 TABLE 1 Mean squares from the analysis of variance of grain yield and other agronomic traits of early-maturing testcrosses across 11 research environments in Nigeria, 2017–2019 SoV Df Yield DSK DTA ASI PHT EHT EPP SLG EASP HC PASP Block (Rep*Env) 432 2 301 576** 14.16** 9.26** 2.89** 515.95** 2.90** .04** 205.97** 1.66** .79** 1.96** Rep (ENV) 11 20 863 535** 19.23** 16.94** 4.37** 2046.05** 911.56** 0.11** 446.62** 3.63** 1.70** 5.83** ENV 10 967 406 125** 4087.07** 2235.22** 52.48** 142 318.60** 56 115.59** 6.65** 46 541.71** 96.98** 136.79** 214.77** Entry (G) 189 76 69 466** 46.96** 39.36** 5.383** 1195.08** 538.43** .08** 463.12** 4.37** 3.25** 3.39** Line 37 15 066 552** 163.14** 13.31** 12.23** 2615.10** 969.57** .14** 1207.56** 7.73** 9.61** 4.86** Tester 4 119 285 223** 222.03** 183.61** 42.01** 20 026.23** 9225.66** 1.26** 2076.78** 81.75** 52.45** 5.72** Contrast 1, 2, 3 vs. 4, 5 1 309 491 471** 129.20** 33.97** 21.15** 8356.32** 16.29 3.55** 5689.86** 16.61** 136.18** 92.46** Contrast 1 vs. 2, 3 1 11 4832 099** 82.26** .45 89.06** 15 779.27** 16 127.77** .00 19.24 3.97 47.00** 75.10** Contrast 2 vs. 3 1 15 896 851** 7.05 7.33 25.19** 25 303.43** 10 111.08** 0.33** 2775.66** 52.99 3.18 1.72 Contrast 4 vs. 5 1 4 230 965 583.60 615.58** 2.39 24 461.23** 5923.17** 1.01** .02 41.09** 1.66 15.32** Line*Tester 148 3 018 130** 21.94** 18.18** 3.98** 306.94** 158.65** 0.05** 263.62** 1.83** 1.40** 1.41** ENV*Entry 1900 1 594 106** 6.95** 1029.09** 2.79** 189.83** 101.25** .03** 19.95** .96** 1.12** .89** ENV*Line 400 3 010 611** 11.77** 9.83** 3.43** 382.52** 188.84** 0.05** 313.58** 1.76** 2.06** 1.27** ENV*Tester 40 13 119 289** 17.93** 12.01** 12.72** 1577.26** 897.69** 0.19** 738.27** 6.98** 12.93** 4.39** ENV*Line*Tester 1480 1 486 181** 8.43** 5.59** 2.84** 228.58 109.84* .03** 173.21* .92** .91** 1.02 Error 1843 804 088 3.92 2.37 1.74 123.20 65.60 .02 14.55 .54 .52 .62 Note: The testers TZEI 31, TZEI 19, TZEI 18, TZEI 7 and TZdEI 352 were designated as 1, 2, 3, 4 and 5, respectively. Abbreviations: ASI, anthesis-silking interval; DSK, days to silking; DTA, days to anthesis; EASP, ear aspect; EHT, ear height; ENV, environments; EPP, ears per plant; HC, husk cover; ns, not significant; PASP, plant aspect; PHT, plant height; RLG, root lodging; SLG, stalk lodging; SoV, source of variation. *Significant at .05 probability levels, respectively. **Significant at .01 probability levels, respectively. 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 228 ADEWALE ET AL. F IGURE 1 Percentage contribution of total genotypic sum of squares of grain yield and other measured traits of early-maturing maize inbreds due to GCA-line, GCA-tester and SCA-line  tester under each and across research conditions between 2017 and 2019. STR, Striga; OPT, optimal; DT, drought; ACR, across; ASI, anthesis–silking interval; LFDT, leaf death score; SR1, Striga damage rating at 8WAP; SR2, Striga damage rating at 10WAP; CO1, Striga emergence counts at 8WAP; CO2, Striga emergence counts at 10WAP. [Color figure can be viewed at wileyonlinelibrary.com] yield, ASI, plant aspect, ear aspect and ears per plant under drought method whereas the DArTseq marker-based GD was able to classify stress conditions. 