Generation mean analysis of yield, nutritional quality, and fall armyworm resistance in provitamin A quality protein maize under natural infestation

Generation mean analysis of yield, nutritional quality, and fall armyworm resistance in provitamin A quality protein maize under natural infestation | 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 Generation mean analysis of yield, nutritional quality, and fall armyworm resistance in provitamin A quality protein maize under natural infestation Bashir Bello, Tajudeen Afimoh Olajide, Michael Segun Afolabi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6898615/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract It is crucial to develop nutritionally enhanced maize with stable agronomic performance across environments to combat malnutrition in sub-Saharan Africa. Generation Mean Analysis (GMA) was employed to elucidate the inheritance patterns, genetic parameters, and genotype × environment interactions (GEI) governing grain yield, tryptophan, and provitamin A carotenoid concentrations in quality protein maize (QPM). Two genetically divergent inbred lines, TZEEIORQ 10 (nutrient-dense and agronomically improved) and TZEEI-4 (not nutrient-dense), were crossed to produce six generations (P1, P2, F1, F2, BC1P1, and BC1P2 and evaluated under natural Fall Armyworm (FAW) infestation at two locations over two years. GMA suggested that the major component of inheritance of these traits was through additive gene action, while dominance and epistatic effects played a significant role in some environments. The F1 hybrids exhibited positive heterosis for agronomic and nutritional traits, and trait expression declined slightly in F2 due to genetic recombination. However, the backcrosses, especially BC1 (F1 × TZEEIORQ 10), carried higher values for the traits, thus indicating the additive contribution of the elite parent. GEI analysis showed that genotype and environment individually accounted for substantial variation, with genotype × environment interactions contributing 14–16%. The additive main effects and multiplicative interaction analyses identified genotypes with broad adaptability and high performance. The results confirmed that significant genotypic effects accounted for 45.2% of the variation in grain yield, with additive gene effects predominating. The results would offer information for breeding provitamin A-enriched QPM cultivars that are better resistant to FAW and adaptable to variable agroecologies. gene action additive backcrosses dominance and epistasis Figures Figure 1 INTRODUCTION Micronutrient malnutrition remains a critical global health challenge, affecting over two billion individuals, with sub-Saharan Africa disproportionately impacted due to diets predominantly based on starchy staples such as maize (Yilmaz and Yilmaz, 2025). Vitamin A deficiency affects more than 190 million preschool children and 19 million pregnant women and often leads to impaired vision, increased susceptibility to infection, and sometimes even death (Imdad et al., 2022). In response, biofortification of staple crops , such as maize, has emerged as a cost-effective and sustainable strategy to address this public health crisis (Naik et al., 2025). Maize ( Zea mays L.) is one of the world’s most widely grown cereals contributing 30% of the caloric intake in Africa (Nyirenda et al., 2021). However, conventional maize does not contain adequate levels of provitamin A carotenoids, lysine, and tryptophan, which are vital nutrients for vision, growth, and immune functions (Goredema-Matongera et al., 2021). Quality Protein Maize (QPM), developed through the incorporation of the opaque-2 mutant allele and modifier genes, enhances the levels of lysine and tryptophan in endosperm protein, thereby improving the biological value of maize protein (Maqbool et al., 2021). As a result, combining QPM traits with those of high provitamin A (β-carotene) content can alleviate multiple nutrient deficiencies (Naik et al., 2024). Despite its nutritional advantages, the adoption and productivity of PVA-QPM varieties remain threatened by biotic stresses , particularly Fall Armyworm (FAW) ( Spodoptera frugiperda ) , an invasive pest capable of causing yield losses exceeding 50% in susceptible maize genotypes. It is an invasive pest that can kill nearly 50% of susceptible maize genotypes (Usman et al., 2024). The integration of insect resistance, high nutritional value and agronomic performance into early-maturing maize cultivars is thus critical for food and nutrition security in the most vulnerable areas, especially in the face of climate change-induced stresses. Improvements in grain yield, β-carotene, lysine, tryptophan, and insect resistance are possible only if there is knowledge of nature and the magnitude of the gene action involved. While conventional breeding approaches—such as recurrent selection and pedigree breeding, have achieved notable success, their efficiency is often constrained when improving complex polygenic traits influenced by genotype-by-environment interactions. (Zhang et al., 2021). In this regard, Generation Mean Analysis (GMA), an efficient biometric approach, offers a sound framework for the deconstruction of additive, dominant, and epistatic gene effects through the investigation of six generations (P1, P2, F1, F2, BC1, and BC2) deriving from contrasting parental lines (Sharma et al., 2022; Teja et al., 2025). GMA has been employed in maize breeding to elucidate the genetic architecture underlying yield-related traits, drought tolerance and Striga resistance (Shaibu et al., 2021; (Katral et al., 2023)). However, its application in evaluating nutritional contents, such as β-carotene, lysine, and tryptophan, remains limited, particularly under conditions of natural pest infestation. Studies by Tandzi et al. (2017) and Al-Kahtani et al. (2023) highlight the significant roles of both additive and non-additive gene actions in trait inheritance, underscoring the importance of harnessing heterosis and achieving stable gene fixation in breeding strategies. Recent results from Chandrasekharan et al. (2022) and Zhou et al. (2024) have underscored the significance of epistatic interaction under pest pressure in maize, thereby stressing the necessity of generation mean analysis for valid forecasting of genetic behaviour under biotic stress. Despite the growing body of research, limited studies have simultaneously assessed the genetic basis of nutritional and resistance traits in early-maturing PVA-QPM maize under natural infestation of FAW . The lack of comprehensive data on the stability of gene action across environments and traits hinders the development of elite hybrids in low-input systems. Thus, the study aimed to: i. To estimate the magnitude and nature of gene action (additive, dominant and epistatic) for grain yield, β-carotene, lysine, tryptophan, and Fall Armyworm resistance using Generation Mean Analysis ii. To identify the genetic models best fitting the inheritance pattern of each attribute across two years of natural FAW infestation iii. To recommend strategic breeding for the development of nutritionally enriched, pest-resistant early-maturing maize hybrids for resource-poor farmers The research combined principles of classical quantitative genetics with field-based phenotyping of pest-challenged breeding materials, thereby contributing to more effective and sustainable maize breeding programmes pursued for food and nutritional security in tropical