Analysis of the combining ability of temperate and tropical maize (Zea mays L.) lines in Burkina Faso

 Corresponding author: Siaka TIAMA Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Analysis of the combining ability of temperate and tropical maize (Zea mays L.) lines in Burkina Faso Siaka TIAMA 1, 2, *, Abdalla DAO 2, Togue Saturnin COULIBALY 2, Jacob SANOU 3 and Mahamadou SAWADOGO 1 1 Department of Plant Biology and Physiology, Biosciences Laboratory, Joseph KI-ZERBO University, 09 BP 1635, Ouagadougou 09, Ouagadougou, Burkina Faso. 2 Plant Production Department, Institute of Environment and Agricultural Research, 01 BP 910, Bobo Dioulasso 01, Bobo Dioulasso, Burkina Faso. 3 Department of Science and Technology, New Dawn University, 01 BP 234, Bobo Dioulasso 01, Bobo Dioulasso, Burkina Faso. World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 Publication history: Received on 21 August 2025; revised on 30 September 2025; accepted on 03 October 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.1.3401 Abstract The development of high-yielding maize varieties requires an adequate choice of potential parents for hybrid development. New yellow inbred lines were used to generate new hybrids through a half-diallel design in 2021. The new hybrids were evaluated in three environments in 2022 and 2023, using the alpha latice design with three replicates. Observations focused on grain yield and its components. The variability within hybrids was determined and the general and specific combining ability (GCA and SCA) of the inbred lines were estimated. The results showed high variability (P < 0.001) within the hybrids, between study environments and their interaction for the majority of traits assessed. In addition, the GCA and SCA variances showed significant differences for all the traits assessed. This is an indication of the presence of both additive and non-additive gene effects in the inheritance of the traits. The inbred lines PI601561, ELN45-1-1-1, FBML10 and TZEI146 lines recorded positive and significant GCA effects for yield and yield-related traits revealing that these lines could be potential inbred lines for high yielding hybrids development as well as new inbred lines extraction. These lines could be used as testers for future studies. The results also showed that hybrids such as FBML10 x PI601009, ELN45-1-1-1 x PI601686, TZEI17 x PI601191, TZEI146 x PI600954, etc. showed positive and significant SCA effects for the grain yield trait. These hybrids could be used to develop three-way and two-way hybrids to improve maize production. Keywords: Burkina Faso; Combining ability; Hybrids; Lines; Maize 1. Introduction Agriculture plays an essential role in the development and food security of African countries. In Burkina Faso, it employs nearly 85% of the total population and contributes 35% of the gross domestic product (GDP) [1]. The sector is dominated by the production of cereals, which play an important role in human and animal nutrition [2]. Among these cereals, maize is one of the staple food. Its production continues to grow, as it finds multiple applications in the fields of human food, livestock breeding and others [3]. Its production has risen from 1700127 tons in 2018 to 1810276 tons in 2023. It has thus become the leading cereal in terms of production [4]. The success of this cereal could be explained by its high natural yield potential compared with other cereals, as well as by the multiple efforts made by research institutes to create new varieties with high yield potential and adapted to the country’s conditions. World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 216 Breeding programs have led to the development of several high-performance hybrid varieties. Despite