Variation in the phenotypic performance of top-cross fall armyworm resistant maize hybrids under optimal growing conditions in a derived savanna agro-ecology

Variation in the phenotypic performance of top-cross fall armyworm resistant maize hybrids under optimal growing conditions in a derived savanna agro-ecology | 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 Variation in the phenotypic performance of top-cross fall armyworm resistant maize hybrids under optimal growing conditions in a derived savanna agro-ecology Adesike Oladoyin Olayinka, Olufemi Stephen Akande, Peter Ayotunde Adebayo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8279943/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Fall armyworm ( Spodoptera frugiperda ) severely threatens sustainable maize production in smallholder farming systems across sub-Saharan Africa. Developing maize hybrids resistant to fall armyworm with high grain yield can enhance productivity and stability, especially in the savanna agro-ecology. This study aimed to identify such resistant hybrids, assess their genetic variability, heritability, and trait relationships under natural infestation and disease pressure. Thirty-two (32) maize hybrids, including commercial checks, were evaluated at two sites on the Teaching and Research farm of Ladoke Akintola University of Technology, Ogbomoso, Nigeria, in an 8 × 4 α-lattice design with three replications. Combined analysis of variance revealed highly significant (p < 0.001) differences among hybrids, environments, and hybrid × environment interactions for grain yield and other agronomic traits. Significant genetic variation for grain yield, foliar diseases, fall armyworm damage, ear rot, and related traits across sites suggests potential for selecting superior resistant hybrids. Approximately 30.7% of tested hybrids outperformed the highest yielding check (G31: Oba Super 9). Hybrid G1 (FAWSYN-1/(TZLComp.1 C6-W-39-1-1)-B-B) had the highest grain yield of 5223.3 kg ha⁻¹, showing a 26.8% yield advantage over the top check. Phenotypic correlations showed grain yield was negatively and significantly (p < 0.001) associated with fall armyworm foliar damage, plant aspect, and husk cover ratings. Regression analyses emphasized plant aspect as the strongest contributor to grain yield. In stepwise regression, fall armyworm infestation had significant detrimental effects (p < 0.01) on grain yield, husk cover, and ear and plant aspect ratings. Genotype by grain yield × trait (GYT) biplot identified five hybrids (G1, G9, G21, G18, and G3) that combined high yield, moderate fall armyworm resistance, and stability across environments. These promising hybrids are recommended for multilocational yield trials aiming to release fall armyworm resistant, high-yielding maize varieties suited for the derived savanna agro-ecology. Fall armyworm resistance Genetic variability Grain yield Maize hybrid Selection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), has been associated with substantial yield losses in cereal crops. This invasive species was first identified in sub-Saharan Africa (SSA) in 2016 (Goergen et al., 2016 ). As a polyphagous and migratory pest, FAW is known to infest diverse economically significant crops with a particularly strong preference for maize (Harrison et al., 2019 ). Maize ( Zea mays L.) is an annual, diploid (2n = 20), C4 monocot belonging to the family Poaceae. As an important staple crop, it provides feed, food, biofuel, starch, and glucose (Yadav et al., 2015 ), indicating its significance to food security in alignment with Sustainable Development Goal 2 (zero hunger). Maize is well-suited to a diverse range of agro-ecological environments worldwide (Bankole and Kolawole, 2023). The rising demand for maize is driven by population growth and climate change. In Nigeria, the average maize yields per hectare on farmers' fields in comparison to the global average are notably low at 1.8t ha − 1 , as opposed to the world's average of 4.5t ha − 1 (Grote et al., 2021 ). This wide disparity in grain yield gap is the result of multiple interacting factors, including biotic stresses such as Striga hermonthica (Del.) Benth, foliar diseases, and insect pests like stem borers and fall armyworms; abiotic stresses such as drought, flooding, heat, and low soil fertility; and suboptimal agronomic practices. In particular, planting time plays a crucial role, as it influences crop exposure to stress and has been shown to correlate linearly with yield outcomes (Rodríguez-del-Bosque et al., 2010 ; Buriro et al., 2015 ). Moreover, limited access to improved seeds, high input costs, poor extension services, and other socioeconomic constraints further contribute to the consistently low productivity of maize (Durocher-Granger et al., 2024 ). Among the various biotic stresses affecting maize production, the FAW is one of the most destructive insect pests, causing estimated grain yield losses ranging from 12% to 58% (De Groote et al., 2020 ). In severe cases, uncontrolled infestations have led to total crop failure (Israni et al., 2020 ), posing a significant threat to food security and the livelihoods of millions of smallholder farmers who depend on maize cultivation (Day et al., 2017 ). Environmental factors, particularly rainfall amount and distribution during periods of FAW infestation, can cause yield losses by interfering with assimilate partitioning. Additionally, suboptimal planting dates, either too early or too late, have been shown to increase susceptibility to insect pest damage, including FAW, thereby reducing maize grain yield (Sanp and Singh, 2018). Given the increasing frequency of pest outbreaks and the unpredictability of climatic conditions, there is a growing need to adopt resilient adaptation strategies. One such strategy is the deployment of maize varieties with native resistance to emerging pests and diseases. According to Tarusikirwa et al. ( 2020 ), host plant resistance (HPR) to insect pests functions through three main mechanisms: antixenosis, which deters pest preference through plant traits; antibiosis, which negatively affects pest survival and reproduction; and tolerance, which enables the plant to maintain acceptable yields despite infestation. Notably, tolerance exerts minimal selection pressure on pest populations and, when integrated with biological and cultural control methods, enhances overall pest management efficacy. As a long-lasting, environmentally safe, and cost-effective strategy, HPR presents a promising avenue for sustainable control of FAW and other insect pests in maize. Since the outbreak of FAW in SSA, researchers have screened maize germplasm under natural and artificial FAW infestation. Due to the high cost and expertise required for artificial infestation, studies often rely on natural infestation at FAW hotspots. Several maize inbred lines resistant to FAW have been developed and deployed (Kasoma et al., 2020 ; Prasanna et al., 2022 ). Currently, only a limited number of commercial maize hybrids with resistance to FAW are available in Africa (Moussa et al., 2023). Cultivating maize varieties with native resistance to FAW is an ecologically and economically sustainable strategy to prevent excessive grain yield losses (Prasanna et al., 2022 ). In selecting FAW resistant varieties, it is essential to prioritize elite hybrids with enhanced agronomic traits and stable grain yield. Given the foregoing, this study was undertaken to (i) assess new top-cross fall armyworm resistant maize hybrids combining high grain yield and tolerance to FAW infestation (ii) estimate the effects of foliar FAW damage on grain yield, (iii) determine agronomic traits correlated with foliar FAW damage in maize hybrids and; (iv) identify superior maize hybrids based on genotype by yield × trait (GYT) biplot method. Materials and Methods Planting materials The new top-cross fall armyworm resistant (FAWR) maize hybrids evaluated in this study were developed by the Maize Improvement Programme (MIP) of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. A total of 32 FAWR maize hybrids, including commercial hybrids used as checks, were evaluated for their responses to FAW leaf damage under natural disease pressure using two planting dates as diverse environments (Table 1 ). The first trial was planted at the beginning of the cropping season (11th of June), which is normally characterized by a very limited amount of rainfall, and the second trial was planted at the peak of the early cropping season (27th of June), which is the normal rain-fed condition of the year, 2024. The range of the planting dates was selected to create contrasting environmental conditions for maize growth and development. Each location by planting date combination was considered as a separate test environment. Weather data (rainfall, air temperature, solar radiation, and relative humidity) between the planting and harvesting period were obtained from the weather station report provided by the Faculty of Agricultural Sciences of Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria (Fig. 1 ). Table 1 Entry code and pedigree of top-cross FAWR maize hybrids and standard checks evaluated in the study Entry code Pedigree Source G1 FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B IITA G2 FAWSYN-1/TZISTR1869 IITA G3 FAWSYN-1/TZISTR1878 IITA G4 FAWSYN-1/TZISTR1121 IITA G5 FAWSYN-1/TZISTR1129 IITA G6 FAWSYN-1/TZISTR2024 IITA G7 FAWSYN-1/TZISTR1305 IITA G8 FAWSYN-1/TZISTR2042 IITA G9 FAWSYN-2/(TZLComp. 1 C6-W-39-1-1)-B-B IITA G10 FAWSYN-2/TZISTR1878 IITA G11 FAWSYN-2/TZISTR1129 IITA G12 FAWSYN-2/TZISTR2024 IITA G13 FAWSYN-2/TZISTR1305 IITA G14 FAWSYN-2/TZISTR2129-2 IITA G15 FAWSYN-3/TZISTR1869 IITA G16 FAWSYN-3/TZISTR1872 IITA G17 FAWSYN-3/TZISTR1878 IITA G18 FAWSYN-3/IITATZI2300 IITA G19 FAWSYN-3/IITATZI2305 IITA G20 FAWSYN-1/IITATZI2300 IITA G21 FAWSYN-2/IITATZI2300 IITA G22 FAWSYN-1/IITATZI2305 IITA G23 FAWSYN-2/IITATZI2305 IITA G24 FAWSYN-1 IITA G25 FAWSYN-2 IITA G26 FAWSYN-3 IITA G27 SAMMAZ 51 Commercial hybrid check G28 Oba Super 11 Commercial hybrid check G29 SC301 Commercial hybrid check G30 Oba Super 7 Commercial hybrid check G31 Oba Super 9 Commercial hybrid check G32 Oba Super 2 Commercial hybrid check IITA = International Institute of Tropical Agriculture Experimental site The experiment took place at the Teaching and Research (T&R) farm of LAUTECH in Ogbomoso, Nigeria. Conducted from June to October 2024, the study included two planting dates, reflecting the typical maize planting periods in the area. The field trials were situated at a latitude of 8°17ʹN and a longitude of 4°28ʹE, with an altitude of 343 meters above sea level. The location experiences a bimodal rainfall pattern each year, accompanied by high relative humidity. The site receives an average annual rainfall of 1000–1100 mm, and the yearly minimum and maximum temperatures average between 28 and 30°C. The soil at the site is generally low in nitrogen and has been identified as Alfisol (USAD, 1999). Experimental layout, design, and cultural practices The experimental field underwent two rounds of ploughing, followed by harrowing with a tractor two weeks later to break down the soil. These steps were crucial before setting up the field layout and planting, as they helped manage weeds, improve soil aeration, facilitate root growth, and boost seed germination and emergence. The blocks were divided by 1.5-meter alleys. The experiment was organized in an 8 × 4 α (0,1) lattice design (Patterson and Williams, 1976 ) with three replications. Each experimental plot comprised two rows, each 4 meters long, with a spacing of 0.75 meters between rows and 0.50 meters between plants within a row, resulting in 9 hills per row (with two plants per hill). The maize hybrids were planted by hand. Three seeds were sown per hill and thinned to two seedlings per stand two weeks after sowing, achieving a plant density of 53,333 plants per hectare. Two guard rows were planted on each side of the experimental field to shield the main maize hybrids being evaluated. Standard agronomic practices, including weeding and fertilization, were followed. The experiments were conducted under rain-fed conditions. The plants were left unprotected to allow for natural FAW infestation. No insecticide was used. On October 21, 2024, the maize hybrids were manually harvested once they reached physiological maturity, with the leaves turning yellow and brown, and the kernels having dried to a grain moisture content of less than 25%. Data collection Considering each location by planting date as a separate test environment, phenotypic data were taken in two environments with three replications per environment. Data were recorded on a plot basis for the number of days to 50% anthesis (DP), 50% silking (DS), anthesis-silking interval (ASI), plant aspect (PASP), ear aspect (EASP), husk cover (HC), plant (PH) and ear height (EH), root and stalk lodging as described by Olayinka et al. ( 2025 ). Additionally, foliar FAW damage (FFAWD) was scored at 4, 8, and 12 weeks after sowing (WAS). The presence of FAW was determined by visual assessment of active larvae, and FFAWD scores were the main indicators of the extent of FAW pressure. FFAWD was recorded following the modified Davis et al. ( 1989 ) rating scale (1–9) as described by Matova et al. ( 2022 ). Severity ratings of Southern corn leaf rust (SCLR), Southern corn leaf blight (SCLB), Curvularia leaf spot (CLS) [ Curvularia lunata (Wakker) Boedijn], and Maize streak virus (MSV) transmitted by Cicadulina leafhoppers were recorded using a scale of 1 to 5 (where 1 = slight leaf infection, and 5 = severe leaf infection). During harvest, ear rot (ER) was assessed by evaluating the number of ears exhibiting signs of infection, using a scale from 1 to 5: 1 indicates no damage and high resistance, 2 represents less than 15% damage and partial resistance, 3 signifies damage between 35% and 50%, indicating susceptibility, 4 denotes damage exceeding 60% but less than 100%, showing high susceptibility, and 5 corresponds to nearly complete damage, indicating high susceptibility. The total number of plants and ears was counted in each plot at the time of harvest. The number of ears per plant (EPP) was estimated as the ratio of the number of harvested ears per plot to the number of plants at harvest in a plot. Grain moisture content was measured using a digital grain moisture content tester. Grain yield (Y) was computed from the ear weight and converted to kg/ha. A shelling percentage of 80% (800 g grain kg − 1 ear weight) was assumed for all hybrids, and the grain yield was adjusted to 15% moisture content (150 g kg − 1 moisture) using the formula described by Carangal et al. ( 1971 ): $$\:Grain\:yield\:\left(kg/ha\right)=ear\:weight\:\left(kg/plot\right)\times\:\frac{100-MC}{85}\:\times\:\frac{\text{10,000}}{plot\:area\:\left({m}^{2}\right)}\:\times\:0.80$$ Where: MC = moisture content (%) in grains at harvest, 85 = percentage dry matter used to adjust for 15% grain moisture content, 10000 = total land area (m 2 ) of a hectare, and 0.80 = 80% shelling percentage Statistical analysis The data from the field experiments were entered into Microsoft Excel 2019 and analysed statistically. The data were subjected to Analysis of Variance (ANOVA) on plot mean basis using the General Linear Model (GLM) procedure of Statistical Analysis System (SAS) version 9.4 (SAS Institute, 2011) to determine hybrid effects on a linear statistical model and enable separation of the variance components (Gomez and Gomez, 1984 ). The location by planting date combination was considered an environment. Data were analysed separately for each environment to assess the significance of different factors, and Bartlett’s test was used to check the homogeneity of error variances before combining the data for further analysis (Steel and Torrie, 1980 ). Analysis of variance for combined data was used to determine the genotype × environment (G × E) interaction. The following linear model was used for the combined analysis: $$\:{y}_{ijkl}=\:\mu\:\:+{r}_{j}{B}_{k}+{E}_{i}+{G}_{l}+{GE}_{il}+{\epsilon\:}_{ijkl}$$ Where Y ijkl = is the observed value of the response variable; µ = grand mean; r j (B) k = effect of the kth block nested in jth replication; E i = the effect of the ith environment; G l = the effect of the lth hybrid; GE il = interaction effect of lth hybrid evaluated in the ith environment, and ɛ ijkl = random experimental error. In the combined ANOVA, the blocks within replications and the environments were considered as random factors, while the maize hybrids were considered fixed effects. The significance