29 out of the 38 inbred lines (76%). In the placement of the inbreds Of the nine inbreds with significant and positive GCA effects for into heterotic groups, there was slight conformity between HSGCA grain yield under Striga infestation, TZEI 1203, TZEI 1252 and TZEI and DArTseq marker-based GD grouping approaches across research 1348 also displayed positive and significant GCA effects as well as environments. For instance, the placement of the inbred lines into negative and significant GCA effects for both Striga damage ratings heterotic groups followed the same trend such that TZEI 1092, TZEI and Striga emergence counts under artificial Striga infested conditions 811 and TZEI 836 were placed in Group 1. (Table 2). The inbred lines TZEI 1321 and TZEI 1323 displayed desir- The HGCAMT, HSGCA and DArT-SNP heterotic grouping able GCA effects (significant and positive) for grain yield under approaches detected 52, 61 and 62 high-yielding crosses out of a total drought stress conditions. The inbreds TZEI 804, TZEI 811, TZEI of 145, 157 and 147 interheterotic crosses, respectively, across 836, TZEI 932, TZEI 1137 and TZEI 1238 had significant and negative 11 research environments (Table 4). The number of between- and GCA effects for leaf death score. The inbred lines TZEI 771, TZEI within-group hybrids classified by HSGCA, HGCAMT and DArTseq 916, TZEI 1203, TZEI 1238, TZEI 1241, TZEI 1271 and TZEI 1305 methods and breeding efficiencies of the grouping methods under showed significant and positive GCA effects for grain yield under opti- Striga infestation, drought stress and nonstress research conditions mal environments. are presented in Table S7. Both the HSGCA and DArT-SNP methods had the highest breeding efficiencies across research conditions, 52.8% and 52.5%, respectively, whereas that of the HGCAMT method 3.3 | Heterotic grouping of the inbred lines and was the least efficient (44.6%). efficiency of heterotic grouping methods The HSGCA, HGCAMT and DArTseq marker-based GD methods clas- 3.4 | Yield performance and stability of hybrids sified the inbred lines into five, three and two heterotic groups, under stress and optimal conditions respectively, across research conditions (Table 3). Similarly, the HSGCA and HGCAMT placed the inbreds into five and three clusters Nine out of the 15 top performing hybrids across stress conditions each under Striga, drought and optimal conditions (Table S6). Across had inbred tester TZdEI 352 involved in their crosses while two each research environments, 33 out of 38 inbred lines (87%) were classified of the worst five hybrids had testers TZEI 31 and TZEI 19 involved in by the testers based on the HSGCA heterotic grouping method, their crosses (Table 5). The hybrid TZEI 7  TZdEI 352 obtained from 26 out of the 38 inbred lines (68%) were grouped by the HGCAMT intermating among the testers recorded the highest grain yield across 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 229 TABLE 2 General combining ability effects of grain yield and other agronomic traits of 38 early maturing maize inbred lines across four Striga-infested, two drought and five optimal environments in Nigeria, 2017–2019 Yield (kg/ha) Days to silking Inbred Striga Drought Optimal Ears per plant Across Days to anthesis TZEI 762 65.83** 417.40* 101.00 .04* 0.32 0.47 TZEI 769 102.73 28.49 22.36 0.01 2.65** 2.44** TZEI 771 250.00 328.20 961.44** 0.02 0.68 0.28 TZEI 804 70.56 140.31 135.22 0.02 2.36** 1.94** TZEI 806 215.43 56.76 328.29 0.05** 0.85 0.64* TZEI 807 177.53 288.79 197.17 0.04 0.24 0.44 TZEI 811 1131.18** 28.72 216.65 0.04* 1.98** 1.61** TZEI 835 701.39** 345.04 386.40 0.08** 0.90** 0.57* TZEI 836 529.03* 38.35 424.14* 0.05** 0.73** 0.74** TZEI 868 674.64** 474.60* 672.86** 0.00 1.13** 0.94** TZEI 907 456.21* 229.66 404.17 