Line X Tester analysis studies in maize under non stress situation

Introduction

Maize (Zea mays L.) is one of the world's most important cereal crops owing to its high yield potential, wide adaptability, and diverse end uses. It belongs to the family Poaceae, tribe Maydeae, and is believed to have originated in Central and South America. Maize is a diploid species (2n = 20), monoecious in nature, and is considered to have been domesticated nearly 8,years ago. Its closest wild relatives include Tripsacum (gama grass) and Teosinte (Zea spp.), which have contributed significantly to understanding maize evolution and genetic improvement. Globally, maize is cultivated across a broad range of agro-climatic regions because of its remarkable genetic diversity and adaptability. It serves as a staple food, livestock feed, and industrial raw material, making it one of the most versatile cereal crops. In India, maize is the third most important cereal after rice and wheat in terms of production. Approximately 52% of the total maize production is utilized as animal and poultry feed, about 23% is consumed as food, and nearly 13% is used for industrial purposes, including starch, ethanol, and processed food industries. The increasing demand for maize in food processing, livestock production, and biofuel industries has further enhanced its economic importance. According to recent estimates, maize occupies nearly 199 million hectares globally, with an annual production of approximately 1,129.20 million tonnes and an average productivity of 5,820 kg ha ¹⁻ (Anon., 2024) [1] . In India, maize is cultivated over 12.84 million hectares, producing 46.15 million tonnes with an average productivity of 3,595 kg ha ¹⁻ . Karnataka is one of the leading maize-producing states, cultivating the crop on 1.90 million hectares with a production of 6.35 million tonnes and an average productivity of 3,339 kg ha ¹⁻ (Anon., 2025-26) [2] . Despite its high genetic yield potential, maize productivity is severely constrained by several abiotic stresses, particularly drought and poor soil fertility. Since a large proportion of tropical maize is cultivated under rainfed conditions, drought remains the most significant environmental factor limiting grain yield.

Moisture stress during flowering and grain filling stages reduces pollination efficiency, kernel set, and grain development, leading to substantial yield losses. Global estimates indicate that drought causes an average annual reduction of approximately 15-17% in maize production, depending on the production environment (Edmeades et al., 2000; Edmeades et al., 2001) [3,4] . Consequently, the development of drought-tolerant, high-yielding hybrids has become a major objective of maize improvement programmes worldwide. The development of elite inbred lines forms the foundation of successful hybrid breeding programmes. Broad-based base populations possessing desirable agronomic traits and stress tolerance are essential for generating superior inbred lines capable of producing high-performing hybrids (Prashanth, 2008) [5] . Among the various breeding approaches, combining ability analysis has proven to be an effective tool for identifying superior parental lines and understanding the nature of gene action governing economically important traits. General combining ability (GCA) primarily reflects additive gene effects, whereas specific combining ability (SCA) represents non-additive genetic effects, including dominance and epistatic interactions (Falconer, 1964) [6] . Estimation of GCA and SCA enables breeders to select appropriate parents and hybrid combinations for developing superior cultivars. The line × tester mating design developed by Kempthorne (1957) [7] is one of the most widely used approaches for evaluating combining ability in maize. It facilitates the identification of superior parental lines and promising hybrid combinations by partitioning genetic variance into GCA and SCA components. Hybrids possessing high SCA effects coupled with superior mean performance are particularly valuable for commercial hybrid development because they effectively exploit heterosis for grain yield and associated traits. Research efforts in maize breeding have increasingly focused on developing highyielding single-cross hybrids adapted to diverse agro-ecological regions while maintaining tolerance to drought and other abiotic stresses. Compared with openpollinated varieties and composite cultivars, single-cross hybrids generally exhibit greater heterosis, better yield stability, and enhanced stress tolerance due to their superior genetic constitution. Therefore, identifying high-performing parental lines with desirable combining ability is essential for accelerating hybrid development. In view of the above considerations, the present investigation was undertaken to evaluate the combining ability of maize inbred lines using a line × tester mating design under non-stress conditions, with the objective of identifying superior parental lines and promising hybrid combinations for grain yield and related agronomic traits that can be exploited in future maize breeding programmes.

Materials and Methods

The present investigation was conducted during kharif, rabi, and summer seasons of 2011-12, 2012-13, and 2013-14 at the Main Agricultural Research Station (MARS), Dharwad, Karnataka, India (15°25′ N latitude, 75°07′ E longitude; 678 m above mean sea level). Meteorological data, including minimum and maximum temperatures and rainfall recorded during the crop growth period, were obtained from the agrometeorological observatory of the station and are presented in Appendix I. During the evaluation period (rabi 2013-14), the mean minimum and maximum temperatures ranged from 16.0 °C to 30.87 °C, and the total rainfall received was 11.40 mm.

Development of Experimental Material

Early-generation testing was undertaken to identify superior inbred lines for combining ability using the line × tester mating design. Initially, 200 maize inbred lines were evaluated, and the best 30 inbred lines were selected based on superior grain yield (mean + 1 SD), shorter anthesis-silking interval (ASI; mean -1 SD), and a higher number of cobs per plant (mean + SD). These selected lines were crossed with five testers during the summer season (03 April 2013), resulting in 150 F hybrids₁ . The harvested hybrid seed was processed, dried, and stored under appropriate conditions until field evaluation.