the existence of these new varieties, the production is still low compared to the demand in multiple sectors. To achieve this, breeding programs should continually create new high-performance hybrids to meet the demand. In addition, information on genetic diversity is also very important for any breeding program, as it enables genetic material to be characterized and assigned to different heterotic groups [5]. Mean performance is one of the most important criteria for evaluating genotypes. Thus, the use of temperate lines in combination with tropical lines would make it possible to introduce new genes into the creation of hybrids. According to [6], it is essential in any breeding program to know the importance of the value of a given trait for the transmission to the progeny of all the favorable genes involved in the expression of this trait during a cross. Knowledge of the combining abilities of these parental lines is essential to generate reliable information and enable breeders to choose representative parents for hybrid development planning [7]. Moreover, it is important to know the mode of inheritance for given before choosing a selection method. Thus, when developing new varieties, the choice of selection methodology is decisive in the action of the genes. Combining ability analysis is a powerful tool for identifying lines for crosses, enabling efficient exploitation of hybrid vigor, but also for identifying high-performance hybrids for future breeding [8]. The objectives of this study were to (i) determine the general and specific combining ability (GCA and SCA) of grain yield and its components for the parental inbred lines, (ii) study the mode of action of genes in the transmission of these traits and (iii) select lines with favorable GCA and hybrids wth favorable SCA effects for desired traits for the development of new high-yielding hybrids. 2. Materials and methods 2.1. Plant material The study material consisted of 78 single yellow hybrids (Table 1) derived from half-diallel crosses of thirteen temperate and tropical lines. These parental lines were sourced from three research centers namely the Institute of Environment and Agricultural Research (INERA), the International Institute of Tropical Agriculture (IITA) and the North Central Regional Plant Introduction Station (NCRPIS). Table 1 List of plant material evaluated N° Hybrids N° Hybrids N° Hybrids 1 PI601561 x PI601686 27 PI601191 x PI601575 53 PI547090 x TZEI146 2 PI601561 x PI601191 28 PI601191 x FBML10 54 PI547090 x TZEI10 3 PI601561 x PI600954 29 PI601191 x TZEI146 55 PI547090 x TZI18 4 PI601561 x PI601009 30 PI601191 x TZEI10 56 PI547090 x TZEI17 5 PI601561 x PI547090 31 PI601191 x TZI18 57 PI547090 x ELN45-1-1-1 6 PI601561 x PI601575 32 PI601191 x TZEI17 58 PI601575 x FBML10 7 PI601561 x FBML10 33 PI601191 x ELN45-1-1-1 59 PI601575 x TZEI146 8 PI601561 x TZEI146 34 PI600954 x PI601009 60 PI601575 x TZEI10 9 PI601561 x TZEI10 35 PI600954 x PI547090 61 PI601575 x TZI18 10 PI601561 x TZI18 36 PI600954 x PI601575 62 PI601575 x TZEI17 11 PI601561 x TZEI17 37 PI600954 x FBML10 63 PI601575 x ELN45-1-1-1 12 PI601561 x ELN45-1-1-1 38 PI600954 x TZEI146 64 FBML10 x TZEI146 13 PI601686 x PI601191 39 PI600954 x TZEI10 65 FBML10 x TZEI10 14 PI601686 x PI600954 40 PI600954 x TZI18 66 FBML10 x TZI18 15 PI601686 x PI601009 41 PI600954 x TZEI17 67 FBML10 x TZEI17 16 PI601686 x PI547090 42 PI600954 x ELN45-1-1-1 68 FBML10 x ELN45-1-1-1 World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 217 17 PI601686 x PI601575 43 PI601009 x PI547090 69 TZEI146 x TZEI10 18 PI601686 x FBML10 44 PI601009 x PI601575 70 TZEI146 x TZI18 19 PI601686 x TZEI146 45 PI601009 x FBML10 71 TZEI146 x TZEI17 20 PI601686 x TZEI10 46 PI601009 x TZEI146 72 TZEI146 x