0.04* 0.21 0.06 TZEI 916 495.12* 120.43 447.52* 0.00 1.93** 1.86** TZEI 932 512.26* 287.30 391.83 0.01 1.32** 1.36** TZEI 933 193.48 170.31 288.51 0.03 1.29** 1.58** TZEI 934 699.46** 212.53 159.45 0.01 0.28 0.16 TZEI 968 493.96* 216.38 79.63 0.04 0.46 0.70** TZEI 972 734.1** 221.94 33.53 0.03 0.81** 0.54* TZEI 1021 171.81 136.66 340.43 0.03 1.14** 0.62* TZEI 1028 27.38 220.54 317.85 0.02 1.54** 1.41** TZEI 1092 68.68 707.56** 559.17** 0.01 2.19** 1.62** TZEI 1107 1283.8** 71.83 708.55** 0.00 0.75** 0.15 TZEI 1134 588.71** 39.49 120.55 0.00 0.24 0.45 TZEI 1137 305.36 281.37 1110.67** 0.03 0.29 0.50 TZEI 1145 70.83** 666.20** 746.32** 0.04 0.03 0.27 TZEI 1203 947.65** 127.62 898.46** 0.07** 0.64* 0.28 TZEI 1207 538.94* 218.71 129.65 0.04 0.91** 0.53 TZEI 1225 199.14 185.44 96.69 0.01 0.08 0.25 TZEI 1237 401.65 170.66 134.42 0.00 0.18 0.02 TZEI 1238 289.82 129.24 720.96** 0.01 0.33 0.33 TZEI 1241 176.41 221.44 682.86** 0.00 1.63** 1.49** TZEI 1252 1178.65** 216.71 249.14 0.05* 0.89** 0.89** TZEI 1271 445.71* 252.63 572.71** 0.03 0.40 0.72** 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 230 ADEWALE ET AL. TABLE 2 (Continued) Yield (kg/ha) Days to silking Inbred Striga Drought Optimal Ears per plant Across Days to anthesis TZEI 1305 394.95 98.71 478.09* 0.02 0.45 0.24 TZEI 1321 0.72 530.75** 103.42 0.04* 0.98** 1.09** TZEI 1323 601.51** 411.97* 372.68 0.02 0.85** 0.61* TZEI 1341 6.18 142.64 23.74 0.00 1.25** 1.49** TZEI 1344 911.45** 103.45 353.07 0.06** 1.97** 1.70** TZEI 1348 1292.49** 306.12 379.75 0.04* 0.40 0.35 S. E 221.07 20.09 217.66 0.02 0.31 0.28 TABLE 2 (Continued) Anthesis-silking Striga damage rating Striga emergence interval at 10 WAP count at 10 WAP Leaf death Inbred Across Ear aspect Plant aspect Husk cover Plant height Striga score TZEI 762 0.18 0.06 0.49** 0.56** 13.57** 0.57** 0.10* 0.56* TZEI 769 0.37* 0.02 0.06 0.67** 1.14 0.28 0.08 0.01 TZEI 771 0.49** 0.21 0.49** 0.04 5.00** 0.12 0.00 0.11 TZEI 804 0.47** 0.05 0.06 0.05 0.35 0.17 0.00 .94** TZEI 806 0.22 0.08 0.07 0.30** 4.81** 0.27 0.05 0.11 TZEI 807 0.25 0.12 0.03 0.20 0.84 0.09 0.02 0.16 TZEI 811 0.46** 0.09 0.17 0.16 0.98 0.96** 0.04 0.59* TZEI 835 0.31 0.40** 0.31** 0.35** 3.75* 0.36* 0.13** 0.04 TZEI 836 0.11 0.25* 0.26* 0.30* 5.01** 0.68** 0.09* 0.49* TZEI 868 0.14 0.35** 0.38** 0.13 5.38** 0.33 0.02 0.14 TZEI 907 0.22 0.12 0.01 0.03 3.09 0.58** 0.04 0.16 TZEI 916 0.08 0.11 0.37** 0.15 3.18 0.03 0.11* 0.39 TZEI 932 0.00 0.03 0.14 0.19 4.31** 0.23 0.10* 0.64** TZEI 933 0.31 0.02 0.14 0.05 2.41 0.27 0.12** 0.16 TZEI 934 0.23 0.11 0.02 0.06 0.62 0.53** 0.19** 0.09 TZEI 968 0.21 0.24* 0.08 0.55** 5.17** 0.81** 0.03 0.71** TZEI 972 0.32* 0.23* 0.11 0.37** 3.79* 0.53** 0.14** 0.76** TZEI 1021 0.47** 0.02 0.06 0.43** 0.51 0.13 0.01 0.71** TZEI 1028 0.13 0.10 0.17 0.07 9.19** 0.01 0.08 0.31 TZEI 1092 0.40* 0.43** 0.33** 0.31* 1.25 0.19 0.16** 1.01** TZEI 1107 0.63** 0.47** 0.24* 0.07 3.74* 1.11** 0.02 0.09 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 231 TABLE 2 (Continued) Anthesis-silking Striga damage rating Striga emergence interval at 10 WAP count at 10 WAP Leaf death Inbred Across Ear aspect Plant aspect Husk cover Plant height Striga score TZEI 1134 0.26 0.06 0.02 0.22 3.79* 0.14 0.11* 0.06 TZEI 1137 0.67** 0.35** 0.38** 0.02 1.24 0.34 0.01 0.84** TZEI 1145 0.67** 0.54** 0.42** 0.28* 2.57 0.38* 0.02 0.11 TZEI 1203 0.23 0.57** 0.27* 0.49** 1.39 0.59** 0.11* 0.34 TZEI 1207 0.33* 0.27* 0.23 0.25* 2.11 0.39* 0.07 0.19 TZEI 1225 0.15 0.03 0.09 0.10 1.17 0.16 0.02 0.01 