Field Evaluation

The 150 F hybrids₁ , along with 35 parental lines and standard commercial checks, were evaluated during the early spring season of 2013 (11 December 2013) under nonstress conditions. The experiment was laid out in a Restricted Randomized Block Design (RBD) with two replications. Each experimental plot consisted of a single row of 4 m length, maintaining a spacing of 75 cm × 20 cm between rows and plants, respectively. All experimental entries were raised following the recommended agronomic practices for maize cultivation. Under non-stress conditions, irrigation was provided through the furrow method at 10-12 day intervals throughout the crop growth period to ensure that the plants did not experience moisture stress.

Data Recording

Observations were recorded on five randomly selected competitive plants from each genotype in each replication. The characters evaluated included days to 50% anthesis, days to 50% silking, anthesis-silking interval (ASI), plant height (cm), ear height (cm), days to maturity, cob length (cm), cob girth (cm), number of cobs per plant, number of kernel rows per cob, number of kernels per row, shelling percentage, 100-grain weight (g), grain yield per plant (g), relative water content (RWC), and chlorophyll content. The mean values of the five sampled plants were used for statistical analysis.

Statistical Analysis

The mean performance of each genotype was subjected to line × tester analysis to estimate the combining ability effects. The total genetic variation among the hybrids was partitioned into general combining ability (GCA) and specific combining ability (SCA) components following the method proposed by Kempthorne (1957) [7] . Statistical analyses were performed using WINDOSTAT version 8.0 software.

Results and Discussion

Analysis of Variance for Combining Ability

The analysis of variance (ANOVA) for combining ability under non-stress conditions is presented in Table 1. The mean sum of squares due to crosses was significant for several important agronomic traits, including cob length, cob girth, number of kernel rows per cob, number of kernels per row, grain yield per plant, grain yield (kg ha ¹), and ⁻ number of cobs per plant, indicating the presence of substantial genetic variability among the hybrids.

The mean squares due to lines were significant for most of the traits studied under nonstress conditions, except days to maturity, chlorophyll content at 60 and 90 days, cob length, number of kernels per row, 100-grain weight, grain yield per plant, grain yield (kg ha ¹), and shelling percentage. Similarly, testers exhibited significant variation for⁻ days to 50% anthesis, chlorophyll content at 90 days, cob length, number of cobs per plant, and grain yield per plant. The line × tester interaction was significant for cob length, cob girth, number of kernel rows per cob, number of kernels per row, grain yield per plant, and grain yield (kg ha ¹),⁻ indicating the importance of specific parental combinations and the presence of nonadditive gene action governing these traits. The estimates of variance components further revealed that the specific combining ability (SCA) variance exceeded the general combining ability (GCA) variance for most of the characters evaluated, except days to 50% anthesis, days to 50% silking, chlorophyll content at 90 days, relative water content at 60 days, and relative water content at 90 days. The predominance of SCA variance over GCA variance, reflected by GCA /SCA ratios of less than unity for most traits, suggests that nonadditive gene effects, including dominance and epistatic interactions, played a major role in the inheritance of these characters. Such findings indicate that the exploitation of heterosis through hybrid breeding would be effective for improving grain yield and associated traits under non-stress conditions. The proportional contribution of lines to the total genetic variation was greater than that of testers for almost all characters studied, indicating the greater influence of female parents on trait expression. Furthermore, the contribution of the line × tester interaction was higher than that of testers alone for most of the traits, except number of cobs per plant, demonstrating the importance of specific parental combinations in hybrid performance.

General Combining Ability Effects

The estimates of general combining ability (GCA) effects of the 30 inbred lines and five testers for different agronomic traits are presented in Table 2. For days to 50% anthesis, the GCA effects of the inbred lines ranged from -1.30 (NK6240 × CI4 Inbred Line 37) to 1.60 (Rajkumar × CML446 Inbred Line 37), whereas the testers ranged from -0.48 (HS7) to 0.41 (CI4). Among the 30 inbred lines, only two exhibited significant GCA effects, and NK6240 × CI4 Inbred Line 37 recorded a significant negative GCA effect (-1.30), indicating its potential as a desirable parent for developing early flowering hybrids. For days to 50% silking, the GCA effects varied from -1.55 (Allrounder × HS17 Inbred Line 34) to 1.34 (Rajkumar × CML446 Inbred Line 37) among the lines and from -0.42 (HS7) to 0.27 (CI4) among the testers. Three inbred lines exhibited significant GCA effects, of which NK6240 × CI4 Inbred Line 37 (-1.35) and Allrounder × HS17 Inbred Line 34 (-1.55) possessed significant negative GCA effects, suggesting their usefulness in breeding programmes aimed at earliness. For anthesis-silking interval (ASI), the GCA effects among the inbred lines ranged from -0.55 (Rajkumar × CML446 Inbred Line 1) to 0.74 (Rajkumar × CML446 Inbred Line 25), while among the testers they ranged from -0.14 (CI4) to 0.06 (HS10). Six inbred lines showed significant GCA effects, and Rajkumar × CML446 Inbred Line 1 (-0.55) together with NK6240 × CI4 Inbred Line 37 exhibited significant negative GCA effects. A reduced anthesis-silking interval is considered a desirable trait under drought and non-stress environments because it promotes synchronized flowering and improves pollination efficiency, ultimately contributing to enhanced grain set and yield. Overall, the identification of parents with significant and desirable GCA effects indicates the presence of favorable additive gene action for flowering traits. Such parental lines can be effectively utilized in maize breeding programmes for the development of early-maturing and highyielding hybrids with improved combining ability.