of the mean squares for the main and interaction effects was tested using the appropriate mean squares obtained from the aforementioned procedure. Coefficients of variation (CV) and determination (R 2 ) values, both in percentage, were used to measure the reliability of the statistical model of ANOVA. To further assess the differences among maize hybrid means, significantly different means were separated using Tukey's Honestly Significant Difference (HSD) test at a 0.05 probability level (Tukey, 1953 ). This post-hoc test provided a detailed comparison of the maize hybrid means, identifying those that are statistically significantly different. Boxplot for every trait was generated using an online web application at https://www.statskingdom.com/boxplot-maker.html . To identify/select superior of top-cross FAWR maize hybrids, the mean grain yield and other measured agronomic traits were used to generate genotype by yield × trait (GYT) biplot according to Yan and Frégeau-Reid ( 2018 ). To account for varying units of traits, standardization was applied so that the mean for each yield-trait combination becomes 0 and the variance becomes 1 as follows: $$\:Z=\:\frac{X\:-\mu\:}{\sigma\:}$$ Where Z = standard score, X = initial trait value, µ = mean of the trait value, and σ = standard deviation of the trait value. R statistical software was used for graphical analysis. Pearson's correlation analysis was computed to determine associations among all traits measured using the 'metan' package in R statistical software (Olivoto and Lúcio, 2020 ). Multiple Linear Regression Model (MLRM) was used to establish the linear relationship between dependent and independent variables according to Montgomery et al. ( 2021 ) using PROC REG in SAS. The maximum likelihood was used to estimate the parameters of the regression model, and the general linear regression model was tested by ANOVA. The general linear model for MLRM, in which the response is related to a set of independent variables (X 1 ), is given: $$\:Y={\alpha\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}\:+\dots\:+\:{\beta\:}_{k}{X}_{k}\:+\:{\epsilon\:}_{i}$$ Where Y = dependent variable, α 0 is the intercept, β 1 , β 2 … β k are coefficients of the variables, X 1 , X 2 … X k are kth independent variables, and ε i is the error term. The variance components and heritability estimates for each trait were computed using R statistical software version 4.2.2 (R core team, 2024). Results Variation among top-cross FAWR maize hybrids Rainfall during the experimental period was unevenly distributed, with higher amounts recorded in the later months, particularly in October, which had a mean rainfall of 7.9 mm (Fig. 1 ). The lowest rainfall was observed in July and August, with mean values ranging from 3 to 3.9 mm. The highest mean temperatures of 26.8°C were recorded in May and October. Sunshine intensity was lower in July and August, ranging from 7.4 to 7.9 MJ/m²/day, while relative humidity peaked in July and August, with values of 93.8% and 92.5%, respectively. Analysis of variance results revealed significant (p < 0.001) differences between environments for grain yield and most measured traits, except for plant height and foliar diseases (Table 2 ). The mean squares for the hybrids were highly significant (p < 0.001) for grain yield, husk cover, plant aspect, number of days to silking and anthesis, Southern corn leaf rust, and blight. Ear rot, plant, and ear heights were highly significant (p < 0.01). The hybrids were significantly different (p < 0.05) in the number of ears per plant. The hybrid mean squares were not significantly different for anthesis-silking interval, ear aspect, maize streak virus, and Curvularia leaf spot. Hybrid × Environment interaction mean squares were significant (p < 0.05) for husk cover, plant aspect, number of days to anthesis and silking, and highly significant (p < 0.001) for grain yield. The sum of squares for the environmental effect represented 54% of the grain yield variation. The differences between the hybrids explained 21% of the total grain yield variation, while the effects of the Hybrid × Environment interaction explained 11%. The coefficient of variation (CV), which determines the precision of the experiment, showed data reliability in that the CV for all agronomic traits, excluding grain yield and foliar disease traits, ranged from 2.6 to 17.7%. Table 2 Combined mean squares for measured traits of top-cross FAWR maize hybrids and checks evaluated at LAUTECH T&R FARM Source Environment (Env) Replication: Rep (Env) Block (Rep × Env) Hybrid Hybrid × Env Error R 2 (%) CV (%) df = 1 4 42 31 31 82 Trait Number of days to anthesis (day) 76.26*** 5.21* 2.94 9.59*** 3.33* 2.13 80.05 2.62 Number of days to silking (day) 285.19*** 10.12*** 3.12* 11.24*** 3.19* 1.94 86.96 2.36 Anthesis-silking interval (day) 656.38*** 2.39 2.11 1.03 1.03 2.69 79.61 48.84 Plant height (cm) 1.34 3754.76*** 383.14** 363.83** 169.33 188.39 76.15 9.86 Ear height (cm) 8303.12*** 589.49*** 174.21*** 150.04** 85.91 71.29 82.24 14.33 Husk cover (1–5) 26.18*** 2.50*** 0.53*** 0.49*** 0.28* 0.17 86.61 13.49 Plant aspect (1–5) 74.38*** 2.26*** 0.47** 0.66*** 0.41* 0.22 89.07 14.88 Ear aspect (1–5) 16.33*** 1.13*** 0.24 0.28 0.26 0.24 72.89 17.08 Ear rot (1–5) 78.80*** 2.03*** 0.51 0.83** 0.42 0.42 81.68 25.98 Number of ears per plant 1.19*** 0.13*** 0.04** 0.04* 0.02 0.02 74.91 17.65 maize streak virus (1–5) 0.00 0.03 0.60 1.04 0.00 0.77 49.32 58.36 Southern corn leaf rust (1–5) 0.00 1.45** 0.47 1.29*** 0.00 0.42 63.35 38.64 Southern corn leaf blight (1–5) 0.00 2.82** 1.41* 3.44*** 0.00 0.82 73.56 32.97 Curvularia leaf spot (1–5) 7.92 6.64 8.13 9.29 7.92 8.27 57.57 62.27 FFAWD 4 WAS 8.33** 8.60*** 1.18 1.08 0.94 0.98 68.00 28.08 FFAWD 8 WAS 180.19*** 11.13*** 1.94** 0.64 0.80 0.92 84.00 32.65 FFAWD 12 WAS 292.55*** 3.29*** 2.18*** 0.94* 0.81 0.57 91.00 23.69 Grain yield (kg/ha) 260640373.30*** 8580273.70*** 884794.4 3233288.30*** 1695309.10*** 741767 89.44 25.48 % Contribution to grain yield 53.75 7.08 7.66 20.67 10.84 *, **, and *** denote significance at p < 0.05, p < 0.01, and p < 0.001, respectively R 2 = Coefficient of determination, CV = coefficient of variation FFAWD 4 WAS = foliar fall armyworm damage at 4 weeks after sowing, FFAWD 8 WAS = foliar fall armyworm damage at 8 weeks after sowing, FFAWD 12 WAS = foliar fall armyworm damage at 12 weeks after sowing Mean performance for grain yield, agronomic traits, foliar FAW damage, and diseases of top-cross FAWR maize hybrids The environmental effects were clearly observed on the variability in performance of grain yield, foliar diseases, FFAWD, and other agronomic traits of the top-cross FAWR maize hybrids (Fig. 2 ). The two trials planted in different environments, hereafter referred to as E1 and E2, showed significant differences in magnitude among all the traits measured. For example, ASI ranged from 1to 2 days in the E1 trial, while it was 3 to 7 days in the E2 trial. Plant and ear heights mean as well as maximum and minimum range, varied across the environments. The maximum range was different; however, the mean plant heights were similar. The overall phenotypic appeal of the plant (plant and ear aspect, husk cover, ear rot ratings) was different in E1 and E2 and showed a wide range for these traits. Grain yield ranged from 715.4 to 3241.2 kg ha − 1 in E1, while it was 901.8 to 7205.3 kg ha − 1 in E2. Note that FFAWD scores at 8 and 12 weeks intervals after sowing were similar, but the values differed at 4 weeks. The foliar diseases (SCLR, SCLB, MSV) scores were at par in E1 and E2; however, the minimum and maximum range were different in CLS. Across the environment, grain yield ranged from 1502.7 kg ha⁻¹ (G32: Oba Super 2) to 5223.3 kg ha⁻¹ (G1: FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B). The highest-yielding hybrid was G1 with 5223.3 kg ha⁻¹, followed closely by G9 (4910.5 kg ha⁻¹) and G21 (4360.8 kg ha⁻¹), and they were not statistically different. The top-yielding hybrid was significantly superior to 44% of the evaluated hybrids, including the checks, according to the HSD test. Among the commercial hybrids used as checks, only G31: Oba super 9 (3823.7 kg ha − 1 ) was comparable to the top-yielding hybrids. Grain yield of the other checks was statistically low (Supplementary Table 1). A few of the evaluated hybrids outperformed the checks, with a 22% higher mean grain yield. They showed 10% improvement in husk cover, 13% and 8% better ratings for plant and ear aspects, respectively, and 9% better resistance to ear rot. The hybrids also exhibited 5% better resistance to Southern corn leaf blight and 12–35% improved FFAWD ratings at 4, 8, and 12 WAS. The mean FFAWD ratings at 4, 8, and 12 WAS were below 6 for all evaluated top-cross FAWR maize hybrids and the checks. Variability among hybrids was minimal at 4 WAS, with most scoring low (1–3). FFAWD scores generally decreased over time. Two hybrids, namely FAWSYN-1/TZISTR1305 (G7) and FAWSYN-3/IITATZI2305 (G19), had consistent performance with low rating across the weeks and were among the top five hybrids with no visible damage and or few short holes on several leaves. Interestingly, the commercial hybrids used as checks (Oba Super 2 and 9) produced higher ratings across the weeks, with several leaves having short holes and a few long lesions. Among the evaluated maize hybrids, FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1), with the highest grain yield potential, showed tolerance to FAW (average FFAWD = 2) according to the Davies scoring scale. Within the commercial hybrids used as checks, SAMMAZ 51(G27) was the best in terms of FAW tolerance. Variance components and broad-sense heritability estimate The phenotypic variance (σ 2 p ) was separated into genotypic (σ 2 g ) and environmental variances (σ 2 e ) to estimate the contribution of each to the total variation observed. The σ 2 e were larger than the σ 2 g ; consequently, the σ 2 p was higher than the σ 2 g for all traits. The highest σ 2 p and σ 2 g were observed for grain yield (3054482.89 and 211188.48), followed by plant height (310.79 and 56.45), ear height (170.62 and 10.63), and number of days to silking (8.18 and 3.67), respectively (Table 3 ). Moderate σ 2 p and σ 2 g were obtained from the number of days to silking. Conversely, other traits measured exhibited very low variation. The highest phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were found for anthesis-silking interval (65.93 and 37.11). In this study, the PCV estimates were higher than the GCV estimates. The Southern corn leaf blight and rust, number of days to anthesis and silking had the highest broad-sense heritability (H 2 ) estimates ranging from 42 to 55%. Ear rot and ear aspect scores had the least H 2 estimates. Table 3 Components of variance and heritability estimates for grain yield, foliar disease, FAW leaf damage and other agronomic traits in top-cross FAWR maize hybrids FFAWD 4 WAS = foliar fall armyworm damage at 4 weeks after sowing, FFAWD 8 WAS = foliar fall armyworm damage at 8 weeks after sowing, FFAWD 12 WAS = foliar fall armyworm damage at 12 weeks after sowing, SE = standard error, σ2g = genotypic variance, σ2e = environmental variance (variance of error mean square), σ2p = phenotypic variance, PCV = phenotypic coefficient of variation, GCV = genotypic coefficient of variation, H2 = broad sense heritability. Range Variance Trait Mean ± SE Minimum Maximum σ 2 g σ 2 e σ 2 p PCV (%) GCV (%) H 2 (%) Number of days to anthesis (day) 55.56 ± 1.03 49.00 61.00 2.88 3.17 6.05 4.43 3.06 47.61 Number of days to silking (day) 58.92 ± 1.23 52.00 64.00 3.67 4.51 8.18 4.86 3.25 44.86 Anthesis-silking interval (day) 3.36 ± 1.47 0.00 10.00 1.55 6.46 4.90 65.93 37.11 31.68 Plant height (cm) 139.18 ± 9.2077 47.40 196.00 56.45 254.34 310.79 12.67 5.40 18.16 Ear height (cm) 58.91 ± 7.30 4.60 96.34 10.63 159.98 170.62 22.17 5.54 6.23 Husk cover (1–5) 3.04 ± 0.39 2.00 5.00 0.03 0.48 0.51 23.55 5.94 6.36 Plant aspect (1–5) 3.17 ± 0.53 2.00 5.00 0.01 0.84 0.85 29.07 3.75 1.67 Ear aspect (1–5) 2.85 ± 0.35 2.00 4.00 0.00 0.36 0.37 21.25 1.92 0.82 Ear rot (1–5) 2.49 ± 0.57 1.00 5.00 0.00 0.96 0.97 39.39 2.41 0.37 Number of ears per plant 0.85 ± 0.11 0.36 1.44 0.00 0.04 0.04 22.51 5.24 5.42 Grain yield (kg ha − 1 ) 3379.73 ± 973.53 412.71 7650.12 211188.48 2843294.41 3054482.89 51.71 13.60 6.91 Maize streak virus (1–5) 1.50 ± 0.43 1.00 5.00 0.20 0.56 0.76 58.04 29.94 26.61 Southern corn leaf rust (1–5) 1.68 ± 0.34 1.00 4.00 0.25 0.34 0.59 45.96 29.87 42.25 Southern corn leaf blight (1–5) 2.74 ± 0.52 1.00 5.00 0.98 0.80 1.78 48.64 36.09 55.07 Curvularia leaf spot (1–5) 4.62 ± 1.65 1.00 43.00 0.50 8.16 8.66 63.69 15.24 5.72 FFAWD 4 WAS 3.32 ± 0.55 2.00 7.00 0.03 0.91 0.94 29.21 5.12 3.07 FFAWD 8 WAS 1.97 ± 0.32 1.00 4.00 0.05 0.30 0.35 29.96 10.78 12.94 FFAWD 12 WAS 1.95 ± 0.31 1.00 4.00 0.03 0.29 0.32 29.20 9.31 10.17 Phenotypic correlations between foliar FAW damage and grain yield and yield-related traits across environments Across the environments, FFAWD had a significant (p < 0.05) negative association (r = -0.36) with grain yield performance, husk cover (r = -0.49), and plant aspect (r = -0.41) (Fig. 3 ). Ear rot had a significant (p < 0.05) negative correlation with the number of days to anthesis (r = -0.45) and silking (r = -0.40). Curvularia leaf spot had a significant (p < 0.01) positive correlation with plant (r = 0.48) and ear (r = 0.44) heights. Plant height positively and significantly (p < 0.001) correlated with grain yield (r = 0.61) and ear height (r = 0.64). Husk cover (r = -0.47) and plant aspect (r = -0.66) correlated significantly (p < 0.01) negatively with grain yield, likewise ear height. Southern corn leaf rust had a positive association with ear rot. Similarly, ear rot had a significant (p < 0.01) positive correlation with ear aspect (r = 0.54), but a negative association with flowering traits. Southern corn leaf blight was significantly (p < 0.01) negatively associated with plant aspect (r = -0.46). Highly significant positive relationships were observed between husk cover and plant aspect (r = 0.74), number of days to anthesis and silking (r = 0.95), anthesis-silking interval with number of days to silking(r = 0.42), and Curvularia leaf spot (r = 0.39). The stepwise multiple regression analysis highlights significant (p < 0.05, p < 0.01, and p < 0.001) relationships between various traits of interest (Table 4 ). The coefficient of determination (R 2 ) value for the regression trend ranged from 11 to 42%. Plant aspect exhibited the highest R 2 . The FFAWD score explains 25% of the variability in husk cover score, with a unit increase in FFAWD score resulting in a 0.48 increase in husk cover score. Similarly, 17% of the variability in plant aspect score is explained by the FFAWD score, where a unit increase leads to a 0.45 increase in plant aspect score. For the ear aspect, FFAWD score accounts for 12% of its variability, with a unit increase causing a 0.26 increase in ear aspect score. Interestingly, the FFAWD score also explains 13% of the grain yield; however, in this case, a unit increase in FFAWD score reduces grain yield by 863.58 kg ha⁻¹. Table 4 Regression statistics for grain yield (y) relative to related traits (x) and other measured traits relative to fall armyworm leaf damage score Variable Trait Regression line equation R 2 Probability Dependent variable: Grain yield Predictor Plant height (cm) Ŷ = -4084.33 + 53.63X 0.37 0.0002 Ear height (cm) Ŷ = -2093.52 + 92.91X 0.41 0.0000 Husk cover (1–5) Ŷ = 6864.59–1147.87X 0.22 0.0071 Plant aspect (1–5) Ŷ = 7832.71–1402.74X 0.42 0.0000 Southern corn leaf rust (1–5) Ŷ = 2402.95 + 582.43X 0.14 0.0354 Southern corn leaf blight (1–5) Ŷ = 2367.80 + 369.37X 0.16 0.0242 Independent variable: FFAWD Response Husk cover (1–5) Ŷ = 1.87 + 0.48X 0.24 0.0040 Plant aspect (1–5) Ŷ = 2.08 + 0.45X 0.17 0.0194 Ear aspect (1–5) Ŷ = 2.23 + 0.26X 0.11 0.0579 Grain yield (kg/ha) Ŷ = 5464.47–863.58X 0.13 0.0429 R 2 = coefficient of determination, FFAWD = foliar FAW damage Table 5 Standardized genotype × yield × trait value and mean superiority index obtained from mean performance for grain yield and agronomic traits of top-cross FAWR maize hybrids and checks across environments ENTRY Y/ DP Y/ DS Y/ ASI Y* PH Y* EH Y/ HC Y/ PASP Y/ EASP Y/ ER Y* EPP Y/ SCLB Y/ SCLR Y/ CLS Y/ F4 Y/ F8 Y/ F12 Mean S.I. G1 2.26 2.24 1.61 2.45 2.66 1.78 2.12 2.83 1.79 2.27 1.35 -0.03 0.37 2.07 1.30 1.41 1.87 G9 1.92 1.94 1.80 1.91 1.91 1.59 1.50 2.04 2.07 1.57 1.18 -0.07 0.38 2.03 2.04 2.35 1.61 G21 1.21 1.22 1.20 1.28 0.81 1.54 1.23 1.81 1.34 0.81 0.48 -0.09 3.51 1.12 