TZEI 1237 0.05 0.13 0.06 0.20 0.54 0.17 0.04 0.01 TZEI 1238 0.12 0.33** 0.44** 0.38** 2.66 0.13 0.01 0.49* TZEI 1241 0.19 0.29** 0.34** 0.16 6.96** 0.06 0.05 0.01 TZEI 1252 0.05 0.28** 0.09 0.17 2.63 1.27** 0.13** 0.11 TZEI 1271 0.28 0.10 0.27* 0.00 10.65** 0.14 0.01 0.11 TZEI 1305 0.21 0.09 0.07 0.26 1.16 0.01 0.07 0.11 TZEI 1321 0.20 0.03 0.21 0.16 2.46 0.19 0.05 0.16 TZEI 1323 0.26 0.19 0.11 0.28** 4.51** 0.14 0.07 0.14 TZEI 1341 0.38* 0.10 0.11 0.08 4.49** 0.07 0.02 0.34 TZEI 1344 0.15 0.32** 0.26* 0.21 6.30** 0.94** 0.10* 0.09 TZEI 1348 0.17 0.34** 0.17 0.37** 3.68* 1.27** 0.11 0.46 S. E 0.17 0.12 0.12 0.13 1.76 0.19 0.04 0.25 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 232 ADEWALE ET AL. TABLE 3 Summary of the heterotic groups of 38 early-maturing white inbred lines identified by different heterotic grouping methods across 11 research environments between 2017 and 2019 Heterotic grouping approach Group 1 Group 2 Group 3 Group 4 Group 5 HSGCA TZEI 18, TZEI 1092, TZEI TZEI 19, TZEI 1021, TZEI TZEI 31, TZEI 1028, TZEI TZEI 7, TZEI 1107, TZEI TZdEI 352, 1225, 1137, 807, 1145, TZEI 1207, TZEI TZEI 1134, TZEI 762, TZEI 811, TZEI TZEI 1238, TZEI 1241, TZEI 868, TZEI 916, TZEI 1237, TZEI 1341, TZEI TZEI 806 836, TZEI 932 TZEI 1271, 933, TZEI 934, TZEI 771, TZEI 804, TZEI TZEI 1305, TZEI 1321, 972 835 TZEI 769, TZEI 907, TZEI 968 HGCAMT TZEI 806, TZEI 934, TZEI TZEI 1203, TZdEI 352, TZEI 1092, TZEI 1107, 7, TZEI 1341, TZEI TZEI 1348, TZEI 1252, TZEI 1137, TZEI 1145, 1321, TZEI 18, TZEI TZEI 1344, TZEI 762 TZEI 811, TZEI 907, 807, TZEI 1225, TZEI TZEI 19, TZEI 835, TZEI 1237, TZEI 1305, TZEI 836, TZEI 868, TZEI 31, 1134 TZEI 968, TZEI 972 DArT-SNP TZEI 1092, TZEI 1107, TZEI 1134, TZEI 1305, based TZEI 18, TZEI 7, TZEI TZEI 1137, TZEI 1203, GD 19, TZdEI 352, TZEI TZEI 1237, TZEI 1271, 811, TZEI 835, TZEI 836 TZEI 1238, TZEI 1241, TZEI 1225, TZEI 1321, TZEI 1323, TZEI 1341, TZEI 1344, TZEI 1145, TZEI 1207, TZEI 1252, TZEI 807, TZEI 1348, TZEI 31, TZEI 771, TZEI 804, TZEI 762, TZEI 806, TZEI 769 TABLE 4 Number of between and within-group hybrids classified performance of the testcrosses with positive index values was gener- by HSGCA, HGCAMT and DArTseq grouping approaches into yield groups 1 (top 63 hybrids), 2 (middle 63 hybrids) and 3 (lowest 64 ally associated with higher grain yield, shorter ASI, taller plants, mini- hybrids), arranged in descending order, as well as breeding efficiency mal Striga damage symptoms, reduced Striga emergence, delayed leaf (B.E) of the grouping methods across 11 research environments, 2017 senescence, improved plant aspect and increased ears per plant. The to 2019 principal component axis (PCA) 1 and 2 of the ‘mean versus stability’ Yield group Cross type HGCAMT HSGCA DArT-SNP view of the GGE biplot accounted for 63.4% of the overall variation in 1 Inter 52 61 62 grain yield of the hybrids across environments (Figure 2). The hybrids 1 Intra 11 2 1 TZEI 7  TZdEI 352, TZEI 1238  TZEI 7 and TZEI 1252  TZEI 7 were identified as high yielding and most stable whereas hybrids 2 Inter 53 54 48 TZEI 807  TZEI 31 and TZEI 972  TZEI 31 were the lowest yielding 2 Intra 10 9 15 and most stable across environments. 