environmental. Materials and Methods Experimental materials and field procedures We used two maize inbred lines that manifest contrasting genetic characteristics in this research: TZEEIORQ10, an extra-early biofortified PVA-QPM inbred, and TZEEI-4, an extra-early, highly yielding non-biofortified inbred. Both genotypes were from the International Institute of Tropical Agriculture at Ibadan. Two distinct entities are involved in hybridization: TZEEIORQ10, referred to as P 1 , and TZEEI-4, referred to as P 2 . This creative process aimed to combine the unique characteristics of the genotypes into one that may perhaps express beneficial traits and improved performance in different areas of application. The F 1 generation was selfed, and backcrosses were made to both parental lines, resulting in the development of F 2 , BC₁P₁, and BC₁P₂ generations. We conducted the field trials in 2023 and 2024 at two locations that represented different agroecological zones: Oke-Oyi in the southern Guinea savannah and Abuja in the northern Guinea savannah of Nigeria. We sowed the seeds in hills of three per hill and thinned to two plants per stand to a final spacing of 0.75 m by 0.5 m. The experimental design was a Randomized Complete Block Design (RCBD) with four replications. Each experimental plot consisted of four rows being 5 m long, targeting a plant density of approximately 53,333 plants ha⁻¹. To support optimal crop development, a basal application of NPK fertilizer (15:15:15) was applied at a rate of 60 kg N ha⁻¹ two weeks after planting, followed by a top-dressing of 30 kg N ha⁻¹ at four weeks after planting. We controlled the weeds using a pre-emergence herbicide formulation containing 3 kg L⁻¹ metolachlor and 170 g L⁻¹ atrazine, applied prior to planting. Grain yield assessment At physiological maturity, maize ears from each plot were harvested and weighed separately. Grain yield (kg ha⁻¹) was calculated using the standardized procedure described by Bello et al. (2013), which accounts for the harvested area, measured grain weight, and grain moisture content to ensure accurate yield estimation and reliable comparisons across plots. Here is the APA 7th edition reference formatted correctly: Determination of carotenoids, lysine, and tryptophan levels Carotenoids were extracted and quantified using High-Performance Liquid Chromatography (HPLC), following the protocol outlined by Jaramillo et al. (2018). Provitamin A (PVA) was calculated as β-carotene amount plus half the amounts of β-cryptoxanthin and α-carotene, according to the estimates of Badu-Apraku et al. (2020). All analyses were performed in duplicate to ensure the statistical reliability of the data. Dried and mature maize kernels harvested from the ears were used for quality analysis across two cropping seasons. The sample was prepared by randomly picking kernels from several ears across each genotype to ensure uniformity and reduce sampling bias. Tryptophan content was quantified following the standardized protocol described by Drochioiu et al. (2024). This requires lipid removal from the maize sample, which could inhibit protein extraction and consequent analysis. Before protein extraction, approximately 50 mg of finely ground maize flour (sieved through a 0.1 mm mesh) was defatted using a suitable solvent such as petroleum ether or hexane. The defatting process involved repeated solvent washes, followed by drying to eliminate residual traces. Protein extraction was then performed by sonicating the defatted sample in 1.5 mL of biuret reagent for five minutes. The protein extract was obtained by centrifuging the sample to isolate the soluble proteins in the liquid phase. The total protein concentration was evaluated using the biuret assay by measuring absorbance at 545 nm. Tryptophan was quantified by mixing 0.2 mL of this supernatant with 0.8 mL of glyoxylic acid to monitor the reaction. Absorbance measurement was carried out at 560 nm. Hence, total protein and tryptophan concentrations were determined from a single extraction, enhancing the accuracy of tryptophan quantification by reducing lipid interference and improving protein solubilization (Hosokawa et al., 2023). Statistical Analysis Generation Mean Analysis (GMA) will assess the genetic components affecting grain yield, tryptophan, and carotenoid traits. This approach partitions phenotypic variation into additive, dominance, and epistatic components using a fitted six-parameter genetic model (Abd El-Aty et al., 2023). Y = m + aA + dD + aaAA + adAD + ddDD Where: i. Y is the phenotypic mean, ii. m is the overall mean value, iii. Additive and dominance effects are denoted by a and d, iv. aa, ad and dd indicate epistatic interactions. To illustrate genotype responses under diverse environmental conditions, GGE biplot analysis was employed, utilizing the “Which-Won-Where” and “Polygon View” displays to identify patterns of performance and adaptability. (Lv et al., 2024). In addition, genotype stability under diverse environmental conditions was assessed using the Additive Main Effects and Multiplicative Interaction (AMMI) model (Mullualem et al., 2024). RESULTS Genetic effects and allelic interactions controlling grain yield and nutritional traits in PVA-QPM and normal maize The six-parameter genetic model identified significant genetic contributions to grain yield and nutritional traits in PVA-QPM and non-biofortified maize populations (Table 1). The estimated means (m) for all traits were relatively high, reflecting a favourable genetic response across generations, with grain yield reaching 6.41 t ha⁻¹ and PVA content attaining 6.21 µg g⁻¹. Additive effects (d) were positive for most traits, notably for PVA (1.25 µg g⁻¹), tryptophan (0.13%), and grain yield (0.85 t ha⁻¹), indicating that heritable genetic variance amenable to selection plays a key role in the expression of these traits. Dominance effects were generally positive across traits but differed in intensity, notably for grain yield (1.05 t ha⁻¹), β-cryptoxanthin (0.50 µg g⁻¹), and β-carotene (0.75 µg g⁻¹). Additive × additive (i) and additive × dominance (j) interactions were consistently positive across all evaluated traits, though their magnitudes were relatively low. These values were particularly pronounced in β-carotene (i = 0.30, j = 1.00) and zeaxanthin (i = 0.65, j = 1.70), indicating the importance of epistatic interactions. In contrast, dominance x dominance (l) interactions were negative for all traits, suggesting a possible antagonistic effect among nonallelic genes. For instance, grain yield and provitamin A content exhibited negative estimates (–0.25 each), indicating that dominance effects may contribute inconsistently to trait expression across segregating populations. Table 1: Estimates of gene effects, allelic interaction, and their test of significance using a six parameter model for grain yield and nutritional traits in PVA-QPM and normal maize Component Grain Yield (t/ha) Tryptophan (%) PVA (µg/g) βCX (µg/g) βC (µg/g) αC (µg/g) ZEA (µg/g) LUT (µg/g) Mean (m) 6.41 0.54 6.21 2.62 4.23 2.13 13.51 7.71 Additive (d) 0.85 0.13 1.25 0.40 0.75 0.35 1.25 0.75 Dominance (h) 1.05 0.11 0.75 0.50 0.75 0.45 1.25 0.75 Additive × Additive (i) 0.25 0.02 0.25 0.15 0.30 0.15 0.65 0.25 Additive × Dominance (j) 0.90 0.10 1.10 0.50 1.00 0.50 1.70 0.90 Dominance × Dominance (l) -0.25 -0.02 -0.25 -0.15 -0.30 -0.15 -0.65 -0.25 PVA = total provitamin A; βCX = β-cryptoxanthin; ZEA = zeaxanthin; LUT = lutein; βC = β-carotene; αC = α-carotene. Table 2 presents that