1.45 1.28 1.22 G18 1.14 1.11 0.45 0.96 0.80 1.52 1.66 0.35 0.49 1.93 0.16 -0.09 2.71 1.47 1.38 1.34 1.06 G3 0.55 0.53 0.14 0.63 0.90 1.05 0.86 0.80 1.24 0.58 -0.63 5.18 0.30 -0.07 0.59 0.52 0.75 G20 0.84 0.80 0.12 1.26 0.79 1.40 1.23 0.28 0.27 0.69 -0.06 -0.43 -0.29 0.45 0.70 0.69 0.51 G17 0.64 0.65 0.63 0.82 0.96 0.68 0.24 0.44 0.54 0.11 -0.45 0.24 0.49 0.23 0.89 0.68 0.44 G22 0.41 0.42 0.41 0.40 0.28 0.41 0.55 0.35 0.66 0.90 -0.91 -0.37 0.92 1.29 0.66 0.61 0.40 GG15 0.53 0.58 1.45 0.20 0.23 0.36 -0.07 0.05 -0.31 0.46 -0.14 0.05 0.29 -0.08 1.03 1.16 0.39 G19 0.48 0.50 0.67 0.26 0.28 0.46 0.53 0.67 -0.01 0.27 -0.38 -0.12 -0.34 0.90 1.11 1.27 0.36 G12 0.27 0.30 0.75 0.72 0.74 0.12 0.15 0.13 -0.17 0.21 1.11 -0.34 -1.74 -0.35 -0.06 -0.07 0.21 G4 0.60 0.53 -0.37 0.28 0.11 0.09 0.62 0.74 -0.44 0.82 0.45 0.12 -0.04 0.36 -0.32 -0.29 0.16 G2 0.26 0.29 0.59 0.22 0.08 -0.30 -0.18 -0.10 0.85 0.27 -0.02 0.01 -0.41 0.60 -0.72 -0.73 0.14 G31 0.58 0.60 0.89 0.26 0.71 0.43 0.44 -0.12 -0.62 0.72 -0.48 -0.57 0.21 -0.27 -0.87 -0.88 0.12 G27 -0.14 -0.07 1.10 -0.08 -0.26 0.10 -0.26 -0.07 1.06 -0.27 -0.40 0.07 0.48 -0.71 0.59 0.33 0.10 G16 0.30 0.25 -0.34 0.20 -0.03 0.16 0.13 -0.14 1.08 0.03 -0.08 -0.17 -0.55 1.28 -0.74 -0.73 0.05 G10 -0.15 -0.16 -0.32 0.12 0.42 -0.04 0.50 0.18 0.49 0.09 -0.51 0.16 0.19 -0.63 0.49 0.26 0.02 G23 0.11 0.12 0.20 -0.21 -0.08 -0.17 0.35 0.10 -0.46 -0.02 -0.73 0.04 -0.06 -0.01 0.19 0.14 -0.09 G30 -0.39 -0.32 1.13 -0.22 -0.32 -0.32 -0.44 -0.17 0.19 -0.01 -0.69 0.62 -0.35 -0.99 -0.10 -0.08 -0.10 G7 -0.32 -0.30 -0.10 -0.38 -0.18 -0.53 -0.60 0.15 0.67 -0.75 -0.40 0.30 0.07 0.31 0.41 0.41 -0.10 G6 -0.50 -0.50 -0.58 -0.36 -0.54 -0.74 -0.53 -0.44 -0.97 -0.35 4.31 -0.11 -0.25 0.12 -0.24 -0.33 -0.14 G14 -0.20 -0.23 -0.68 -0.41 0.12 -0.03 0.14 -0.41 -0.49 0.03 -0.74 -0.21 -0.28 -0.07 0.44 0.38 -0.18 G25 -0.50 -0.51 -0.58 -0.57 -0.55 -0.01 -0.34 -0.68 -0.87 0.00 -0.08 -0.39 -0.05 0.12 -0.37 -0.37 -0.36 G11 -0.69 -0.69 -0.77 -0.82 -0.67 -0.01 -0.37 -0.06 0.31 -0.42 -0.32 0.27 -0.57 -0.67 -0.28 -0.30 -0.39 G8 -0.94 -0.96 -1.07 -0.85 -0.59 -0.29 -0.82 -0.67 -0.55 -0.75 -0.03 -0.55 0.64 -0.54 -0.38 -0.41 -0.51 G13 -0.51 -0.52 -0.73 -0.63 -0.93 -0.41 -0.12 -0.43 -0.04 -0.77 -0.53 -0.46 -0.66 -0.22 -0.99 -0.66 -0.57 G29 0.02 0.01 -0.13 0.02 -0.22 -0.72 -0.47 -0.92 -1.37 -0.58 -0.78 -0.58 -0.96 -1.08 -0.64 -0.66 -0.58 G26 -0.61 -0.64 -0.93 -0.72 -0.73 -1.05 -1.08 -0.57 -0.93 -0.72 0.72 -0.07 -0.59 -0.86 -1.00 -0.67 -0.65 G5 -1.19 -1.18 -0.90 -1.10 -1.05 -1.11 -1.19 -0.80 -0.95 -1.64 -0.50 -0.45 -1.14 -1.01 -1.49 -1.46 -1.07 G24 -1.65 -1.67 -1.80 -1.59 -1.48 -1.50 -1.50 -1.23 -1.06 -2.09 0.72 -0.85 -0.97 -0.93 -1.24 -1.35 -1.24 G28 -2.07 -2.08 -1.93 -1.90 -2.03 -2.11 -2.09 -2.07 -1.70 -1.51 -0.71 -0.52 -0.59 -1.75 -1.87 -1.87 -1.62 G32 -2.27 -2.26 -1.93 -2.15 -2.17 -2.34 -2.19 -2.05 -2.12 -1.87 -0.90 -0.61 -0.71 -2.11 -1.94 -1.95 -1.81 DP = number of days to anthesis, DS = number of days to silking, ASI = anthesis-silking interval, PH = plant height, EH = ear height, HC = husk cover, PASP = plant aspect, EASP = ear aspect, ER = ear rot, EPP = number of ears per plant, Y = grain yield, SCLR = Southern corn leaf blight, SCLB = Southern corn leaf rust, CLS = Curvularia leaf spot, F4, 8, 18 = foliar FAW damage scored at 4, 8 and 12 week intervals after sowing respectively. SI = superiority index. Entry G1-G32 = top-cross fall armyworm resistant maize hybrids and checks evaluated Grain yield is influenced by several additional factors. Plant height accounts for 37% of the variability in grain yield, with a unit increase in plant height resulting in a 53.63 kg ha⁻¹ increase. Ear height explains 41% of the variability, where a unit increase raises grain yield by 92.91 kg ha⁻¹. Conversely, husk cover score, which explains 22% of grain yield variability, shows a negative association; a unit increase in husk cover score reduces grain yield by 1147.87 kg ha⁻¹. Similarly, plant aspect score accounts for 42% of the variability in grain yield, with a unit increase decreasing yield by 1402.74 kg ha⁻¹. Two foliar diseases, Southern corn leaf rust and blight, also impact grain yield, although the R 2 values suggest weak explanatory power. Genotype by yield × trait (GYT) biplot Significant Hybrid × Environment interaction and its high contribution to total variation for grain yield necessitate the use of GYT to identify promising hybrids. The GYT biplot displays about 82% (PC1 74.4% and PC2 7.7%) of the total variations explained among the traits (Fig. 4 ). For clarity, on the biplot, entry number corresponding to each hybrid was used for ease of graphical presentation. The trait profile of the hybrids (the weakness and strength of the genotypes) was observed. The acute angle between the vectors of the hybrids shows their value. Accordingly, FAWSYN-2/(TZLComp. 1 C6-W-39-1-1)-B-B (G9) and FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) showed higher Y × ER and Y × CURV, FAWSYN-2/IITATZI2300 (G21) and FAWSYN-3/IITATZI2300 (G18) showed higher Y × RUST, Y × EASP, Y × PASP, FAWSYN-1/IITATZI2300 (G20) showed higher Y × ASI, Y × DP, Y × DS Y × EPP, FAWSYN-1/IITATZI2305 (G22) showed higher Y × BLIGHT, FAWSYN-3/TZISTR1878 (G17) and FAWSYN-1/TZISTR1878 (G3) showed higher Y × STREAK. Based on the “which won where” analysis (Fig. 5 ), the perpendicular lines divided the polygon into seven sectors, the vertex hybrids in each sector are indicated by the polygon peaks, and hybrids that were desirable for a GYT were found in its sector as a group. Thus, entries G1, G9, G15, G18, G21, G27 and G31 were closely correlated with Y × DP, Y × DS, Y × EH, Y × PH, Y × HC, Y × PASP, Y × EASP, Y × ER, Y × EPP, Y × RUST, Y × CURV. Entries G17, G20, and G22 were closely correlated with Y × BLIGHT, and entries G4, G10, G14, and G23 were closely correlated with Y × STREAK. Other hybrids and checks evaluated did not correlate with any GYT combinations. Out of the seven polygons, only three had traits in their sectors. The Average Tester Coordination (ATC) view of the GYT biplot includes a small circle (abscissa) within the biplot, representing the average of yield–trait combinations. A line, called the Average Tester Axis (ATA), passes through the origin of the biplot and the point corresponding to the average yield–trait combinations. This ATA is used to rank maize hybrids based on their overall superiority. Hybrids positioned close to the ATA tend to exhibit balanced trait profiles, while those farther from the ATA, in either direction, demonstrate pronounced strengths or weaknesses in specific traits (Fig. 6 ). The outstanding hybrids identified were G1 > G9 > G21 > G18 > G20 whereas; G32, G28, G24, G5 and G26 were identified as the poorer hybrids. These results were confirmed by the superiority index generated from the yield-trait combinations (Table 4 ). Entries 1 and 9 had desirable lower ratings for ear rot, Southern corn leaf rust, Curvularia leaf spot, husk cover, ear, and plant aspects. Entries G21, GG18, and 20 were good in number of ears per plant, ear, and plant heights. Other entries, such as G22, G17, G4, and G3, were good in Southern corn leaf blight, maize streak virus, anthesis-silking interval, number of days to anthesis and silking. Discussion Insufficient maize production, largely caused by FAW infestations, jeopardizes the livelihoods of smallholder farmers and hinders the Sustainable Development Goals' hunger reduction efforts. The cultivation of maize hybrids with native resistance reduces FFAWD and mitigates grain yield losses (Kamweru et al., 2022 ). This study evaluated top-cross FAWR maize hybrids across environments under natural FAW infestation for their resistance to FAW and potential grain yield performance. Due to the erratic rainfall pattern experienced, the FAW infestation pressure in both environments was sufficient to cause a differential response across maize hybrids. The significant mean squares of hybrid for grain yield and other traits measured indicate the presence of variation among the evaluated maize hybrids, suggesting that superior hybrids could be identified, selected, and recommended for commercial evaluation. The differences among the hybrids could also be attributed to the genetic background of the parental lines. Similar findings had been observed among maize hybrids for grain yield and other traits (Bocianowski et al., 2024 ). For grain yield, assessment of the total sum of squares revealed that the environmental sums of squares accounted for 54% of the variations observed, with the hybrid contributing 21%, highlighting greater environmental influence and variability. This aligns with findings from multi-environment trials in SSA (Sserumaga et al., 2018 ; Eze et al., 2020 ). Significant hybrid × environment interactions for grain yield, flowering traits, and phenotypic appeal of the plant indicated that the maize hybrids performed differently across the test environments and suggested a low level of stability. Mafouasson et al. ( 2018 ) and Boreddy et al. ( 2020 ) reported significant mean squares of G × E interaction for grain yield and other agronomic traits. The differences among the environments in soil fertility, rainfall, relative humidity, and temperature affected the FAWR maize hybrids stability. This depicts the need for evaluation in several locations and over several seasons in order to ascertain adaptation and stability of the newly developed top-cross FAWR maize hybrids. Similarly, Zebire et al. ( 2024 ) reported significant environmental effects on maize grain yield and other agronomic traits. Moreover, hybrid × environment interaction lacked significant mean squares for ASI, ear aspect, ear rot, number of ears per plant, foliar diseases, ear and plant heights, indicating stability across environments. The coefficients of variation (CV) for all agronomic traits were below 20%, signifying low experimental error. Similar findings were reported by Maphumulo et al. ( 2015 ) and Kolawole and Olayinka ( 2022 ), where secondary traits consistently showed CVs under 20%, indicating reliable trial performance. Other CVs greater than 20% can be treated as high, indicating high experimental error, for ASI, foliar diseases, and FFAWD. The high CV obtained for ASI may be due to the method of calculation, where the negative values reduced the mean without affecting the variance. For foliar diseases and FFAWD, high CV likely reflects inter-environment variability (Ramos Guimarães et al. 2021 ) The HSD (0.05) revealed that the hybrids were different, given an opportunity to select outstanding maize hybrids. The boxplot analysis revealed variability in means among FAWR maize hybrids within each environment, with trait magnitudes highlighting hybrids with extreme performance. Differences in mean values across environments confirmed the impact of environmental conditions on hybrid performance, aligning with ANOVA results. These results emphasize the need to consider environmental factors when evaluating maize hybrid performance for quantitative traits. The relative grain yield differed significantly between the two environments (E1 and E2), with a gap of 2330.2 kg ha − 1 . This high disparity was related to the prolonged drought between July and August, particularly during the periods before anthesis and following silking, resulting in to delay in ASI. This led to lower yields compared to the findings of Manjunatha et al. ( 2018 ), who reported approximately 6–11 ton ha − 1 of grain yield. However, grain yields reported in this study were comparable to those of Vah et al. ( 2017 ), who also evaluated maize hybrids across different environmental conditions. The sensitivity of maize to drought during the seedling stage, flowering, and grain-filling periods, coupled with high FAW pressure, was mainly responsible for the low yield recorded in E1. Traits that reflect internode elongation (plant and ear heights) did not respond similarly. They showed different trends for E1 and E2. The frequent rainfall after planting in E1 made the plant significantly taller; ear height, being determined earlier in the plant's development than plant height, was consistently higher. When the July-August drought occurred, the plants in E2 became progressively shorter. Across the environments, the hybrids exhibited significant variation across most measured traits, providing selection opportunities. Grain yield ranged from 1502.7 kg ha − 1 (G32: Oba Super 2) to 5223.3 kg ha − 1 (G1: FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B), with a mean of 3379.7 kg ha − 1 . This was consistent with the findings of Kolawole and Olayinka ( 2022 ), who evaluated maize hybrid in a similar environment. FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) had a 26.8% yield advantage compared to the best check, Oba Super 9 (G31). Relative to the average grain yield, 47% of the maize hybrids evaluated surpassed the average grain yield, while only one of the checks (G3: Oba Super 9) had comparable performance. FFAWD scores were recorded three times, beginning in the fourth week after sowing. Initial observations revealed high infestation levels, which diminished as the maize hybrids matured, aligning with the findings of Sebayang et al. ( 2022 ). The level of FAW resistance of the evaluated maize hybrids translated to the difference in grain yield observed. Most of the evaluated maize hybrids exhibited high to moderate resistance to FAW damage, demonstrating their potential resilience against FAW herbivory (Asare et al., 2023 ). The lowest score was recorded in FAWSYN-3/IITATZI2305 (G19) and FAWSYN-1/TZISTR1305 (G7), and the highest score was observed in Oba Super 2 (G32) and Oba Super 9 (G31). All commercial hybrids used as checks had a similar response to FAW, except for SAMMAZ 51(G27). Phenotypic variances exceeded genotypic variances for all traits, indicating a significant influence of environmental factors and genotype × environment interactions on trait expression. Lower σ²g compared to σ²e for grain yield and other traits resulted in low (0–27%) to moderate (32–55%) H² estimates for all traits measured. This suggests that genetic differences contribute only a small proportion of the phenotypic variation in ear and plant heights, husk cover, plant aspect, ear aspect, ear rot, number of ears per plant, grain yield, maize streak virus, Curvularia leaf spot and FFAWD with σ²e being the primary influence on the observed expression of these traits (Falconer and Mackay, 2009 ; Lynch and Walsh, 1998 ). These results may not be repeatable, as genetic variation was minimal for most traits. Previous studies (Muliadi et al., 2021 ; Matova et al., 2022 ) reported higher H² for maize hybrid grain yield. These differences can be explained by the differences in the genetic materials evaluated and the environments tested. Conversely, the moderate H² for traits such as number of days to anthesis and silking, anthesis-silking interval, and Southern corn leaf rust and blight imply a stronger genetic influence. Higher PCV values compared to GCV indicate a significant environmental impact on trait expression, as confirmed by the high environmental main effects in the ANOVA. A similar observation was reported by Simelane et al. ( 2024 ). Statistically significant correlated traits should be prioritized in maize breeding to address multiple yield-limiting factors simultaneously. The significant negative correlation coefficients (r) between grain yield and FFAWD, plant aspect, and husk cover scores could impact grain yield positively. Previous studies reported negative correlations between grain yield and FFAWD (Moussa et al., 2023; Kamweru et al., 2023 ) and also between grain yield and husk cover (Muliadi et al., 2023 ). The direct consequence of FAW feeding on leaf tissues is the reduction of the photosynthetic capacity of the plant, which is essential for grain production. The positive and significant association among FFAWD, plant aspect, and husk cover suggests that any of these traits could be used to predict the other. Poor husk coverage exposes the cobs to pests, diseases, and environmental stress, ultimately reducing grain yield, and plants with poor overall phenotypic appeal are less productive. Therefore, lower scores for these traits are desirable as they could lead to increased grain yield. On the other hand, grain yield showed strong positive correlations with ear and plant heights, likely due to increased dry matter accumulation from a higher leaf count in taller plants (Nzuve et al., 2014 ). As a physiological process, plant height was highly correlated with ear height. The association between grain yield with plant and ear heights was reported by Vah