3 Inter 40 42 37 3 Intra 24 22 27 B.E 44.6 52.8 52.5 4 | DISCUSSION The presence of significant mean squares for grain yield and most stress and under nonstress environments. Grain yield of the testcross studied traits for the hybrids in the present study indicated sufficient hybrids varied from 1281 kg/ha for TZEI 968  TZEI 19 to 4447 kg/ genetic variability among the testcrosses to allow good advances from ha for TZEI 1305  TZdEI 352 across stress conditions and 3688 kg/ selection for improvement in grain yield as well as drought tolerance ha for TZEI 972  TZEI 31 to 6982 kg/ha for TZEI 771  TZdEI and Striga resistance adaptive traits. The significant G  E mean 352 across nonstress conditions. The highest yielding testcross squares for grain yield and most other measured traits under each and hybrids out-yielded the best hybrid check TZE-W Pop DT C5 STR across research conditions implied contrasting responses of the geno- C5  ENT 11 by 13% across stress conditions. Grain yield of the TZEI types in contrasting environments and the necessity for identifying 7  TZdEI 352 obtained from intermating among the testers outper- high-yielding as well as stable hybrids across environments (Amegbor formed the best check by 20% across stress conditions. The superior et al., 2017). The significant GCAline, GCAtester and SCAline  tester 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 233 TABLE 5 Grain yield and other agronomic traits of selected hybrids (best 15 and worst 5 based on the multiple base index) plus checks evaluated under multiple stress and nonstress environments in Nigeria, 2017 and 2019 Yield DYSK ASI PLHT RAT1 RAT2 CO1 CO2 EPP LDS PASP BI ST NS ST Hybrid kg ha1 ST NS ST NS cm NS ST ST NS ST ST NS TZEI 7  TZdEI 352 4843 7125 55 55 1.2 1.8 153 168 4.0 4.0 26.5 38.3 0.91 1.03 2.3 3.5 3.0 18.4 TZEI 1207  TZdEI 352 4414 5370 56 53 1.8 1.0 158 171 2.5 3.0 16.5 22.3 0.91 0.94 3.2 3.8 3.6 18.1 TZEI 1305  TZdEI 352 4447 6261 57 53 2.0 0.9 163 184 2.3 2.9 16.9 24.0 0.82 0.99 3.9 4.5 3.5 15.9 TZEI 1321  TZdEI 352 4042 5784 56 53 2.0 1.1 153 170 2.8 3.3 15.0 19.9 0.88 0.94 3.4 3.8 3.5 15.7 TZEI 1344  TZdEI 352 4131 5000 56 52 2.3 1.5 146 157 2.0 2.4 12.8 14.9 0.90 1.00 3.6 5.0 4.1 15.5 TZEI 1021  TZdEI 352 4088 6075 55 51 1.1 0.3 162 181 2.9 3.6 17.1 20.9 0.87 1.02 4.2 4.3 3.8 14.2 TZEI 1252  TZEI 7 4154 5394 55 52 2.5 0.9 145 165 2.8 3.5 20.4 21.4 0.87 1.01 2.9 4.3 4.6 13.9 TZEI 1237  TZdEI 352 3854 6306 57 53 2.3 0.5 155 170 2.6 3.4 13.0 18.4 0.93 0.92 4.4 5.0 3.9 13.4 TZEI 1028  TZdEI 352 4146 6051 58 56 1.8 1.3 165 183 3.0 3.6 20.0 27.1 0.85 0.99 3.7 5.0 3.8 13.0 TZEI 1348  TZEI 7 3962 5642 56 53 2.3 0.8 150 160 3.1 3.6 26.3 27.8 0.84 0.94 3.0 4.5 4.4 12.3 TZEI 771  TZdEI 352 3904 6982 59 54 3.0 0.9 156 187 3.0 3.4 18.4 21.9 0.81 0.99 3.9 5.0 2.8 11.5 TZEI 762  TZEI 7 3602 5501 56 52 1.6 1.0 139 152 3.1 4.0 16.4 21.1 0.85 0.94 2.8 5.0 4.8 11.32 TZEI 1238  TZEI 7 3684 7107 57 53 2.4 0.9 154 172 3.9 4.6 16.6 21.0 0.76 1.00 2.9 4.5 3.6 10.67 TZEI 1348  TZEI 18 4302 6203 58 54 2.6 1.6 147 169 2.9 3.5 23.5 31.5 0.81 0.97 4.1 5.0 4.4 9.89 TZEI 1203  TZEI 7 3599 6093 56 53 2.5 0.8 146 170 3.6 4.5 13.1 21.88 0.81 0.97 2.9 5.3 4.3 9.18 TZEI 835  TZEI 7 1804 4811 61 54 5.2 1.1 139 167 5.0 5.9 34.1 46.8 0.48 0.81 3.9 6.5 4.8 14.9 TZEI 1021  TZEI 19 1513 5787 59 53 2.8 1.0 142 175 5.8 6.4 41.3 44.3 0.51 0.94 5.1 6.3 4.4 15.1 TZEI 807  TZEI 31 1310 4490 59 54 3.1 0.8 137 168 5.3 6.0 25.4 33.9 0.48 0.85 4.2 7.5 4.6 16.0 TZEI 972  TZEI 31 1431 3688 59 54 3.0 0.3 134 162 5.8 6.4 37.8 46.0 0.48 0.92 4.4 6.5 5.0 16.9 TZEI 968  TZEI 19 1281 5431 59 53 4.8 1.5 138 164 6.4 7.5 38.6 46.9 0.44 0.89 5.2 5.8 4.4 21.1 TZE-W pop DT C5 STR C5  ENT 11 (check) 3889 7520 56 53 1.3 1.0 162 193 4.3 5.0 26.0 35.0 0.88 0.98 3.3 4.0 4.0 12.1 ENT 11  TZEI 65 (check) 3285 6315 56 53 2.5 0.3 135 165 4.0 4.3 38.5 39.0 