Days to Maturity

Under non-stress conditions, the GCA effects of lines and testers for days to maturity ranged from -2.22 (NK6240 × CI4 Inbredline 37) to 1.07 (Allrounder × HS17 Inbredline 45) and -0.36 (HS4) to 0.27 (HS7), respectively. Among the evaluated parents, one line exhibited significant GCA effects, with NK6240 × CI4 Inbredline 37 (-2.22) showing a desirable negative GCA effect, indicating its potential contribution towards early maturity.

The GCA effects for chlorophyll content at 60 days under nonstress conditions varied from -3.90 (Rajkumar × CML446 Inbredline 25) to 4.00 (NAC6002 × CI4 Inbredline 7) among lines and from -0.78 (KDMI16) to 0.68 (HS7) among testers. Four parents showed significant GCA effects, of which NAC6002 × CI4 Inbredline 7 (4.00) exhibited a significant positive GCA effect, suggesting its usefulness for improving chlorophyll retention and photosynthetic efficiency.

For chlorophyll content at 90 days, GCA effects among lines ranged from -2.93 (NK6240 × CI4 Inbredline 18) to 3.29 (Rajkumar × CML446 Inbredline 37), while tester effects ranged from -1.26 (KDMI16) to 1.94 (HS7). One line, Rajkumar × CML446 Inbredline 37 (3.29), showed a significant positive GCA effect, indicating its superiority for maintaining higher chlorophyll content during later crop growth stages.

Under non-stress conditions, the GCA effects for relative water content at 60 days ranged from -0.079 (NK6240 × CI4 Inbredline 42) to 0.068 (Allrounder × HS17 Inbredline 34) among lines and from -0.007 (HS4) to 0.011 (HS10) among testers. Five parents exhibited significant GCA effects. Among these, Allrounder × HS17 Inbredline 39 (0.057), Allrounder × HS17 Inbredline 21 (0.063), and Allrounder × HS17 Inbredline 34 (0.068) recorded significant positive GCA effects, indicating their potential for improving plant water status.

The GCA effects for relative water content at 90 days varied from -0.132 (NK6240 × CI4 Inbredline 37) to 0.130 (Allrounder × HS17 Inbredline 21) among lines and from -0.031 (CI4) to 0.026 (HS10) among testers. Six parents exhibited significant GCA effects. The lines Allrounder × HS17 Inbredline 34 (0.10), Allrounder × HS17 Inbredline 25 (0.12), and Allrounder × HS17 Inbredline 21 (0.13) showed significant positive GCA effects, suggesting their importance in improving water retention ability. Plant Height (cm) the GCA effects for plant height ranged from -22.97 (Rajkumar × CML446 Inbredline 3) to 12.82 (Rajkumar × CML446 Inbredline 38) among lines and from -2.56 (KDMI16) to 3.09 (HS10) among testers. Three lines exhibited significant GCA effects. Among them, Rajkumar × CML446 Inbredline 38 (12.82) showed a desirable positive GCA effect, whereas Rajkumar × CML446 Inbredline 8 (-18.17) and Rajkumar × CML446 Inbredline 3 (-22.97) exhibited significant negative effects.

For ear height, GCA effects among lines ranged from -17.63 (Rajkumar × CML446 Inbredline 3) to 14.41 (Rajkumar × CML446 Inbredline 1), whereas tester effects ranged from -2.20 (HS4) to 3.29 (HS10). Three lines showed significant positive GCA effects, namely NK6240 × CI4 Inbredline 42 (9.86), NK6240 × HS17 Inbredline 10 (11.86), and Rajkumar × CML446 Inbredline 1 (14.41). Conversely, Rajkumar × CML446 Inbredline 18 (-11.38), Rajkumar × CML446 Inbredline 38 (-13.38), and Rajkumar × CML446 Inbredline 3 (-17.63) exhibited significant negative GCA effects.

The GCA effects for cob length ranged from -1.70 (Rajkumar × CML446 Inbredline 8) to 2.13 (Rajkumar × CML446 Inbredline 25) among lines and from -0.56 (KDMI16) to 0.86 (HS7) among testers. Significant positive GCA effects were recorded in Rajkumar × CML446 Inbredline 18 (1.67) and Rajkumar × CML446 Inbredline 25 (2.13). Among testers, HS7 (0.86) showed a significant positive GCA effect, indicating its suitability for improving cob length.

Under non-stress conditions, GCA effects for cob girth ranged from -0.42 (Rajkumar × CML446 Inbredline 8) to 0.25 (Rajkumar × CML446 Inbredline 18) among lines and from -0.054 (HS10) to 0.038 (HS7) among testers. Four lines showed significant positive GCA effects, namely Allrounder × HS17 Inbredline 25 (0.16), NK6240 × CI4 Inbredline 42 (0.19), NK6240 × CI4 Inbredline 24 (0.21), and Rajkumar × CML446 Inbredline 18 (0.25), indicating their usefulness in improving cob development traits. The variation observed in GCA effects among parental lines indicates considerable genetic diversity for the studied traits. Parents exhibiting significant positive GCA effects for yieldassociated and physiological traits can serve as valuable sources in hybrid breeding programmes. The presence of desirable negative GCA effects for traits such as days to anthesis, silking and maturity suggests the possibility of developing early maturing hybrids. Significant positive GCA effects for chlorophyll content and relative water content indicate the contribution of favourable alleles associated with enhanced photosynthetic activity and improved plant physiological status. These findings confirm that selection of superior combiners is essential for improving complex quantitative traits in maize breeding programmes. The predominance of significant GCA effects for several traits also suggests the importance of additive gene effects in the inheritance of these characters under nonstress conditions.