et al. ( 2017 ). The simultaneous occurrence of high foliar disease and grain yield suggests that the evaluated top-cross FAWR maize hybrids possess tolerance mechanisms or disease escape traits, allowing them to sustain productivity under both erratic rainfall and high disease pressure environments. The significant effects of other traits on grain yield indicate that the correlation results align with the regression findings, corroborating the report of Maphumulo et al ( 2015 ). Traits with R 2 greater than 20% had a significant direct contribution to the yield of hybrids, whereas traits with R 2 less than 20% with significant associations had less direct influence on grain yield, but cannot be ignored, because their cumulative contribution to grain yield could be highly influential. From the regression models, plant aspect was the most significant factor influencing grain yield, followed closely by ear and plant heights. This result emphasizes how crucial plant aspect ratings are in determining grain yield in maize breeding programmes. The overall phenotypic appeal of the plant encompasses vigorous growth, resistance to lodging, minimal leaf defoliation, and absence of disease symptoms, contributing to higher grain yield (Kolawole et al., 2018 ). Ear and plant heights are also important traits because taller plants typically possess a larger leaf area, which boosts photosynthetic efficiency and biomass accumulation, ultimately resulting in higher yields. FAW infestation significantly affects husk cover, grain yield, and ear traits in maize hybrids, highlighting its negative impact on yield-determining traits. The direct impact of FAW on maize plants results from FAW larvae feeding on leaves and whorls, reducing photosynthetic efficiency and impairing assimilate translocation for vegetative growth, cob formation, and grain filling. Older larvae further damage maize ears and kernels by burrowing into cobs, increasing susceptibility to secondary infections (Anjorin et al., 2022 ). Grain yield is a genetically complex trait influenced by the combined effects of multiple agronomic factors, so the direct selection of grain yield is usually ineffective (Muliadi et al., 2023 ). Conversely, phenology and growth-related traits are simpler with moderate to high heritability traits and lower susceptibility to G × E interaction (Blancon et al., 2024 ). Considering multi-trait selection indices such as the GYT that balance productivity, pest/disease resistance, and overall plant phenotypic appeal. The high variance explained by the first two PCs confirms the biplot's effectiveness in depicting relationships among traits (Hosseini et al., 2025). The GYT biplot ranked the maize hybrid based on its worth in combining grain yield with other desirable traits, alongside a comprehensive visualization of the weaknesses and strengths of the hybrids. The top 10 maize hybrids identified by the GYT superiority index confirm the efficiency of the GYT biplot, as the top-cross FAWR maize hybrids outperform the standard checks evaluated. According to Yan and Frégeau-Reid ( 2018 ), the geometry of the biplot indicates that the hybrid at a vertex has the largest values for the yield-trait combinations within the corresponding sector. Thus, FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) and closely placed FAWSYN-2/(TZLComp. 1 C6-W-39-1-1)-B-B (G9) were the best in combining grain yield with desirable flowering, growth, overall phenotypic appeal, and foliar disease tolerance. These hybrids also had balanced trait profiles because of their short projections to the ATA. FAWSYN-3/TZISTR1878 (G17) had a contrasting trait profile but was high-yielding. Previous studies (Faheem et al., 2023 ; Hassani et al., 2024 ), including this one, highlight the GYT biplot as a valuable tool for insightful genotype analysis. Conclusion The research faced limitations due to unpredictable rainfall patterns, including rainfall cessation after planting, which likely caused moisture stress during crucial early growth phases, and excessive rainfall after maize reached maturity, leading to lodging and affecting yield evaluations. Despite these challenges, the study identified genetic variability among the FAWR maize hybrids tested. The environment significantly impacted the phenotypic expression of hybrids, potentially affecting trait expression and hybrid rankings. Nonetheless, a few hybrids demonstrated high grain yield performance across different environments. Grain yield losses linked to FFAWD and poor plant traits directly result in financial losses for farmers. Preventative measures, such as cultivating the identified FAWR maize hybrids, can improve profitability and production sustainability in the derived savanna agro-ecology. Therefore, the top-cross FAWR maize hybrids identified in this study hold significant potential to reduce the impact of FAW damage. Further testing and validation are suggested to confirm their performance across various environments and management conditions. The findings of this study could benefit smallholder farmers by providing new top-cross FAWR maize hybrids tailored to specific locations. Abbreviations fall armyworm (FAW), fall armyworm resistant (FAWR), genotype by yield × trait (GYT), sub-Saharan Africa (SSA) Declarations Ethics approval and consent to participate : Not applicable Consent for publication : Not applicable Funding: This research was supported by the 2024 Nigeria Tertiary Education Trust Fund (TETFund) Institution-Based Research (IBR) Intervention, awarded to Dr. Adesike Oladoyin Olayinka of the Department of Crop Production and Soil Science, Faculty of Agricultural Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria. Author Contribution Conceptualization, Olayinka, Adesike Oladoyin; Data curation, Akande, Olufemi Stephen, Adebayo, Peter Ayotunde and Ujah, Godswill Ofuowoichoyama; Formal analysis, Olayinka, Adesike Oladoyin; Investigation, Akande, Olufemi Stephen, Adebayo, Peter Ayotunde and Ujah, Godswill Ofuowoichoyama; Methodology, Olayinka, Adesike Oladoyin, and Odewole, Adeola Foluke; Project administration, Olayinka, Adesike Oladoyin; Supervision, Olayinka, Adesike Oladoyin; Visualization, Olayinka, Adesike Oladoyin and Odewole, Adeola Foluke; Writing – review & editing, Olayinka, Adesike Oladoyin. Acknowledgements We appreciate the efforts of the Maize Improvement Programme (MIP) of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, for the provision of genetic materials used for this study. 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11:38:58","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":255582,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/4a40b306b91dc4427a244ccb.html"},{"id":100358222,"identity":"5885409a-b87f-4630-b99e-8dccbb3a5b56","added_by":"auto","created_at":"2026-01-16 07:20:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242182,"visible":true,"origin":"","legend":"\u003cp\u003eMeteorological data during the 2024 rainy season at the experimental sites.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/0ec5f2ca2ae24befab1bf246.png"},{"id":99882580,"identity":"e5f2e644-13dd-4302-8b5b-e3bcd87193ee","added_by":"auto","created_at":"2026-01-09 11:39:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":565907,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots analysis for grain yield and other agronomic traits evaluated in 32 maize hybrids for two environments. Solid lines inside the boxes indicate the median value while the dotted lines indicate the mean. The top and bottom lines of the boxes indicate the upper and lower quartiles. Solid dots outside the whiskers show outliers.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/d06333b28f314ef31ce215d4.png"},{"id":99882563,"identity":"40b0f5c0-de90-4057-84ab-d5877c333818","added_by":"auto","created_at":"2026-01-09 11:38:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":391889,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficients based on pooled data of grain yield and other agronomic traits measured evaluated in 32 maize hybrids across environments. ns, nonsignificant.\u003c/p\u003e\n\u003cp\u003eDP = number of days to anthesis, DS = number of days to silking, \u0026nbsp;ASI = anthesis-silking interval, PH = plant height, EH = ear height, \u0026nbsp;HC = husk cover, PASP = plant aspect, EASP = ear aspect, ER = ear rot, EPP = number of ears per plant, Y = grain yield, MSV = maize streak virus, SCLR = Southern corn leaf blight, SCLB = Southern corn leaf rust, CLS = \u003cem\u003eCurvularia\u003c/em\u003e leaf spot. FAW = foliar FAW damage\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/3aeb3d3720efa943284ad7d7.png"},{"id":100358359,"identity":"0c1ac928-1b12-4d06-9adc-feaf71d25c07","added_by":"auto","created_at":"2026-01-16 07:20:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216419,"visible":true,"origin":"","legend":"\u003cp\u003eGYT biplot showing the relationship among the yield-trait combination of the 32 top-cross FAWR maize hybrids including the checks.\u003c/p\u003e\n\u003cp\u003eDP = number of days to anthesis, DS = number of days to silking, \u0026nbsp;ASI = anthesis-silking interval, PH = plant height, EH = ear height, \u0026nbsp;HC = husk cover, PASP = plant aspect, EASP = ear aspect, ER = ear rot, EPP = number of ears per plant, Y = grain yield, MSV = maize streak virus, SCLR = Southern corn leaf blight, SCLB = Southern corn leaf rust, CLS = \u003cem\u003eCurvularia\u003c/em\u003e leaf spot.\u003c/p\u003e\n\u003cp\u003eEntry 1-32, representing maize hybrid as presented on Table 1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/4c1efafa5573137ce55c92d7.png"},{"id":99882579,"identity":"bf446eee-6638-4a59-951a-92fcf436873f","added_by":"auto","created_at":"2026-01-09 11:38:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":176072,"visible":true,"origin":"","legend":"\u003cp\u003eWhich-won-where view of the GYT biplot for the 32 top-cross FAWR maize hybrids including the checks.\u003c/p\u003e\n\u003cp\u003eDP = number of days to anthesis, DS = number of days to silking, \u0026nbsp;ASI = anthesis-silking interval, PH = plant height, EH = ear height, \u0026nbsp;HC = husk cover, PASP = plant aspect, EASP = ear aspect, ER = ear rot, EPP = number of ears per plant, Y = grain yield, MSV = maize streak virus, SCLR = Southern corn leaf blight, SCLB = Southern corn leaf rust, CLS = \u003cem\u003eCurvularia\u003c/em\u003e leaf spot.\u003c/p\u003e\n\u003cp\u003eEntry 1-32, representing maize hybrid as presented on Table 1.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/6c54f9965729c4b30dd9e3d9.png"},{"id":99882546,"identity":"81606c4d-3912-4814-a3ce-fbf2b4f6025a","added_by":"auto","created_at":"2026-01-09 11:38:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116576,"visible":true,"origin":"","legend":"\u003cp\u003eThe Average Tester Coordination view of the genotype by yield*trait (GYT) biplot for the 32 top-cross FAWR maize hybrids, including the checks.\u003c/p\u003e\n\u003cp\u003eDP = number of days to anthesis, DS = number of days to silking, \u0026nbsp;ASI = anthesis-silking interval, PH = plant height, EH = ear height, \u0026nbsp;HC = husk cover, PASP = plant aspect, EASP = ear aspect, ER = ear rot, EPP = number of ears per plant, Y = grain yield, MSV = maize streak virus, SCLR = Southern corn leaf blight, SCLB = Southern corn leaf rust, CLS = \u003cem\u003eCurvularia\u003c/em\u003e leaf spot.\u003c/p\u003e\n\u003cp\u003eEntry 1-32, representing the maize hybrid as presented in Table 1.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/909a771084b64752636cbf9d.png"},{"id":100406314,"identity":"d0b56af1-1b4e-41a7-8e9c-19699dec6754","added_by":"auto","created_at":"2026-01-16 13:00:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3050385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/68870e04-215a-4b9a-b027-0cc4c9c8c6eb.pdf"},{"id":100358328,"identity":"456854a2-3418-4855-8f43-ffd4a29f49fd","added_by":"auto","created_at":"2026-01-16 07:20:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32416,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-8279943/v1/7c91deaf9b8e9701cc47f565.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Variation in the phenotypic performance of top-cross fall armyworm resistant maize hybrids under optimal growing conditions in a derived savanna agro-ecology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe fall armyworm (FAW), \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e (J. E. Smith) (Lepidoptera: Noctuidae), has been associated with substantial yield losses in cereal crops. This invasive species was first identified in sub-Saharan Africa (SSA) in 2016 (Goergen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a polyphagous and migratory pest, FAW is known to infest diverse economically significant crops with a particularly strong preference for maize (Harrison et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Maize (\u003cem\u003eZea mays\u003c/em\u003e L.) is an annual, diploid (2n\u0026thinsp;=\u0026thinsp;20), C4 monocot belonging to the family Poaceae. As an important staple crop, it provides feed, food, biofuel, starch, and glucose (Yadav et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), indicating its significance to food security in alignment with Sustainable Development Goal 2 (zero hunger). Maize is well-suited to a diverse range of agro-ecological environments worldwide (Bankole and Kolawole, 2023). The rising demand for maize is driven by population growth and climate change. In Nigeria, the average maize yields per hectare on farmers' fields in comparison to the global average are notably low at 1.8t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, as opposed to the world's average of 4.5t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Grote et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This wide disparity in grain yield gap is the result of multiple interacting factors, including biotic stresses such as \u003cem\u003eStriga hermonthica\u003c/em\u003e (Del.) Benth, foliar diseases, and insect pests like stem borers and fall armyworms; abiotic stresses such as drought, flooding, heat, and low soil fertility; and suboptimal agronomic practices. In particular, planting time plays a crucial role, as it influences crop exposure to stress and has been shown to correlate linearly with yield outcomes (Rodr\u0026iacute;guez-del-Bosque et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Buriro et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, limited access to improved seeds, high input costs, poor extension services, and other socioeconomic constraints further contribute to the consistently low productivity of maize (Durocher-Granger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the various biotic stresses affecting maize production, the FAW is one of the most destructive insect pests, causing estimated grain yield losses ranging from 12% to 58% (De Groote et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In severe cases, uncontrolled infestations have led to total crop failure (Israni et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), posing a significant threat to food security and the livelihoods of millions of smallholder farmers who depend on maize cultivation (Day et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Environmental factors, particularly rainfall amount and distribution during periods of FAW infestation, can cause yield losses by interfering with assimilate partitioning. Additionally, suboptimal planting dates, either too early or too late, have been shown to increase susceptibility to insect pest damage, including FAW, thereby reducing maize grain yield (Sanp and Singh, 2018).\u003c/p\u003e \u003cp\u003eGiven the increasing frequency of pest outbreaks and the unpredictability of climatic conditions, there is a growing need to adopt resilient adaptation strategies. One such strategy is the deployment of maize varieties with native resistance to emerging pests and diseases. According to Tarusikirwa et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), host plant resistance (HPR) to insect pests functions through three main mechanisms: antixenosis, which deters pest preference through plant traits; antibiosis, which negatively affects pest survival and reproduction; and tolerance, which enables the plant to maintain acceptable yields despite infestation. Notably, tolerance exerts minimal selection pressure on pest populations and, when integrated with biological and cultural control methods, enhances overall pest management efficacy. As a long-lasting, environmentally safe, and cost-effective strategy, HPR presents a promising avenue for sustainable control of FAW and other insect pests in maize.