0.75 1.01 3.3 6.0 4.3 2.95 TZE-W pop DT STR C4  TZEI 7 (check) 2725 5945 55 53 2.2 1.5 139 164 5.3 5.63 33.4 34.9 0.75 0.92 3.1 4.8 4.5 0.79 TZEI 86  TZEI 60 (check) 2525 6362 57 52 2.7 0.5 152 180 5.4 5.9 40.0 39.3 0.67 0.84 4.3 4.8 3.9 3.81 ENT 3  TZEI 65 2280 5057 59 52 4.6 1.6 136 160 5.6 6.1 32.6 39.6 0.65 0.91 3.3 5.3 4.3 8.21 S.e.d 317 362 1 1 0.5 0.3 5 4 0.3 0.3 5.0 5.0 0.05 0.04 0.4 0.6 0.3 Grand mean 2748 5405 58 53 2.6 1.1 146 169 4.4 5.1 26.6 31.6 0.72 0.93 3.6 5.5 4.3 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 234 ADEWALE ET AL. F IGURE 2 Mean yield performance and stability of 15 best and worst five early maturing maize hybrids plus five hybrid checks evaluated across 11 environments (Striga-infested, drought and optimal) in Nigeria, 2017–2019. MKSTR17—Mokwa Striga—infested 2017; MKSTR18— Mokwa Striga—infested 2018; MKOPT17—Mokwa optimal 2017; MKOPT18—Mokwa optimal 2018; ABSTR17—Abuja Striga—infested 2017; ABSTR18—Abuja Striga—infested 2018; IKOPT17—Ikenne optimal 2017; IKOPT18—Ikenne optimal 2018; IKDT17—Ikenne drought 2017; IKDT18—Ikenne drought 2018; KDOPT18—Kadawa optimal, 2018. [Color figure can be viewed at wileyonlinelibrary.com] mean squares obtained for grain yield and most measured traits under genetic action was more important for grain yield and other traits each and across research conditions revealed significant genetic dif- under Striga infestation. The predominance of SCA over GCA action ferences in the performance of the inbred lines and testers, and both (nonadditive gene action) for grain yield and majority of the traits additive and nonadditive genetic effects were important in the set of under drought stress conditions is consistent with the findings of Guei inbreds. The significance of linear contrasts for most orthogonal com- and Wassom (1992) and Badu-Apraku, Oyekunle, et al. (2011). Con- parisons across research environments for grain yield and other trarily, Adebayo et al. (2014), Ertiro et al. (2017) and Adewale et al. assayed traits emphasized the differences in the relative rankings of (2018) reported the preponderance of additive gene action for grain the inbreds by the testers. The lack of significant orthogonal compari- yield and other traits under drought stress conditions. The disparity sons observed between Tester 4 (TZEI 7) and Tester 5 (TZdEI 352) for between the findings from this study and those of earlier researchers grain yield across research environments indicated close correspon- may be attributed to the sources of the inbred lines used for this dence in the relative rankings of the inbred lines for grain yield study, the intensity of drought stress conditions in the drought stress performance. experiments and the influence of environmental conditions such as The predominance of GCA over SCA sum of squares for grain the types of soil and climate that might have directly affected the yield and most other assayed traits under Striga-infested, optimal and emergence, severity and biotypes of Striga species in the different across research conditions was an indication that additive gene action locations and years of the Striga experiments. was more important in the inheritance of these traits and that GCA Inbred lines with outstanding GCA effects for maize grain yield was the major component accounting for the variations among the and other agronomic traits could be used to develop heterotic popula- 190 testcrosses assessed. The predominance of GCA action over SCA tions for further improvement and