Diallel analysis of turcicum leaf blight resistance and grain yield in maize and its implications for genetic improvement

Diallel analysis of turcicum leaf blight resistance and grain yield in maize and its implications for genetic improvement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diallel analysis of turcicum leaf blight resistance and grain yield in maize and its implications for genetic improvement Susmita Cherukuri, Hemalatha Vipparthi, Yathish Kondajji Rangappa, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7925151/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A half-diallel mating design involving nine diverse maize genotypes was employed to investigate combining ability for resistance to Turcicum leaf blight (TLB) and for grain yield performance under both artificial inoculation and natural infection at two hotspot locations in India. Analysis of variance (ANOVA) revealed significant differences among parents and crosses for all studied traits, indicating substantial genetic variability. Diallel analysis revealed significant general combining ability (GCA) and specific combining ability (SCA) effects for disease-related traits as well as yield, highlighting the role of both additive and non-additive gene actions. Additive effects were predominant for TLB resistance, whereas non-additive effects mainly governed yield under both artificially inoculated and natural infected conditions. GCA effects identified P 1 , P 2 , P 3 , P 5 and P 9 as key donors of additive alleles conferring TLB resistance, while P 2 , P 3 , and P 5 consistently showed positive GCA effects for both disease resistance and grain yield across both disease conditions. Among the hybrids, P 3 × P 7 showed strong positive SCA effects for grain yield along with significant negative SCA effects for disease resistance. Other promising crosses, including P 1 × P 5 , P 4 × P 6 , P 5 × P 6 , P 2 × P 4 , P 5 × P 7 and P 4 × P 9 , also combined favorable SCA effects for grain yield and disease resistance. Hence, integrating GCA and SCA analyses could provide an effective strategy for identifying superior parental lines and hybrids, supporting the development of TLB-resistant, high-yielding maize cultivars. To check for stability and adaptability, these promising combinations should be validated through multi-location evaluation. Combining ability Diallel analysis Disease resistance Gene action Grain yield Figures Figure 1 Figure 2 Figure 3 Introduction Maize ( Zea mays L.) is a globally important cereal, serving as a vital source of food, animal feed, and raw material for various industrial products (Paroda and Kumar 2000 ; Augustine and Kalyanasundaram 2021 ). In India, it ranks third among cereals after rice and wheat. Owing to its broad adaptability, maize thrives across diverse agro-climatic conditions, ranging from semi-arid regions to well-irrigated ecosystems in tropical, subtropical, and temperate zones. Despite its versatility, the crop is highly susceptible to biotic and abiotic stresses, with foliar fungal diseases posing a major constraint to yield and productivity (Njeru et al. 2023 ). Turcicum leaf blight (TLB), also referred to as northern corn leaf blight (NCLB), caused by Setosphaeria turcica (anamorph Exserohilum turcicum Leonard and Suggs), is a widespread foliar disease that severely affects maize production worldwide, including India (Hooda et al. 2017 ). In India, TLB is predominantly observed during the kharif (rainy) season in Zones I, II, and IV of the All India Coordinated Research Project (AICRP) on Maize, which correspond to the Northern Hill Zone (temperate region), North West Plain Zone (sub-tropical region), and Peninsular Zone (tropical region), respectively. Yield losses reported due to TLB vary from 27% to 90% depending on disease severity (Jha 1993 ; Pandurangegowda et al. 1993 ; Harlapur et al. 2000 ), and the disease also increases susceptibility to stalk rot while reducing forage quality (Chenulu and Hora 1962 ). Characteristic symptoms include elongated, elliptical tan lesions starting on lower leaves and progressing upward. High humidity and moderate to high temperatures favor disease development, which can affect plants from the vegetative stage through grain filling (Palaversic et al. 2012 ; Abebe 2023 ). Disease severity is influenced by the host genotype, prevailing climatic conditions, and cultivation practices. Despite the availability of chemical and cultural control measures, their effectiveness often declines under high disease pressure. Fungicides such as mancozeb, propiconazole, and tebuconazole are effective when applied r during early infection stages; however, excessive reliance may accelerate the development of fungicide-resistant S. turcica populations. Cultural practices, including crop rotation with non-host crops, deep ploughing, removal of infected residues, and balanced fertilization with optimal spacing, help reduce inoculum and disease spread. However, under conducive environmental conditions, these measures often fail to provide satisfactory disease control. Moreover, economic constraints, environmental concerns, and rapid disease spread during the kharif season (Parime et al. 2023 ) further limit their sustainability. Therefore, host-plant resistance remains the most practical, cost-effective, and environmentally sound strategy for long-term management of Turcicum leaf blight (Fehr 1987 ). Effective breeding for durable resistance requires understanding the genetic basis of resistance. Combining ability analysis, comprising general combining ability (GCA) and specific combining ability (SCA), provides insight into additive and non-additive gene effects (Griffing 1956 ). While GCA is crucial for selecting stable parental lines, SCA identifies superior hybrid combinations. Such analyses enable targeted parental selection and hybrid development, maximizing genetic gains in both TLB resistance and grain yield. Several studies have identified maize germplasm and inbred lines with TLB resistance through systematic screening (Chandrashekara et al. 2014 ; Kiran et al. 2017 ; Singh et al. 2018 ; Jakhar et al. 2021 ). Although the inheritance of TLB resistance and yield stability has been explored via combining ability analyses, most studies have considered these traits separately, focusing either on yield or disease resistance (Njore and Gichurru 2013; Nedi et al. 2018 ; Suresh et al. 2021 ). Only a few studies have simultaneously evaluated both traits, either in single environment (Mogesse et al. 2020 ; Ohunakin et al. 2021 ; Antony et al. 2023 ) or across multiple locations (Badu-Apraku et al. 2021 ; Abdelsalam et al. 2022 ; Kutuka et al. 2024 ). The present study, therefore, aimed to assess the combining ability of diallel crosses in disease hotspot environments for TLB resistance and grain yield. The objective was to identify superior parental lines and cross combinations based on GCA and SCA effects, prioritizing resistance with minimal yield penalty under disease pressure. Material and Methods Development of genetic material for field evaluation Genetic material comprised nine parental genotypes (Table 1 ) and their 36 half-diallel crosses, generated by following a 9 × 9 half-diallel mating design during rabi , 2023-24 at Winter Nursery Centre, ICAR-Indian Institute of Maize Research (IIMR), Hyderabad (17⁰32′58.22″N, 78⁰39′70.31″E), India. Parental genotypes comprised of resistant (3), moderately resistant (2), moderately susceptible (1) and susceptible (3) to TLB, selected based on their differential reaction to TLB disease under artificially inoculated conditions during kharif , 2022 at two TLB hotspot locations viz. , Mandya [Zonal Agricultural Research Station (ZARS), (12⁰34′8.97′′N, 76⁰ 48'47.86′′E)] and Dharwad [AICRP on Maize, UAS, Main Agricultural Research Station (MARS), (15⁰27′2.63″N and 75⁰0′11.52′′E)] located in Karnataka, India. Table 1 Details of inbred parents used in the present study S. No. Entry identity Entry code Reaction to TLB 1. MIL2-27P P 1 Resistant 2. EC0758084 P 2 Resistant 3. HKI163 P 3 Resistant 4. IC212886 P 4 Susceptible 5. SKV50 P 5 Moderately Resistant 6. IC212893 P 6 Susceptible 7. HKI161 P 7 Susceptible 8. CM600 P 8 Moderately Susceptible 9. UMI1200 P 9 Moderately Resistant TLB culture preparation and inoculation The primary source of fungus, E. turcicum (Pass.) Leonard and Suggs. inoculum was collected in the form of TLB-infected leaves of maize plants available in the local fields. The infected leaf tissues were surface sterilized using 0.1% mercuric chloride or 1% sodium hypochlorite solution for 30–60 seconds and rinsed thoroughly with sterile distilled water to remove any residual disinfectant. The sterilized leaf segments were then plated on Potato Dextrose Agar (PDA) medium and incubated at 25 ± 1°C for 7–10 days to facilitate fungal growth. Standard tissue-isolation technique was used to obtain pure cultures of E. turcicum through hyphal tip transfer, subsequently maintained for further use for mass multiplication. Sterilized sorghum grains were used as substrate for the mass multiplication of the pathogen (Joshi et al. 1969 ). Sorghum grains, due to their high nutritional value support robust fungal sporulation. Approximately 100–150 g of sorghum grains were soaked overnight, autoclaved, and inoculated with actively growing pure mycelium culture of E. turcicum . The inoculated grains were incubated at 25–28°C for 10–12 days under alternate light and dark cycles to induce sporulation. Once the grains were heavily colonized with the fungus, they were shade-dried and ground to a fine powder. Artificial inoculation was employed using the widely adopted leaf whorl inoculation technique (Raymundo and Hooker 1981 ; Carson 1998 ). It was carried out manually at 30–35 days after sowing (DAS), corresponding to the V 6 -V 8 growth stages of maize plants, the most susceptible stages by placing finely ground fungal inoculum (1.0-1.5 g) into the central leaf whorl of each plant. A fine mist of water was then applied to create a humid microenvironment conducive to fungal germination/ infection and also to ensure uniform infection and minimize disease pressure variability in the field. Further, a second round of inoculation was conducted seven days after the first inoculation to reinforce infection and achieve uniform disease establishment across experimental plots, thereby minimizing the risk of disease escape and account any variation in plant growth stages. Phenotyping for TLB response and grain yield The diallel crosses were evaluated in a trial under both TLB epiphytotic, created artificially through E. turcicum inoculation as well as naturally infected (uninoculated) conditions at TLB disease hotspot locations namely, Mandya and Dharwad during kharif , 2024. The trials were conducted by following randomized complete block design (RCBD) in two replications. Each entry in the trial was sown in single row of four-meter length with 0.6 and 0.2 metre spacing between rows, and plants within each row, respectively. The data on grain yield and TLB incidence were recorded under both conditions. The TLB disease score was recorded based on percentage of leaf area infected by visualizing the leaf area covered by lesions using 1–9 scale on five randomly selected plants in each row during 75–90 DAS (dough stage), the period coincided with peak disease expression (Chung et al. 2010 ). Based on disease severity, the genotypes were grouped into four categories: resistant (≤ 3), moderately resistant (3.1-5.0), moderately susceptible (5.1-7.0) and susceptible (> 7.0). Furthermore, additional data on lesion length (cm), infected leaves per plant, and lesions per plant were recorded from five plants that were scored for TLB response. The data on grain yield was recorded on per-plot basis by harvesting and drying the maize ears, adjusting them to 15% moisture content, and subsequently converted to tonnes per hectare (t/ha). Statistical analysis Analysis of variance (ANOVA) was performed for each trait at individual locations. The trait means were then subjected to Levene’s test for homogeneity of variances, after which pooled ANOVA across locations was conducted by considering crosses as fixed effects and locations as random effects. To partition the genetic variation into GCA and SCA effects, diallel ANOVA was performed across environments following Griffing’s Method II, Model I (Griffing, 1956 ), using AGD-R software (Rodríguez et al. 2015 ). The statistical model employed was as follows: $$\:{Y}_{\text{i}\text{j}\mathcal{l}r}=\mu\:+{L}_{\mathcal{l}}+{R}_{\mathcal{l}r}+{g}_{\text{i}}+{g}_{\text{j}}+{s}_{\text{i}\text{j}}+{\left({g}_{\text{i}}\right)}_{\mathcal{l}}+{\left({g}_{\text{j}}\right)}_{\mathcal{l}}+{\left({s}_{\text{i}\text{j}}\right)}_{\mathcal{l}}+{\epsilon\:}_{\text{i}\text{j}\mathcal{l}r}$$ where \(\:{Y}_{\text{i}\text{j}\mathcal{l}r}\) ​ = observation of cross i×j in location (site/environment) ℓ and replication r, µ = overall mean, \(\:{L}_{\mathcal{l}}\) ​ = effect of the ℓ th location, \(\:{R}_{\mathcal{l}r}\) = replication effect nested within location, \(\:{g}_{\text{i}}\) ​, \(\:{g}_{\text{j}}\) = GCA effects of parents i and j, \(\:{s}_{\text{i}\text{j}}\) = SCA effect of the i×j cross, \(\:{\left({g}_{\text{i}}\right)}_{\mathcal{l}}\) ​, \(\:{\left({g}_{\text{j}}\right)}_{\mathcal{l}}\) ​ = GCA × location interaction effects, \(\:{\left({s}_{\text{i}\text{j}}\right)}_{\mathcal{l}}\) ​ = SCA × location interaction effect, \(\:{\epsilon\:}_{\text{i}\text{j}\mathcal{l}r}\) ​ = experimental error. The significance of the GCA of parents and SCA of half-diallel crosses was tested using a t-test based on the standard errors of the GCA and SCA effects. Variance components, Baker’s predictability ratio, and heritability estimates were estimated using the Mixed A model of AGD-R. In this model GCA and SCA were modelled as random, and variance components were estimated by Restricted Maximum Likelihood (REML) in order to obtain unbiased estimates of variance components. Additive and dominance variances were derived as σ 2 A = 4σ 2 GCA /p and σ 2 D ​=4σ 2 SCA ​/[p(p−1)] respectively, where p is the number of parents. Broad- and narrow-sense heritability, as well as Baker’s predictability ratio, were computed from these variance components. Baker’s ratio was calculated as 2σ 2 GCA /2σ 2 GCA + σ 2 SCA (Baker, 1978 ). The GCA effects of parents for disease traits were visualized using bar graphs in Excel, whereas the SCA effects of crosses were represented through a bubble plot generated with the ggplot2 package in R. A heatmap, displaying the performance of the crosses and parents based on mean values for percent reduction in yield, yield under artificial inoculation and natural infection, was generated using the Pheatmap function in R software. The heatmap facilitates easy identification of entries with stable performance for yield and higher resilience to disease. Results Analysis of variance The homogeneity of variance test revealed no significant differences across environments for both grain yield and disease traits, indicating uniform error variances among the tested locations. Diallel ANOVA revealed that environmental variances were highly significant for all the traits evaluated, reflecting substantial differences in mean trait values across the tested environments (Table 2 ). The GCA and SCA variances were also highly significant for all the studied traits, including grain yield under both artificially inoculated and naturally infected conditions. The highly significant variance observed among entries (parents and crosses) indicated the presence of substantial genetic variability for the studied traits. The analysis of site × combining ability interactions revealed contrasting patterns between disease traits and grain yield. For disease traits, both site × GCA and site × SCA interactions were found to be non-significant, suggesting that the combining ability effects for disease resistance remained consistent across the two testing environments. This indicated that the ranking of genotypes for disease resistance did not change significantly between locations, pointing towards stable genetic control of this trait. In contrast, significant site × GCA and site × SCA interaction variances were detected for grain yield. These results demonstrated that the relative performance of genotypes and their combining abilities varied across environments for yield. The significance of these interactions reflects the influence of environmental conditions on the expression of yield and indicated the presence of genotype × environment interactions (GEI) for this trait. Table 2 Mean squares for disease traits and yield pooled across two environments Source of variation Df Disease score Lesions per plant Lesion length Infected leaves per plant Grain yield q/ha (artificial inoculation) Grain yield q/ha (natural