Number of Kernel Rows per Cob

Under non-stress conditions, the GCA effects for number of kernel rows per cob ranged from -1.10 (Rajkumar × CML446 Inbredline 1) to 1.35 (Rajkumar × CML446 Inbredline 40) among lines and from -0.17 (KDMI16) to 0.30 (CI4) among testers. Five lines exhibited positive GCA effects, namely NK6240 × CI4 Inbredline 35 (0.69), NAC6002 × CI4 Inbredline 7 (0.89), Allrounder × HS17 Inbredline 25 (0.96), Rajkumar × CML446 Inbredline 46 (1.09), and Rajkumar × CML446 Inbredline 40 (1.35). Among testers, CI4 (0.30) recorded a desirable positive GCA effect, indicating its potential contribution towards increasing kernel row number.

Number of Kernels per Row

The GCA effects for number of kernels per row ranged from -3.09 (NK6240 × HS17 Inbredline 14) to 3.53 (Rajkumar × CML446 Inbredline 25) among lines and from -0.55 (KDMI16) to 0.75 (HS7) among testers. Two lines, namely Rajkumar × CML446 Inbredline 37 (2.63) and Rajkumar × CML446 Inbredline 25 (3.53), exhibited significant positive GCA effects, indicating their suitability for improving kernel number per row.

Number of Cobs per Plant

Under non-stress conditions, the GCA effects for number of cobs per plant varied from -0.18 (Rajkumar × CML446 Inbredline 25) to 0.41 (NK6240 × CI4 Inbredline 12) among lines and from -0.072 (HS10) to 0.09 (HS4) among testers. Three lines showed positive GCA effects, namely NK6240 × CI4 Inbredline 24 (0.21), Allrounder × HS17 Inbredline 34 (0.28), and NK6240 × CI4 Inbredline 12 (0.41). Among testers, HS7 (0.07) and HS4 (0.09) showed positive GCA effects, suggesting their usefulness in improving cob number.

The GCA effects for 100-grain weight ranged from -4.74 (Rajkumar × CML446 Inbredline 8) to 1.78 (Rajkumar × CML446 Inbredline 46) among lines and from -0.22 (CI4) to 0.26 (HS10) among testers. None of the evaluated lines exhibited significant positive GCA effects for this trait, indicating limited contribution of additive genetic effects among the studied parental lines for improving grain weight.

For grain yield per plant under non-stress conditions, GCA effects among lines ranged from -20.72 (Parbhat × CI4 Inbredline 5) to 45.08 (Rajkumar × CML446 Inbredline 18), while tester effects ranged from -11.38 (KDMI16) to 12.96 (HS7). Among the evaluated parents, Rajkumar × CML446 Inbredline 18 (45.08) exhibited a significant positive GCA effect. Among testers, HS7 (12.96) showed a desirable positive GCA effect, indicating its usefulness for improving grain yield per plant.

The GCA effects for grain yield ranged from -931.74 (Parbhat × CI4 Inbredline 5) to 854.57 (NK6240 × CI4 Inbredline 24) among lines and from -298.55 (HS10) to 206.21 (CI4) among testers. Two lines exhibited significant positive GCA effects, namely NK6240 × CI4 Inbredline 12 (784.73) and NK6240 × CI4 Inbredline 24 (854.57). Among testers, CI4 (206.21) showed a desirable positive GCA effect, indicating its potential as a good general combiner for grain yield improvement.