for developing high-yielding and under Striga infestation indicated that selection for resistance to multiple stress-tolerant varieties in WCA (Akinwale et al., 2014). S. hermonthica based solely on the prediction from GCA would be Desirable GCA effects of TZEI 1203, TZEI 1252 and TZEI 1348 effective in early generations (Akinwale et al., 2014; Badu-Apraku, observed for grain yield under Striga and across research environ- Oyekunle, et al., 2011; Yallou et al., 2009; Zebire et al., 2020). Con- ments, Striga damage ratings and number of emerged Striga plants trarily, Kim (1991) and Sangaré et al. (2018) reported that nonadditive implied that these inbred lines possessed beneficial alleles and might 14390523, 2023, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/pbr.13077 by Nigeria Hinari NPL, Wiley Online Library on [07/08/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ADEWALE ET AL. 235 have contributed alleles for higher grain yield to their progenies. Simi- implied that the tester TZdEI 352 possessed multiple stress tolerant larly, inbred lines TZEI 1321 and TZEI 1323 manifested significant and genes, especially for Striga resistance. The results of the present positive GCA effects for grain yield under drought conditions indicating study buttressed the findings of Akaogu et al. (2019) who recom- that the lines might have contributed beneficial alleles for higher grain mended that TZdEI 352 should be used as a parent in hybrid devel- yield to their hybrids under drought stress. Under optimal conditions, opment, to maximize maize production and productivity in Striga TZEI 771, TZEI 916, TZEI 1203, TZEI 1238, TZEI 1241, TZEI 1271 and endemic agro-ecologies of SSA. The hybrid TZEI 7  TZdEI TZEI 1305 were identified as inbred parents with significant positive/ 352 obtained from intermating among the testers in the present negative GCA effects for grain yield and other agronomic traits. Paren- study, testcross hybrids TZEI 1238  TZEI 7 and TZEI 1252  TZEI tal lines identified in the present study with favourable and stable GCA 7 were identified as the high-yielding and most stable hybrids across effects for grain yield and other desirable agronomic traits could be Striga-infested, drought and nonstress research conditions. These used in hybrid development and recurrent selection programmes for hybrids should be tested extensively in on-farm trials for commer- the development of synthetic populations that could be improved for cialization in SSA. Striga resistance and drought tolerance, or used for inbred recycling as well as testers for evaluating newly developed inbred lines (Akinwale et al., 2014; Ertiro et al., 2017; Makumbi et al., 2011). 5 | CONCLUSIONS Unlike the temperate maize germplasm, distinct heterotic pat- terns have not been identified among tropical maize germplasm and The inheritance of grain yield under Striga infestation, optimal and this has been attributed to the existence of several cultivars which are across research environments was influenced by additive genetic yet to be field tested (Badu-Apraku, Fakorede, & Akinwale, 2017). The effects but there was greater influence of nonadditive effects under 38 inbred lines used in our study were classified into two, three and drought stress conditions, Nine, seven and two inbred lines had signif- five heterotic groups by the DArTseq-SNP based GD, HGCAMT and icant and positive GCA effects for grain yield under Striga infestation, HSGCA methods, respectively. Regarding the placement of