infection) Env 1 8.36** 453.97** 218.21** 18.63** 15806.62** 19715.85** Rep (Env) 2 0.13 6.67 0.33 10.44** 4.75 4.51 Entry 44 5.66** 50.82** 97.71** 22.88** 2237.18** 2243.42** GCA 8 27.93** 82.30** 382.35** 99.29** 3279.45** 2953.60** SCA 36 0.71** 43.83** 34.46** 5.91** 2005.57** 2085.60** Entry × Env 44 0.16 3.09 2.77 0.88 337.69** 356.16** Env × GCA 8 0.43 4.18 7.58 2.35 240.23** 336.79** Env × SCA 36 0.10 2.84 1.70 0.55 359.34** 360.47** Error 88 0.22 4.39 5.45 1.36 23.82 37.03 *, ** significant at 0.05% and 0.01% level of probability respectively; Df-Degrees of freedom Env-environment; Rep-replication; Entry-crosses and parents The proportional mean squares (MS) due to GCA and SCA were relatively consistent across both disease conditions, with GCA values exceeding those of SCA (Table 2 ). This suggests a predominance of additive genetic effects under both conditions, supporting the utility of selection-based improvement. However, variance components estimated using AGD-R under the Mixed A model revealed higher SCA variance than GCA variance across both environments (Table 3 ). This apparent contradiction arises from methodological differences; while ANOVA-derived mean squares reflect the magnitude of effects and are influenced by replication and design structure, REML-based estimates more accurately partition genetic variance by accounting for random effects, data imbalance, and estimation error (Piepho et al. 2008 ). Table 3 Estimates of variance and other genetic parameters Genetic variance estimates Disease score Lesions per plant Lesion length Infected leaves per plant Grain yield q/ha (artificial inoculation) Grain yield q/ha (natural infection) GCA Variance 0.61 (62.44) 0.84 (5.59) 7.77 (37.39) 2.08 (44.27) 31.66 (4.99) 20.27 (3.12) SCA Variance 0.15 (15.63) 10.25 (67.84) 8.19 (39.40) 1.34 (28.49) 411.56 (64.83) 431.28 (66.32) GCA x Env Variance 0.01(1.52) 0.06 (0.40) 0.27 (1.29) 0.08 (1.74) 0.00 (0.00) 0.00 (0.00) SCA x Env Variance 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) 167.76 (26.43) 161.72 (24.87) Error Variance 0.20 (20.42) 3.95 (26.17) 4.56 (21.93) 1.20 (25.51) 23.82 (3.75) 37.03 (5.69) Additive Variance 1.22 1.69 15.55 4.16 63.32 40.53 Dominance Variance 0.15 10.25 8.19 1.34 411.56 431.28 GCA-SCA ratio 4.00 0.08 0.95 1.55 0.08 0.05 Baker ratio 0.89 0.14 0.65 0.76 0.13 0.09 Narrow Heritability 0.78 0.11 0.55 0.62 0.13 0.08 Broad Heritability 0.87 0.75 0.84 0.82 0.95 0.93 Env-environment, values in parenthesis represent % contribution of various genetic effects to total variance Under artificially inoculated conditions, SCA variance contributed substantially more to grain yield (64.8%) compared to GCA variance (4.9%), indicating that yield performance under stress was predominantly governed by non-additive gene actions such as dominance and epistasis (Table 3 ). Baker’s predictability ratio, which was below 0.5, further confirmed the predominance of non-additive effects. Similarly, under natural infection conditions, SCA contribution remained high (66.3%) compared with GCA (3.1%), reaffirming the dominance of non-additive gene action across environments. Interestingly, the GCA contribution under stress (4.9%) was slightly higher than under natural conditions (3.1%), suggesting that additive effects may play a relatively greater role in yield performance when disease pressure is present. This observation aligns with the proportional mean squares, where GCA consistently exceeded SCA (Table 2 ), indicating that both additive and non-additive gene actions were involved in controlling grain yield across environments. In contrast for disease resistance, GCA contributed 62.4% while SCA accounted for 15.6%, indicating that additive genetic effects from parental lines played a major role in conferring resistance to TLB under stress conditions (Table 3 ). A similar trend was observed for infected leaves per plant, where GCA contributed 44.2% and SCA 28.5%, again highlighting the predominance of additive gene action. For other disease-related traits, however, the pattern shifted towards non-additive effects. In the case of lesion length, SCA contributed 39.4% compared to GCA with 37.4%, while for lesions per plant, SCA dominated with 67.8% against only 5.6% from GCA. This points to a stronger influence of non-additive gene action for these two traits, though GCA contributions remained statistically significant, especially for lesion length, suggesting that additive effects were still present. The MS due to GCA were significant across all disease-related traits (lesion length, lesion number, and infected leaves per plant) (Table 2 ), that further supported the role of additive variance in their inheritance. Heritability estimates revealed high narrow-sense (0.78) and broad-sense (0.87) heritability for disease score, as well as moderately high values for infected leaves per plant (0.62 and 0.82) and lesion length (0.55 and 0.84). Baker’s ratio was also high for disease score (0.89), lesion length (0.65), and infected leaves per plant (0.76), with values above 0.5 indicating the relative importance of additive genetic effects for these traits. GCA effects Among the nine parental genotypes evaluated, five (P 1 , P 2 , P 3 , P 5 , and P 9 ) exhibited significant negative GCA effects for disease score and key disease-related traits, including lesion length and infected leaves per plant (Table 4 ; Fig. 1 ). Additionally, four parents (P 1 , P 2 , P 5 , and P 9 ) showed significant negative GCA effects for lesions per plant. The results corroborated the resistance or moderately resistant reaction against TLB recorded previously on these set of genotypes at different disease hot spots in India (data unpublished). Since, the parents were selected based on their resistance reaction to TLB, the present results further ensured the reliability of genotypes, for further use in genetic and plant breeding experiments/studies including their use in development in development of TLB resistant hybrids. The GCA effects for disease score ranged from − 0.97 (P 1 ) to 0.98 (P 4 ), while for lesions per plant, they varied from − 2.17 (P 2 ) to 1.60 (P 6 ). Lesion length GCA effects ranged between − 4.68 (P 1 ) to 3.79 (P 8 ), and for infected leaves per plant, from − 1.90 (P 1 ) to 2.06 (P 8 ). Table 4 General combining ability effects of parental lines for disease and yield Parent Disease score Lesions per plant Lesion length Infected leaves per plant Grain yield q/ha (artificial inoculation) Grain yield q/ha (Natural infection) P 1 -0.97** -1.60** -4.68** -1.90** 1.72** -1.16 P 2 -0.89** -2.17** -3.44** -1.62** 6.80** 4.29** P3 -0.69** 0.93** -1.48** -1.07** 6.22** 5.39** P 4 0.98** 0.73** 3.25** 1.27** -6.41** -3.73** P 5 -0.51** -0.88** -0.79** -1.18** 15.96** 16.07** P 6 0.66** 1.60** 2.75** 1.03** -7.91** -8.03** P 7 0.86 0.50* 0.70* 1.54** 0.65 3.25** P 8 0.68** 1.53** 3.79** 2.06** -6.94** -5.64** P 9 -0.12** -0.63* -0.11 -0.13 -10.09** -10.45** S.E. 0.05 0.21 0.23 0.12 0.49 0.61 *, ** significant at 0.05% and 0.01% level of probability respectively GCA effects for grain yield were assessed under both artificial epiphytotic and natural infection conditions to evaluate the value of parental genotypes under disease pressure. Under artificial inoculated condition, four parents (P 1 , P 2, P 3 , and P 5 ) showed highly significant positive GCA effects for yield, ranging from 1.72 (P 1 ) to 15.96 (P 5 ), while the overall GCA effects ranged from − 10.09 (P 9 ) to 15.96 (P 5 ). Under natural infection condition, four parents (P 2 , P 3 , P 5 , and P 7 ) recorded significant positive GCA effects, ranging from 3.25 (P 7 ) to 16.07 (P 5 ), with overall GCA effects ranging from − 10.45 (P 9 ) to 16.07 (P 5 ). SCA effects The SCA effects for grain yield in the 36 half-diallel crosses were statistically significant in most combinations under both artificial and natural infection conditions (Table 5 ). Among the evaluated crosses, 25 (~ 69%) showed significant SCA effects for grain yield under artificially inoculated conditions, whereas 24 (~ 66%) showed significant SCA effects under natural infection. Notably, 22 (~ 92%) of these crosses were common across both disease conditions, while only two crosses (~ 8%) were unique to either the natural or stress condition. The high overlap of significant crosses across the two disease conditions suggests that many favorable non-additive interactions are stable and can be reliably exploited in hybrid breeding programs. Differences observed between artificial and natural conditions likely reflect the influence of disease intensity, pathogen pressure, or other factors on the expression of non-additive effects. Nonetheless, the presence of a few cross-specific differences (~ 8%) indicates that non-additive gene expression may vary depending on the disease scenario and the associated level of disease pressure. Table 5 Specific combining ability effects of F 1 hybrids for disease and yield Hybrids Disease score Lesions per plant Lesion length Infected leaves per plant Grain yield q/ha (artificial inoculation) Grain yield q/ha (Natural infection) P 1 × P 2 -0.41 -0.21 2.32* 0.08 12.60** 9.41** P 1 × P 3 0.86** 3.97** 2.38* 1.12* 5.47* 0.88 P 2 × P 3 0.35 0.59 1.64 -0.12 12.28** 12.76** P 1 × P 4 0.06 0.85 -0.90 -0.28 22.91** 23.16** P 2 × P 4 -0.36 -1.93* -0.97 0.41 8.92** 11.52** P 3 × P 4 0.54** 8.75** -1.20 0.70 6.08** 8.07** P 1 × P 5 -0.23 0.81 -0.66 -1.53** 19.15** 23.48** P 2 × P 5 0.11 1.54 1.63 0.18 9.43** 7.36* P 3 × P 5 0.42* 3.44** 2.89** 1.13* 30.72** 35.28** P 4 × P 5 0.46* 2.48** 0.40 1.52** -7.36** -5.72* P 1 × P 6 0.78** -1.56 -1.76 1.46** -0.49 -0.59 P 2 × P 6 0.46* 2.27* 1.04 0.83 13.23** 15.33** P 3 × P 6 -0.48* -3.67** 1.85 -0.36 4.32 6.21* P 4 × P 6 -0.22 -4.12** -0.43 0.54 13.94** 10.70** P 5 × P 6 -0.12 -6.07** -2.30* 0.24 12.34** 10.53** P 1 × P 7 -0.16 -2.19* -2.92** -0.45 2.40 0.40 P 2 × P 7 -0.06 -0.52 0.99 -0.34 30.55** 28.21** P 3 × P 7 -0.44* -1.54 -2.14* -0.67 18.78** 20.34** P 4 × P 7 -0.01 0.82 1.67 0.54 -4.77* 6.41* P 5 × P 7 -0.22 0.57 -3.43** 0.71 20.47** 13.81** P 6 × P 7 -0.36 -1.45 -4.27** 0.63 -1.38 -4.34 P 1 × P 8 0.37 1.91* 2.81* 0.20 4.91* 6.63* P 2 × P 8 0.43* 0.63 1.22 -0.05 8.42** 10.73** P 3 × P 8 0.05 -3.94** 1.76* -1.00 6.49** 7.71** P 4 × P 8 -0.68* -2.94** -1.97 -0.19 2.28 -2.16 P 5 × P 8 -0.16 -1.66 -4.65** 0.09 6.50** 3.69 P 6 × P 8 -0.07 2.91** 2.56* 0.96 0.70 1.17 P 7 × P 8 0.43* 5.04** -0.92 0.64 8.21** 7.46* P 1 × P 9 -0.07 0.03 1.51 0.97 13.08** 16.34** P 2 × P 9 0.42 0.10 1.94 1.60** 3.93 4.00 P 3 × P 9 -0.13 -1.73 2.71* 0.44 -0.28 -0.69 P 4 × P 9 -0.12 -3.00** -6.12** -0.25 17.74** 12.12** P 5 × P 9 0.02** -3.21** -0.29 0.40 -0.51 -1.53 P 6 × P 9 -0.62** 2.61** 1.88 -0.04 3.90 4.61 P 7 × P 9 0.49* 2.25* 4.33** 2.98** 6.92** 11.77** P 8 × P 9 -0.21 6.00** -0.56 0.004 11.87** 14.86** S.E. 0.21 0.96 1.07 0.53 2.23 2.78 *, ** significant at 0.05% and 0.01% level of probability respectively While variation in yield was evident across the crosses, SCA analysis revealed that certain crosses also contributed significantly to disease resistance (Table 5 ). Evaluation of the crosses further highlighted substantial variation across multiple disease-related traits (Fig. 2 ). Specifically, 14 crosses (~ 39%) exhibited significant differences for disease score, indicating differential resistance or susceptibility. For the number of lesions per plant, 20 crosses (~ 56%) were significant, reflecting substantial variability in pathogen infection intensity. Similarly, 15 crosses (42%) showed significant differences in lesion length, demonstrating variation in disease severity or progression. In contrast, only seven crosses (~ 19%) exhibited significant variation for the number of infected leaves per plant, suggesting that this trait was relatively less variable or less responsive across the genetic combinations evaluated. Considering the SCA effects for both yield and disease resistance, a subset of crosses exhibited simultaneously favorable contributions to both traits. A substantial proportion of these crosses (~ 69%) exhibited significant SCA effects for both the traits. Considering desirable SCA effects, 24 crosses (~ 67%) showed significantly positive effects for yield under natural conditions, while 23 crosses (~ 64%) were significant under artificially inoculated conditions. In contrast, the number of crosses with desirable negative SCA effects for disease traits was markedly lower. Specifically, four crosses (~ 11%) showed significantly negative SCA effects for disease score, eight crosses (~ 22%) for lesions per plant, seven crosses (~ 19%) for lesion length, and only one cross (~ 3%) for infected leaves per plant. Notably, only a few hybrids combined positive SCA effects for yield with negative SCA effects for disease traits. Notably, the cross P 3 × P 7 involving a resistant parent (HKI163) and a susceptible parent (HKI161), exhibited highly significant positive SCA effects for grain yield under both artificial and natural conditions, along with highly significant negative SCA effects for disease reaction under artificial inoculation. Other crosses showing significant positive SCA effects for grain yield coupled with negative SCA effects for disease-related traits included P 2 × P 4 , P 4 × P 6 , P 5 × P 6 , and P 4 × P 9 for lesions per plant; P 5 × P 6 , P 5 ×P 7 , P 4 × P 9 , and P 3 × P 7 for lesion length, and P 1 × P 5 for infected leaves per plant. Complementing the crosses with strong SCA effects for both traits, ten crosses maintained positive SCA for grain yield despite showing limited improvement in disease resistance. The crosses viz. , P 1 × P 5 , P 4 × P 6 , P 5 × P 6 , P 2 × P 7 , P 1 × P 2 , P 2 × P 4 , P 5 × P 7 , P 1 × P 9 , P 4 × P 9 , and P 8 × P 9 showed non-significant negative SCA effects for TLB infection. Although these crosses did not show significant improvement in overall disease resistance, they maintained positive and statistically significant SCA effects for grain yield under both artificial and natural infection conditions. Among them, six crosses (P 1 × P 5 , P 4 × P 6 , P 5 × P 6 P 2 × P 4 , P 5 × P 7 , and P 4 × P 9 ) additionally exhibited significant negative SCA effects for one or more disease-related traits (lesion number, lesion length, infected leaves), while consistently maintaining positive SCA effects for yield. Collectively, these crosses represented diverse parental combinations, indicating that favorable yield performance was achieved across a broad genetic background. Specifically, they included, one cross with both resistant parents (P 1 × P 2 ); four crosses between resistant and susceptible or moderately resistant parents (P 2 × P 7 , P 2 × P 4 , P1 × P5, P 1 × P 9 ); two crosses involving moderately resistant and susceptible parents (P 5 × P 6 , P 5 × P 7 ); one cross between susceptible and moderately resistant parents (P 4 × P 9 ); one cross between moderately susceptible and moderately resistant parents (P 8 × P 9 ), and one cross with both susceptible parents (P 4 × P 6 ). A heatmap was generated to visualize the comparative performance of entries under artificially inoculation and natural infection conditions, assessing both disease response and the impact of disease on yield (Fig. 3 ). The heatmap displayed mean trait values across locations, allowing for simultaneous comparison of all entries. Entries with low percentage reduction in yield under artificially inoculation relative to natural infection were represented in blue, indicating maintenance of higher yields under both conditions. Conversely, entries with higher percentage reduction in yield were shown in red, reflecting a greater decrease in yield under artificially inoculated condition. This representation highlighted genotypes that combined high yield potential with reduced yield losses due to disease. Discussion The significant differences in GCA variances indicate the presence of additive genetic variance and potential for effective selection among parental lines. In contrast, the significant SCA variances across all traits reflect the presence of non-additive genetic variance, suggesting differential complementarity between genotypes and a high degree of heterozygosity that can be effectively exploited through selection (Vencovsky 1973 ; Oliveria et al. 2016). This confirms that hybrid performance depends on specific genotype combinations rather than solely on the average combining ability of the parents. The observed genetic variability, together with the non-additive variance, highlights the potential for exploiting heterosis to achieve higher grain yield with improved TLB resistance. Similar findings of significant differences among parents and crosses for grain yield and TLB score have been reported by Njoroge and Gichuru ( 2013 ), Abdelsalam et al. ( 2022 ), and Antony et al. ( 2023 ). The absence of significant site × GCA and SCA interactions for disease traits indicates