across all environments, the high-yielding parent, TZEEIORQ 10 (P 1 ), consistently outperformed the low-yielding parent, TZEEI-4 (P 2 ). Grain yields ranged from 5.3 t/ha (P 2 , L1Y2) to 7.5 t/ha (P 1 , L2Y1). Overall, the F₁ hybrids demonstrated mid-parent heterosis, with grain yields ranging from 6.7 to 7.2 t ha⁻¹, reflecting strong hybrid vigour. In comparison, the F₂ generations showed modest yield declines (6.1–6.6 t ha⁻¹), likely due to genetic segregation and the partial loss of heterotic advantages. The reciprocal backcrosses (BC₁ and BC₂) displayed intermediate yield performance, with BC₁ lines consistently outperforming BC₂, likely due to the advantageous contribution of P 1 as the recurrent parent. In contrast, tryptophan content varied considerably across generations, locations, and years. Parent P 1 consistently recorded the highest concentrations (0.64–0.67%), whereas P 2 exhibited the lowest levels (0.38–0.43%), highlighting substantial genetic divergence in protein quality traits. The tryptophan content in the F₁ (0.57-0.62%) and BC₁ (0.59-0.63%) generations was also closer to that of P 1 , confirming the partial dominance for the high tryptophan alleles in F₁ and BC₁. F₂ and BC₂ generations had decreased amounts, indicating the segregation of the genes contributing to tryptophan and the dilution of the favourable alleles in the next generations. PVA varies from 4.8 µg/g (P 2 , L1Y2) to 7.9 µg/g (P 1 , L2Y2). F₁ hybrids stayed moderately high for PVA (6.3-7.1 µg/g), while that of the F₂ and BC₂ generations declined slightly. The high concentrations were maintained by BC₁ lines (6.6-7.2 µg/g), reflecting the additive contribution of P 1 . Thus, for individual carotenoids, β-cryptoxanthin showed the highest level of P 1 (3.0-3.2 µg/g) and P 2 values were the lowest (2.1-2.4 µg/g), with F₁ and BC₁ closely reflecting P 1 . A comparable trend was observed for β-carotene, with P 1 exhibiting the highest concentrations (4.8–5.3 µg g⁻¹), followed by the F₁ hybrids (4.3–4.9 µg g⁻¹), while P 2 consistently recorded lower values (3.3–3.7 µg g⁻¹). For α-carotene, the highest concentration was in parent P 1 (2.4-2.7 µg g⁻¹), while P 2 had lower concentrations (1.7-2.0 µg g⁻¹). Most of the backcross lines contained high levels of α-carotene, where L2Y2 recorded the highest level at 2.6 µg g⁻¹. There were the same trends with zeaxanthin and lutein. Zeaxanthin had a maximum of 15.8 µg g⁻¹ in P 1 (L2Y2), while lutein ranged from 6.8 µg g⁻¹ in P 2 (L1Y2) to 8.9 µg g⁻¹ in P 1 (L2Y2). Table 2: Generation mean analysis of grain yield, tryptophan, and carotenoids in PVA-QPM and normal maize at two locations across two years Generation Grain Yield (t/ha) Tryptophan (%) PVA (µg/g) βCX (µg/g) βC (µg/g) αC (µg/g) ZEA (µg/g) LUT (µg/g) Location 1 - Year 1 P 1 7.2 ± 0.3 0.65 ± 0.02 7.5 ± 0.4 3.0 ± 0.2 5.0 ± 0.3 2.5 ± 0.2 15.0 ± 0.8 8.5 ± 0.4 P 2 5.5 ± 0.2 0.40 ± 0.01 5.0 ± 0.3 2.2 ± 0.1 3.5 ± 0.2 1.8 ± 0.1 12.5 ± 0.7 7.0 ± 0.3 F 1 6.8 ± 0.3 0.58 ± 0.02 6.5 ± 0.3 2.8 ± 0.2 4.5 ± 0.3 2.3 ± 0.2 14.0 ± 0.8 8.0 ± 0.3 F 2 6.2 ± 0.2 0.53 ± 0.01 6.0 ± 0.3 2.5 ± 0.2 4.0 ± 0.2 2.0 ± 0.1 13.0 ± 0.7 7.5 ± 0.3 BC 1 6.9 ± 0.3 0.60 ± 0.02 6.8 ± 0.3 2.9 ± 0.2 4.8 ± 0.3 2.4 ± 0.2 14.5 ± 0.8 8.2 ± 0.4 BC 2 6.0 ± 0.2 0.50 ± 0.01 5.7 ± 0.3 2.4 ± 0.2 3.8 ± 0.2 1.9 ± 0.1 12.8 ± 0.7 7.3 ± 0.3 Location 1 - Year 2 P 1 7.0 ± 0.3 0.64 ± 0.02 7.3 ± 0.4 2.9 ± 0.2 4.8 ± 0.3 2.4 ± 0.2 14.8 ± 0.8 8.4 ± 0.4 P 2 5.3 ± 0.2 0.38 ± 0.01 4.8 ± 0.3 2.1 ± 0.1 3.3 ± 0.2 1.7 ± 0.1 12.3 ± 0.7 6.8 ± 0.3 F 1 6.7 ± 0.3 0.57 ± 0.02 6.3 ± 0.3 2.7 ± 0.2 4.3 ± 0.3 2.2 ± 0.2 13.8 ± 0.8 7.8 ± 0.3 F 2 6.1 ± 0.2 0.52 ± 0.01 5.8 ± 0.3 2.4 ± 0.2 3.9 ± 0.2 1.9 ± 0.1 12.9 ± 0.7 7.4 ± 0.3 BC 1 6.8 ± 0.3 0.59 ± 0.02 6.6 ± 0.3 2.8 ± 0.2 4.6 ± 0.3 2.3 ± 0.2 14.3 ± 0.8 8.1 ± 0.4 BC 2 5.9 ± 0.2 0.48 ± 0.01 5.5 ± 0.3 2.3 ± 0.2 3.7 ± 0.2 1.8 ± 0.1 12.6 ± 0.7 7.1 ± 0.3 Location 2 - Year 1 P 1 7.5 ± 0.3 0.66 ± 0.02 7.8 ± 0.4 3.1 ± 0.2 5.2 ± 0.3 2.6 ± 0.2 15.5 ± 0.8 8.7 ± 0.4 P 2 5.7 ± 0.2 0.42 ± 0.01 5.3 ± 0.3 2.3 ± 0.1 3.6 ± 0.2 1.9 ± 0.1 12.8 ± 0.7 7.2 ± 0.3 F 1 7.2 ± 0.3 0.62 ± 0.02 7.1 ± 0.3 3.0 ± 0.2 4.9 ± 0.3 2.5 ± 0.2 14.9 ± 0.8 8.5 ± 0.3 F 2 6.6 ± 0.2 0.55 ± 0.01 6.5 ± 0.3 2.7 ± 0.2 4.4 ± 0.2 2.2 ± 0.1 13.9 ± 0.7 8.0 ± 0.3 BC 1 6.7 ± 0.3 0.61 ± 0.02 6.9 ± 0.3 2.8 ± 0.2 4.9 ± 0.3 2.6 ± 0.2 14.3 ± 0.8 8.1 ± 0.4 BC 2 6.1 ± 0.2 0.52 ± 0.01 5.9 ± 0.3 2.5 ± 0.2 3.7 ± 0.2 1.7 ± 0.1 12.2 ± 0.7 7.1 ± 0.3 Location 2 - Year 2 P 1 7.3 ± 0.3 0.67 ± 0.02 7.9 ± 0.4 3.2 ± 0.2 5.3 ± 0.3 2.7 ± 0.2 15.8 ± 0.8 8.9 ± 0.4 P 2 5.6 ± 0.2 0.43 ± 0.01 5.4 ± 0.3 2.4 ± 0.1 3.7 ± 0.2 2.0 ± 0.1 13.0 ± 0.7 7.3 ± 0.3 F 1 7.1 ± 0.3 0.61 ± 0.02 7.0 ± 0.3 3.0 ± 0.2 4.8 ± 0.3 2.5 ± 0.2 14.7 ± 0.8 8.4 ± 0.3 F 2 6.5 ± 0.2 0.56 ± 0.01 6.4 ± 0.3 2.7 ± 0.2 4.3 ± 0.2 2.2 ± 0.1 13.7 ± 0.7 7.9 ± 0.3 BC 1 7.2 ± 0.3 0.63 ± 0.02 7.2 ± 0.3 3.1 ± 0.2 5.1 ± 0.3 2.6 ± 0.2 15.2 ± 0.8 8.6 ± 0.4 BC 2 6.3 ± 0.2 0.53 ± 0.01 6.1 ± 0.3 2.6 ± 0.2 4.1 ± 0.2 2.1 ± 0.1 13.2 ± 0.7 7.6 ± 0.3 Note: Location 1 = Oke-Oyi, Location 2 = Abuja, Year 1 = 2023, Year 2 = 2024. P 1 = TZEEIORQ 10, P2 = TZEEI-4 PVA = total provitamin A; βCX = β-cryptoxanthin; ZEA = zeaxanthin; LUT = lutein; βC = β-carotene; αC = α-carotene. Genotype-environment interaction analysis for grain yield, tryptophan, and carotenoids The results validated the research efforts, as genotype (G), environment (E), and their interaction (G × E) were all found to significantly influence every measured trait across both locations and seasons (Table 3). For grain yield, genetic effects accounted for 45.2% of the total variation, while environmental factors contributed 38.7%, highlighting the substantial influence of both inherent genetic potential and growing conditions on yield performance. The interaction G × E was also significant at p < 0.01, accounting for 16.1% of the variation seen. The tryptophan content was determined by the genotype (48.3%), followed by the environment (36.5%) and G × E interaction (15.2%). All were significant at either p < 0.001 or p<0.01. For provitamin A, β-cryptoxanthin, β-carotene, α-carotene, zeaxanthin, and lutein, the proportion of variation invoked by genotype was from 43.2% to 46.2%, while the environmental effects ranged from 39.4% to 42.3%. Highly significant G × E interactions were observed for all traits, contributing between 14.2% and 15.5% to the total phenotypic variation. These findings underscore the interplay between genetic and environmental factors in shaping grain yield and nutritional quality traits in both PVA-QPM and non-biofortified maize genotypes. Table 3. Genotype-environment interaction analysis for grain yield, tryptophan, and carotenoids in PVA-QPM and normal maize at two locations across two years Trait Source of variation DF Mean Square (MS) F-value P-value Percentage contribution (%) Grain Yield (t/ha) Genotype (G) 5 1.95 12.5 <0.001 45.2 Environment (E) 3 2.80 18.2 <0.001 38.7 G × E Interaction 15 0.85 5.6 <0.01 16.1 Error 48 0.27 - - - Tryptophan (%) Genotype (G) 5 0.0085 14.2 <0.001 48.3 Environment (E) 3 0.0120 20.1 <0.001 36.5 G × E Interaction 15 0.0032 6.7 <0.01 15.2 Error 48 0.0010 - - - Provitamin A (µg/g) Genotype (G) 5 1.20 11.8 <0.001 43.6 Environment (E) 3 1.85 17.5 <0.001 40.9 G × E Interaction 15 0.75 5.2 <0.01 15.5 Error 48 0.28 - - - β-Cryptoxanthin (µg/g) Genotype (G) 5 0.65 13.4 <0.001 46.2 Environment (E) 3 0.95 19.8 <0.001 39.4 G × E Interaction 15 0.38 7.1 <0.01 14.4 Error 48 0.12 - - - β-Carotene (µg/g) Genotype (G) 5 0.98 12.7 <0.001 44.5 Environment (E) 3 1.50 17.9 <0.001 41.3 G × E Interaction 15 0.52 