ELN45-1-1-1 21 PI601686 x TZI18 47 PI601009 x TZEI10 73 TZEI10 x TZI18 22 PI601686 x TZEI17 48 PI601009 x TZI18 74 TZEI10 x TZEI17 23 PI601686 x ELN45-1-1-1 49 PI601009 x TZEI17 75 TZEI10 x ELN45-1-1-1 24 PI601191 x PI600954 50 PI601009 x ELN45-1-1-1 76 TZI18 x TZEI17 25 PI601191 x PI601009 51 PI547090 x PI601575 77 TZI18 x ELN45-1-1-1 26 PI601191 x PI547090 52 PI547090 x FBML10 78 TZEI17 x ELN45-1-1-1 2.2. Study sites The study was conducted across two locations in 2022 and 2023, namely Vina and Bama at Burkina Faso (Figure 1). Vina is located in Tuy province at 130 km from Bobo Dioulasso at 11°34’ north latitude and 3°24’ west longitude, at an altitude of between 300 and 400 meters. In this location, hybrids were evaluated during rainy season from june to september without supplementary irrigation. The second location Bama in Kou valley is situated at 25 km from BoboDioulasso. The experimentation was conducted in INERA experiment sites. This area is located at 10°20’ north latitude, 4°20’ west longitude, 450 m above sea level [9]. The hybrids were assessed during rainy season with supplementary irrigation at the needs from july to september and during dry season from november to march. During the dry season, the water was supplied by gravity irrigation. Cumulative rainfall in both years exceeded 1,000 mm. Ean temperatures ranging from 23°C to 34°C [10]. Figure 1 Representation of study sites World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 218 2.3. Methods 2.3.1. Experimental design The seventy-eight hybrids obtained were evaluated using the 6 x 13 alpha lattice design with three replications in three different environments over two years. The plot size was a single 5 m line. The spacing between the holes was 0.4 m while between lines 0.80 m. 2.3.2. Trial management Organic matter (compost) was applied at a rate of 5 tons per hectare at the time of sowing in each trial. Weeding was carried out as required. Fertilization (NPK 14-23-14) was supplied at 15th day after sowing (DAS) at the rate of 300 kg/ha. Urea 46 was used for top-dressing at the rate 100 kg/ha at 30 day after sowing and 50 kg/ha at 45 day after sowing. The herbicide “Glyphosate 360 g/L” combined with the pre-emergence herbicide “Atrazine 500 g/L” was used to control weeds. In addition, Emamectin benzoate (50 g/kg) was used to control armyworms and locusts as soon as their first symptoms appeared. 2.3.3. Data collection Observations werefocused on grain yield and its components as described below: • Grain yield (GY) is the estimate of the variety's production potential based on the weight of harvested ears and grains, and the relative humidity in the study plot. This grain yield is calculated using the following formula: GY = [FW x (GSW/ESW) x [(100-GM) / 85] x (62500/NP)] Where GY= grain weight (kg/ha); FW = Field weight (kg); GSW = grain sample weight (kg); ESW = ears sample weight (kg); GM = grain moisture; NP = number of plants. • Ear diameter (ED) is the average diameter of ten ears from each useful plot in millimeter. • Ear length (EL) is the measurement in centimeter of the length of the ear samples harvested per useful plot. • Number of rows (NR) was obtained by counting the number of rows of grains in the ear. • Number of grains per row (NGR) was obtained by counting the number of grains in each row. 2.3.4. Statistical analysis The data collected was entered, organized and processed using Microsoft Excel version 2016 spreadsheet software. Data processing initially consisted of considering all plots with a plant density at harvest (DH) of less than 30% as missing data. In addition, the difference between the highest and lowest yields of the three replicates was calculated for each genotype studied. If the yield of a