that combining ability effects for resistance were stable, which is advantageous for breeding as it allows reliable selection of resistant genotypes across environments. Although the overall level of disease pressure may differ depending on pathogen virulence, the genetic ranking of genotypes is likely to remain consistent. Similar stability of resistance traits across locations has been reported by Chaudhary and Mani ( 2010 ), Beyene et al. ( 2012 ), and Njoroge and Gichuru ( 2013 ). In contrast, the significant site × GCA and SCA interactions observed for grain yield reflect the complexity of this quantitative trait and its high sensitivity GEI. Since the combining ability for yield is influenced by environmental conditions, genotypes that perform well in one location may not necessarily maintain their superiority in another. Such patterns are commonly observed in maize and other cereals and highlight the importance of multi-environment testing. Under these circumstances, it is often recommended to identify and promote location-specific genotypes in order to exploit their full genetic potential (Akaogu et al. 2017 ; Akaogu et al. 2020 ). Beyond the observed GEI effects, the partitioning of variance components provides further insights into the genetic basis of yield. The higher proportion of SCA variance under both stress and natural conditions clearly demonstrates that grain yield is largely controlled by non-additive genetic effects. This implies that hybrid development, which captures dominance and epistatic interactions, is an effective strategy for improving yield. The low Baker’s ratio further strengthens this interpretation, as values below 0.5 are characteristic of non-additive gene action. At the same time, the relatively higher contribution of GCA under stress compared to natural conditions suggests that additive gene effects may provide stability to yield expression under disease pressure. This is an important consideration for breeding programs, as it indicates that while heterosis can be exploited for higher yield potential, additive variance should not be overlooked in environments with biotic stress. The presence of both additive and non-additive effects across environments highlights the need for a balanced breeding strategy, involving the selection of parental lines with strong additive effects while exploiting specific crosses to capture non-additive interactions. Non-additive effects primarily drive yield, particularly under stress, but additive effects gain relative importance when disease pressure is high, making both genetic components valuable targets for selection. Thus, integrating both additive and non-additive gene actions provides an effective approach to enhance grain yield under natural and stressful conditions (Makumbi et al., 2011 ; Umar et al., 2013; Beyene et al., 2017 ; Badu-Apraku et al., 2021 ). In contrast to grain yield, disease resistance traits showed a different genetic pattern. While yield was predominantly influenced by non-additive variance, disease resistance traits showed a contrasting pattern. The predominance of GCA variance over SCA variance for disease score and infected leaves per plant suggests that additive gene action is the major determinant of TLB resistance. High heritability estimates further indicate that a substantial portion of phenotypic variation is attributable to additive genetic factors, reinforcing the potential for effective improvement through recurrent or pedigree selection. However, lesion length and lesion number displayed higher SCA contributions, suggesting that heterosis may contribute to these components, although additive effects remain important. The high Baker’s ratios (0.65–0.89) strongly support the predominance of additive gene action for key resistance traits, particularly disease score (Sibiya et al. 2013 ; Ayiga-Aluba et al. 2015 ; Abdelsalam et al. 2022 ; Antony et al. 2023 ). Further examination of GCA effects among parents provides deeper insights into their breeding value. The significant negative GCA effects observed in P 1 , P 2 , P 3 , P 5 , and P 9 indicate that these genotypes carry favorable alleles contributing to TLB resistance, supporting their potential use in hybrid development. In particular, P 9 showed negative GCA effects across all disease traits, suggesting it carries minor-effect additive alleles that can provide durable, broad-spectrum resistance when used in half-diallel crosses. These results emphasize that phenotypic resistance alone may not fully reflect a parent’s breeding value; additive gene action and genetic complementation determine hybrid performance (Hettiarachchi et al. 2009 ; Ding et al. 2015 ; Jakhar et al. 2021 ). In addition to resistance, the GCA effects for grain yield revealed complementary patterns. Among the nine parental lines, P 1 , P 2 , P 3 , and P 5 exhibited significant positive GCA effects for grain yield under artificially inoculated conditions, while under natural infection conditions, significant positive effects were observed in P 2 , P 3 , P 5 , and P 7 . The consistent positive GCA effects of P 2 , P 3 , and P 5 across both disease conditions suggest these genotypes possess stable additive alleles for yield, largely independent of disease pressure, highlighting their adaptability and potential for use in breeding programs targeting yield improvement (Wegary et al. 2014 ; Farfan et al. 2015 ; Ali 2016 ; Annor et al. 2019 ). The three resistant genotypes (P 1 , P 2 , and P 3 ) exhibited distinct GCA patterns, with P 2 and P 3 showing highly significant negative GCA effects for disease and disease-related traits and highly significant positive GCA effects for grain yield under both artificial epiphytotic and naturally infected conditions. Whereas, P 1 showed significantly positive GCA effects for yield under artificial inoculation but negative effects under natural infection, reflecting inconsistency across disease conditions. This suggests that its contribution to yield is context-dependent rather than stable across conditions. Among the two moderately resistant genotypes (P 5 and P 9 ), P 5 showed highly significant negative GCA effects for disease and disease-related traits and highly significant positive GCA effects for grain yield under both conditions. These results indicate the underlying contribution of alleles contributing for additive genetic variance. Such genotypes are of immense value in breeding programmes aimed at development of high yielding TLB disease resistant germplasm or cultivars which underscores their value as a donor of adaptive alleles that enhance performance under adverse environments (Makumbi 2005; Millet et al. 2016 ; Mageto et al. 2017 ). Among three susceptible genotypes (P 4 , P 6 , and P 7 ), two genotypes P 4 and P 6 showed positive GCA effects for disease and disease-related traits and negative GCA effects for grain yield under both conditions, which is in expected line. However, one genotype each of moderately resistant (P 9 ) and susceptible (P 7 ) stood contrast and distinct among all the genotypes by defying the expected results. For example, P 9 exhibited highly significant negative GCA effects for yield under both artificially inoculated and natural conditions, suggesting a potential trade-off between disease resistance and growth-related traits. This indicates that, although P 9 carries favorable alleles for TLB resistance, it may lack alleles that contribute to higher yield or could be inherently low-yielding. In contrast, P 7 showed highly significant positive GCA effects for yield despite being susceptible to TLB, indicating that alleles controlling yield can be present and effective independently of disease resistance alleles. Together, these contrasting patterns in P 9 and P 7 highlight the independent segregation of alleles governing yield and disease resistance, emphasizing the complexity of simultaneously improving both traits in breeding programs. These contrasting patterns underscore the need to identify genotypes that consistently provide favorable alleles for both yield and disease resistance, highlighting their potential as superior general combiners in hybrid breeding programs. In this study, P 1 , P 2 , P 3 , and P 5 simultaneously exhibited significant negative GCA effects for disease traits and positive GCA effects for grain yield, indicating that these genotypes carry additive alleles that can enhance both TLB resistance and productivity. Among them, P 2 , P 3 , and P 5 were particularly consistent across both disease conditions, demonstrating stable expression of favorable alleles regardless of environmental variation or disease pressure. This stability makes these genotypes highly valuable as parental lines in breeding programs, as they can contribute both high yield potential and durable disease resistance to hybrids. These findings align with previous studies emphasizing the importance of selecting parental genotypes that combine high yield potential with disease resistance to develop superior hybrids with predictable performance (Badu-Apraku et al. 2021 ; Ohunakin et al. 2021 ; Abdelsalam et al. 2022 ; Antony et al. 2023 ; Kutuka et al. 2024 ). The widespread significance of SCA effects across both disease conditions highlights the predominance of non-additive gene interactions in determining hybrid performance for grain yield. Although most favorable non-additive interactions were stable across both disease conditions, a small fraction of crosses showed condition-dependent differences. This suggests that the expression of non-additive effects can be influenced by the level of disease pressure or pathogen dynamics, emphasizing the value of evaluating genotypes under both artificial inoculation and natural infection to capture hybrids with consistent performance. To further explore how parental alleles influence hybrid performance, GCA effects of selected genotypes were analyzed. Parent P 9 consistently showed highly significant negative GCA effects for yield under both artificial and natural conditions, suggesting a potential trade-off between disease resistance and growth, or a lack of favorable yield alleles. In contrast, P 1 displayed significantly positive GCA effects under artificial inoculation, indicating its capacity to donate adaptive alleles that improve yield under controlled conditions. However, the same genotype showed negative GCA effects under natural infection, highlighting the context-dependent expression of favorable alleles. This contrast may be explained by pathogen-induced activation of defense-related signaling pathways, which could interact with yield-contributing alleles, altering trait expression (Guo et al. 2023 ; Derbyshire et al. 2024 ; Gao et al. 2024 ). Additionally, under artificial inoculation, P 1 may enhance specific combining ability through favorable gene interactions that are not fully expressed under natural conditions, suggesting that hybrid performance can be influenced by the intensity or uniformity of disease pressure (Souza et al. 2009 ; Hallauer et al. 2010 ; Adu et al. 2023 ). While these hypotheses require systematic investigation for confirmation, the results underscore the critical role of specific parental interactions in achieving superior hybrid performance under stress. Overall, these findings underscore the critical importance of specific parental combinations in achieving superior hybrid performance under stress. They suggest that hybrid development should consider both parental GCA and favorable SCA interactions to maximize yield under variable disease pressures. Future studies are required to validate the proposed hypotheses regarding pathogen-mediated modulation of yield-contributing alleles. The observed variation among crosses for disease score and associated traits highlights the differential genetic potential for TLB resistance within the population. The higher proportion of significant crosses for lesion number and length suggests that these traits are more sensitive indicators of pathogen response and may be more amenable to selection in breeding programs. Conversely, the relatively low variability for infected leaves per plant indicates that this trait may be more stable across genetic backgrounds or less influenced by minor genetic differences. These findings emphasize the importance of evaluating multiple disease-related traits to capture the full spectrum of genetic diversity for resistance and to guide the selection of superior parental combinations for hybrid development. The relatively low frequency of crosses exhibiting desirable SCA effects for both yield and disease resistance suggests that these traits are governed by different genetic mechanisms and gene interactions, and may be influenced by environmental conditions. While positive SCA effects were common for yield, negative SCA for disease traits was limited, reinforcing the predominance of additive genetic variance in controlling TLB resistance. These findings are consistent with previous studies (Gemechu et al., 2018 ; Nkurunziza et al., 2019 ; Badu-Apraku et al., 2021 ; Ohunakin et al., 2021 ). Consequently, breeding strategies should prioritize the selection of parents with high GCA for disease resistance, as relying solely on hybrid-specific SCA combinations may be less effective. Considering the emphasis on parental GCA and the limited frequency of favorable SCA combinations, the P 3 × P 7 cross (HKI163 × HKI161) emerged as the most ideal combination, demonstrating both high yield potential and strong resistance to TLB. This observation aligns with its commercial success as HQPM 5, a high-yielding quality protein maize (QPM) hybrid released in 2007 and widely adopted across India. The favorable SCA effects of this cross, positive for grain yield and negative for disease incidence, reflect strong non-additive gene interactions, which likely contributed to its superior performance. In addition to this hybrid, several other crosses demonstrated positive SCA effects for grain yield despite limited overall SCA for TLB resistance. Notably, six crosses (P 1 × P 5 , P 4 × P 6 , P 5 × P 6 , P 2 × P 4 , P 5 × P 7 , P 4 × P 9 ) exhibited significant negative SCA effects for specific disease-related traits, indicating partial resistance potentially governed by quantitative loci or minor-effect alleles that modulate lesion development and disease progression (Zila et al., 2013 ; Kiran et al., 2017 ). The complementary distribution of resistance alleles among parental lines may facilitate allele complementation in hybrids, enhancing partial resistance while sustaining high yield. For instance, the P 4 × P 6 cross illustrates that moderately susceptible and susceptible parents can contribute favorable alleles to reduce lesion length. These observations highlight the importance of evaluating lesion-specific metrics, in addition to mean disease score, to accurately capture the genetic basis of TLB resistance (Harlapur et al., 2008 ). Collectively, these results demonstrate that both superior individual crosses and complementary parental combinations can be strategically exploited to develop high-yielding, heterotic hybrids with quantitative TLB resistance, emphasizing the practical significance of favorable non-additive interactions in hybrid breeding. These findings are supported by previous studies that document the genetic underpinnings of successful hybrid combinations (Fasahat et al., 2016 ; Mogesse et al., 2020 ; Antony et al., 2023 ). The heatmap effectively complements GCA and SCA analyses by visually highlighting superior and stable genotypes under varying disease pressures. Entries showing low yield reduction maintained high productivity under both disease conditions, indicating the presence of favorable alleles for yield and TLB resistance. Conversely, entries with high yield reduction were more susceptible to disease stress. This visualization aids in the rapid identification of hybrids that balance high yield with resistance, supporting targeted selection in breeding programs. Conclusion This study highlights the predominance of additive gene action in controlling TLB resistance, as indicated by the significant GCA variance for disease and its related traits. For grain yield, the proportional mean squares of GCA and SCA under both stress and natural conditions were relatively similar, suggesting that both additive and non-additive genetic effects contribute to yield performance. The substantial contribution of significant GCA and SCA effects in the desired direction for both TLB resistance and grain yield suggests that general and specific combining abilities can be effectively utilized to develop high-yielding, disease-resistant heterotic hybrids. In this particular study, parental lines P 1 , P 2 , P 3 , P 5 , and P 9 were identified as key donors of additive alleles that confer resistance to TLB, indicating that these genotypes possess stable genetic factors that can be reliably transmitted to their progeny. Among them, P 2 , P 3 , and P 5 consistently contributed favorable alleles for both disease resistance and grain yield across artificial and natural infection conditions, demonstrating their dual value in breeding programs. This consistency highlights their potential as superior general combiners, capable of enhancing resistance while simultaneously improving yield, thereby making them highly suitable for the development of high-performing, disease-resilient maize hybrids. Among the hybrids, P 3 × P 7 emerged as the most promising combination, exhibiting strong positive SCA effects for grain yield under both conditions, coupled with significant negative SCA effects for TLB infection. Considering the importance of lesion metrics, six crosses P 1 × P 5 , P 4 × P 6 , P 5 × P 6 , P 2 × P 4 , P 5 × P 7 and P 4 × P 9 were identified as the best performers for imparting TLB resistance and also enhancing grain yield. Thus, integrating combining ability analysis using diallel crosses could provide a robust framework for developing TLB-resistant and high-yielding maize hybrids. However, multi-location evaluations of these promising combinations are essential before their effective deployment in maize breeding programs aimed at managing TLB disease. Declarations Authors contribution Conceptualization of research (KRY, CGK, SC, VH); Designing of the experiments (CGK, KRY, SC, VH); Contribution and maintenance of experimental materials (CGK, KRY, RKD, OK, SN, SP, JK, SRJ, BS, SP); Execution of field/lab experiments and data collection (SC, MN, PGU, KRY, CGK); Analysis of data and interpretation (SC, CGK, KRY); Preparation of the manuscript (SC, KRY, CGK). Acknowledgements The authors acknowledge the Indian Council of Agricultural Research (ICAR) - Indian Institute of Maize Research, Ludhiana; All India Co-ordinated Research Project (AICRP) on Maize, Zonal Agricultural Research Station, Mandya and AICRP on Maize, Main Agricultural Research Station, UAS, Dharwad AICRP for support in carrying out the research work. 