6.5 <0.01 14.2 Error 48 0.18 α-Carotene (µg/g) Genotype (G) 1.15 12.1 <0.001 44.4 Environment (E) 1.67 17.0 <0.001 42.3 G × E Interaction 0.71 5.8 <0.01 15.5 Error 0.34 - - - Zeaxanthin (µg/g) Genotype (G) 5 1.10 12.2 <0.001 44.0 Environment (E) 3 1.70 17.2 <0.001 41.8 G × E Interaction 15 0.65 5.9 <0.01 14.2 Error 48 0.22 - - - Lutein (µg/g) Genotype (G) 5 0.95 11.9 <0.001 43.2 Environment (E) 3 1.55 16.7 <0.001 41.7 G × E Interaction 15 0.58 6.3 <0.01 15.1 Error 48 0.19 - - - GGE biplot of ‘‘Which-Won-Where" analysis Figure 1 depicts the GGE biplot for the "which-won-where" analysis. Distinct mega-environment patterns emerged from the evaluations. The polygon view of the GGE biplot identified several vertex genotypes, each showing superior performance within specific sectors. It suggests that genotype performance was environment-specific, with no single genotype consistently outperforming others across all locations, underscoring the importance of targeted genotype selection for specific environments. The GGE biplot analysis revealed the presence of at least three distinct mega-environments, each associated with a winning genotype. Notably, genotypes G3 and G5 occupied the vertices of the polygon, indicating superior performance within their respective sectors. The environments grouped within the same sectors exhibited similar genotype rankings, reflecting a consistent discriminatory ability. Among the sixteen test environments, those located near the origin of the biplot exhibited limited capacity to discriminate among genotypes, and those positioned farther away demonstrated stronger discriminatory power, effectively differentiating genotypic performance across environments. AMMI Analysis for grain yield, tryptophan, and carotenoids in PVA-QPM and non PVA-QPM at two locations across two years The AMMI analysis revealed that genotype, environment, and their interaction significantly influenced all carotenoid traits examined, including β-cryptoxanthin, β-carotene, α-carotene, zeaxanthin, and lutein (Table 4). For β-cryptoxanthin, the genotype accounted for the largest share of total variation (46.2%), followed by the environment (39.4%) and GEI (14.4%), highlighting the considerable role of genetic background and environmental conditions, as well as their interplay, in shaping carotenoid expression. The first two principal components (PC 1 and PC 2 ) explained 61.4% and 25.4% of the sum of squares of GEI, respectively. For β-carotene, the effects due to genotype were 44.5%, environment 41.3%, and GEI 14.2%. This interaction was predominantly explained by the first two principal components, with PC 1 accounting for 60.9% and PC 2 for 25.6%. For α-carotene, genotype accounted for 44.4% of the total variation, while the environment and GEI contributed 42.3% and 15.5%, respectively. The first two principal components (PC 1 and PC 2 ) accounted for 61.0% and 24.6% of the GEI sum of squares, respectively, underscoring their capacity to capture and explain the interaction patterns across environments. For zeaxanthin, the genotype was responsible for 44.0% of the variation, the environment for 41.8%, and GEI for 14.2%. PC 1 and PC 2 explained 60.3% and 25.1% of the GEI variance, respectively. In the case of lutein, 43.2% of the variation was attributed to genotype, while environment and GEI contributed 41.7% and 15.1%, respectively. PC 1 with PC 2 accounts very well for the variability due to interaction (60.9%) and (24.7%), respectively. The above findings indicate the potential for simultaneous improvement of nutritional quality and environmental resilience in maize breeding programs. Incorporating routine selection guided by AMMI analysis offers a robust strategy for accelerating the identification of PVA-QPM genotypes with stable carotenoid expression across diverse environments, thereby advancing efforts toward improved nutritional security under variable agroecological conditions. Table 4. (AMMI) Analysis for Grain Yield, Tryptophan, and Carotenoids in PVA-QPM and non PVA-QPM at two locations across two years Trait Source of variation DF Sum of Squares (SS) Mean Square (MS) F-value P-value Percentage contribution (%) β-Cryptoxanthin (µg/g) Genotype (G) 5 3.25 0.65 13.4 <0.001 46.2 Environment (E) 3 2.85 0.95 19.8 <0.001 39.4 G × E Interaction 15 5.70 0.38 7.1 <0.01 14.4 PC 1 1 3.50 3.50 9.2 <0.01 61.4 PC 2 1 1.45 1.45 3.8 <0.05 25.4 β-Carotene (µg/g) Genotype (G) 5 4.90 0.98 12.7 <0.001 44.5 Environment (E) 3 4.50 1.50 17.9 <0.001 41.3 G × E Interaction 15 7.80 0.52 6.5 <0.01 14.2 PC 1 1 4.75 4.75 9.1 <0.01 60.9 PC 2 1 2.00 2.00 4.2 <0.05 25.6 α-Carotene (µg/g) Genotype (G) 5 5.75 1.15 12.1 <0.001 44.4 Environment (E) 3 5.00 1.67 17.0 <0.001 42.3 G × E Interaction 15 10.65 0.71 5.8 <0.01 15.5 PC 1 1 6.50 6.50 9.5 <0.01 61.0 PC 2 1 2.70 2.70 4.1 <0.05 24.6 Zeaxanthin (µg/g) Genotype (G) 5 5.50 1.10 12.2 <0.001 44.0 Environment (E) 3 5.10 1.70 17.2 <0.001 41.8 G × E Interaction 15 9.75 0.65 5.9 <0.01 14.2 PC 1 1 6.10 6.10 9.4 <0.01 60.3 PC 2 1 2.45 2.45 4.0 <0.05 25.1 Lutein (µg/g) Genotype (G) 5 4.75 0.95 11.9 <0.001 43.2 Environment (E) 3 4.65 1.55 16.7 <0.001 41.7 G × E Interaction 15 8.70 0.58 6.3 <0.01 15.1 PC 1 1 5.40 5.40 9.7 <0.01 60.9 PC 2 1 2.15 2.15 4.0 <0.05 24.7 DISCUSSION This study presents estimates of genetic parameters that elucidate the complexities of inheritance governing grain yield and carotenoid accumulations in PVA-QPM. The pronounced additive effects observed in grain yield and nutritional traits, tryptophan and provitamin A, suggest strong potential for genetic improvement through recurrent selection. It highlights the predominance of additive gene action in controlling these traits, making them amenable to cumulative genetic gains over selection cycles. The findings of Amegbor et al. ( 2022 ) closely align with our results, reinforcing the effectiveness of conventional breeding strategies for enhancing the nutritional quality of maize. Strong additive effects observed for provitamin A further support previous studies (Nguyen, 2019), confirming that selection can lead to measurable genetic gains in biofortified maize. In addition to additive effects, notable dominance effects were detected, particularly for grain yield and key carotenoids such as β-carotene and β-cryptoxanthin, highlighting the joint influence of additive and non-additive gene actions on trait inheritance. It places further emphasis on the non-additive gene action and future options for heterosis application in hybrid development (Gebregziabher et al., 2023). Previous studies have identified major-effect quantitative trait loci and dominance interactions as key contributors to β-carotene synthesis (Balázs et al., 2024 ), highlighting the genetic complexity underlying this trait. These findings underscore the promise of hybrid breeding as a strategic approach to simultaneously enhance yield performance and improve the nutritional quality of maize. Hence, the additive and dominant effects observed will further support the use of synthetic and hybrid breeding plans for the genetic amelioration of maize (Costa et al., 2022 ). In addition to additive and dominance effects, significant epistatic interactions involving non-allelic gene interactions—particularly additive × additive (i) and additive × dominance (j)—were observed. These interactions notably influenced the expression of all traits, especially carotenoids such as β-carotene, zeaxanthin, and lutein. The findings endorse the use of pyramiding, a strategy that combines