replicate was lower than this difference, then the yield value for that plot was deleted and considered missing data. A correlation test between the variables studied was carried out using R 4.2.1 software to determine the nature of the relationship between the variables. In addition, the measured variables were subjected to an analysis of variance (ANOVA) using AGD-R 5.1 software. An additive model was used to determine the variability within the study population for all the traits following the formula: Yij = µ + Gi + Bi + ɛij where Yij = observed value of genotype i on block j, μ = overall mean of the experiment, Gi = effect of genotype i, Bj = effect of block j and εij = effect due to experimental error. For the analysis of variance, each combination year by location was considered as an environment. Then six environments were involved in the data analysis. The factors “environment, replicates and blocks” were considered as random effects, while hybrids were taken as fixed effects. This analysis made it possible to calculate the ratio of [11], in order to determine the predominance of the genes’ mode of action in the inheritance of the traits, based on the formula: R=2VGCA/(2VGCA+VSCA) World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 219 where VGCA = variance due to general combining ability and VSCA = variance due to specific combining ability. Thus, if the ratio is close to unity (1) for a given trait, this indicates a predominance of additive gene effects. However, if it is close to zero (0 ≤ R ≤ 0.5), this reflects the dominance of non-additive gene effects [12]. Regarding genetic analysis, the same software was used to determine combining ability. This analysis allows breeding stock to be classified according to their genetic value and the best hybrid combinations to be identified. The analysis of variance was used to estimate general and specific combining abilities (GCA and SCA) using the additive model of the IV method (F1 without reciprocal or parents) [13] as follows: 𝒀𝒊𝒋𝒌 = µ + 𝒈𝒊 + 𝒈𝒋 + 𝒔𝒊𝒋 + 𝒓𝒌 + 𝒆𝒊𝒋𝒌 where Yijk = value of the cross obtained from female parent (i) and male parent (j) in the kth repetition, µ = overall population mean, gi = effect of GCA of female parent i, gj = effect of GCA of female parent j, sij = effect of SCA of cross ij, rk = effect of the kth repetition, and eijk = effect of the environment on individual ij. 3. Results 3.1. Analysis of variance of the grain yield trait and its components The results of the analysis of variance (Table 2) showed that there was significant difference (P< 0.001) among genotypes as well environments for all the studied traits. Significant genotype by environment interaction effect was also observed for all variables. However, variations between hybrids were low for yield components (CV < 15%), but high for the grain yield trait (CV > 15%). Grain yield of the hybrids varied from 1101.82 kg/ha for PI601191 x PI600954 to 9971.30 kg/ha for PI601686 x TZEI10 with an estimated mean value of 5559.88 kg/ha. The average ear diameter (ED) and ear length (EL) was respectively 42.65 mm and 15.05 cm. The number of grains per row (NGR) ranged from 20 to 47 grains, with a mean of around 35 grains and the number of rows (NR) from 11 to 20 rows with a mean value of 14 rows per ear. Table 2 Mean squares from analysis of variance for evaluated traits Source DF GY ED EL NGR NR Rep (Env) 12 20.31*** 46.41*** 88.70*** 31.44*** 40.30*** Env 5 180.78*** 1021.73*** 434.37*** 182.35*** 131.95*** Hybrid 77 5.02*** 8.27*** 6.58*** 7.48*** 9.52*** Env x Hybrid 385 2.00*** 2.49*** 2.01*** 1.73*** 2.18*** Minimum 1101.82 30.38 9.20 20.00 11.00 Maximum 9971.30 56.20 21.00 47.00 20.00 Mean 5559.88 42.65 15.05 35.00 14.00 CV (%) 25.56 10.57 14.78 14.23 9.52 