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Pandurangegowda KT, Shetty HS, Gowda BJ, Prakash HS, Sangamlal (1993) Comparison of two methods for assessment of yield losses due to turcicum leaf blight of maize. Ind Phytopath 45:316-320. Parime BC, Mallikarjuna N, Satya Kala K, Hooda KS, Karjagi CG (2023) Efficacy of new fungicides against turcicum leaf blight disease of maize. Ecol Environ Conserv 29(Suppl):338-342. https://doi.org/10.53550/EEC.2023.v29i04s.051 Paroda RS, Kumar P (2000) Food production and demand in South Asia. Agric Econ Res Rev 13:1-25. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209-228. https://doi.org/10.1007/s10681-007-9449-8 Raymundo AD, Hooker AL (1981) Measuring the relationship between northern corn leaf blight and yield losses. Plant Dis 65:325-327. https://doi.org/10.1094/PD-65-325 Rodríguez FS, Alvarado G, Pacheco A, Crossa J, Burgueño J (2015) AGD-R (Analysis of Genetic Designs with R for Windows) Version 4.0. CIMMYT, Mexico. Singh SB, Karjagi CG, Hooda KS, Mallikarjuna N, Harlapur SI, Rajashekara H, Devlash R, Kumar S, Kasana RK, Kumar S, Gangoliya SS, Rakshit S (2018) Identification of resistant sources against turcicum leaf blight of maize ( Zea mays L.). Maize J 7:64-71. Sibiya J, Tongoona P, Derera J (2013) Combining ability and GGE biplot analyses for resistance to northern leaf blight in tropical and subtropical elite maize inbred lines. Euphytica 191:245-257. https://doi.org/10.1007/s10681-012-0806-x Souza LV, Miranda GV, Galvão JCC, Guimarães LJ, Santos IC (2009) Combining ability of maize grain yield under different levels of environmental stress. Pesq Agropec Bras 44:1297-1303. https://doi.org/10.1590/S0100-204X2009001000013 Suresh LD, Kachapur RM, Talekar SC, Gurumurthy R (2021) Combining ability and heterosis study for yield and its attributing traits in maize ( Zea mays L.). Maydica 66:12. Umar UU, Ado SG, Aba DA, Bugaje SM (2014) Estimates of combining ability and gene action in maize ( Zea mays L.) under water stress and non-stress conditions. J Biol Agric Healthc 4:247-253. Vencovsky R (1973) Princípios de genética quantitativa. Escola Superior de Agricultura "Luiz de Queiroz", Universidade de São Paulo, Piracicaba, São Paulo, Brazil:97. Wegary D, Vivek BS, Labuschagne MT (2014) Combining ability of certain agronomic traits in quality protein maize under stress and nonstress environments in Eastern and Southern Africa. Crop Sci 54:1004-1014. https://doi.org/10.2135/cropsci2013.09.0585 Zila C, Samayoa L, Santiago R, Butrón A, Holland J (2013) A genome-wide association study reveals genes associated with fusarium ear rot resistance in a maize core diversity panel. G3 Genes Genom Genet 3:2095-2104. https://doi.org/10.1534/g3.113.007328 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7925151","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547369881,"identity":"8bcc1d79-2df4-4825-a4bc-a12297e3069b","order_by":0,"name":"Susmita Cherukuri","email":"","orcid":"","institution":"ICAR-Winter Nursery Center, Indian Institute of Maize Research","correspondingAuthor":false,"prefix":"","firstName":"Susmita","middleName":"","lastName":"Cherukuri","suffix":""},{"id":547369883,"identity":"03e00d4d-1dd1-4101-a2fb-88b78ba4a841","order_by":1,"name":"Hemalatha Vipparthi","email":"","orcid":"","institution":"Professor Jayashankar Telangana State Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hemalatha","middleName":"","lastName":"Vipparthi","suffix":""},{"id":547369884,"identity":"a2f17f93-696a-4c86-ada3-3d28dfb8b5fb","order_by":2,"name":"Yathish Kondajji Rangappa","email":"","orcid":"","institution":"ICAR-Winter Nursery Center, Indian Institute of Maize Research","correspondingAuthor":false,"prefix":"","firstName":"Yathish","middleName":"Kondajji","lastName":"Rangappa","suffix":""},{"id":547369887,"identity":"d16fe18a-ac77-4b35-bfd9-d8cb7402e755","order_by":3,"name":"Chikkappa Gangadhar 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07:59:52","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213791,"visible":true,"origin":"","legend":"","description":"","filename":"a274c4d40e654a3f9fd652047dcb3e7e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/442f9d34d28c23fa25fddaea.xml"},{"id":96445844,"identity":"ad48e3f3-3517-432b-89e5-2b6777319730","added_by":"auto","created_at":"2025-11-21 07:59:52","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217913,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/4cd08b66afda9488f989c760.html"},{"id":96445833,"identity":"f66cb7dd-9ba9-4f82-afcf-ef4b00abdd4d","added_by":"auto","created_at":"2025-11-21 07:59:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27029,"visible":true,"origin":"","legend":"\u003cp\u003eGraph displaying GCA effects of parents for various disease related traits\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/91ea629c292c0ab4e7bbf594.png"},{"id":96454603,"identity":"674873cb-486f-4f84-9dab-21ae75cc4f99","added_by":"auto","created_at":"2025-11-21 10:02:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61220,"visible":true,"origin":"","legend":"\u003cp\u003eBubble plot of SCA effects for four disease traits in hybrids. Disease Score (x-axis) and Lesions per Plant (y-axis) indicate disease severity. Bubble size shows lesion length, and color intensity (purple to red) represents infected leaves per plant. Larger, red bubbles in the upper-right denote higher susceptibility; smaller, purple bubbles in the lower-left indicate greater resistance.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/3211e54760a0609096523f55.png"},{"id":96445834,"identity":"4e19d3d5-765e-4ac4-99d4-6a2830bc61e2","added_by":"auto","created_at":"2025-11-21 07:59:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62831,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap depicting \u003cem\u003eper se\u003c/em\u003e performance of entries for yield under AI (artificially inoculation) and NI (natural infection) conditions, along with the percentage reduction in AI yield relative to NI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/15c6944379b18bc271b0c15b.png"},{"id":97898530,"identity":"23b17e14-0833-4004-b7fb-390cf6d82e7d","added_by":"auto","created_at":"2025-12-10 15:39:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560957,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7925151/v1/16a35adf-1bdb-48a2-9d42-23ca781e7475.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diallel analysis of turcicum leaf blight resistance and grain yield in maize and its implications for genetic improvement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is a globally important cereal, serving as a vital source of food, animal feed, and raw material for various industrial products (Paroda and Kumar \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Augustine and Kalyanasundaram \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In India, it ranks third among cereals after rice and wheat. Owing to its broad adaptability, maize thrives across diverse agro-climatic conditions, ranging from semi-arid regions to well-irrigated ecosystems in tropical, subtropical, and temperate zones. Despite its versatility, the crop is highly susceptible to biotic and abiotic stresses, with foliar fungal diseases posing a major constraint to yield and productivity (Njeru et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTurcicum leaf blight (TLB), also referred to as northern corn leaf blight (NCLB), caused by \u003cem\u003eSetosphaeria turcica\u003c/em\u003e (anamorph \u003cem\u003eExserohilum turcicum\u003c/em\u003e Leonard and Suggs), is a widespread foliar disease that severely affects maize production worldwide, including India (Hooda et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In India, TLB is predominantly observed during the \u003cem\u003ekharif\u003c/em\u003e (rainy) season in Zones I, II, and IV of the All India Coordinated Research Project (AICRP) on Maize, which correspond to the Northern Hill Zone (temperate region), North West Plain Zone (sub-tropical region), and Peninsular Zone (tropical region), respectively. Yield losses reported due to TLB vary from 27% to 90% depending on disease severity (Jha \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Pandurangegowda et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Harlapur et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the disease also increases susceptibility to stalk rot while reducing forage quality (Chenulu and Hora \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). Characteristic symptoms include elongated, elliptical tan lesions starting on lower leaves and progressing upward. High humidity and moderate to high temperatures favor disease development, which can affect plants from the vegetative stage through grain filling (Palaversic et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abebe \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Disease severity is influenced by the host genotype, prevailing climatic conditions, and cultivation practices.\u003c/p\u003e\u003cp\u003eDespite the availability of chemical and cultural control measures, their effectiveness often declines under high disease pressure. Fungicides such as mancozeb, propiconazole, and tebuconazole are effective when applied r during early infection stages; however, excessive reliance may accelerate the development of fungicide-resistant \u003cem\u003eS. turcica\u003c/em\u003e populations. Cultural practices, including crop rotation with non-host crops, deep ploughing, removal of infected residues, and balanced fertilization with optimal spacing, help reduce inoculum and disease spread. However, under conducive environmental conditions, these measures often fail to provide satisfactory disease control. Moreover, economic constraints, environmental concerns, and rapid disease spread during the \u003cem\u003ekharif\u003c/em\u003e season (Parime et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further limit their sustainability. Therefore, host-plant resistance remains the most practical, cost-effective, and environmentally sound strategy for long-term management of Turcicum leaf blight (Fehr \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Effective breeding for durable resistance requires understanding the genetic basis of resistance. Combining ability analysis, comprising general combining ability (GCA) and specific combining ability (SCA), provides insight into additive and non-additive gene effects (Griffing \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). While GCA is crucial for selecting stable parental lines, SCA identifies superior hybrid combinations. Such analyses enable targeted parental selection and hybrid development, maximizing genetic gains in both TLB resistance and grain yield.\u003c/p\u003e\u003cp\u003eSeveral studies have identified maize germplasm and inbred lines with TLB resistance through systematic screening (Chandrashekara et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kiran et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jakhar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the inheritance of TLB resistance and yield stability has been explored via combining ability analyses, most studies have considered these traits separately, focusing either on yield or disease resistance (Njore and Gichurru 2013; Nedi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Suresh et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Only a few studies have simultaneously evaluated both traits, either in single environment (Mogesse et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ohunakin et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Antony et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or across multiple locations (Badu-Apraku et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abdelsalam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kutuka et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The present study, therefore, aimed to assess the combining ability of diallel crosses in disease hotspot environments for TLB resistance and grain yield. The objective was to identify superior parental lines and cross combinations based on GCA and SCA effects, prioritizing resistance with minimal yield penalty under disease pressure.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eDevelopment of genetic material for field evaluation\u003c/p\u003e\u003cp\u003eGenetic material comprised nine parental genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and their 36 half-diallel crosses, generated by following a 9 \u0026times; 9 half-diallel mating design during \u003cem\u003erabi\u003c/em\u003e, 2023-24 at Winter Nursery Centre, ICAR-Indian Institute of Maize Research (IIMR), Hyderabad (17⁰32\u0026prime;58.22\u0026Prime;N, 78⁰39\u0026prime;70.31\u0026Prime;E), India. Parental genotypes comprised of resistant (3), moderately resistant (2), moderately susceptible (1) and susceptible (3) to TLB, selected based on their differential reaction to TLB disease under artificially inoculated conditions during \u003cem\u003ekharif\u003c/em\u003e, 2022 at two TLB hotspot locations \u003cem\u003eviz.\u003c/em\u003e, Mandya [Zonal Agricultural Research Station (ZARS), (12⁰34\u0026prime;8.97\u0026prime;\u0026prime;N, 76⁰ 48'47.86\u0026prime;\u0026prime;E)] and Dharwad [AICRP on Maize, UAS, Main Agricultural Research Station (MARS), (15⁰27\u0026prime;2.63\u0026Prime;N and 75⁰0\u0026prime;11.52\u0026prime;\u0026prime;E)] located in Karnataka, India.