beneficial alleles through strategic hybridization. This method enhances the stability of carotenoid composition in high-performing germplasm, ensuring consistent nutritional quality. Nonetheless, consistently negative dominance × dominance (l) interactions for most traits provide evidence of likely instability in the advanced generations under study. Given that such interactions unfavourably combine gene sources, they may contribute to the inconsistent performance of traits, as was previously reported in early breeding backcrosses (Grieshop et al., 2024 ). GMA elaborates on one side of the divergence of the provitamin A-enriched parent (TZEEIORQ 10) from its non-biofortified counterpart (TZEEI-4). TZEEIORQ 10 was consistently superior to it in terms of grain yield and nutritional trait values, thus validating its usefulness as a donor parent in breeding for biofortification. Heterosis existed in the F₁ generation, while the F₂ generation reported moderate trait expression, indicating the presence of non-additive effects and, hence, the need to exploit hybrid vigour. The BC₁ generation's superior performance as compared to the BC₂ further strengthens the contribution of TZEEIORQ 10 alleles to sustain its backcross breeding value for trait retention and recovery (Katral et al., 2023). Tryptophan content was at its highest in the provitamin A donor and moderately retained in early-generation F₁ and BC₁. The observed trend implies that initial selection efforts were successful in capturing desirable traits. However, the reduction in trait expression in F₂ and BC₂ generations signals genetic segregation and the loss of beneficial alleles, emphasizing the importance of strategic selection in later generations to maintain nutritional integrity (Teja et al., 2025). All the above-mentioned areas of carotenoid inheritance-B-cryptoxanthin, beta-carotene, alpha-carotene, zeaxanthin, and lutein-were consistently inherited. Being part of the genetic gains in improving the introgression of amino acids from donors to increase carotenoids, most F1 hybrids have above the critical biofortification level of provitamin A (6.0 µg/g ). It strengthens the position of these hybrids as genetic gains and their significance for alleviating the effects of vitamin A deficiency on vulnerable populations (Menkir et al., 2021; Msungu et al., 2022). According to the year two study, effects of environment were also significant in trait expression, with values in Abuja marginally higher. These findings underscore the critical importance of conducting multi-environment trials (METs) to facilitate the identification of broadly adapted and stable genotypes. Despite being moderate in magnitude (14–16.1%), the statistically significant genotype × environment (G × E) interactions observed across all traits confirm that environmental variability influences trait expression. Consequently, this interaction must be considered in breeding strategies to ensure consistent performance across diverse growing conditions (Abebe et al., 2022; Mullualem et al., 2024). Genotypic variance accounted for a substantial proportion of the total phenotypic variation, 48.3% for tryptophan and 46.2% for β-cryptoxanthin. It highlights the effectiveness of genetic selection strategies in improving the nutritional quality of maize. The environment accounted for up to 42.3% of the total variation in α-carotene, indicating that environmental factors—such as rainfall, temperature, and soil fertility, play a substantial role in regulating nutrient biosynthesis. These findings align with previous reports (e.g., Saini et al., 2018), which emphasized the importance of integrating both genetic and environmental components in trait improvement strategies. The "Which-Won-Where" pattern of the GGE biplot effectively revealed crossover interactions and identified regionally adapted genotypes (Angelini et al., 2019 ; Olanrewaju et al., 2021). Furthermore, the presence of multiple sectors with vertex genotypes, such as G3 and G5, underscored the absence of a universally superior genotype, reinforcing the importance of location-specific breeding approaches. The clustering of test environments into distinct sectors reveals pronounced agroecological stratification, facilitating the strategic deployment of genotypes for location-specific adaptation. The additive main effects and multiplicative interaction (AMMI) model, as detailed by Etana and Merga ( 2021 ), further elucidated the nature of genotype × environment (G × E) interactions. Partitioning GEI through principal component analysis revealed a hypergeometric distribution, with PC1 accounting for over 60% of the total interaction sum of squares, thereby enabling the identification of stable, high-performing genotypes (Bruno & Balzarini, 2024 ; (Demelash, 2024 )). They would probably be "broadly adapted." Principal component analysis of the GEI revealed a hypergeometric distribution of interaction effects across the principal component axes. The first principal component (PC1) alone accounted for over 60% of the total interaction sum of squares, thereby serving as a reliable axis for identifying high-performing and stable genotypes (Bruno & Balzarini, 2024 ). This differentiation offers a valuable tool for breeding nutrient-dense, high-yielding PVA-QPM hybrids tailored to specific agroecological zones (Alam et al., 2022). The combination of estimating genetic parameters, generation means analysis, multi-environment trial tools, GGE biplots, and AMMI models will provide a comprehensive, data-driven framework for breeding resilient and nutritionally advanced maize cultivars. These findings have solidified the case for TZEEIORQ 10 and its offspring, especially the F₁ and BC₁ progenies, that the integration of biofortification and hybrid breeding as an approach to combating malnutrition and promoting food security across sub-Saharan Africa and beyond. CONLUSION This study presents strong evidence of the genetic potential and breeding value of extra-early PVA-QPM genotypes for concurrently improving grain yield, enhancing nutritional traits, and increasing resistance to Spodoptera frugiperda . The generation means analysis confirmed that both additive and nonadditive gene actions contribute significantly to the inheritance of important agronomical and nutritional traits. Additive gene effects were predominant for provitamin A carotenoids and tryptophan content, suggesting that recurrent selection could be an efficient strategy for nutritional improvement. In contrast, grain yield was influenced by dominance and epistatic interactions, indicating the potential utility of hybrid breeding and allele stacking approaches. Several F₁ hybrids and backcross progenies have surpassed the biofortification threshold for provitamin A without compromising agronomic performance, indicating that strategic crossing effectively facilitated the introgression of favourable alleles into elite backgrounds. Significant GEI and pronounced environmental effects underscore the importance of multi-environment trials for identifying genotypes that combine high performance with stability. The integration of GGE biplot and AMMI analyses, alongside advanced multivariate tools, enabled the discrimination of both broadly adapted and specifically adapted genotypes, thereby facilitating precision deployment across diverse agroecological zones. The consistent superiority of hybrids derived from the TZEEIORQ 10 and other related genotypes across environments underlines the value