\u003c/p\u003e \u003cp\u003eSince the outbreak of FAW in SSA, researchers have screened maize germplasm under natural and artificial FAW infestation. Due to the high cost and expertise required for artificial infestation, studies often rely on natural infestation at FAW hotspots. Several maize inbred lines resistant to FAW have been developed and deployed (Kasoma et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prasanna et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, only a limited number of commercial maize hybrids with resistance to FAW are available in Africa (Moussa et al., 2023). Cultivating maize varieties with native resistance to FAW is an ecologically and economically sustainable strategy to prevent excessive grain yield losses (Prasanna et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In selecting FAW resistant varieties, it is essential to prioritize elite hybrids with enhanced agronomic traits and stable grain yield.\u003c/p\u003e \u003cp\u003eGiven the foregoing, this study was undertaken to (i) assess new top-cross fall armyworm resistant maize hybrids combining high grain yield and tolerance to FAW infestation (ii) estimate the effects of foliar FAW damage on grain yield, (iii) determine agronomic traits correlated with foliar FAW damage in maize hybrids and; (iv) identify superior maize hybrids based on genotype by yield \u0026times; trait (GYT) biplot method.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003ePlanting materials\u003c/p\u003e \u003cp\u003eThe new top-cross fall armyworm resistant (FAWR) maize hybrids evaluated in this study were developed by the Maize Improvement Programme (MIP) of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. A total of 32 FAWR maize hybrids, including commercial hybrids used as checks, were evaluated for their responses to FAW leaf damage under natural disease pressure using two planting dates as diverse environments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first trial was planted at the beginning of the cropping season (11th of June), which is normally characterized by a very limited amount of rainfall, and the second trial was planted at the peak of the early cropping season (27th of June), which is the normal rain-fed condition of the year, 2024. The range of the planting dates was selected to create contrasting environmental conditions for maize growth and development. Each location by planting date combination was considered as a separate test environment. Weather data (rainfall, air temperature, solar radiation, and relative humidity) between the planting and harvesting period were obtained from the weather station report provided by the Faculty of Agricultural Sciences of Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eEntry code and pedigree of top-cross FAWR maize hybrids and standard checks evaluated in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntry code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePedigree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003cp\u003eFAWSYN-2/TZISTR1129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2/TZISTR2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2/TZISTR1305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2/TZISTR2129-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3/TZISTR1869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3/TZISTR1872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3/TZISTR1878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3/IITATZI2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3/IITATZI2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-1/IITATZI2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2/IITATZI2300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-1/IITATZI2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2/IITATZI2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAWSYN-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIITA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAMMAZ 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOba Super 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOba Super 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOba Super 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOba Super 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommercial hybrid check\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eIITA\u0026thinsp;=\u0026thinsp;International Institute of Tropical Agriculture\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExperimental site\u003c/p\u003e \u003cp\u003eThe experiment took place at the Teaching and Research (T\u0026amp;R) farm of LAUTECH in Ogbomoso, Nigeria. Conducted from June to October 2024, the study included two planting dates, reflecting the typical maize planting periods in the area. The field trials were situated at a latitude of 8\u0026deg;17ʹN and a longitude of 4\u0026deg;28ʹE, with an altitude of 343 meters above sea level. The location experiences a bimodal rainfall pattern each year, accompanied by high relative humidity. The site receives an average annual rainfall of 1000\u0026ndash;1100 mm, and the yearly minimum and maximum temperatures average between 28 and 30\u0026deg;C. The soil at the site is generally low in nitrogen and has been identified as Alfisol (USAD, 1999).\u003c/p\u003e \u003cp\u003eExperimental layout, design, and cultural practices\u003c/p\u003e \u003cp\u003eThe experimental field underwent two rounds of ploughing, followed by harrowing with a tractor two weeks later to break down the soil. These steps were crucial before setting up the field layout and planting, as they helped manage weeds, improve soil aeration, facilitate root growth, and boost seed germination and emergence. The blocks were divided by 1.5-meter alleys. The experiment was organized in an 8 \u0026times; 4 α (0,1) lattice design (Patterson and Williams, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) with three replications. Each experimental plot comprised two rows, each 4 meters long, with a spacing of 0.75 meters between rows and 0.50 meters between plants within a row, resulting in 9 hills per row (with two plants per hill). The maize hybrids were planted by hand. Three seeds were sown per hill and thinned to two seedlings per stand two weeks after sowing, achieving a plant density of 53,333 plants per hectare. Two guard rows were planted on each side of the experimental field to shield the main maize hybrids being evaluated. Standard agronomic practices, including weeding and fertilization, were followed. The experiments were conducted under rain-fed conditions. The plants were left unprotected to allow for natural FAW infestation. No insecticide was used. On October 21, 2024, the maize hybrids were manually harvested once they reached physiological maturity, with the leaves turning yellow and brown, and the kernels having dried to a grain moisture content of less than 25%.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eConsidering each location by planting date as a separate test environment, phenotypic data were taken in two environments with three replications per environment. Data were recorded on a plot basis for the number of days to 50% anthesis (DP), 50% silking (DS), anthesis-silking interval (ASI), plant aspect (PASP), ear aspect (EASP), husk cover (HC), plant (PH) and ear height (EH), root and stalk lodging as described by Olayinka et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, foliar FAW damage (FFAWD) was scored at 4, 8, and 12 weeks after sowing (WAS). The presence of FAW was determined by visual assessment of active larvae, and FFAWD scores were the main indicators of the extent of FAW pressure. FFAWD was recorded following the modified Davis et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) rating scale (1\u0026ndash;9) as described by Matova et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Severity ratings of Southern corn leaf rust (SCLR), Southern corn leaf blight (SCLB), \u003cem\u003eCurvularia\u003c/em\u003e leaf spot (CLS) [\u003cem\u003eCurvularia lunata\u003c/em\u003e (Wakker) Boedijn], and Maize \u003cem\u003estreak\u003c/em\u003e virus (MSV) transmitted by \u003cem\u003eCicadulina\u003c/em\u003e leafhoppers were recorded using a scale of 1 to 5 (where 1\u0026thinsp;=\u0026thinsp;slight leaf infection, and 5\u0026thinsp;=\u0026thinsp;severe leaf infection). During harvest, ear rot (ER) was assessed by evaluating the number of ears exhibiting signs of infection, using a scale from 1 to 5: 1 indicates no damage and high resistance, 2 represents less than 15% damage and partial resistance, 3 signifies damage between 35% and 50%, indicating susceptibility, 4 denotes damage exceeding 60% but less than 100%, showing high susceptibility, and 5 corresponds to nearly complete damage, indicating high susceptibility. The total number of plants and ears was counted in each plot at the time of harvest. The number of ears per plant (EPP) was estimated as the ratio of the number of harvested ears per plot to the number of plants at harvest in a plot. Grain moisture content was measured using a digital grain moisture content tester. Grain yield (Y) was computed from the ear weight and converted to kg/ha. A shelling percentage of 80% (800 g grain kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ear weight) was assumed for all hybrids, and the grain yield was adjusted to 15% moisture content (150 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e moisture) using the formula described by Carangal et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1971\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Grain\\:yield\\:\\left(kg/ha\\right)=ear\\:weight\\:\\left(kg/plot\\right)\\times\\:\\frac{100-MC}{85}\\:\\times\\:\\frac{\\text{10,000}}{plot\\:area\\:\\left({m}^{2}\\right)}\\:\\times\\:0.80$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: MC\u0026thinsp;=\u0026thinsp;moisture content (%) in grains at harvest, 85\u0026thinsp;=\u0026thinsp;percentage dry matter used to adjust for 15% grain moisture content, 10000\u0026thinsp;=\u0026thinsp;total land area (m\u003csup\u003e2\u003c/sup\u003e) of a hectare, and 0.80\u0026thinsp;=\u0026thinsp;80% shelling percentage\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data from the field experiments were entered into Microsoft Excel 2019 and analysed statistically. The data were subjected to Analysis of Variance (ANOVA) on plot mean basis using the General Linear Model (GLM) procedure of Statistical Analysis System (SAS) version 9.4 (SAS Institute, 2011) to determine hybrid effects on a linear statistical model and enable separation of the variance components (Gomez and Gomez, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The location by planting date combination was considered an environment. Data were analysed separately for each environment to assess the significance of different factors, and Bartlett\u0026rsquo;s test was used to check the homogeneity of error variances before combining the data for further analysis (Steel and Torrie, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Analysis of variance for combined data was used to determine the genotype \u0026times; environment (G \u0026times; E) interaction. The following linear model was used for the combined analysis:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{y}_{ijkl}=\\:\\mu\\:\\:+{r}_{j}{B}_{k}+{E}_{i}+{G}_{l}+{GE}_{il}+{\\epsilon\\:}_{ijkl}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Y\u003csub\u003eijkl\u003c/sub\u003e = is the observed value of the response variable; \u0026micro;\u0026thinsp;=\u0026thinsp;grand mean; r\u003csub\u003ej\u003c/sub\u003e(B)\u003csub\u003ek\u003c/sub\u003e = effect of the kth block nested in jth replication; E\u003csub\u003ei\u003c/sub\u003e = the effect of the ith environment; G\u003csub\u003el\u003c/sub\u003e = the effect of the lth hybrid; GE\u003csub\u003eil\u003c/sub\u003e = interaction effect of lth hybrid evaluated in the ith environment, and ɛ\u003csub\u003eijkl\u003c/sub\u003e = random experimental error.\u003c/p\u003e \u003cp\u003eIn the combined ANOVA, the blocks within replications and the environments were considered as random factors, while the maize hybrids were considered fixed effects. The significance of the mean squares for the main and interaction effects was tested using the appropriate mean squares obtained from the aforementioned procedure. Coefficients of variation (CV) and determination (R\u003csup\u003e2\u003c/sup\u003e) values, both in percentage, were used to measure the reliability of the statistical model of ANOVA. To further assess the differences among maize hybrid means, significantly different means were separated using Tukey's Honestly Significant Difference (HSD) test at a 0.05 probability level (Tukey, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1953\u003c/span\u003e). This post-hoc test provided a detailed comparison of the maize hybrid means, identifying those that are statistically significantly different. Boxplot for every trait was generated using an online web application at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.statskingdom.com/boxplot-maker.html\u003c/span\u003e\u003cspan address=\"https://www.statskingdom.com/boxplot-maker.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. To identify/select superior of top-cross FAWR maize hybrids, the mean grain yield and other measured agronomic traits were used to generate genotype by yield \u0026times; trait (GYT) biplot according to Yan and Fr\u0026eacute;geau-Reid (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To account for varying units of traits, standardization was applied so that the mean for each yield-trait combination becomes 0 and the variance becomes 1 as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Z=\\:\\frac{X\\:-\\mu\\:}{\\sigma\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Z\u0026thinsp;=\u0026thinsp;standard score, X\u0026thinsp;=\u0026thinsp;initial trait value, \u0026micro;\u0026thinsp;=\u0026thinsp;mean of the trait value, and σ\u0026thinsp;=\u0026thinsp;standard deviation of the trait value. R statistical software was used for graphical analysis.\u003c/p\u003e \u003cp\u003ePearson's correlation analysis was computed to determine associations among all traits measured using the 'metan' package in R statistical software (Olivoto and L\u0026uacute;cio, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Multiple Linear Regression Model (MLRM) was used to establish the linear relationship between dependent and independent variables according to Montgomery et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) using PROC REG in SAS. The maximum likelihood was used to estimate the parameters of the regression model, and the general linear regression model was tested by ANOVA. The general linear model for MLRM, in which the response is related to a set of independent variables (X\u003csub\u003e1\u003c/sub\u003e), is given:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Y={\\alpha\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}\\:+\\dots\\:+\\:{\\beta\\:}_{k}{X}_{k}\\:+\\:{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Y\u0026thinsp;=\u0026thinsp;dependent variable, α\u003csub\u003e0\u003c/sub\u003e is the intercept, β\u003csub\u003e1\u003c/sub\u003e, β\u003csub\u003e2\u003c/sub\u003e\u0026hellip; β\u003csub\u003ek\u003c/sub\u003e are coefficients of the variables, X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e \u0026hellip; X\u003csub\u003ek\u003c/sub\u003e are kth independent variables, and ε\u003csub\u003ei\u003c/sub\u003e is the error term.\u003c/p\u003e \u003cp\u003eThe variance components and heritability estimates for each trait were computed using R statistical software version 4.2.2 (R core team, 2024).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariation among top-cross FAWR maize hybrids\u003c/h2\u003e \u003cp\u003eRainfall during the experimental period was unevenly distributed, with higher amounts recorded in the later months, particularly in October, which had a mean rainfall of 7.9 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The lowest rainfall was observed in July and August, with mean values ranging from 3 to 3.9 mm. The highest mean temperatures of 26.8\u0026deg;C were recorded in May and October. Sunshine intensity was lower in July and August, ranging from 7.4 to 7.9 MJ/m\u0026sup2;/day, while relative humidity peaked in July and August, with values of 93.8% and 92.5%, respectively.