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of inbred parents used in the present study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEntry identity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEntry code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReaction to TLB\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIL2-27P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEC0758084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHKI163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIC212886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSusceptible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSKV50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerately Resistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIC212893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSusceptible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHKI161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSusceptible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCM600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerately Susceptible\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUMI1200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerately Resistant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTLB culture preparation and inoculation\u003c/p\u003e\u003cp\u003eThe primary source of fungus, \u003cem\u003eE. turcicum\u003c/em\u003e (Pass.) Leonard and Suggs. inoculum was collected in the form of TLB-infected leaves of maize plants available in the local fields. The infected leaf tissues were surface sterilized using 0.1% mercuric chloride or 1% sodium hypochlorite solution for 30\u0026ndash;60 seconds and rinsed thoroughly with sterile distilled water to remove any residual disinfectant. The sterilized leaf segments were then plated on Potato Dextrose Agar (PDA) medium and incubated at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C for 7\u0026ndash;10 days to facilitate fungal growth. Standard tissue-isolation technique was used to obtain pure cultures of \u003cem\u003eE. turcicum\u003c/em\u003e through hyphal tip transfer, subsequently maintained for further use for mass multiplication. Sterilized sorghum grains were used as substrate for the mass multiplication of the pathogen (Joshi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Sorghum grains, due to their high nutritional value support robust fungal sporulation. Approximately 100\u0026ndash;150 g of sorghum grains were soaked overnight, autoclaved, and inoculated with actively growing pure mycelium culture of \u003cem\u003eE. turcicum\u003c/em\u003e. The inoculated grains were incubated at 25\u0026ndash;28\u0026deg;C for 10\u0026ndash;12 days under alternate light and dark cycles to induce sporulation. Once the grains were heavily colonized with the fungus, they were shade-dried and ground to a fine powder.\u003c/p\u003e\u003cp\u003eArtificial inoculation was employed using the widely adopted leaf whorl inoculation technique (Raymundo and Hooker \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Carson \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). It was carried out manually at 30\u0026ndash;35 days after sowing (DAS), corresponding to the V\u003csub\u003e6\u003c/sub\u003e-V\u003csub\u003e8\u003c/sub\u003e growth stages of maize plants, the most susceptible stages by placing finely ground fungal inoculum (1.0-1.5 g) into the central leaf whorl of each plant. A fine mist of water was then applied to create a humid microenvironment conducive to fungal germination/ infection and also to ensure uniform infection and minimize disease pressure variability in the field. Further, a second round of inoculation was conducted seven days after the first inoculation to reinforce infection and achieve uniform disease establishment across experimental plots, thereby minimizing the risk of disease escape and account any variation in plant growth stages.\u003c/p\u003e\u003cp\u003ePhenotyping for TLB response and grain yield\u003c/p\u003e\u003cp\u003eThe diallel crosses were evaluated in a trial under both TLB epiphytotic, created artificially through \u003cem\u003eE. turcicum\u003c/em\u003e inoculation as well as naturally infected (uninoculated) conditions at TLB disease hotspot locations namely, Mandya and Dharwad during \u003cem\u003ekharif\u003c/em\u003e, 2024. The trials were conducted by following randomized complete block design (RCBD) in two replications. Each entry in the trial was sown in single row of four-meter length with 0.6 and 0.2 metre spacing between rows, and plants within each row, respectively. The data on grain yield and TLB incidence were recorded under both conditions.\u003c/p\u003e\u003cp\u003eThe TLB disease score was recorded based on percentage of leaf area infected by visualizing the leaf area covered by lesions using 1\u0026ndash;9 scale on five randomly selected plants in each row during 75\u0026ndash;90 DAS (dough stage), the period coincided with peak disease expression (Chung et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Based on disease severity, the genotypes were grouped into four categories: resistant (\u0026le;\u0026thinsp;3), moderately resistant (3.1-5.0), moderately susceptible (5.1-7.0) and susceptible (\u0026gt;\u0026thinsp;7.0). Furthermore, additional data on lesion length (cm), infected leaves per plant, and lesions per plant were recorded from five plants that were scored for TLB response. The data on grain yield was recorded on per-plot basis by harvesting and drying the maize ears, adjusting them to 15% moisture content, and subsequently converted to tonnes per hectare (t/ha).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAnalysis of variance (ANOVA) was performed for each trait at individual locations. The trait means were then subjected to Levene\u0026rsquo;s test for homogeneity of variances, after which pooled ANOVA across locations was conducted by considering crosses as fixed effects and locations as random effects. To partition the genetic variation into GCA and SCA effects, diallel ANOVA was performed across environments following Griffing\u0026rsquo;s Method II, Model I (Griffing, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), using AGD-R software (Rodr\u0026iacute;guez et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The statistical model employed was as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{\\text{i}\\text{j}\\mathcal{l}r}=\\mu\\:+{L}_{\\mathcal{l}}+{R}_{\\mathcal{l}r}+{g}_{\\text{i}}+{g}_{\\text{j}}+{s}_{\\text{i}\\text{j}}+{\\left({g}_{\\text{i}}\\right)}_{\\mathcal{l}}+{\\left({g}_{\\text{j}}\\right)}_{\\mathcal{l}}+{\\left({s}_{\\text{i}\\text{j}}\\right)}_{\\mathcal{l}}+{\\epsilon\\:}_{\\text{i}\\text{j}\\mathcal{l}r}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{\\text{i}\\text{j}\\mathcal{l}r}\\)\u003c/span\u003e\u003c/span\u003e​ = observation of cross i\u0026times;j in location (site/environment) ℓ and replication r, \u0026micro;\u0026thinsp;=\u0026thinsp;overall mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{L}_{\\mathcal{l}}\\)\u003c/span\u003e\u003c/span\u003e​ = effect of the ℓ\u003csup\u003eth\u003c/sup\u003e location, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{\\mathcal{l}r}\\)\u003c/span\u003e\u003c/span\u003e = replication effect nested within location, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e​, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e = GCA effects of parents i and j, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e = SCA effect of the i\u0026times;j cross, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left({g}_{\\text{i}}\\right)}_{\\mathcal{l}}\\)\u003c/span\u003e\u003c/span\u003e​, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left({g}_{\\text{j}}\\right)}_{\\mathcal{l}}\\)\u003c/span\u003e\u003c/span\u003e​ = GCA \u0026times; location interaction effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left({s}_{\\text{i}\\text{j}}\\right)}_{\\mathcal{l}}\\)\u003c/span\u003e\u003c/span\u003e​ = SCA \u0026times; location interaction effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{\\text{i}\\text{j}\\mathcal{l}r}\\)\u003c/span\u003e\u003c/span\u003e​ = experimental error. The significance of the GCA of parents and SCA of half-diallel crosses was tested using a t-test based on the standard errors of the GCA and SCA effects. Variance components, Baker\u0026rsquo;s predictability ratio, and heritability estimates were estimated using the Mixed A model of AGD-R. In this model GCA and SCA were modelled as random, and variance components were estimated by Restricted Maximum Likelihood (REML) in order to obtain unbiased estimates of variance components. Additive and dominance variances were derived as σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eA\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eGCA\u003c/sub\u003e/p and σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eD\u003c/sub\u003e ​=4σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eSCA\u003c/sub\u003e ​/[p(p\u0026minus;1)] respectively, where p is the number of parents. Broad- and narrow-sense heritability, as well as Baker\u0026rsquo;s predictability ratio, were computed from these variance components. Baker\u0026rsquo;s ratio was calculated as 2σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eGCA\u003c/sub\u003e/2σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eGCA\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eSCA\u003c/sub\u003e (Baker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). The GCA effects of parents for disease traits were visualized using bar graphs in Excel, whereas the SCA effects of crosses were represented through a bubble plot generated with the ggplot2 package in R. A heatmap, displaying the performance of the crosses and parents based on mean values for percent reduction in yield, yield under artificial inoculation and natural infection, was generated using the Pheatmap function in R software. The heatmap facilitates easy identification of entries with stable performance for yield and higher resilience to disease.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAnalysis of variance\u003c/p\u003e\u003cp\u003eThe homogeneity of variance test revealed no significant differences across environments for both grain yield and disease traits, indicating uniform error variances among the tested locations. Diallel ANOVA revealed that environmental variances were highly significant for all the traits evaluated, reflecting substantial differences in mean trait values across the tested environments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The GCA and SCA variances were also highly significant for all the studied traits, including grain yield under both artificially inoculated and naturally infected conditions. The highly significant variance observed among entries (parents and crosses) indicated the presence of substantial genetic variability for the studied traits. The analysis of site \u0026times; combining ability interactions revealed contrasting patterns between disease traits and grain yield. For disease traits, both site \u0026times; GCA and site \u0026times; SCA interactions were found to be non-significant, suggesting that the combining ability effects for disease resistance remained consistent across the two testing environments. This indicated that the ranking of genotypes for disease resistance did not change significantly between locations, pointing towards stable genetic control of this trait. In contrast, significant site \u0026times; GCA and site \u0026times; SCA interaction variances were detected for grain yield. These results demonstrated that the relative performance of genotypes and their combining abilities varied across environments for yield. The significance of these interactions reflects the influence of environmental conditions on the expression of yield and indicated the presence of genotype \u0026times; environment interactions (GEI) for this trait.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean squares for disease traits and yield pooled across two environments\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource of variation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDisease score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLesions per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLesion length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInfected leaves per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGrain yield q/ha\u003c/p\u003e\u003cp\u003e(artificial inoculation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGrain yield\u003c/p\u003e\u003cp\u003eq/ha (natural infection)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.36**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e453.97**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e218.21**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.63**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15806.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19715.85**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRep (Env)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.44**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.66**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.82**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e97.71**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.88**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2237.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2243.42**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.93**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82.30**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e382.35**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e99.29**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3279.45**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2953.60**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.83**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34.46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.91**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2005.57**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2085.60**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEntry \u0026times; Env\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e337.69**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e356.16**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnv \u0026times; GCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e240.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e336.79**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnv \u0026times; SCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e359.34**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e360.47**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e37.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003e*, ** significant at 0.05% and 0.01% level of probability respectively; Df-Degrees of freedom\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eEnv-environment; Rep-replication; Entry-crosses and parents\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe proportional mean squares (MS) due to GCA and SCA were relatively consistent across both disease conditions, with GCA values exceeding those of SCA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests a predominance of additive genetic effects under both conditions, supporting the utility of selection-based improvement. However, variance components estimated using AGD-R under the Mixed A model revealed higher SCA variance than GCA variance across both environments (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This apparent contradiction arises from methodological differences; while ANOVA-derived mean squares reflect the magnitude of effects and are influenced by replication and design structure, REML-based estimates more accurately partition genetic variance by accounting for random effects, data imbalance, and estimation error (Piepho et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimates of variance and other genetic parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenetic variance estimates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisease score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLesions per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLesion length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInfected leaves per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrain yield q/ha (artificial inoculation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGrain yield q/ha (natural infection)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCA Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.61 (62.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84 (5.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.77 (37.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.08 (44.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.66 (4.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.27 (3.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15 (15.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.25 (67.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.19 (39.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.34 (28.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e411.56 (64.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e431.28 (66.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCA x Env Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.01(1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06 (0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.27 (1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08 (1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCA x Env Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e167.76 (26.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e161.72 (24.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20 (20.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.95 (26.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.56 (21.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20 (25.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.82 (3.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37.03 (5.