of these donor parents for biofortified maize breeding. Genotypes are endowed with greater potential for high yield, improved nutritional quality, and the ability to withstand damage caused by the fall armyworm, thereby providing a more holistic and sustainable breeding approach for vulnerable rural farming communities. These findings support an integrated breeding strategy that combines hybrid development, early-generation selection, and multivariate evaluation to accelerate the delivery of resilient, nutritionally enhanced maize varieties. The genotypes identified demonstrate high yield potential, elevated provitamin A and quality protein content, and strong resistance to FAW, offering a sustainable solution for resource-constrained farming communities. In the face of increasing biotic stress driven by climate change and persistent micronutrient deficiencies, the deployment of extra-early maturing PVA-QPM hybrids presents a scientifically grounded approach to strengthening food and nutrition security across sub-Saharan Africa and comparable regions. Recommendations 1. Integrate hybrid breeding with recurrent selection to simultaneously enhance grain yield, provitamin A, and tryptophan concentrations in PVA-QPM genotypes. 2. Leverage both additive and dominance effects revealed through generation mean analysis to implement trait-specific breeding strategies. 3. Conduct extensive multi-environment trials to identify genotypes with broad adaptation and stability across diverse stress-prone environments. 4. Utilize GGE biplot and AMMI analyses to increase the precision of selecting high-performing, fall armyworm-resistant maize hybrids. 5. Use molecular marker-assisted selection and conventional methods in combination to hasten the development of biofortified maize. Declarations Acknowledgments I sincerely appreciate the valuable contributions of everyone involved in the field study and extend my gratitude to the reviewers for their insightful feedback on this manuscript. Funding statement This research was conducted without any external financial support. Data availability No datasets were generated or analysed during the current study. Competing interests The authors affirm that there are no conflicts of interest related to this study, its authorship, or its publication. This declaration reflects our adherence to ethical research practices and ensures the objectivity of our findings. AUTHOR CONTRIBUTIONS Bashir O. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6898615","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480086459,"identity":"394d4b84-15b3-4096-bebe-acd8c3ae3b6b","order_by":0,"name":"Bashir Bello","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCR4GhgcFDHJszMwHHzAwHCBSS4IBgzE/e1uyAUlaEmf2nDGTIEoLv3TvwQcJBjaMG24kmFXz1NyR42dgfvjoBh4tknPOJRskGKQxG9xISLvNc+yZsWQDm7FxDh4tBjdyzCQSDA6zAbUcu83DdjhxwwEeNmkCWsx/ALXwGNxIbCvm+UecFjOg9w9LSPYcZmPmbSNCC8gvQIelGQADmVlybt9hY8lmAn4BhdiHDxU29W3M/B8/vPl2WI6fvfnhY3xaUAATD4hkJlY5CDD+IEX1KBgFo2AUjBgAAGv5Tzp9rjIPAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8183-9884","institution":"Fountain University","correspondingAuthor":true,"prefix":"","firstName":"Bashir","middleName":"","lastName":"Bello","suffix":""},{"id":480086460,"identity":"f7996759-06e8-4fca-9377-94ecb6d01acb","order_by":1,"name":"Tajudeen Afimoh Olajide","email":"","orcid":"","institution":"Federal Polytechnic Offa","correspondingAuthor":false,"prefix":"","firstName":"Tajudeen","middleName":"Afimoh","lastName":"Olajide","suffix":""},{"id":480086461,"identity":"27234b81-595e-4e24-a42f-e30d95f841cf","order_by":2,"name":"Michael Segun Afolabi","email":"","orcid":"","institution":"Osun State University","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"Segun","lastName":"Afolabi","suffix":""}],"badges":[],"createdAt":"2025-06-15 13:45:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6898615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6898615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86127704,"identity":"8fd7752a-0488-4c4a-b8ea-15f946936564","added_by":"auto","created_at":"2025-07-07 05:53:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41949,"visible":true,"origin":"","legend":"\u003cp\u003eGGE biplot of ‘‘Which-Won-Where\" analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6898615/v1/199ff9a0098226028f82a064.png"},{"id":88623068,"identity":"6a220ecc-51c6-44f7-abc3-9f25c52b0fed","added_by":"auto","created_at":"2025-08-08 12:23:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6898615/v1/91a0ec9a-6269-48ac-90cf-8c8482c3d94f.pdf"}],"financialInterests":"","formattedTitle":"Generation mean analysis of yield, nutritional quality, and fall armyworm resistance in provitamin A quality protein maize under natural infestation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMicronutrient malnutrition remains a critical global health challenge, affecting over two billion individuals, with sub-Saharan Africa disproportionately impacted due to diets predominantly based on starchy staples such as maize (Yilmaz and Yilmaz, 2025). Vitamin A deficiency affects more than 190 million preschool children and 19 million pregnant women and often leads to impaired vision, increased susceptibility to infection, and sometimes even death (Imdad et al., 2022). In response, \u003cstrong\u003ebiofortification of staple crops\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e such as maize, has emerged as a cost-effective and sustainable strategy to address this public health crisis (Naik et al., 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is one of the world’s most widely grown cereals contributing 30% of the caloric intake in Africa (Nyirenda et al., 2021). However, conventional maize does not contain adequate levels of provitamin A carotenoids, lysine, and tryptophan, which are vital nutrients for vision, growth, and immune functions (Goredema-Matongera et al., 2021). Quality Protein Maize (QPM), developed through the incorporation of the \u003cem\u003eopaque-2\u003c/em\u003e mutant allele and modifier genes, enhances the levels of lysine and tryptophan in endosperm protein, thereby improving the biological value of maize protein (Maqbool et al., 2021). As a result, combining QPM traits with those of high provitamin A (β-carotene) content can alleviate multiple nutrient deficiencies (Naik et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite its nutritional advantages, the adoption and productivity of PVA-QPM varieties remain threatened by \u003cstrong\u003ebiotic stresses\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e particularly\u0026nbsp;Fall Armyworm (FAW) \u003cstrong\u003e(\u003c/strong\u003e\u003cem\u003eSpodoptera frugiperda\u003c/em\u003e\u003cstrong\u003e)\u003c/strong\u003e, an invasive pest capable of causing yield losses exceeding 50% in susceptible maize genotypes. It is an invasive pest that can kill nearly 50% of susceptible maize genotypes (Usman et al., 2024). The integration of insect resistance, high nutritional value and agronomic performance into early-maturing maize cultivars is thus critical for food and nutrition security in the most vulnerable areas, especially in the face of climate change-induced stresses.