\u003c/p\u003e \u003cp\u003eAnalysis of variance results revealed significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differences between environments for grain yield and most measured traits, except for plant height and foliar diseases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean squares for the hybrids were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for grain yield, husk cover, plant aspect, number of days to silking and anthesis, Southern corn leaf rust, and blight. Ear rot, plant, and ear heights were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The hybrids were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the number of ears per plant. The hybrid mean squares were not significantly different for anthesis-silking interval, ear aspect, maize streak virus, and \u003cem\u003eCurvularia\u003c/em\u003e leaf spot. Hybrid \u0026times; Environment interaction mean squares were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for husk cover, plant aspect, number of days to anthesis and silking, and highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for grain yield. The sum of squares for the environmental effect represented 54% of the grain yield variation. The differences between the hybrids explained 21% of the total grain yield variation, while the effects of the Hybrid \u0026times; Environment interaction explained 11%. The coefficient of variation (CV), which determines the precision of the experiment, showed data reliability in that the CV for all agronomic traits, excluding grain yield and foliar disease traits, ranged from 2.6 to 17.7%.\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\u003eCombined mean squares for measured traits of top-cross FAWR maize hybrids and checks evaluated at LAUTECH T\u0026amp;R FARM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment (Env)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReplication:\u003c/p\u003e \u003cp\u003eRep (Env)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003cp\u003e(Rep \u0026times; Env)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003eEnv\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf =\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of days to anthesis (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.33*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of days to silking (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.12*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.19*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthesis-silking interval (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e656.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3754.76***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e383.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e363.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e188.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8303.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e589.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.21***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150.04**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusk cover (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e72.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar rot (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.80***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ears per plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e74.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaize streak virus (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e49.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern corn leaf rust (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern corn leaf blight (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e73.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCurvularia\u003c/em\u003e leaf spot (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e62.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 4 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 8 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180.19***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.13***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e84.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 12 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield (kg/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260640373.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8580273.70***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e884794.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3233288.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1695309.10***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e741767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Contribution to grain yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*, **, and *** denote significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Coefficient of determination, CV\u0026thinsp;=\u0026thinsp;coefficient of variation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eFFAWD 4 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 4 weeks after sowing, FFAWD 8 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 8 weeks after sowing, FFAWD 12 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 12 weeks after sowing\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMean performance for grain yield, agronomic traits, foliar FAW damage, and diseases of top-cross FAWR maize hybrids\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe environmental effects were clearly observed on the variability in performance of grain yield, foliar diseases, FFAWD, and other agronomic traits of the top-cross FAWR maize hybrids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The two trials planted in different environments, hereafter referred to as E1 and E2, showed significant differences in magnitude among all the traits measured. For example, ASI ranged from 1to 2 days in the E1 trial, while it was 3 to 7 days in the E2 trial. Plant and ear heights mean as well as maximum and minimum range, varied across the environments. The maximum range was different; however, the mean plant heights were similar. The overall phenotypic appeal of the plant (plant and ear aspect, husk cover, ear rot ratings) was different in E1 and E2 and showed a wide range for these traits. Grain yield ranged from 715.4 to 3241.2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in E1, while it was 901.8 to 7205.3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in E2. Note that FFAWD scores at 8 and 12 weeks intervals after sowing were similar, but the values differed at 4 weeks. The foliar diseases (SCLR, SCLB, MSV) scores were at par in E1 and E2; however, the minimum and maximum range were different in CLS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross the environment, grain yield ranged from 1502.7 kg ha⁻\u0026sup1; (G32: Oba Super 2) to 5223.3 kg ha⁻\u0026sup1; (G1: FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B). The highest-yielding hybrid was G1 with 5223.3 kg ha⁻\u0026sup1;, followed closely by G9 (4910.5 kg ha⁻\u0026sup1;) and G21 (4360.8 kg ha⁻\u0026sup1;), and they were not statistically different. The top-yielding hybrid was significantly superior to 44% of the evaluated hybrids, including the checks, according to the HSD test. Among the commercial hybrids used as checks, only G31: Oba super 9 (3823.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ) was comparable to the top-yielding hybrids. Grain yield of the other checks was statistically low (Supplementary Table\u0026nbsp;1). A few of the evaluated hybrids outperformed the checks, with a 22% higher mean grain yield. They showed 10% improvement in husk cover, 13% and 8% better ratings for plant and ear aspects, respectively, and 9% better resistance to ear rot. The hybrids also exhibited 5% better resistance to Southern corn leaf blight and 12\u0026ndash;35% improved FFAWD ratings at 4, 8, and 12 WAS. The mean FFAWD ratings at 4, 8, and 12 WAS were below 6 for all evaluated top-cross FAWR maize hybrids and the checks. Variability among hybrids was minimal at 4 WAS, with most scoring low (1\u0026ndash;3). FFAWD scores generally decreased over time. Two hybrids, namely FAWSYN-1/TZISTR1305 (G7) and FAWSYN-3/IITATZI2305 (G19), had consistent performance with low rating across the weeks and were among the top five hybrids with no visible damage and or few short holes on several leaves. Interestingly, the commercial hybrids used as checks (Oba Super 2 and 9) produced higher ratings across the weeks, with several leaves having short holes and a few long lesions. Among the evaluated maize hybrids, FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1), with the highest grain yield potential, showed tolerance to FAW (average FFAWD\u0026thinsp;=\u0026thinsp;2) according to the Davies scoring scale. Within the commercial hybrids used as checks, SAMMAZ 51(G27) was the best in terms of FAW tolerance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariance components and broad-sense heritability estimate\u003c/h3\u003e\n\u003cp\u003eThe phenotypic variance (σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e) was separated into genotypic (σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e) and environmental variances (σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e) to estimate the contribution of each to the total variation observed. The σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e were larger than the σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e; consequently, the σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e was higher than the σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e for all traits. The highest σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e and σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e were observed for grain yield (3054482.89 and 211188.48), followed by plant height (310.79 and 56.45), ear height (170.62 and 10.63), and number of days to silking (8.18 and 3.67), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moderate σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e and σ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e were obtained from the number of days to silking. Conversely, other traits measured exhibited very low variation. The highest phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were found for anthesis-silking interval (65.93 and 37.11). In this study, the PCV estimates were higher than the GCV estimates. The Southern corn leaf blight and rust, number of days to anthesis and silking had the highest broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) estimates ranging from 42 to 55%. Ear rot and ear aspect scores had the least H\u003csup\u003e2\u003c/sup\u003e estimates.\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\u003eComponents of variance and heritability estimates for grain yield, foliar disease, FAW leaf damage and other agronomic traits in top-cross FAWR maize hybrids FFAWD 4 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 4 weeks after sowing, FFAWD 8 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 8 weeks after sowing, FFAWD 12 WAS\u0026thinsp;=\u0026thinsp;foliar fall armyworm damage at 12 weeks after sowing, SE\u0026thinsp;=\u0026thinsp;standard error, σ2g\u0026thinsp;=\u0026thinsp;genotypic variance, σ2e\u0026thinsp;=\u0026thinsp;environmental variance (variance of error mean square), σ2p\u0026thinsp;=\u0026thinsp;phenotypic variance, PCV\u0026thinsp;=\u0026thinsp;phenotypic coefficient of variation, GCV\u0026thinsp;=\u0026thinsp;genotypic coefficient of variation, H2\u0026thinsp;=\u0026thinsp;broad sense heritability.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of days to anthesis (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of days to silking (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthesis-silking interval (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.18\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e254.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e310.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.91\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e159.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e170.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHusk cover (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar rot (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of ears per plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3379.73\u0026thinsp;\u0026plusmn;\u0026thinsp;973.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e412.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7650.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e211188.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2843294.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3054482.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize streak virus (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern corn leaf rust (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern corn leaf blight (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCurvularia\u003c/em\u003e leaf spot (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 4 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 8 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFAWD 12 WAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePhenotypic correlations between foliar FAW damage and grain yield and yield-related traits across environments\u003c/h3\u003e\n\u003cp\u003eAcross the environments, FFAWD had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) negative association (r = -0.36) with grain yield performance, husk cover (r = -0.49), and plant aspect (r = -0.41) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Ear rot had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) negative correlation with the number of days to anthesis (r = -0.45) and silking (r = -0.40). \u003cem\u003eCurvularia\u003c/em\u003e leaf spot had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) positive correlation with plant (r\u0026thinsp;=\u0026thinsp;0.48) and ear (r\u0026thinsp;=\u0026thinsp;0.44) heights. Plant height positively and significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) correlated with grain yield (r\u0026thinsp;=\u0026thinsp;0.61) and ear height (r\u0026thinsp;=\u0026thinsp;0.64). Husk cover (r = -0.47) and plant aspect (r = -0.66) correlated significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) negatively with grain yield, likewise ear height. Southern corn leaf rust had a positive association with ear rot. Similarly, ear rot had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) positive correlation with ear aspect (r\u0026thinsp;=\u0026thinsp;0.54), but a negative association with flowering traits. Southern corn leaf blight was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) negatively associated with plant aspect (r = -0.46). Highly significant positive relationships were observed between husk cover and plant aspect (r\u0026thinsp;=\u0026thinsp;0.74), number of days to anthesis and silking (r\u0026thinsp;=\u0026thinsp;0.95), anthesis-silking interval with number of days to silking(r\u0026thinsp;=\u0026thinsp;0.42), and \u003cem\u003eCurvularia\u003c/em\u003e leaf spot (r\u0026thinsp;=\u0026thinsp;0.39).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe stepwise multiple regression analysis highlights significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) relationships between various traits of interest (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) value for the regression trend ranged from 11 to 42%. Plant aspect exhibited the highest R\u003csup\u003e2\u003c/sup\u003e. The FFAWD score explains 25% of the variability in husk cover score, with a unit increase in FFAWD score resulting in a 0.48 increase in husk cover score. Similarly, 17% of the variability in plant aspect score is explained by the FFAWD score, where a unit increase leads to a 0.45 increase in plant aspect score. For the ear aspect, FFAWD score accounts for 12% of its variability, with a unit increase causing a 0.26 increase in ear aspect score. Interestingly, the FFAWD score also explains 13% of the grain yield; however, in this case, a unit increase in FFAWD score reduces grain yield by 863.58 kg ha⁻\u0026sup1;.