inbred optimal and drought stress environments, respectively. These inbred lines into the same heterotic group, there was little conformity lines could be important sources of beneficial alleles for development between the HSGCA and the DArTseq-SNP methods. An efficient of outstanding tropical maize hybrids and improvement of breeding heterotic grouping method is expected to identify groups that allow populations. The DArTseq marker-based GD method, which was one interheterotic group crosses to display higher heterosis than within- of the most efficient methods and classified the inbred lines into two group crosses. The HSGCA and DArTseq grouping methods were the groups, should be adopted for grouping and realigning the early most efficient in classifying the inbreds into heterotic groups with the maturing white maize inbred lines into two heterotic groups. Out- highest breeding efficiencies of 52.8% and 52.5%, respectively across standing hybrids TZEI 7  TZdEI 352, TZEI 1238  TZEI 7 and TZEI 11 research environments. In a similar study, Annor et al. (2020) iden- 1252  TZEI 7 should be tested extensively in on-farm trials for com- tified the HSGCA grouping method as the most effective in classifying mercialization in SSA. early yellow tropical maize inbred lines across similar environmental conditions (Striga infestation, drought and optimal). Molecular markers ACKNOWLEDGEMENTS have also proved to be powerful tools for defining heterotic groups This research was supported by the Bill and Melinda Gates Foundation and examining relationships among inbred lines. For instance, Badu- (OPP1134248) under the Stress Tolerant Maize for Africa (STMA) pro- Apraku et al. (2015) identified the SNP-marker method as the most effi- ject. The authors are also grateful to the IITA Maize Improvement Pro- cient in classifying early maturing quality protein inbred lines into heter- gramme and Bioscience staff for providing technical assistance. otic groups under multiple stress environments. Therefore, for a practical maize breeding programme, the DArTseq marker-based GD CONFLICT OF INTEREST method, which was identified as one of the most efficient grouping The authors declare that there is no competing interest. methods for the early maturing white inbred lines of the IITA-MIP in the present study and classified the inbred lines into two groups, should AUTHOR CONTRIBUTIONS be adopted for grouping and realigning the IITA-MIP early maturing Samuel Adewale, Baffour Badu-Apraku and Richard Akinwale concep- white maize inbred lines into two heterotic groups. The adoption of the tualized, designed and executed the experiments. Samuel Adewale DArTseq marker-based GD method for grouping of the early white analysed the data and drafted the manuscript. All authors critically inbred lines would facilitate the achievement of the present goal of the reviewed the manuscript. IITA-MIP to reduce the number of the heterotic groups of the early maturing white endosperm maize inbred lines into heterotic groups A DATA AVAILABILITY STATEMENT and B categories (Badu-Apraku et al., 2021). This will greatly improve The datasets used in this study have been deposited at the IITA CKAN the efficiency of parent selection of the hybrid programme while reduc- repository. ing the number of heterotic groups to a manageable number. Using the multiple trait base index, 9 of the 15 top-performing ORCID testcrosses had inbred TZdEI 352 involved in their crosses. 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