Legend: DF = degree of freedom, GY = grain yield, ED = ear diameter, EL = ear length, NGR = number of grains per row, NR = number of rows, Env = environment, Rep = replication, CV = coefficient of variation and *, **, *** = Significant at 0.05, 0.01 and 0.001 probability levels, respectively 3.2. Relationship between the quantitative characteristics studied The results of the correlation test between the variables studied showed strong or weak, positive or negative correlations at the 5% threshold (Figure 2). A strong positive correlation (r = 0.62) was observed between grain yield (GY) and ear diameter (ED). The same was observed between ear length (EL) and GY (r = 0.66). However, the correlation was positive but weak between the number of grains per row (NGR) and GY (r = 0.49). On the other hand, the correlation between GY and the number of rows (NR) per ear was weak at the 5% threshold (r = 0.15). As for the ear diameter (ED), it was positively and weakly correlated with the number of rows (r = 0.37), ear length (r = 0.34) and number of kernels per row (r = 0.18). The variables NGR and LE were strongly and positively correlated (r = 0.77) with each other, while the correlation between the variables LE and NR was very weak (r = 0.04). Finally, a weak negative correlation (r = - 0.10) was observed between the number of grains per row (NGR) and the number of rows (NR). World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 220 Legend: GY = grain yield, ED = ear diameter, EL = ear length, NGR = number of grains per row and NR = number of rows Figure 2 Correlation between grain yield trait and its components 3.3. Evaluation of gene action Mean squares of the lines (GCA) and the combination (SCA) were significant (P <0.001) for grain yield and all the others measured traits (Table 3). Additionally, the means of the interaction line by environment (Env x GCA) and the interaction combination by environment (Env x SCA) were significant (P < 0.001) for all the traits. Baker ratio (R) showed dominance of GCA effects compared to SCA effects for all the observed variables. Table 3 Results of the variance in general and specific combining abilities Source DF GY ED EL NGR NR GCA 12 20.89*** 89.79*** 28.06*** 38.33*** 116.16*** SCA 65 8.74*** 8.19*** 11.63*** 9.43*** 3.47*** Env x GCA 60 2.98*** 6.96*** 4.40*** 3.68*** 5.89*** Env x SCA 325 1.92*** 1.66*** 1.72*** 1.62*** 1.51*** RGCA/SCA 0.83 0.96 0.83 0.89 0.99 Legend: DF = degree of freedom, GY = grain yield, ED = ear diameter, EL = ear length, NGR = number of grains per row, NR = number of rows, Env = environment, GCA = general combining ability, SCA = specific combining ability, *, **, *** = Significant at 0.05, 0.01 and 0.001 probability levels, respectively and RGCA/SCA= ratio between the variances of GCA and SCA. 3.4. Estimation of the general combining ability (GCA) effects of the lines The results of the estimation of the combining ability of the lines are showed in Table 4. The tropical line ELN45-1-1-1 recorded positive and highly significant (P < 0.001) GCA effects for all evaluated variables. The temperate line PI601561 showed positive and highly significant (P < 0.001) GCA effects for four traits, namely GY, EL, NGR and NR. The tropical lines, FBML10 and TZEI146 showed significant (P < 0.001) and positive GCA effect for the grain yield (GY). Two temperate lines, PI600954 and PI547090 recorded significant (P <0.001) and positive GCA effects for ear diameter. Lines PI601686, PI601009, PI601575 and TZEI17 displayed significant GCA effects for ear length, lines PI601009, World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 221 TZEI10, TZI18 and TZEI17 for number of grains per row and lines PI601191, PI600954, PI547090 and TZEI10 for number of