\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\u003eRegression statistics for grain yield (y) relative to related traits (x) and other measured traits relative to fall armyworm leaf damage score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression line equation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDependent variable: Grain yield\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = -4084.33\u0026thinsp;+\u0026thinsp;53.63X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEar height (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = -2093.52\u0026thinsp;+\u0026thinsp;92.91X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusk cover (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 6864.59\u0026ndash;1147.87X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 7832.71\u0026ndash;1402.74X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern corn leaf rust (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 2402.95\u0026thinsp;+\u0026thinsp;582.43X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern corn leaf blight (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 2367.80\u0026thinsp;+\u0026thinsp;369.37X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndependent variable: FFAWD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusk cover (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 1.87\u0026thinsp;+\u0026thinsp;0.48X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 2.08\u0026thinsp;+\u0026thinsp;0.45X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEar aspect (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 2.23\u0026thinsp;+\u0026thinsp;0.26X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield (kg/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eŶ = 5464.47\u0026ndash;863.58X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;coefficient of determination, FFAWD\u0026thinsp;=\u0026thinsp;foliar FAW damage\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eStandardized genotype \u0026times; yield \u0026times; trait value and mean superiority index obtained from mean performance for grain yield and agronomic traits of top-cross FAWR maize hybrids and checks across environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENTRY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eASI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY*\u003c/p\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eY*\u003c/p\u003e \u003cp\u003eEH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003ePASP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eEASP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eY*\u003c/p\u003e \u003cp\u003eEPP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eSCLB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eSCLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eCLS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eF8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eY/\u003c/p\u003e \u003cp\u003eF12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eMean S.I.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\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\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\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\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\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\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\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\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45\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\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\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\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\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\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.34\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\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG23\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\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.58\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\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG11\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\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG13\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-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG29\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\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.18\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-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.93\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\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eDP\u0026thinsp;=\u0026thinsp;number of days to anthesis, DS\u0026thinsp;=\u0026thinsp;number of days to silking, ASI\u0026thinsp;=\u0026thinsp;anthesis-silking interval, PH\u0026thinsp;=\u0026thinsp;plant height, EH\u0026thinsp;=\u0026thinsp;ear height, HC\u0026thinsp;=\u0026thinsp;husk cover, PASP\u0026thinsp;=\u0026thinsp;plant aspect, EASP\u0026thinsp;=\u0026thinsp;ear aspect, ER\u0026thinsp;=\u0026thinsp;ear rot, EPP\u0026thinsp;=\u0026thinsp;number of ears per plant, Y\u0026thinsp;=\u0026thinsp;grain yield, SCLR\u0026thinsp;=\u0026thinsp;Southern corn leaf blight, SCLB\u0026thinsp;=\u0026thinsp;Southern corn leaf rust, CLS\u0026thinsp;=\u0026thinsp;\u003cem\u003eCurvularia\u003c/em\u003e leaf spot, F4, 8, 18\u0026thinsp;=\u0026thinsp;foliar FAW damage scored at 4, 8 and 12 week intervals after sowing respectively. SI\u0026thinsp;=\u0026thinsp;superiority index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"18\"\u003eEntry G1-G32\u0026thinsp;=\u0026thinsp;top-cross fall armyworm resistant maize hybrids and checks evaluated\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGrain yield is influenced by several additional factors. Plant height accounts for 37% of the variability in grain yield, with a unit increase in plant height resulting in a 53.63 kg ha⁻\u0026sup1; increase. Ear height explains 41% of the variability, where a unit increase raises grain yield by 92.91 kg ha⁻\u0026sup1;. Conversely, husk cover score, which explains 22% of grain yield variability, shows a negative association; a unit increase in husk cover score reduces grain yield by 1147.87 kg ha⁻\u0026sup1;. Similarly, plant aspect score accounts for 42% of the variability in grain yield, with a unit increase decreasing yield by 1402.74 kg ha⁻\u0026sup1;. Two foliar diseases, Southern corn leaf rust and blight, also impact grain yield, although the R\u003csup\u003e2\u003c/sup\u003e values suggest weak explanatory power.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenotype by yield \u0026times; trait (GYT) biplot\u003c/h2\u003e \u003cp\u003eSignificant Hybrid \u0026times; Environment interaction and its high contribution to total variation for grain yield necessitate the use of GYT to identify promising hybrids. The GYT biplot displays about 82% (PC1 74.4% and PC2 7.7%) of the total variations explained among the traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For clarity, on the biplot, entry number corresponding to each hybrid was used for ease of graphical presentation. The trait profile of the hybrids (the weakness and strength of the genotypes) was observed. The acute angle between the vectors of the hybrids shows their value. Accordingly, FAWSYN-2/(TZLComp. 1 C6-W-39-1-1)-B-B (G9) and FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) showed higher Y \u0026times; ER and Y \u0026times; CURV, FAWSYN-2/IITATZI2300 (G21) and FAWSYN-3/IITATZI2300 (G18) showed higher Y \u0026times; RUST, Y \u0026times; EASP, Y \u0026times; PASP, FAWSYN-1/IITATZI2300 (G20) showed higher Y \u0026times; ASI, Y \u0026times; DP, Y \u0026times; DS Y \u0026times; EPP, FAWSYN-1/IITATZI2305 (G22) showed higher Y \u0026times; BLIGHT, FAWSYN-3/TZISTR1878 (G17) and FAWSYN-1/TZISTR1878 (G3) showed higher Y \u0026times; STREAK.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the \u0026ldquo;which won where\u0026rdquo; analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the perpendicular lines divided the polygon into seven sectors, the vertex hybrids in each sector are indicated by the polygon peaks, and hybrids that were desirable for a GYT were found in its sector as a group. Thus, entries G1, G9, G15, G18, G21, G27 and G31 were closely correlated with Y \u0026times; DP, Y \u0026times; DS, Y \u0026times; EH, Y \u0026times; PH, Y \u0026times; HC, Y \u0026times; PASP, Y \u0026times; EASP, Y \u0026times; ER, Y \u0026times; EPP, Y \u0026times; RUST, Y \u0026times; CURV. Entries G17, G20, and G22 were closely correlated with Y \u0026times; BLIGHT, and entries G4, G10, G14, and G23 were closely correlated with Y \u0026times; STREAK. Other hybrids and checks evaluated did not correlate with any GYT combinations. Out of the seven polygons, only three had traits in their sectors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Average Tester Coordination (ATC) view of the GYT biplot includes a small circle (abscissa) within the biplot, representing the average of yield\u0026ndash;trait combinations. A line, called the Average Tester Axis (ATA), passes through the origin of the biplot and the point corresponding to the average yield\u0026ndash;trait combinations. This ATA is used to rank maize hybrids based on their overall superiority. Hybrids positioned close to the ATA tend to exhibit balanced trait profiles, while those farther from the ATA, in either direction, demonstrate pronounced strengths or weaknesses in specific traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The outstanding hybrids identified were G1\u0026thinsp;\u0026gt;\u0026thinsp;G9\u0026thinsp;\u0026gt;\u0026thinsp;G21\u0026thinsp;\u0026gt;\u0026thinsp;G18\u0026thinsp;\u0026gt;\u0026thinsp;G20 whereas; G32, G28, G24, G5 and G26 were identified as the poorer hybrids. These results were confirmed by the superiority index generated from the yield-trait combinations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Entries 1 and 9 had desirable lower ratings for ear rot, Southern corn leaf rust, \u003cem\u003eCurvularia\u003c/em\u003e leaf spot, husk cover, ear, and plant aspects. Entries G21, GG18, and 20 were good in number of ears per plant, ear, and plant heights. Other entries, such as G22, G17, G4, and G3, were good in Southern corn leaf blight, maize streak virus, anthesis-silking interval, number of days to anthesis and silking.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eInsufficient maize production, largely caused by FAW infestations, jeopardizes the livelihoods of smallholder farmers and hinders the Sustainable Development Goals' hunger reduction efforts. The cultivation of maize hybrids with native resistance reduces FFAWD and mitigates grain yield losses (Kamweru et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study evaluated top-cross FAWR maize hybrids across environments under natural FAW infestation for their resistance to FAW and potential grain yield performance. Due to the erratic rainfall pattern experienced, the FAW infestation pressure in both environments was sufficient to cause a differential response across maize hybrids.\u003c/p\u003e \u003cp\u003eThe significant mean squares of hybrid for grain yield and other traits measured indicate the presence of variation among the evaluated maize hybrids, suggesting that superior hybrids could be identified, selected, and recommended for commercial evaluation. The differences among the hybrids could also be attributed to the genetic background of the parental lines. Similar findings had been observed among maize hybrids for grain yield and other traits (Bocianowski et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For grain yield, assessment of the total sum of squares revealed that the environmental sums of squares accounted for 54% of the variations observed, with the hybrid contributing 21%, highlighting greater environmental influence and variability. This aligns with findings from multi-environment trials in SSA (Sserumaga et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eze et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Significant hybrid \u0026times; environment interactions for grain yield, flowering traits, and phenotypic appeal of the plant indicated that the maize hybrids performed differently across the test environments and suggested a low level of stability. Mafouasson et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Boreddy et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported significant mean squares of G \u0026times; E interaction for grain yield and other agronomic traits. The differences among the environments in soil fertility, rainfall, relative humidity, and temperature affected the FAWR maize hybrids stability. This depicts the need for evaluation in several locations and over several seasons in order to ascertain adaptation and stability of the newly developed top-cross FAWR maize hybrids. Similarly, Zebire et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported significant environmental effects on maize grain yield and other agronomic traits. Moreover, hybrid \u0026times; environment interaction lacked significant mean squares for ASI, ear aspect, ear rot, number of ears per plant, foliar diseases, ear and plant heights, indicating stability across environments. The coefficients of variation (CV) for all agronomic traits were below 20%, signifying low experimental error. Similar findings were reported by Maphumulo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Kolawole and Olayinka (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where secondary traits consistently showed CVs under 20%, indicating reliable trial performance. Other CVs greater than 20% can be treated as high, indicating high experimental error, for ASI, foliar diseases, and FFAWD. The high CV obtained for ASI may be due to the method of calculation, where the negative values reduced the mean without affecting the variance. For foliar diseases and FFAWD, high CV likely reflects inter-environment variability (Ramos Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe HSD (0.05) revealed that the hybrids were different, given an opportunity to select outstanding maize hybrids. The boxplot analysis revealed variability in means among FAWR maize hybrids within each environment, with trait magnitudes highlighting hybrids with extreme performance. Differences in mean values across environments confirmed the impact of environmental conditions on hybrid performance, aligning with ANOVA results. These results emphasize the need to consider environmental factors when evaluating maize hybrid performance for quantitative traits. The relative grain yield differed significantly between the two environments (E1 and E2), with a gap of 2330.2 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This high disparity was related to the prolonged drought between July and August, particularly during the periods before anthesis and following silking, resulting in to delay in ASI. This led to lower yields compared to the findings of Manjunatha et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported approximately 6\u0026ndash;11 ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of grain yield. However, grain yields reported in this study were comparable to those of Vah et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who also evaluated maize hybrids across different environmental conditions. The sensitivity of maize to drought during the seedling stage, flowering, and grain-filling periods, coupled with high FAW pressure, was mainly responsible for the low yield recorded in E1. Traits that reflect internode elongation (plant and ear heights) did not respond similarly. They showed different trends for E1 and E2. The frequent rainfall after planting in E1 made the plant significantly taller; ear height, being determined earlier in the plant's development than plant height, was consistently higher. When the July-August drought occurred, the plants in E2 became progressively shorter.\u003c/p\u003e \u003cp\u003eAcross the environments, the hybrids exhibited significant variation across most measured traits, providing selection opportunities. Grain yield ranged from 1502.