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdditive Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDominance Variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e411.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e431.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCA-SCA ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaker ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNarrow Heritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad Heritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eEnv-environment, values in parenthesis represent % contribution of various genetic effects to total variance\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUnder artificially inoculated conditions, SCA variance contributed substantially more to grain yield (64.8%) compared to GCA variance (4.9%), indicating that yield performance under stress was predominantly governed by non-additive gene actions such as dominance and epistasis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Baker\u0026rsquo;s predictability ratio, which was below 0.5, further confirmed the predominance of non-additive effects. Similarly, under natural infection conditions, SCA contribution remained high (66.3%) compared with GCA (3.1%), reaffirming the dominance of non-additive gene action across environments. Interestingly, the GCA contribution under stress (4.9%) was slightly higher than under natural conditions (3.1%), suggesting that additive effects may play a relatively greater role in yield performance when disease pressure is present. This observation aligns with the proportional mean squares, where GCA consistently exceeded SCA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating that both additive and non-additive gene actions were involved in controlling grain yield across environments.\u003c/p\u003e\u003cp\u003eIn contrast for disease resistance, GCA contributed 62.4% while SCA accounted for 15.6%, indicating that additive genetic effects from parental lines played a major role in conferring resistance to TLB under stress conditions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A similar trend was observed for infected leaves per plant, where GCA contributed 44.2% and SCA 28.5%, again highlighting the predominance of additive gene action. For other disease-related traits, however, the pattern shifted towards non-additive effects. In the case of lesion length, SCA contributed 39.4% compared to GCA with 37.4%, while for lesions per plant, SCA dominated with 67.8% against only 5.6% from GCA. This points to a stronger influence of non-additive gene action for these two traits, though GCA contributions remained statistically significant, especially for lesion length, suggesting that additive effects were still present. The MS due to GCA were significant across all disease-related traits (lesion length, lesion number, and infected leaves per plant) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), that further supported the role of additive variance in their inheritance. Heritability estimates revealed high narrow-sense (0.78) and broad-sense (0.87) heritability for disease score, as well as moderately high values for infected leaves per plant (0.62 and 0.82) and lesion length (0.55 and 0.84). Baker\u0026rsquo;s ratio was also high for disease score (0.89), lesion length (0.65), and infected leaves per plant (0.76), with values above 0.5 indicating the relative importance of additive genetic effects for these traits.\u003c/p\u003e\u003cp\u003eGCA effects\u003c/p\u003e\u003cp\u003eAmong the nine parental genotypes evaluated, five (P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e9\u003c/sub\u003e) exhibited significant negative GCA effects for disease score and key disease-related traits, including lesion length and infected leaves per plant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, four parents (P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e9\u003c/sub\u003e) showed significant negative GCA effects for lesions per plant. The results corroborated the resistance or moderately resistant reaction against TLB recorded previously on these set of genotypes at different disease hot spots in India (data unpublished). Since, the parents were selected based on their resistance reaction to TLB, the present results further ensured the reliability of genotypes, for further use in genetic and plant breeding experiments/studies including their use in development in development of TLB resistant hybrids. The GCA effects for disease score ranged from \u0026minus;\u0026thinsp;0.97 (P\u003csub\u003e1\u003c/sub\u003e) to 0.98 (P\u003csub\u003e4\u003c/sub\u003e), while for lesions per plant, they varied from \u003cb\u003e\u0026minus;\u003c/b\u003e\u0026thinsp;2.17 (P\u003csub\u003e2\u003c/sub\u003e) to 1.60 (P\u003csub\u003e6\u003c/sub\u003e). Lesion length GCA effects ranged between \u003cb\u003e\u0026minus;\u003c/b\u003e\u0026thinsp;4.68 (P\u003csub\u003e1\u003c/sub\u003e) to 3.79 (P\u003csub\u003e8\u003c/sub\u003e), and for infected leaves per plant, from \u003cb\u003e\u0026minus;\u003c/b\u003e\u0026thinsp;1.90 (P\u003csub\u003e1\u003c/sub\u003e) to 2.06 (P\u003csub\u003e8\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral combining ability effects of parental lines for disease and yield\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisease score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLesions per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLesion length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInfected leaves per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrain yield q/ha (artificial inoculation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGrain yield q/ha (Natural infection)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.97**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.68**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.90**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.72**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.89**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.17**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.44**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.80**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.29**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.69**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.48**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.22**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.39**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.25**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.27**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-6.41**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-3.73**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.51**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.88**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.79**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.18**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.96**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.07**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.66**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.75**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.03**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-7.91**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-8.03**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.25**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.53**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.79**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.06**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-6.94**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-5.64**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.63*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-10.09**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-10.45**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS.E.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*, ** significant at 0.05% and 0.01% level of probability respectively\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGCA effects for grain yield were assessed under both artificial epiphytotic and natural infection conditions to evaluate the value of parental genotypes under disease pressure. Under artificial inoculated condition, four parents (P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2,\u003c/sub\u003e P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e) showed highly significant positive GCA effects for yield, ranging from 1.72 (P\u003csub\u003e1\u003c/sub\u003e) to 15.96 (P\u003csub\u003e5\u003c/sub\u003e), while the overall GCA effects ranged from \u0026minus;\u0026thinsp;10.09 (P\u003csub\u003e9\u003c/sub\u003e) to 15.96 (P\u003csub\u003e5\u003c/sub\u003e). Under natural infection condition, four parents (P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e7\u003c/sub\u003e) recorded significant positive GCA effects, ranging from 3.25 (P\u003csub\u003e7\u003c/sub\u003e) to 16.07 (P\u003csub\u003e5\u003c/sub\u003e), with overall GCA effects ranging from \u0026minus;\u0026thinsp;10.45 (P\u003csub\u003e9\u003c/sub\u003e) to 16.07 (P\u003csub\u003e5\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eSCA effects\u003c/p\u003e\u003cp\u003eThe SCA effects for grain yield in the 36 half-diallel crosses were statistically significant in most combinations under both artificial and natural infection conditions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among the evaluated crosses, 25 (~\u0026thinsp;69%) showed significant SCA effects for grain yield under artificially inoculated conditions, whereas 24 (~\u0026thinsp;66%) showed significant SCA effects under natural infection. Notably, 22 (~\u0026thinsp;92%) of these crosses were common across both disease conditions, while only two crosses (~\u0026thinsp;8%) were unique to either the natural or stress condition. The high overlap of significant crosses across the two disease conditions suggests that many favorable non-additive interactions are stable and can be reliably exploited in hybrid breeding programs. Differences observed between artificial and natural conditions likely reflect the influence of disease intensity, pathogen pressure, or other factors on the expression of non-additive effects. Nonetheless, the presence of a few cross-specific differences (~\u0026thinsp;8%) indicates that non-additive gene expression may vary depending on the disease scenario and the associated level of disease pressure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecific combining ability effects of F\u003csub\u003e1\u003c/sub\u003e hybrids for disease and yield\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHybrids\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisease score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLesions per\u003c/p\u003e\u003cp\u003eplant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLesion length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInfected leaves per plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrain yield q/ha (artificial inoculation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGrain yield q/ha\u003c/p\u003e\u003cp\u003e(Natural infection)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.32*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.41**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.97**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.38*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.47*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.28**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.76**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.91**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.16**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.93*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.92**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.52**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.75**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.08**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.07**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.53**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.48**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.36*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.44**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.89**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.13*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.72**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.28**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.48**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.52**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-7.36**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-5.72*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.46*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.33**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.48*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.67**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.21*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-4.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.94**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.70**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-6.07**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.30*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.34**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.53**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.19*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.92**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.55**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.21**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.44*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.78**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.34**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-4.77*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.41*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-3.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.47**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.81**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e6\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.27**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-4.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.91*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.81*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.91*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.63*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.42**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.73**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.94**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.76*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.49**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.71**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.68*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.94**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-2.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.65**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.50**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e6\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.91**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.56*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e7\u003c/sub\u003e \u0026times; P\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.43*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.04**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.21**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.46*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.08**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.34**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.71*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.00**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.12**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.74**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.12**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.21**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e6\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.61**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e7\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.49*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.25*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.33**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.98**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.92**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.77**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003csub\u003e8\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.00**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.87**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.86**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eS.E.