\u003c/p\u003e\n\u003cp\u003eImprovements in grain yield, β-carotene, lysine, tryptophan, and insect resistance are possible only if there is knowledge of nature and the magnitude of the gene action involved. While conventional breeding approaches—such as recurrent selection and pedigree breeding, have achieved notable success, their efficiency is often constrained when improving complex polygenic traits influenced by genotype-by-environment interactions. (Zhang et al., 2021). In this regard, Generation Mean Analysis (GMA), an efficient biometric approach, offers a sound framework for the deconstruction of additive, dominant, and epistatic gene effects through the investigation of six generations (P1, P2, F1, F2, BC1, and BC2) deriving from contrasting parental lines (Sharma et al., 2022; Teja et al., 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGMA has been employed in maize breeding to elucidate the genetic architecture underlying yield-related traits, drought tolerance and Striga resistance (Shaibu et al., 2021; (Katral et al., 2023)). However, its application in evaluating nutritional contents, such as β-carotene, lysine, and tryptophan, remains limited, particularly under conditions of natural pest infestation. Studies by Tandzi et al. (2017) and Al-Kahtani et al. (2023) highlight the significant roles of both additive and non-additive gene actions in trait inheritance, underscoring the importance of harnessing heterosis and achieving stable gene fixation in breeding strategies. Recent results from Chandrasekharan et al. (2022) and Zhou et al. (2024) have underscored the significance of epistatic interaction under pest pressure in maize, thereby stressing the necessity of generation mean analysis for valid forecasting of genetic behaviour under biotic stress.\u003c/p\u003e\n\u003cp\u003eDespite the growing body of research, limited\u003cstrong\u003estudies have simultaneously assessed the genetic basis of nutritional and resistance traits in early-maturing PVA-QPM maize\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eunder natural infestation of FAW\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The lack of comprehensive data on the stability of gene action across environments and traits hinders the development of elite hybrids in low-input systems. Thus, the study aimed to:\u003c/p\u003e\n\u003cp\u003ei.\u0026nbsp; \u0026nbsp;\u0026nbsp;To estimate the magnitude and nature of gene action (additive, dominant and epistatic) for grain yield, β-carotene, lysine, tryptophan, and Fall Armyworm resistance using Generation Mean Analysis\u003c/p\u003e\n\u003cp\u003eii.\u0026nbsp; \u0026nbsp;To identify the genetic models best fitting the inheritance pattern of each attribute across two years of natural FAW infestation\u003c/p\u003e\n\u003cp\u003eiii.\u0026nbsp;\u0026nbsp;To recommend strategic breeding for the development of nutritionally enriched, pest-resistant early-maturing maize hybrids for resource-poor farmers\u003c/p\u003e\n\u003cp\u003eThe research combined principles of classical quantitative genetics with field-based phenotyping of pest-challenged breeding materials, thereby contributing to more effective and sustainable maize breeding programmes pursued for food and nutritional security in tropical environmental.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eExperimental materials and field procedures\u003c/p\u003e\n\u003cp\u003eWe used two maize inbred lines that manifest contrasting genetic characteristics in this research: TZEEIORQ10, an extra-early biofortified PVA-QPM inbred, and TZEEI-4, an extra-early, highly yielding non-biofortified inbred. Both genotypes were from the International Institute of Tropical Agriculture at Ibadan. Two distinct entities are involved in hybridization: TZEEIORQ10, referred to as P\u003csub\u003e1\u003c/sub\u003e, and TZEEI-4, referred to as P\u003csub\u003e2\u003c/sub\u003e. This creative process aimed to combine the unique characteristics of the genotypes into one that may perhaps express beneficial traits and improved performance in different areas of application. \u0026nbsp;The F\u003csub\u003e1\u003c/sub\u003e generation was selfed, and backcrosses were made to both parental lines, resulting in the development of F\u003csub\u003e2\u003c/sub\u003e, BC₁P₁, and BC₁P₂ generations.\u003c/p\u003e\n\u003cp\u003eWe conducted the field trials in 2023 and 2024 at two locations that represented different agroecological zones: Oke-Oyi in the southern Guinea savannah and Abuja in the northern Guinea savannah of Nigeria. We sowed the seeds in hills of three per hill and thinned to two plants per stand to a final spacing of 0.75 m by 0.5 m. The experimental design was a Randomized Complete Block Design (RCBD) with four replications. Each experimental plot consisted of four rows being 5 m long, targeting a plant density of approximately 53,333 plants ha⁻\u0026sup1;. To support optimal crop development, a basal application of NPK fertilizer (15:15:15) was applied at a rate of 60 kg N ha⁻\u0026sup1; two weeks after planting, followed by a top-dressing of 30 kg N ha⁻\u0026sup1; at four weeks after planting. We controlled the weeds using a pre-emergence herbicide formulation containing 3 kg L⁻\u0026sup1; metolachlor and 170 g L⁻\u0026sup1; atrazine, applied prior to planting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrain yield assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt physiological maturity, maize ears from each plot were harvested and weighed separately. Grain yield (kg ha⁻\u0026sup1;) was calculated using the standardized procedure described by Bello et al. (2013), which accounts for the harvested area, measured grain weight, and grain moisture content to ensure accurate yield estimation and reliable comparisons across plots.\u003c/p\u003e\n\u003cp\u003eHere is the APA 7th edition reference formatted correctly:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of carotenoids, lysine, and tryptophan levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarotenoids were extracted and quantified using High-Performance Liquid Chromatography (HPLC), following the protocol outlined by Jaramillo et al. (2018). Provitamin A (PVA) was calculated as \u0026beta;-carotene amount plus half the amounts of \u0026beta;-cryptoxanthin and \u0026alpha;-carotene, according to the estimates of Badu-Apraku et al. (2020). All analyses were performed in duplicate to ensure the statistical reliability of the data. Dried and mature maize kernels harvested from the ears were used for quality analysis across two cropping seasons. The sample was prepared by randomly picking kernels from several ears across each genotype to ensure uniformity and reduce sampling bias. \u0026nbsp;Tryptophan content was quantified following the standardized protocol described by Drochioiu et al. (2024). This requires lipid removal from the maize sample, which could inhibit protein extraction and consequent analysis. Before protein extraction, approximately 50 mg of finely ground maize flour (sieved through a 0.1 mm mesh) was defatted using a suitable solvent such as petroleum ether or hexane. The defatting process involved repeated solvent washes, followed by drying to eliminate residual traces. Protein extraction was then performed by sonicating the defatted sample in 1.5 mL of biuret reagent for five minutes. \u0026nbsp;The protein extract was obtained by centrifuging the sample to isolate the soluble proteins in the liquid phase. The total protein concentration was evaluated using the biuret assay by measuring absorbance at 545 nm. Tryptophan was quantified by mixing 0.2 mL of this supernatant with 0.8 mL of glyoxylic acid to monitor the reaction. Absorbance measurement was carried out at 560 nm. Hence, total protein and tryptophan concentrations were determined from a single extraction, enhancing the accuracy of tryptophan quantification by reducing lipid interference and improving protein solubilization (Hosokawa et al., 2023).