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (G32: Oba Super 2) to 5223.3 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (G1: FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B), with a mean of 3379.7 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. This was consistent with the findings of Kolawole and Olayinka (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who evaluated maize hybrid in a similar environment. FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) had a 26.8% yield advantage compared to the best check, Oba Super 9 (G31). Relative to the average grain yield, 47% of the maize hybrids evaluated surpassed the average grain yield, while only one of the checks (G3: Oba Super 9) had comparable performance. FFAWD scores were recorded three times, beginning in the fourth week after sowing. Initial observations revealed high infestation levels, which diminished as the maize hybrids matured, aligning with the findings of Sebayang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The level of FAW resistance of the evaluated maize hybrids translated to the difference in grain yield observed. Most of the evaluated maize hybrids exhibited high to moderate resistance to FAW damage, demonstrating their potential resilience against FAW herbivory (Asare et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The lowest score was recorded in FAWSYN-3/IITATZI2305 (G19) and FAWSYN-1/TZISTR1305 (G7), and the highest score was observed in Oba Super 2 (G32) and Oba Super 9 (G31). All commercial hybrids used as checks had a similar response to FAW, except for SAMMAZ 51(G27).\u003c/p\u003e \u003cp\u003ePhenotypic variances exceeded genotypic variances for all traits, indicating a significant influence of environmental factors and genotype \u0026times; environment interactions on trait expression. Lower σ\u0026sup2;g compared to σ\u0026sup2;e for grain yield and other traits resulted in low (0\u0026ndash;27%) to moderate (32\u0026ndash;55%) H\u0026sup2; estimates for all traits measured. This suggests that genetic differences contribute only a small proportion of the phenotypic variation in ear and plant heights, husk cover, plant aspect, ear aspect, ear rot, number of ears per plant, grain yield, maize streak virus, \u003cem\u003eCurvularia\u003c/em\u003e leaf spot and FFAWD with σ\u0026sup2;e being the primary influence on the observed expression of these traits (Falconer and Mackay, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lynch and Walsh, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). These results may not be repeatable, as genetic variation was minimal for most traits. Previous studies (Muliadi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Matova et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported higher H\u0026sup2; for maize hybrid grain yield. These differences can be explained by the differences in the genetic materials evaluated and the environments tested. Conversely, the moderate H\u0026sup2; for traits such as number of days to anthesis and silking, anthesis-silking interval, and Southern corn leaf rust and blight imply a stronger genetic influence. Higher PCV values compared to GCV indicate a significant environmental impact on trait expression, as confirmed by the high environmental main effects in the ANOVA. A similar observation was reported by Simelane et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistically significant correlated traits should be prioritized in maize breeding to address multiple yield-limiting factors simultaneously. The significant negative correlation coefficients (r) between grain yield and FFAWD, plant aspect, and husk cover scores could impact grain yield positively. Previous studies reported negative correlations between grain yield and FFAWD (Moussa et al., 2023; Kamweru et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and also between grain yield and husk cover (Muliadi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The direct consequence of FAW feeding on leaf tissues is the reduction of the photosynthetic capacity of the plant, which is essential for grain production. The positive and significant association among FFAWD, plant aspect, and husk cover suggests that any of these traits could be used to predict the other. Poor husk coverage exposes the cobs to pests, diseases, and environmental stress, ultimately reducing grain yield, and plants with poor overall phenotypic appeal are less productive. Therefore, lower scores for these traits are desirable as they could lead to increased grain yield. On the other hand, grain yield showed strong positive correlations with ear and plant heights, likely due to increased dry matter accumulation from a higher leaf count in taller plants (Nzuve et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As a physiological process, plant height was highly correlated with ear height. The association between grain yield with plant and ear heights was reported by Vah et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The simultaneous occurrence of high foliar disease and grain yield suggests that the evaluated top-cross FAWR maize hybrids possess tolerance mechanisms or disease escape traits, allowing them to sustain productivity under both erratic rainfall and high disease pressure environments.\u003c/p\u003e \u003cp\u003eThe significant effects of other traits on grain yield indicate that the correlation results align with the regression findings, corroborating the report of Maphumulo et al (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Traits with R\u003csup\u003e2\u003c/sup\u003e greater than 20% had a significant direct contribution to the yield of hybrids, whereas traits with R\u003csup\u003e2\u003c/sup\u003e less than 20% with significant associations had less direct influence on grain yield, but cannot be ignored, because their cumulative contribution to grain yield could be highly influential. From the regression models, plant aspect was the most significant factor influencing grain yield, followed closely by ear and plant heights. This result emphasizes how crucial plant aspect ratings are in determining grain yield in maize breeding programmes. The overall phenotypic appeal of the plant encompasses vigorous growth, resistance to lodging, minimal leaf defoliation, and absence of disease symptoms, contributing to higher grain yield (Kolawole et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ear and plant heights are also important traits because taller plants typically possess a larger leaf area, which boosts photosynthetic efficiency and biomass accumulation, ultimately resulting in higher yields. FAW infestation significantly affects husk cover, grain yield, and ear traits in maize hybrids, highlighting its negative impact on yield-determining traits. The direct impact of FAW on maize plants results from FAW larvae feeding on leaves and whorls, reducing photosynthetic efficiency and impairing assimilate translocation for vegetative growth, cob formation, and grain filling. Older larvae further damage maize ears and kernels by burrowing into cobs, increasing susceptibility to secondary infections (Anjorin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrain yield is a genetically complex trait influenced by the combined effects of multiple agronomic factors, so the direct selection of grain yield is usually ineffective (Muliadi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, phenology and growth-related traits are simpler with moderate to high heritability traits and lower susceptibility to G \u0026times; E interaction (Blancon et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Considering multi-trait selection indices such as the GYT that balance productivity, pest/disease resistance, and overall plant phenotypic appeal. The high variance explained by the first two PCs confirms the biplot's effectiveness in depicting relationships among traits (Hosseini et al., 2025). The GYT biplot ranked the maize hybrid based on its worth in combining grain yield with other desirable traits, alongside a comprehensive visualization of the weaknesses and strengths of the hybrids. The top 10 maize hybrids identified by the GYT superiority index confirm the efficiency of the GYT biplot, as the top-cross FAWR maize hybrids outperform the standard checks evaluated. According to Yan and Fr\u0026eacute;geau-Reid (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the geometry of the biplot indicates that the hybrid at a vertex has the largest values for the yield-trait combinations within the corresponding sector. Thus, FAWSYN-1/(TZLComp. 1 C6-W-39-1-1)-B-B (G1) and closely placed FAWSYN-2/(TZLComp. 1 C6-W-39-1-1)-B-B (G9) were the best in combining grain yield with desirable flowering, growth, overall phenotypic appeal, and foliar disease tolerance. These hybrids also had balanced trait profiles because of their short projections to the ATA. FAWSYN-3/TZISTR1878 (G17) had a contrasting trait profile but was high-yielding. Previous studies (Faheem et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hassani et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including this one, highlight the GYT biplot as a valuable tool for insightful genotype analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research faced limitations due to unpredictable rainfall patterns, including rainfall cessation after planting, which likely caused moisture stress during crucial early growth phases, and excessive rainfall after maize reached maturity, leading to lodging and affecting yield evaluations. Despite these challenges, the study identified genetic variability among the FAWR maize hybrids tested. The environment significantly impacted the phenotypic expression of hybrids, potentially affecting trait expression and hybrid rankings. Nonetheless, a few hybrids demonstrated high grain yield performance across different environments. Grain yield losses linked to FFAWD and poor plant traits directly result in financial losses for farmers. Preventative measures, such as cultivating the identified FAWR maize hybrids, can improve profitability and production sustainability in the derived savanna agro-ecology. Therefore, the top-cross FAWR maize hybrids identified in this study hold significant potential to reduce the impact of FAW damage. Further testing and validation are suggested to confirm their performance across various environments and management conditions. The findings of this study could benefit smallholder farmers by providing new top-cross FAWR maize hybrids tailored to specific locations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003efall armyworm (FAW), fall armyworm resistant (FAWR), genotype by yield × trait (GYT), \u0026nbsp;sub-Saharan Africa (SSA) \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003e \u003cem\u003eEthics approval and consent to participate\u003c/em\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cem\u003eConsent for publication\u003c/em\u003e:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was supported by the 2024 Nigeria Tertiary Education Trust Fund (TETFund) Institution-Based Research (IBR) Intervention, awarded to Dr. Adesike Oladoyin Olayinka of the Department of Crop Production and Soil Science, Faculty of Agricultural Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Olayinka, Adesike Oladoyin; Data curation, Akande, Olufemi Stephen, Adebayo, Peter Ayotunde and Ujah, Godswill Ofuowoichoyama; Formal analysis, Olayinka, Adesike Oladoyin; Investigation, Akande, Olufemi Stephen, Adebayo, Peter Ayotunde and Ujah, Godswill Ofuowoichoyama; Methodology, Olayinka, Adesike Oladoyin, and Odewole, Adeola Foluke; Project administration, Olayinka, Adesike Oladoyin; Supervision, Olayinka, Adesike Oladoyin; Visualization, Olayinka, Adesike Oladoyin and Odewole, Adeola Foluke; Writing \u0026ndash; review \u0026amp; editing, Olayinka, Adesike Oladoyin.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe appreciate the efforts of the Maize Improvement Programme (MIP) of the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, for the provision of genetic materials used for this study. We are grateful to the students of the Department of Crop Production and Soil Science, Faculty of Agricultural Sciences, under the supervision of the first author, for their technical assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnjorin FB, Odeyemi OO, Akinbode OA, Kareem KT. Fall armyworm (\u003cem\u003eSpodoptera frugiperda\u003c/em\u003e)(JE Smith)(Lepidoptera: Noctuidae) infestation: maize yield depression and physiological basis of tolerance. 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CABI Agric Biosci. 2024;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s43170-024-00239-w\u003c/span\u003e\u003cspan address=\"10.1186/s43170-024-00239-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fall armyworm resistance, Genetic variability, Grain yield, Maize hybrid, Selection","lastPublishedDoi":"10.21203/rs.3.rs-8279943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8279943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFall armyworm (\u003cem\u003eSpodoptera frugiperda\u003c/em\u003e) severely threatens sustainable maize production in smallholder farming systems across sub-Saharan Africa. Developing maize hybrids resistant to fall armyworm with high grain yield can enhance productivity and stability, especially in the savanna agro-ecology. This study aimed to identify such resistant hybrids, assess their genetic variability, heritability, and trait relationships under natural infestation and disease pressure. Thirty-two (32) maize hybrids, including commercial checks, were evaluated at two sites on the Teaching and Research farm of Ladoke Akintola University of Technology, Ogbomoso, Nigeria, in an 8 \u0026times; 4 α-lattice design with three replications. Combined analysis of variance revealed highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differences among hybrids, environments, and hybrid \u0026times; environment interactions for grain yield and other agronomic traits. Significant genetic variation for grain yield, foliar diseases, fall armyworm damage, ear rot, and related traits across sites suggests potential for selecting superior resistant hybrids. Approximately 30.7% of tested hybrids outperformed the highest yielding check (G31: Oba Super 9). Hybrid G1 (FAWSYN-1/(TZLComp.1 C6-W-39-1-1)-B-B) had the highest grain yield of 5223.3 kg ha⁻\u0026sup1;, showing a 26.8% yield advantage over the top check. Phenotypic correlations showed grain yield was negatively and significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) associated with fall armyworm foliar damage, plant aspect, and husk cover ratings. Regression analyses emphasized plant aspect as the strongest contributor to grain yield. In stepwise regression, fall armyworm infestation had significant detrimental effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) on grain yield, husk cover, and ear and plant aspect ratings. Genotype by grain yield \u0026times; trait (GYT) biplot identified five hybrids (G1, G9, G21, G18, and G3) that combined high yield, moderate fall armyworm resistance, and stability across environments. These promising hybrids are recommended for multilocational yield trials aiming to release fall armyworm resistant, high-yielding maize varieties suited for the derived savanna agro-ecology.\u003c/p\u003e","manuscriptTitle":"Variation in the phenotypic performance of top-cross fall armyworm resistant maize hybrids under optimal growing conditions in a derived savanna agro-ecology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 11:38:23","doi":"10.21203/rs.3.rs-8279943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T10:06:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T20:04:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-19T21:25:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T10:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126670665462285916842533600605177212610","date":"2026-01-12T19:37:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217536954565650940367839094453373389782","date":"2026-01-09T18:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250691978048608251288083746539882451948","date":"2026-01-09T11:06:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T10:55:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-19T06:55:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T10:41:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T12:35:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Plants","date":"2025-12-12T12:25:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-plants","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Plants](https://link.springer.com/journal/44372)","snPcode":"44372","submissionUrl":"https://submission.springernature.com/new-submission/44372/3","title":"Discover Plants","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"873eb7d3-93b8-41af-ab28-12cc58d89905","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T09:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 11:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8279943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8279943","identity":"rs-8279943","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}