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*, ** significant at 0.05% and 0.01% level of probability respectively\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhile variation in yield was evident across the crosses, SCA analysis revealed that certain crosses also contributed significantly to disease resistance (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Evaluation of the crosses further highlighted substantial variation across multiple disease-related traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, 14 crosses (~\u0026thinsp;39%) exhibited significant differences for disease score, indicating differential resistance or susceptibility. For the number of lesions per plant, 20 crosses (~\u0026thinsp;56%) were significant, reflecting substantial variability in pathogen infection intensity. Similarly, 15 crosses (42%) showed significant differences in lesion length, demonstrating variation in disease severity or progression. In contrast, only seven crosses (~\u0026thinsp;19%) exhibited significant variation for the number of infected leaves per plant, suggesting that this trait was relatively less variable or less responsive across the genetic combinations evaluated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConsidering the SCA effects for both yield and disease resistance, a subset of crosses exhibited simultaneously favorable contributions to both traits. A substantial proportion of these crosses (~\u0026thinsp;69%) exhibited significant SCA effects for both the traits. Considering desirable SCA effects, 24 crosses (~\u0026thinsp;67%) showed significantly positive effects for yield under natural conditions, while 23 crosses (~\u0026thinsp;64%) were significant under artificially inoculated conditions. In contrast, the number of crosses with desirable negative SCA effects for disease traits was markedly lower. Specifically, four crosses (~\u0026thinsp;11%) showed significantly negative SCA effects for disease score, eight crosses (~\u0026thinsp;22%) for lesions per plant, seven crosses (~\u0026thinsp;19%) for lesion length, and only one cross (~\u0026thinsp;3%) for infected leaves per plant. Notably, only a few hybrids combined positive SCA effects for yield with negative SCA effects for disease traits.\u003c/p\u003e\u003cp\u003eNotably, the cross P\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e involving a resistant parent (HKI163) and a susceptible parent (HKI161), exhibited highly significant positive SCA effects for grain yield under both artificial and natural conditions, along with highly significant negative SCA effects for disease reaction under artificial inoculation. Other crosses showing significant positive SCA effects for grain yield coupled with negative SCA effects for disease-related traits included P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, and P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e for lesions per plant; P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times;P \u003csub\u003e7\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e, and P\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e for lesion length, and P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e for infected leaves per plant.\u003c/p\u003e\u003cp\u003eComplementing the crosses with strong SCA effects for both traits, ten crosses maintained positive SCA for grain yield despite showing limited improvement in disease resistance. The crosses \u003cem\u003eviz.\u003c/em\u003e, P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e, P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e, P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e, and P\u003csub\u003e8\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e showed non-significant negative SCA effects for TLB infection. Although these crosses did not show significant improvement in overall disease resistance, they maintained positive and statistically significant SCA effects for grain yield under both artificial and natural infection conditions. Among them, six crosses (P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e, and P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e) additionally exhibited significant negative SCA effects for one or more disease-related traits (lesion number, lesion length, infected leaves), while consistently maintaining positive SCA effects for yield. Collectively, these crosses represented diverse parental combinations, indicating that favorable yield performance was achieved across a broad genetic background. Specifically, they included, one cross with both resistant parents (P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e2\u003c/sub\u003e); four crosses between resistant and susceptible or moderately resistant parents (P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P1 \u0026times; P5, P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e); two crosses involving moderately resistant and susceptible parents (P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e); one cross between susceptible and moderately resistant parents (P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e); one cross between moderately susceptible and moderately resistant parents (P\u003csub\u003e8\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e), and one cross with both susceptible parents (P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e).\u003c/p\u003e\u003cp\u003eA heatmap was generated to visualize the comparative performance of entries under artificially inoculation and natural infection conditions, assessing both disease response and the impact of disease on yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The heatmap displayed mean trait values across locations, allowing for simultaneous comparison of all entries. Entries with low percentage reduction in yield under artificially inoculation relative to natural infection were represented in blue, indicating maintenance of higher yields under both conditions. Conversely, entries with higher percentage reduction in yield were shown in red, reflecting a greater decrease in yield under artificially inoculated condition. This representation highlighted genotypes that combined high yield potential with reduced yield losses due to disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe significant differences in GCA variances indicate the presence of additive genetic variance and potential for effective selection among parental lines. In contrast, the significant SCA variances across all traits reflect the presence of non-additive genetic variance, suggesting differential complementarity between genotypes and a high degree of heterozygosity that can be effectively exploited through selection (Vencovsky \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Oliveria et al. 2016). This confirms that hybrid performance depends on specific genotype combinations rather than solely on the average combining ability of the parents. The observed genetic variability, together with the non-additive variance, highlights the potential for exploiting heterosis to achieve higher grain yield with improved TLB resistance. Similar findings of significant differences among parents and crosses for grain yield and TLB score have been reported by Njoroge and Gichuru (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Abdelsalam et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Antony et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The absence of significant site \u0026times; GCA and SCA interactions for disease traits indicates that combining ability effects for resistance were stable, which is advantageous for breeding as it allows reliable selection of resistant genotypes across environments. Although the overall level of disease pressure may differ depending on pathogen virulence, the genetic ranking of genotypes is likely to remain consistent. Similar stability of resistance traits across locations has been reported by Chaudhary and Mani (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Beyene et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Njoroge and Gichuru (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, the significant site \u0026times; GCA and SCA interactions observed for grain yield reflect the complexity of this quantitative trait and its high sensitivity GEI. Since the combining ability for yield is influenced by environmental conditions, genotypes that perform well in one location may not necessarily maintain their superiority in another. Such patterns are commonly observed in maize and other cereals and highlight the importance of multi-environment testing. Under these circumstances, it is often recommended to identify and promote location-specific genotypes in order to exploit their full genetic potential (Akaogu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Akaogu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond the observed GEI effects, the partitioning of variance components provides further insights into the genetic basis of yield. The higher proportion of SCA variance under both stress and natural conditions clearly demonstrates that grain yield is largely controlled by non-additive genetic effects. This implies that hybrid development, which captures dominance and epistatic interactions, is an effective strategy for improving yield. The low Baker\u0026rsquo;s ratio further strengthens this interpretation, as values below 0.5 are characteristic of non-additive gene action. At the same time, the relatively higher contribution of GCA under stress compared to natural conditions suggests that additive gene effects may provide stability to yield expression under disease pressure. This is an important consideration for breeding programs, as it indicates that while heterosis can be exploited for higher yield potential, additive variance should not be overlooked in environments with biotic stress. The presence of both additive and non-additive effects across environments highlights the need for a balanced breeding strategy, involving the selection of parental lines with strong additive effects while exploiting specific crosses to capture non-additive interactions. Non-additive effects primarily drive yield, particularly under stress, but additive effects gain relative importance when disease pressure is high, making both genetic components valuable targets for selection. Thus, integrating both additive and non-additive gene actions provides an effective approach to enhance grain yield under natural and stressful conditions (Makumbi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Umar et al., 2013; Beyene et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Badu-Apraku et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast to grain yield, disease resistance traits showed a different genetic pattern. While yield was predominantly influenced by non-additive variance, disease resistance traits showed a contrasting pattern. The predominance of GCA variance over SCA variance for disease score and infected leaves per plant suggests that additive gene action is the major determinant of TLB resistance. High heritability estimates further indicate that a substantial portion of phenotypic variation is attributable to additive genetic factors, reinforcing the potential for effective improvement through recurrent or pedigree selection. However, lesion length and lesion number displayed higher SCA contributions, suggesting that heterosis may contribute to these components, although additive effects remain important. The high Baker\u0026rsquo;s ratios (0.65\u0026ndash;0.89) strongly support the predominance of additive gene action for key resistance traits, particularly disease score (Sibiya et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ayiga-Aluba et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Abdelsalam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Antony et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther examination of GCA effects among parents provides deeper insights into their breeding value. The significant negative GCA effects observed in P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e9\u003c/sub\u003e indicate that these genotypes carry favorable alleles contributing to TLB resistance, supporting their potential use in hybrid development. In particular, P\u003csub\u003e9\u003c/sub\u003e showed negative GCA effects across all disease traits, suggesting it carries minor-effect additive alleles that can provide durable, broad-spectrum resistance when used in half-diallel crosses. These results emphasize that phenotypic resistance alone may not fully reflect a parent\u0026rsquo;s breeding value; additive gene action and genetic complementation determine hybrid performance (Hettiarachchi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jakhar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to resistance, the GCA effects for grain yield revealed complementary patterns. Among the nine parental lines, P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e exhibited significant positive GCA effects for grain yield under artificially inoculated conditions, while under natural infection conditions, significant positive effects were observed in P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e7\u003c/sub\u003e. The consistent positive GCA effects of P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e across both disease conditions suggest these genotypes possess stable additive alleles for yield, largely independent of disease pressure, highlighting their adaptability and potential for use in breeding programs targeting yield improvement (Wegary et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Farfan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ali \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Annor et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe three resistant genotypes (P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, and P\u003csub\u003e3\u003c/sub\u003e) exhibited distinct GCA patterns, with P\u003csub\u003e2\u003c/sub\u003e and P\u003csub\u003e3\u003c/sub\u003e showing highly significant negative GCA effects for disease and disease-related traits and highly significant positive GCA effects for grain yield under both artificial epiphytotic and naturally infected conditions. Whereas, P\u003csub\u003e1\u003c/sub\u003e showed significantly positive GCA effects for yield under artificial inoculation but negative effects under natural infection, reflecting inconsistency across disease conditions. This suggests that its contribution to yield is context-dependent rather than stable across conditions. Among the two moderately resistant genotypes (P\u003csub\u003e5\u003c/sub\u003e and P\u003csub\u003e9\u003c/sub\u003e), P\u003csub\u003e5\u003c/sub\u003e showed highly significant negative GCA effects for disease and disease-related traits and highly significant positive GCA effects for grain yield under both conditions. These results indicate the underlying contribution of alleles contributing for additive genetic variance. Such genotypes are of immense value in breeding programmes aimed at development of high yielding TLB disease resistant germplasm or cultivars which underscores their value as a donor of adaptive alleles that enhance performance under adverse environments (Makumbi 2005; Millet et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mageto et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among three susceptible genotypes (P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e6\u003c/sub\u003e, and P\u003csub\u003e7\u003c/sub\u003e), two genotypes P\u003csub\u003e4\u003c/sub\u003e and P\u003csub\u003e6\u003c/sub\u003e showed positive GCA effects for disease and disease-related traits and negative GCA effects for grain yield under both conditions, which is in expected line. However, one genotype each of moderately resistant (P\u003csub\u003e9\u003c/sub\u003e) and susceptible (P\u003csub\u003e7\u003c/sub\u003e) stood contrast and distinct among all the genotypes by defying the expected results. For example, P\u003csub\u003e9\u003c/sub\u003e exhibited highly significant negative GCA effects for yield under both artificially inoculated and natural conditions, suggesting a potential trade-off between disease resistance and growth-related traits. This indicates that, although P\u003csub\u003e9\u003c/sub\u003e carries favorable alleles for TLB resistance, it may lack alleles that contribute to higher yield or could be inherently low-yielding. In contrast, P\u003csub\u003e7\u003c/sub\u003e showed highly significant positive GCA effects for yield despite being susceptible to TLB, indicating that alleles controlling yield can be present and effective independently of disease resistance alleles. Together, these contrasting patterns in P\u003csub\u003e9\u003c/sub\u003e and P\u003csub\u003e7\u003c/sub\u003e highlight the independent segregation of alleles governing yield and disease resistance, emphasizing the complexity of simultaneously improving both traits in breeding programs.