Under non-stress conditions, GCA effects for shelling percentage ranged from -6.63 (NK6240 × CI4 Inbredline 18) to 4.50 (NK6240 × CI4 Inbredline 42) among lines and from -0.67 (CI4) to 0.49 (HS10) among testers. Two lines, namely NK6240 × CI4 Inbredline 35 (4.507) and NK6240 × CI4 Inbredline 42 (4.508), recorded significant positive GCA effects, suggesting their suitability for improving shelling percentage in maize breeding programmes. The significant variation in GCA effects among parental lines and testers for yield and yield-related traits indicates the presence of substantial genetic variability within the breeding material. Traits such as number of kernel rows per cob, kernels per row, number of cobs per plant and grain yield showed desirable positive combiners, which can be effectively utilized in hybrid breeding programmes. The identification of NK6240 × CI4 Inbredline 12 and NK6240 × CI4 Inbredline 24 as superior general combiners for grain yield indicates their potential contribution of favourable alleles for productivity improvement. Similarly, the positive GCA effect of Rajkumar × CML446 Inbredline 18 for grain yield per plant highlights its usefulness in developing high-yielding hybrids. The predominance of favourable GCA effects for yield components suggests the importance of additive genetic variance in controlling these traits under non-stress conditions. Such parental lines can be utilized in recurrent selection programmes for accumulation of favourable genes and in hybrid development programmes for exploiting heterosis. The superior combining ability of tester CI4 and HS7 further emphasizes their importance in identifying promising hybrid combinations. These findings demonstrate that combining ability analysis provides an effective approach for selecting elite parents and improving grain yield potential in maize. The specific combining ability effects of 150 crosses are presented in Table 2. Days to 50 per cent anthesis, Under nonstress situation, the SCA effects for days to 50 per cent anthesis ranged from -2.91 (NK6240 × CI 4 Inbredline 24) × HS7) to 2.03 (NK6240 × CI 4 Inbredline 37) × KDMI16) (Table16). One hybrid viz. (NK6240 × CI 4 Inbredline24)×HS7 (-2.91) showed significant SCA effects in negative direction. Days to 50 per cent silking, Under non-stress situation, the SCA effects for days to 50 per cent silking ranged from -3.47 (Rajkumar × CML446 Inbredline 18) × CI4) to 2.55 (NK6240 × CI4 Inbredline 37) × KDMI16). One hybrid viz. (Rajkumar × CML446 Inbredline 18) × CI4 (-3.47) showed significant SCA effect in negative direction. Whereas under stress situation, SCA effects for days to 50 per cent silking ranged from (Allrounder × HS17 Inbredline 21) × HS7 (-3.58) to (Allrounder × HS17 Inbredline 45) × CI4 (3.55). One hybrid viz. (Allrounder × HS17 Inbredline 21) × HS7 (-3.58) showed significant SCA effect in negative direction. Anthesis to silking interval (ASI), Under non-stress situation, the SCA effects for ASI ranged from -1.26 (Rajkumar × CML446 Inbredline 18) × HS7 to 1.74 (NK6240 × HS17 Inbredline 20) × HS10. One hybrid viz. (Rajkumar × CML446 Inbredline 18) × HS7 (-1.26) showed significant SCA effect in negative direction. Days to maturity, Under non-stress situation, the SCA effects for days to maturity ranged from -2.64 (Rajkumar × CML446 Inbredline 46) × HS4) to 3.17 18. 150 hybrids along with 35 parents and 3 checks, the hybrids viz. (NK6240 × HS17 inbredline 10) recorded highest (1.81) and (Allrounder × HS17 inbredline 39) × CI4 lowest (0.04) DSI values for grain yield (kg/ha) with an overall mean of (0.87). Analysis of variance indicated mean sum of squares due to lines was significant for anthesis to silking interval (ASI), days to 50 per cent anthesis, days to 50 per cent silking, relative water content at 60 days, relative water content at 90 days, plant height, ear height, cob girth, number of kernel rows per cob and number of cobs per plant under non-stress situation. indicating there was considerable variability for these traits among lines under non-stress situation (Table 1). The variability due to tester was significant for days to 50 per cent anthesis, chlorophyll at 90 days, cob length, number of cobs per plant and grain yield per plant under non-stress situation indicating the presence of variability among the selected lines. Mean sum of square for line × tester was significant for cob length, cob girth, number of kernel rows per cob, number of kernels per row, grain yield per plant and grain yield (kg/ha) under non-stress situation indicated the presence of average heterosis. These results are in agreement with that of Joshi et al. (2002) [8] , Asif et al. (2007) [9] , Premlatha and Kalamani (2010) [10] , Aminu and Izge (2013) [11] and Kuchanur et al. (2013) [12] . Maize a highly allogamous species is expected to contain predominant of non additive gene action. As per the expectation, the variance due to SCA was considerably high compared to that of variance due to GCA for all the characters under non-stress situation, except number of cobs per plant under non-stress situation. The ratio of GCA to SCA variance was much below unity indicating preponderance of non additive gene effects in the inheritance of all traits under study for nonstress situation, except number of cobs per plant under nonstress situation. The predominance of non-additive gene action for most of the growth and productivity traits have been reported by Sharma et al. (2004) [13] , Sandhu et al. (2006) [14] , Singh et al. (1983) [15] , Paul and Duara (1991) [16] , Sedhom (1994) [17] , Mohammad (1993) [18] and Satyanarayana et al. (1994) [19] . Whereas, Olaoye (2005) [20] and Aminu and Izge (2013) [11] reported predominance of both GCA and SCA for most of the traits under stress and non stress situations. These results also in agreement with Rojas and Sprague (1952) [21] and Gama et al. (1995) who worked on millet and maize respectively. These results exhibited that parental lines would be utilized in the development of maize hybrids. Exploitation of hybrid vigour has gained much significance in view of enormous higher yield in hybrids, which is the only way to harness the predominant non-additive gene action. The contribution of lines to total variances was higher than testers for most of the traits which signifies the prevalence of high diversity among the lines used for this investigation. This is mainly attributed to the enormous diversity in different source populations from which these lines have been derived. The contribution of either lines or tester revealed the importance of specific contribution for expression of all the traits studied in hybrids. Similar observations were also reported by Kadlubiec et al. (2000) [22] , Ramesh et al. (2000) [23] , Rosa et al. (2000) [24] , Mandal et al. (2001) [25] and Sandhu et al. (2006) [14] .