rows. Negative and significant (P < 0.001) GCA effects for grain yield were recorded by the temperate lines PI601191, PI600954 and PI601009 and the tropical lines TZI18 and TZEI17. Table 4 General combining ability (GCA) of parental lines Lines GY ED EL NGR NR PI601561 416.25*** -0.02 0.74*** 0.69*** 0.47*** PI601686 -82.30 0.09 0.16** -0.05 -0.80*** PI601191 -434.85*** -0.02 -0.55*** -1.69*** 0.87*** PI600954 -203.49*** 1.10*** -0.68*** -1.82*** 0.55*** PI601009 -238.98*** -0.61*** 0.11* 1.02*** -0.62*** PI547090 73.49 1.17*** 0.003 -0.83** 0.50*** PI601575 -48.84 0.01 0.12* -1.06*** -0.11** FBLM10 377.49*** 1.59*** -0.15** 0.16 -0.60*** TZEI146 242.41*** -1.12*** -0.19*** -1.04*** -0.71*** TZEI10 -23.79 -1.12*** -0.07 1.15*** 0.24*** TZI18 -236.12*** -1.47*** -0.16** 0.84*** -0.38*** TZEI17 -257.51*** -1.50*** 0.22*** 1.22*** -0.27*** ELN45-1-1-1 416.24*** 1.89*** 0.44*** 1.41*** 0.85*** Legend: GY = grain yield, ED = ear diameter, EL = ear length, NGR = number of grains per row, NR = number of rows and *, **, *** = Significant at 0.05, 0.01 and 0.001 probability levels, respectively 3.5. Estimation of the specific combining ability (SCA) effects of hybrids The results of the estimation of the specific combining ability (SCA) of hybrids showed in Table 5 revealed significant or non-significant SCA effects for the studied traits. Four combinations, viz ELN45-1-1-1 x PI601686, TZEI10 x PI547090, FBML10 x PI601575 and TZEI17 x PI60157 had positive and significant SCA effects for grain yield at the threshold P < 0.05, two combinations including, FBML10 x PI601191 and PI601575 x PI547090 had positive significant SCA effects for grain yield at the threshold P < 0.01 and 12 combinations (PI601575 x PI601686, TZEI10 x PI601191, TZI18 x PI601191, TZEI17 x PI601191, FBML10 x PI600954, TZEI146 x PI600954, TZEI17 x PI600954, ELN45-1-1-1 x PI600954, FBML10 x PI601009, TZEI146 x PI601009, TZEI10 x PI601009 and TZI18 x PI547090) revealed significant positive SCA effects at the threshold P < 0.001. Thus, it appears that “tropical x temperate” combinations are more numerous (16 positive and significant combinations) than “temperate x temperate” combinations (two combinations). In addition, eight (08) combinations, all of the “tropical x temperate” type (ELN45-1-1-1 x PI601686, FBML10 x PI601575, FBML10 x PI601191, FBML10 x PI600954, TZEI146 x PI600954, ELN45-1-1-1 x PI600954, FBML10 x PI601009 and TZEI146 x PI601009) have at least one parent that exhibits a positive and significant GCA effect on grain yield (GY). For ear length, three hybrids from “temperate x tropical” combinations (TZEI17 x PI600954, TZEI10 x PI601009 and TZEI146 x PI601191) showed positive and significant SCA effects. Four “tropical x temperate” combinations, namely “TZI18 x PI600954, TZEI17 x PI601191, FBML10 x PI600954 and TZEI146 x PI600954,” showed significant positive SCA effects for the number of kernels per row. Regarding the number of rows, the results show that two hybrids, “TZI18 x PI601561 and ELN45-1-1-1 x PI601191,” showed positive and significant SCA effects. However, none of the combinations showed significant positive SCA effect for the ear diameter. World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 222 Table 5 Specific combining ability (SCA) effects of hybrids Hybrids GY ED EL NGR NR Hybrids GY ED EL NGR NR PI601686 x PI601561 204.78 -0.09 0.29 0.77 -0.23 TZI18 x PI600954 53.04 0.83 0.79 2.89** 0.16 PI601191 x PI601561 208.70 0.67 0.81 1.15 0.19 TZEI17 x PI600954 633.48*** 0.31 1.03* 0.88 0.23 PI600954 x PI601561 317.11 0.82 0.44 0.30 -0.05 ELN45-1-1-1 x PI600954 945.70*** 0.88 0.69 1.39 0.32 PI601009 x PI601561 -112.52 1.15 -0.63 0.17 -0.04 PI547090 x PI601009 - 733.81*** -0.97 -0.64 -2.95** -0.39 PI547090 x PI601561 -179.89 -0.60 -0.87 - 2.13 * -0.56 PI601575 x PI601009 - 925.85*** -1.39 -1.39** -4.44*** -0.07 PI601575 x PI601561 204.91 0.71 -0.03 0.80 -0.19 FBML10 x PI601009 647.68*** 0.85 0.00 -0.56 0.55 FBML10 x PI601561 -325.42 -2.03 -0.47 0.84 -0.61 TZEI146 x PI601009 627.09*** -0.04 0.73 1.36 0.23 TZEI146 x PI601561 101.03 0.54 0.05 0.37 0.04 TZEI10 x PI601009 742.11*** 0.14 1.05* 2.06 -0.32 TZEI10 x PI601561 -12.77 -0.23 0.09 -0.42 0.32 TZI18 x PI601009 195.13 0.26 0.51 1.98 0.25 TZI18 x PI601561 -347.28 0.20 0.63 -0.73 0.75* TZEI17 x PI601009 -454.40* -0.05 -0.66 -0.75 0.00 TZEI17 x PI601561 99.81 -0.98 0.69 1.66 0.23 ELN45-1-1-1 x PI601009 -370.54 -0.72 -0.82 -0.39 -0.24 ELN45-1-11 x PI601561 -158.44 -0.17 -1.01* - 2.77 ** 0.14 PI601575 x PI547090 536.89** 0.48 0.25 1.62 0.11 PI601191 x PI601686 -163.96 0.26 -0.64 -1.17 -0.10 FBML10 x PI547090 18.31 0.51 0.25 0.22 0.29 PI600954 x PI601686 - 432.17* -0.25 -0.89 - 2.73 * -0.30 TZEI146 x PI547090 168.21 -0.57 -0.50 -0.82 -0.38 PI601009 x PI601686 65.35 0.65 0.72 2.03 0.19 TZEI10 x PI547090 407.02* 1.75 1.30* 1.76 0.57 PI547090 x PI601686 - 513.30* * - 2.29 * -0.96 -1.91 0.29 TZI18 x PI547090 678.74*** 0.44 0.38 0.71 0.17 PI601575 x PI601686 706.44* ** 0.73 0.58 1.96 0.19 TZEI17 x PI547090 347.28 0.49 0.37 1.01 -0.19 FBML10 x PI601686 122.82 0.44 0.89 -1.37 0.32 ELN45-1-1-1 x PI547090 -285.55 0.35 0.48 1.44 -0.38 TZEI146 x PI601686 -105.32 0.34 0.40 1.74 0.13 FBML10 x PI601575 395.36* 0.45 -0.29 1.02 -0.08 TZEI10 x PI601686 -228.82 -0.56 -0.43 -0.54 0.34 TZEI146 x PI601575 -221.14 0.74 -0.21 -0.66 0.07 World Journal of Advanced Research and Reviews, 2025, 28(01), 215-226 223 TZI18 x PI601686 -54.38 0.34 0.04 -0.11 -0.85** TZEI10 x PI601575 105.61 0.31 0.76 1.73 -0.14 TZEI17 x PI601686 -41.45 -0.18 -0.26 0.48 0.22 TZI18 x PI601575 174.36 -0.05 -0.12 -0.06 0.35 ELN45-1-11 x PI601686 440.00* 0.61 0.27 0.84 -0.18 TZEI17 x PI601575 404.12* 1.09 0.81 1.97 -0.10 PI600954 x PI601191 - 2522.71 *** - 4.80 *** - 3.16** * - 6.72 *** 0.03 ELN45-1-1-1 x PI601575 -98.05 0.45 0.33 0.26 0.26 PI601009 x PI601191 202.48 -0.28 0.14 1.72 -0.56 TZEI146 x FBML10 - 1054.42** * -1.72 -0.48 -0.65 -0.39 PI547090 x PI601191 -364.23 0.14 0.20 -0.35 0.34 TZEI10 x FBML10 84.75 -0.43 0.00 -0.30 0.15 PI601575 x PI601191 - 874.05* ** -1.23 -0.57 - 2.15 * 0.10 TZI18 x FBML10 57.75 0.06 -0.26 -0.77 -0.07 FBML10 x PI601191 606.43* * 1.46 0.43 0.92 -0.44 TZEI17 x FBML10 -190.89 -0.09 -0.36 -1.77 0.27 TZEI146 x PI601191 103.76 0.95 1.11* 0.94 0.21 ELN45-1-1-1 x FBML10 - 1031.90** * -1.42 -0.14 -0.51 0.20 TZEI10 x PI601191 839.90* ** -0.20 0.50 1.85 -0.41 TZEI10 x TZEI146 -308.39 -0.64 -0.56 -1.20 -0.30 TZI18 x PI601191 999.54* ** 1.34 0.65 1.38 -0.04 TZI18 x TZEI146 -57.71 -0.34 -0.11 0.03 0.35 TZEI17 x PI601191 1002.59 *** 1.73 0.98 2.78 ** 0.03 TZEI17 x TZEI146 -239.05 -0.48 -0.63 -1.83 -0.12 ELN45-1-11 x PI601191 -38.45 -0.04 -0.44 -0.36 0.65* ELN45-1-1-1 x TZEI146 285.01 -0.52 -0.26 -1.50 0.32 PI601009 x PI600954 117.28 0.41 0.98 -0.23 0.39 TZI18 x TZEI10 - 791.61*** -0.30 -1.06* -2.75** -0.02 PI547090 x PI600954 -79.68 0.26 -0.25 1.42 0.12 TZEI17 x TZEI10 - 721.34*** -0.04 -1.20* -2.11* 0.19 PI601575 x PI600954 - 408.61* - 2.29 * -0.12 -2.06 -0.48 ELN45-1-1-1 x TZEI10 -122.56 0.03 -0.06 0.24 -0.31 FBML10 x PI600954 669.52* ** 1.92 0.42 2.94 ** -0.18 TZEI17 x TZI18 - 1091.26** * -2.56 -1.59** -3.11** -0.52 TZEI146 x PI600954 700.93* ** 1.75 0.47 2.24 * -0.16 ELN45-1-1-1 x TZI18 183.67 -0.21 0.13 0.54 -0.53 TZEI10 x PI600954 6.11 0.16 -0.39 -0.32 -0.08 ELN45-1-1-1 x TZEI17 251.12 0.76 0.83 0.80 -0.25 Legend: GY = grain yield, ED = ear diameter, EL = ear length, NGR = number of grains per row, NR = number of rows, and *, **, *** = Significant at 0.05, 0.01 and 0.001 probability levels, respectively