\u003c/p\u003e\u003cp\u003eThese contrasting patterns underscore the need to identify genotypes that consistently provide favorable alleles for both yield and disease resistance, highlighting their potential as superior general combiners in hybrid breeding programs. In this study, P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e simultaneously exhibited significant negative GCA effects for disease traits and positive GCA effects for grain yield, indicating that these genotypes carry additive alleles that can enhance both TLB resistance and productivity. Among them, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e were particularly consistent across both disease conditions, demonstrating stable expression of favorable alleles regardless of environmental variation or disease pressure. This stability makes these genotypes highly valuable as parental lines in breeding programs, as they can contribute both high yield potential and durable disease resistance to hybrids. These findings align with previous studies emphasizing the importance of selecting parental genotypes that combine high yield potential with disease resistance to develop superior hybrids with predictable performance (Badu-Apraku et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ohunakin et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Abdelsalam et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Antony et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kutuka et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe widespread significance of SCA effects across both disease conditions highlights the predominance of non-additive gene interactions in determining hybrid performance for grain yield. Although most favorable non-additive interactions were stable across both disease conditions, a small fraction of crosses showed condition-dependent differences. This suggests that the expression of non-additive effects can be influenced by the level of disease pressure or pathogen dynamics, emphasizing the value of evaluating genotypes under both artificial inoculation and natural infection to capture hybrids with consistent performance. To further explore how parental alleles influence hybrid performance, GCA effects of selected genotypes were analyzed. Parent P\u003csub\u003e9\u003c/sub\u003e consistently showed highly significant negative GCA effects for yield under both artificial and natural conditions, suggesting a potential trade-off between disease resistance and growth, or a lack of favorable yield alleles. In contrast, P\u003csub\u003e1\u003c/sub\u003e displayed significantly positive GCA effects under artificial inoculation, indicating its capacity to donate adaptive alleles that improve yield under controlled conditions. However, the same genotype showed negative GCA effects under natural infection, highlighting the context-dependent expression of favorable alleles. This contrast may be explained by pathogen-induced activation of defense-related signaling pathways, which could interact with yield-contributing alleles, altering trait expression (Guo et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Derbyshire et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, under artificial inoculation, P\u003csub\u003e1\u003c/sub\u003e may enhance specific combining ability through favorable gene interactions that are not fully expressed under natural conditions, suggesting that hybrid performance can be influenced by the intensity or uniformity of disease pressure (Souza et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hallauer et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Adu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these hypotheses require systematic investigation for confirmation, the results underscore the critical role of specific parental interactions in achieving superior hybrid performance under stress. Overall, these findings underscore the critical importance of specific parental combinations in achieving superior hybrid performance under stress. They suggest that hybrid development should consider both parental GCA and favorable SCA interactions to maximize yield under variable disease pressures. Future studies are required to validate the proposed hypotheses regarding pathogen-mediated modulation of yield-contributing alleles.\u003c/p\u003e\u003cp\u003eThe observed variation among crosses for disease score and associated traits highlights the differential genetic potential for TLB resistance within the population. The higher proportion of significant crosses for lesion number and length suggests that these traits are more sensitive indicators of pathogen response and may be more amenable to selection in breeding programs. Conversely, the relatively low variability for infected leaves per plant indicates that this trait may be more stable across genetic backgrounds or less influenced by minor genetic differences. These findings emphasize the importance of evaluating multiple disease-related traits to capture the full spectrum of genetic diversity for resistance and to guide the selection of superior parental combinations for hybrid development.\u003c/p\u003e\u003cp\u003eThe relatively low frequency of crosses exhibiting desirable SCA effects for both yield and disease resistance suggests that these traits are governed by different genetic mechanisms and gene interactions, and may be influenced by environmental conditions. While positive SCA effects were common for yield, negative SCA for disease traits was limited, reinforcing the predominance of additive genetic variance in controlling TLB resistance. These findings are consistent with previous studies (Gemechu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nkurunziza et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Badu-Apraku et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ohunakin et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, breeding strategies should prioritize the selection of parents with high GCA for disease resistance, as relying solely on hybrid-specific SCA combinations may be less effective.\u003c/p\u003e\u003cp\u003eConsidering the emphasis on parental GCA and the limited frequency of favorable SCA combinations, the P\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e cross (HKI163 \u0026times; HKI161) emerged as the most ideal combination, demonstrating both high yield potential and strong resistance to TLB. This observation aligns with its commercial success as HQPM 5, a high-yielding quality protein maize (QPM) hybrid released in 2007 and widely adopted across India. The favorable SCA effects of this cross, positive for grain yield and negative for disease incidence, reflect strong non-additive gene interactions, which likely contributed to its superior performance. In addition to this hybrid, several other crosses demonstrated positive SCA effects for grain yield despite limited overall SCA for TLB resistance. Notably, six crosses (P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e) exhibited significant negative SCA effects for specific disease-related traits, indicating partial resistance potentially governed by quantitative loci or minor-effect alleles that modulate lesion development and disease progression (Zila et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kiran et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The complementary distribution of resistance alleles among parental lines may facilitate allele complementation in hybrids, enhancing partial resistance while sustaining high yield. For instance, the P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e cross illustrates that moderately susceptible and susceptible parents can contribute favorable alleles to reduce lesion length. These observations highlight the importance of evaluating lesion-specific metrics, in addition to mean disease score, to accurately capture the genetic basis of TLB resistance (Harlapur et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Collectively, these results demonstrate that both superior individual crosses and complementary parental combinations can be strategically exploited to develop high-yielding, heterotic hybrids with quantitative TLB resistance, emphasizing the practical significance of favorable non-additive interactions in hybrid breeding. These findings are supported by previous studies that document the genetic underpinnings of successful hybrid combinations (Fasahat et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mogesse et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Antony et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe heatmap effectively complements GCA and SCA analyses by visually highlighting superior and stable genotypes under varying disease pressures. Entries showing low yield reduction maintained high productivity under both disease conditions, indicating the presence of favorable alleles for yield and TLB resistance. Conversely, entries with high yield reduction were more susceptible to disease stress. This visualization aids in the rapid identification of hybrids that balance high yield with resistance, supporting targeted selection in breeding programs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the predominance of additive gene action in controlling TLB resistance, as indicated by the significant GCA variance for disease and its related traits. For grain yield, the proportional mean squares of GCA and SCA under both stress and natural conditions were relatively similar, suggesting that both additive and non-additive genetic effects contribute to yield performance. The substantial contribution of significant GCA and SCA effects in the desired direction for both TLB resistance and grain yield suggests that general and specific combining abilities can be effectively utilized to develop high-yielding, disease-resistant heterotic hybrids.\u003c/p\u003e\u003cp\u003eIn this particular study, parental lines P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e, and P\u003csub\u003e9\u003c/sub\u003e were identified as key donors of additive alleles that confer resistance to TLB, indicating that these genotypes possess stable genetic factors that can be reliably transmitted to their progeny. Among them, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e consistently contributed favorable alleles for both disease resistance and grain yield across artificial and natural infection conditions, demonstrating their dual value in breeding programs. This consistency highlights their potential as superior general combiners, capable of enhancing resistance while simultaneously improving yield, thereby making them highly suitable for the development of high-performing, disease-resilient maize hybrids. Among the hybrids, P\u003csub\u003e3\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e emerged as the most promising combination, exhibiting strong positive SCA effects for grain yield under both conditions, coupled with significant negative SCA effects for TLB infection. Considering the importance of lesion metrics, six crosses P\u003csub\u003e1\u003c/sub\u003e \u0026times; P\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e \u0026times; P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e \u0026times; P\u003csub\u003e7\u003c/sub\u003e and P\u003csub\u003e4\u003c/sub\u003e \u0026times; P\u003csub\u003e9\u003c/sub\u003e were identified as the best performers for imparting TLB resistance and also enhancing grain yield. Thus, integrating combining ability analysis using diallel crosses could provide a robust framework for developing TLB-resistant and high-yielding maize hybrids. However, multi-location evaluations of these promising combinations are essential before their effective deployment in maize breeding programs aimed at managing TLB disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization of research (KRY, CGK, SC, VH); Designing of the experiments (CGK, KRY, SC, VH); Contribution and maintenance of experimental materials (CGK, KRY, RKD, OK, SN, SP, \u0026nbsp;JK, SRJ, BS, SP); Execution of field/lab experiments and data collection (SC, MN, PGU, KRY, CGK); Analysis of data and interpretation (SC, CGK, KRY); Preparation of the manuscript (SC, KRY, CGK).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Indian Council of Agricultural Research (ICAR) - Indian Institute of Maize Research, Ludhiana; All India Co-ordinated Research Project (AICRP) on Maize, Zonal Agricultural Research Station, Mandya and AICRP on Maize, Main Agricultural Research Station, UAS, Dharwad AICRP for support in carrying out the research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work is funded by ICAR under Consortium Research Platform on Agrobiodiversity (CRPAB).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdelsalam NR, Balbaa MG, Osman HT, Ghareeb RY, Desoky EM, Elshehawi AM, Aljuaid BS, Elnahal ASM (2022) Inheritance of resistance against northern leaf blight of maize using conventional breeding methods. 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Crop Sci 54:1004-1014. https://doi.org/10.2135/cropsci2013.09.0585\u003c/li\u003e\n \u003cli\u003eZila C, Samayoa L, Santiago R, Butr\u0026oacute;n A, Holland J (2013) A genome-wide association study reveals genes associated with fusarium ear rot resistance in a maize core diversity panel. G3 Genes Genom Genet 3:2095-2104. https://doi.org/10.1534/g3.113.007328\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Combining ability, Diallel analysis, Disease resistance, Gene action, Grain yield","lastPublishedDoi":"10.21203/rs.3.rs-7925151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7925151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA half-diallel mating design involving nine diverse maize genotypes was employed to investigate combining ability for resistance to Turcicum leaf blight (TLB) and for grain yield performance under both artificial inoculation and natural infection at two hotspot locations in India. Analysis of variance (ANOVA) revealed significant differences among parents and crosses for all studied traits, indicating substantial genetic variability. Diallel analysis revealed significant general combining ability (GCA) and specific combining ability (SCA) effects for disease-related traits as well as yield, highlighting the role of both additive and non-additive gene actions. Additive effects were predominant for TLB resistance, whereas non-additive effects mainly governed yield under both artificially inoculated and natural infected conditions. \u0026nbsp;GCA effects identified P\u003csub\u003e1\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e and P\u003csub\u003e9\u003c/sub\u003e as key donors of additive alleles conferring TLB resistance, while P\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, and P\u003csub\u003e5\u003c/sub\u003e consistently showed positive GCA effects for both disease resistance and grain yield across both disease conditions.\u0026nbsp; Among the hybrids, P\u003csub\u003e3\u003c/sub\u003e × P\u003csub\u003e7\u003c/sub\u003e showed strong positive SCA effects for grain yield along with significant negative SCA effects for disease resistance. Other promising crosses, including P\u003csub\u003e1\u003c/sub\u003e × P\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e4\u003c/sub\u003e × P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e5\u003c/sub\u003e × P\u003csub\u003e6\u003c/sub\u003e, P\u003csub\u003e2\u003c/sub\u003e × P\u003csub\u003e4\u003c/sub\u003e, P\u003csub\u003e5 \u003c/sub\u003e× P\u003csub\u003e7\u003c/sub\u003e and P\u003csub\u003e4\u003c/sub\u003e × P\u003csub\u003e9\u003c/sub\u003e, also combined favorable SCA effects for grain yield and disease resistance. Hence, integrating GCA and SCA analyses could provide an effective strategy for identifying superior parental lines and hybrids, supporting the development of TLB-resistant, high-yielding maize cultivars. To check for stability and adaptability, these promising combinations should be validated through multi-location evaluation.\u003c/p\u003e","manuscriptTitle":"Diallel analysis of turcicum leaf blight resistance and grain yield in maize and its implications for genetic improvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 07:59:47","doi":"10.21203/rs.3.rs-7925151/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ff10940-5fb9-47bc-8fe8-30e6f48c9a80","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T01:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 07:59:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7925151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7925151","identity":"rs-7925151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}