A high GCA effect for specific trait in a parent indicates presence of additive gene effects for that trait. When parents possessing high GCA effects were combined by hybridization, a large proportion of progenies would have high value for the trait concerned facilitating easy selection for that trait. In the present study, a score was given based on the GCA effects of the parents (Table 2). The total score of each parent across the characters was worked out for the classification of parents as high or best combiner and low or poor combiner (Thenmozhi, 1983) [26] . Under non-stress situation, among the lines, Rajkumar × CML446 inberdline 37 and NK6240 × CI4 inberdline 24 exhibited maximum positive GCA effect for quantitative traits, the pooled score for GCA effects for these two female parents was highest (+4) proving them to be potential general combiner (Table 15). NK6240 × CI4 inberdline 12, Allrounder × HS17 inberdline 34 and Allrounder × HS17 inberdline 25 were the next best general combiners with pool score of +3. Among the other lines, Rajkumar × CML446 inberdline 18, Allrounder × HS17 inberdline 21 and NAC6002 × CI4 inberdline 7 recorded a positive score of +for GCA effects. Among the testers, HS7 recorded pool score of +4 indicating its superiority for utilization in hybridization programme. For anthesis to silking interval two parents viz., NK6240 × CI4 inberdline 37 and Rajkumar × CML446 inberdline 1 exhibited significant GCA effects in desirable direction under non stress situation. Anthesis silking interval is a trait used mostly in screening genotypes for tolerance to stresses (EL-Hosary et al., 1994) [27] . It is a measure of nicking (synchronization) of pollen shed with silking as reported by Paul and Debenth (1999) [28] and (Olaoye et al., 2005) [20] . For number of cobs per plant three parents viz., NK6240 × CI4 inberdline 24, Allrounder × HSinberdline 34 and NK6240 × CI4 inberdline 12 exhibited significant GCA effects in desirable direction. Among testers, HS7 and HS4 exhibited significant GCA effects in desirable direction under non stress situation. The GCA effects for grain yield per plant Rajkumar × CML446 inberdline exhibited significant GCA effect in desirable direction non-stress situation. Among testers HS7 exhibited significant GCA effect in desirable direction non-stress situation. The parents with a high GCA effects for traits could produce superior segregants in F₂ as well as in later generations. Significant GCA effects in desirable direction for most of the quantitative traits. Therefore, these parents could be utilized in a hybridization programme for selection of superior recombinants. These findings are in accordance with Mandal et al. (2001) [25] , Malik et al. (2004) [29] , Koinuma et al. (2004) [30] , Fan et al. (2004) [31] , kanta et al. (2005), Prashanth (2008) [5] , Kuchanur (2010) [32] , Premlatha and Kalamani (2010) [10] , Izge and Dugje (2011) [33] , Aminu and Izge (2013) [11] and Praveen Kumar et al. (2014) [34] The estimates of specific combining ability effects for traits in maize nonstress situation are presented in Table 16. These estimates are used to identify the best cross-combinations for hybrids production (Izge et al., 2007) [35] . Among 150 hybrids evaluated, 45 hybrid combinations recorded positive score under non-stress situation across triats, indicating them as heterotic hybrids for these traits. 26 hybrid combinations were found to be inferior with negative score for SCA effects across traits under non-stress. 79 hybrid combinations scored zero under nonstress, because of cancellation positive and negative significant SCA effects for different characters. Such hybrids may also be considered as heterotic hybrids depending on the importance of productivity trait with positive significant SCA effects (Table 16). The study revealed that hybrids with high SCA effects involved at least one or two of the several higher general combiners as parent. Gama et al. (1995), Kadams et al. (1999) [36] Asif et al. (2007) [9] , Kuchanur (2010) [32] , Premlatha and Kalamani (2010) [10] , Aminu and Izge (2013) [11] and Praveen Kumar et al. (2014) [34] reported similar result where a hybrid with high SCA effects involved one or both of the good general combiners as parents. Specific combining ability effects of selected triats. The results of these triats presented in (Table 2). Under non stress situation, for anthesis to silking interval the hybrid viz. (Rajkumar × CML446 Inberdline 18) × HS7 exhibited significant SCA effect in negative direction. Whereas, under stress situation, the hybrids viz. (Rajkumar × CML446 Inberdline 40) × KDMI16 and (NK6240 × CIInberdline 12) × CIexhibited significant SCA effects in negative direction. Negativity of these traits is important, implying that these hybrids could mature earlier and could eSCApe drought. Similar results were reported by Bello and Olaoye (2009) [37] , Kuchanur (2010) [32] and Aminu and Izge (2013) [11] . Under non stress situation, for grain yield per plant the hybrids viz. (NK6240 × CI Inberdline 37) × HS7 and (Rajkumar × CML446 Inberdline 38) × HS7 (Rajkumar × CML446 Inberdline 18) × HSand (NK6240 × HS17 Inberdline 14) × HS7 exhibited significant SCA effects in positive direction, These are good hybrids when breeding for drought stress and grain yield. These hybrids probably have potential as parents of hybrid varieties, as well as for inclusion in breeding programmes, since they may contribute superior alleles in new populations for high grain yield and other abiotic stresses in maize production especially in areas where low rainfall. These results are in line with earlier independent studies of Perez-Velasquez et al. (1996) [38] , [9] , Kuchanur (2010) [32] , Premlatha and Kalamani (2010) [10] and Aminu and Izge (2013) [11] . Drought susceptibility index (DSI) among 150 hybrids along with 35 parents and checks (Allrounder × HS17 inbredline 39) × CI4 (0.04) lowest DSI values for grain yield (kg/ ha).

It is conclded that under non stress situation, Three hybrids exhibited significant SCA effects with a superior mean performance for grain yield (kg/ha) viz. (Rajkumar × CML446 Inberdline 3) × HS4 (NK6240 ×CIInberdline 12) × HSand (Allrounder × HS17 Inberdline 34) × HS7. These three hybrids need to be further evaluated in large SCAle trials before these are identified for commercial cultivation.

References

1. Anonymous.https://indiastat.com.2024.
2. Anonymous.https://indiastat.com.2025-2026.
3. Edmeades GO, Bolanos J, Elings A, Ribaut JM, Banziger M, Westgate ME. Physiology and modeling kernel set in maize, "The role regulation of the anthesis-silking interval in maize". Westagate ME, Boote BJ, eds, CSSA Special Publication 29, CSSA, Madison, WI. 2000:43-73.
4. Edmeades GO, Cooper M, Lafitte R, Zinselmeier C, Ribaut JM, Habben JE, et al. Abiotic stress and staple crops. In: Nosberger J, Geiger HH, Struic PC, editors. Crop science: Progress and Prospects. Proc. of Third Int. Crop Sci. Congress. CAB Int., Wallingford, Oxon, U. K. 2001:137154.
5. Prashanth M. Isolation and early generation evaluation of inbred lines derived from yellow pool population of maize (Zea mays L.). Ph.D Thesis submitted to University of Agricultural Sciences, Dharwad, Karnataka (India). 2008.
6. Falconer DE. Introduction to Quantitative Genetics. Ronald Press Company, New. 1964.
7. Kempthorne O. The Contributions of Statistics to Agronomy. Advances in Agronomy. 1957:177204.https://doi.org/10.1016/s0065-2113(08)60113-3
8. Joshi VN, Dubey RB, Marker S. Combining ability for polygenic traits in maturity hybrids of maize (Zea mays L.). Indian J. Genet. Plant Breed. 2002;62:312-315.
9. Asif MI, Nehvi FA, Wani SA, Rehana Q, Zaboor AD. Combining ability analysis for yield and yield relarted traits in Maize (Zea mays L.). International J. Plant Breed. Genet. 2007;1(2):101-105.
10. Premlatha M, Kalamani A. Heterosis and combining ability studies for grain yield and growth characters in maize (Zea mays L.). Indian J. Agric. Res. 2010;44(1):62-65.
11. Aminu D, Izge AU. Gene action and heterosis for yield and yield traits in maize (Zea mays L.), under drought conditions in Northern guinea and Sudan savannas of Borno State, Nigeria. J. Agri. Sci. 2013;1(1):17-23.
12. Kuchanur PH, Salimath PM, Wali MC. Genetic analyses in maize (Zea mays L.) under moisture stress conditions. Indian J. Genet. 2013;73(1):36-43.
13. Sharma S, Narwal MS, Kumar R, Dass S. Line × tester analysis in maize (Zea mays L.). Forage Res. 2004;30:2830.
14. Sandhu SS, Harjinder Singh, Saxena VK. Detection of promising lines of maize from new germplasm for heterotic breeding in different seasons. Crop Improv. 2006;33(1):2530.
15. Singh SN, Singh KN, Singh HG. Combining ability studies in maize. Crop Improv. 1983;10(1):6-9.
16. Paul SK, Duara PK. Combining ability for yield and maturity in maize (Zea mays L.). International J. Trop. Agri. 1991;9(4):205-254.
17. Sedhom SA. Estimation of general and specific combining ability in maize under different planting dates. Ann. Agril. Sci. 1994;28(1):25-30.
18. Mohammad AA. Effect of nitrogen fertilization levels on the performance and combining ability of maize hybrids (Zea mays L.). Ann. Agril. Sci. 1993;38(2):531-549.
19. Satyanarayana E, Kumar RS, Sharma MY. Inheritance studies of maturity components and yield in selected hybrids of maize (Zea mays L.). Mysore J. Agril. Sci. 1994;28(1):25-30.
20. Olaoye G, Bello GB, Ajani AK, Ademuwagun TK. Changes in quantitative and qualitative characters in the segregating F1 population of sweet corn (Zea mays L.) crosses. Proceedings of the 30th Annual Conference of the Genetics Society of Nigeria 5-6 Sept 2005. University of Nigeria, Nsukka. 2005:74-84.
21. Rojas BA, Sprague GF. A comparison of variance components in corn yield trails. III. General and specific combining ability and their interaction with locations and years. Agron. J. 1952;44:462-566.
22. Kadlubiec WI, Karwowkka C, Kuzezych Z, Kuriata R, Walczowska KS. Combining ability of maize inbred lines. Biueltyn Instytutu Hodowlii Aklimatyzacji Roslin. 2000;216:371-378.
23. Ramesh V, Rezai A, Arzani A. Estimates of genetic parameters for yield and yield components in corn inbred lines using diallel crosses. J. Sci. Technol. Agril. Natural Resources. 2000;4:95-104.
24. Rosa A De La, leon De H, Hartinez G, Rincon F. Heterosis, combining ability and genetic diversity in commercial hybrids of maize (Zea mays L.). Agron. Mesoamericana. 2000;11:113-122.
25. Mandal SS, Mandal HK, Verma DK, Akhtam SA. Combining ability analysis for grain yield and component traits in maize. J. Inter Academicia. 2001;5:132-137.
26. Thenmozhi P. Studies on heterosis and combining ability in Lab lab purpurius L. sweet. M. Sc. (Agri.) Thesis, Tamil Nadu Agril. Univ., Coimbatore, Tamil Nadu; 1983.
27. El-Hosary AA, Mohamed MK, Sedhom SA, Abo-ElHassan GKA. Performance and combining ability in diallel crosses of maize. Ann. Agril. Sci. 1994;32(1):203-216.
28. Paul KK, Debanth SC. Combining ability analysis in maize (Zea mays L.). Pakistan J. Scientific and Industrial Res. 1999;42(3):141-144.
29. Malik SI, Malik HN, Minhas NM, Muhammad M. General and specific combining ability studies in maize dialle crosses. International J. Agril. Biol. 2004;6(5):856-859.
30. Koinuma K, Miura Y, Sato H, Hasegawa H, Monma E, Enoki H, Shigemorit, Takamiya Y, Okabe T. Breeding of flint maize inbred line "HO49" and its characteristics. bulletin of the National agricultural Research Center for Hokkaido Region. 2004;180:19-31.
31. Fan XM, Tan J, Yang JY, Chen HM. Combining ability and Heterotic grouping of ten temperate, subtropical and tropical quality protein maize inbreds. Maydica. 2004;49(4):267-272.
32. Kuchanur PH. Identification of drought tolerance maize (Zea mays L) germplasm. Ph.D. Thesis, Univ. Agril. Sci., Dharwad, Karnataka (India). 2010.
33. Izge AU, Dugje IY. Performance of drought tolerant threeway and top cross maize hybrids in Sudan Savanna of North Eastern Nigeria. J. Plant Breed. Crop Sci. 2011;3(11):269675. 34. Praveen Kumar G, Narsimha RV, Sudheerkumar S, Venkateshwara RP. Combining ability studies in newly developed inbred lines in maize (Zea mays L.). International J. Plant Animal Env. Sci. 2014;4(4):229-234.
34. Izge AU, Kadams AM, Gungula DT. Heterosis and inheritance of quantitative characters in a diallel cross of pearl millet (Pennisetum glaucum L.). J. Agron. 2007;6(2):278-285.
35. Kadams AM, Ugherughe PO, Abakura JB. Genetic correlation and path coefficient analysis of yield and its components in wheat. Niger. J. Trop. Agric. 1999;1:107114.
36. Bello OB, Olaoye G. Combining ability for maize grain yield and other agronomic characters in a typical Southern Guinea Savanna ecology of Nigeria. Afr. J. Biotechnol. 2009;8(11):2518-2522.
37. F. Perez-Munoz, T. S. Colvin. Continuous Grain Yield Monitoring. Transactions of the ASAE. 1996;39(3):775783.https://doi.org/10.13031/2013.27560
38. Anonymous. Abiotic Stressess in Maize: Some Issues and Solutions Directorate of Maize Research, Pusa Campus, New Delhi, Indian Council of Agricultural Research. 2009:16.
39. CIMMYT. World maize facts and trends. Meeting world maize needs: technological opportunities and priorities for the public sector. CIMMYT, Mexico, DF. 1999-2000:4-20.
40. Debnath A. Molecular Regulation of Salt Stress Tolerance in Fruit Crops with Special Emphasis on WRKY Transcription Factors. Plant Science Review. 2020.https://doi.org/10.51470/PSR.2020.01.01.09
41. Debnath A, Sarkar A. Genetic and Molecular Basis of Stress Tolerance in Temperate and Tropical Fruit Crops. Agriculture Reviews: An International Journal. 2022.https://doi.org/10.51470/AR.2022.1.1.01
42. Debnath A. Hormonal Crosstalk and Signal Transduction Mechanisms Regulating Plant Reproductive Development. Agriculture Reviews: An International Journal. 2022.https://doi.org/10.51470/AR.2022.1.1.05
43. Debnath A. Climate Resilient Horticultural Crops and Breeding Strategies for Abiotic Stress Adaptation. Xplore Environment: An International Journal. 2021.https://doi.org/10.51470/XE.2021.1.1.01
44. Debnath A. Environmental Implications of Peat-Based Growing Media on Carbon Balance and Greenhouse Gas Emissions in Horticultural Systems. Xplore Environment: An International Journal. 2021.https://doi.org/10.51470/XE.2021.1.2.01
45. Chauhan K. The Impact of Microfinance and Self-Help Groups on Women's Empowerment and Social Inclusion. Discover Economics and Finance: An International Journal. 2026.https://doi.org/10.51470/DEF.2026.4.1.01
46. Shaghaf K, Mishra A, Sunita T. Application of Convolutional Neural Networks for Ripeness Classification of Fruits. Agriculture Reviews: An International Journal. 2025.https://doi.org/10.51470/AR.2025.4.2.15
47. Syed S, Tiwari JK, Singh D, Sharma R. Combined Heat and Drought Stress in Crops: Physiological Responses and Adaptation Mechanisms. Agriculture Reviews: An International Journal. 2026.https://doi.org/10.51470/AR.2026.5.1.42