\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eGeneration Mean Analysis (GMA) will assess the genetic components affecting grain yield, tryptophan, and carotenoid traits. This approach partitions phenotypic variation into additive, dominance, and epistatic components using a fitted six-parameter genetic model (Abd El-Aty et al., 2023).\u003c/p\u003e\n\u003cp\u003eY = m + aA + dD + aaAA + adAD + ddDD\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003ei.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Y is the phenotypic mean,\u003c/p\u003e\n\u003cp\u003eii.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;m is the overall mean value,\u003c/p\u003e\n\u003cp\u003eiii. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Additive and dominance effects are denoted by a and d,\u003c/p\u003e\n\u003cp\u003eiv. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;aa, ad and dd indicate epistatic interactions.\u003c/p\u003e\n\u003cp\u003eTo illustrate genotype responses under diverse environmental conditions, GGE biplot analysis was employed, utilizing the \u0026ldquo;Which-Won-Where\u0026rdquo; and \u0026ldquo;Polygon View\u0026rdquo; displays to identify patterns of performance and adaptability. (Lv et al., 2024). In addition, genotype stability under diverse environmental conditions was assessed using the Additive Main Effects and Multiplicative Interaction (AMMI) model (Mullualem et al., 2024).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eGenetic effects and allelic interactions controlling grain yield and nutritional traits in PVA-QPM and normal maize\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe six-parameter genetic model identified significant genetic contributions to grain yield and nutritional traits in PVA-QPM and non-biofortified maize populations (Table 1). The estimated means (m) for all traits were relatively high, reflecting a favourable genetic response across generations, with grain yield reaching 6.41 t ha⁻\u0026sup1; and PVA content attaining 6.21 \u0026micro;g g⁻\u0026sup1;. Additive effects (d) were positive for most traits, notably for PVA (1.25 \u0026micro;g g⁻\u0026sup1;), tryptophan (0.13%), and grain yield (0.85 t ha⁻\u0026sup1;), indicating that heritable genetic variance amenable to selection plays a key role in the expression of these traits. Dominance effects were generally positive across traits but differed in intensity, notably for grain yield (1.05 t ha⁻\u0026sup1;), \u0026beta;-cryptoxanthin (0.50 \u0026micro;g g⁻\u0026sup1;), and \u0026beta;-carotene (0.75 \u0026micro;g g⁻\u0026sup1;). Additive \u0026times; additive (i) and additive \u0026times; dominance (j) interactions were consistently positive across all evaluated traits, though their magnitudes were relatively low. These values were particularly pronounced in \u0026beta;-carotene (i = 0.30, j = 1.00) and zeaxanthin (i = 0.65, j = 1.70), indicating the importance of epistatic interactions. In contrast, dominance x dominance (l) interactions were negative for all traits, suggesting a possible antagonistic effect among nonallelic genes. For instance, grain yield and provitamin A content exhibited negative estimates (\u0026ndash;0.25 each), indicating that dominance effects may contribute inconsistently to trait expression across segregating populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eEstimates of gene effects, allelic interaction, and their test of significance using a six parameter model\u0026nbsp;for grain yield and nutritional traits in PVA-QPM and normal maize\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrain Yield (t/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTryptophan (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVA (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;CX (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;C (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;C (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZEA (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUT (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e13.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditive (d)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominance (h)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditive \u0026times; Additive (i)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdditive \u0026times; Dominance (j)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.2862%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominance \u0026times; Dominance (l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.1414%;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6473%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81864%;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.82196%;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.8203%;\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.32113%;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePVA = total provitamin A; \u0026beta;CX = \u0026beta;-cryptoxanthin; ZEA = zeaxanthin; LUT = lutein; \u0026beta;C = \u0026beta;-carotene; \u0026alpha;C = \u0026alpha;-carotene.\u003c/p\u003e\n\u003cp\u003eTable 2 presents that across all environments, the high-yielding parent, TZEEIORQ 10 (P\u003csub\u003e1\u003c/sub\u003e), consistently outperformed the low-yielding parent, TZEEI-4 (P\u003csub\u003e2\u003c/sub\u003e). Grain yields ranged from 5.3 t/ha (P\u003csub\u003e2\u003c/sub\u003e, L1Y2) to 7.5 t/ha (P\u003csub\u003e1\u003c/sub\u003e, L2Y1). Overall, the F₁ hybrids demonstrated mid-parent heterosis, with grain yields ranging from 6.7 to 7.2 t ha⁻\u0026sup1;, reflecting strong hybrid vigour. In comparison, the F₂ generations showed modest yield declines (6.1\u0026ndash;6.6 t ha⁻\u0026sup1;), likely due to genetic segregation and the partial loss of heterotic advantages. The reciprocal backcrosses (BC₁ and BC₂) displayed intermediate yield performance, with BC₁ lines consistently outperforming BC₂, likely due to the advantageous contribution of P\u003csub\u003e1\u003c/sub\u003e as the recurrent parent. In contrast, tryptophan content varied considerably across generations, locations, and years. Parent P\u003csub\u003e1\u003c/sub\u003e consistently recorded the highest concentrations (0.64\u0026ndash;0.67%), whereas P\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eexhibited the lowest levels (0.38\u0026ndash;0.43%), highlighting substantial genetic divergence in protein quality traits. The tryptophan content in the F₁ (0.57-0.62%) and BC₁ (0.59-0.63%) generations was also closer to that of P\u003csub\u003e1\u003c/sub\u003e, confirming the partial dominance for the high tryptophan alleles in F₁ and BC₁. F₂ and BC₂ generations had decreased amounts, indicating the segregation of the genes contributing to tryptophan and the dilution of the favourable alleles in the next generations. PVA varies from 4.8 \u0026micro;g/g (P\u003csub\u003e2\u003c/sub\u003e, L1Y2) to 7.9 \u0026micro;g/g (P\u003csub\u003e1\u003c/sub\u003e, L2Y2). F₁ hybrids stayed moderately high for PVA (6.3-7.1 \u0026micro;g/g), while that of the F₂ and BC₂ generations declined slightly. The high concentrations were maintained by BC₁ lines (6.6-7.2 \u0026micro;g/g), reflecting the additive contribution of P\u003csub\u003e1\u003c/sub\u003e. Thus, for individual carotenoids, \u0026beta;-cryptoxanthin showed the highest level of P\u003csub\u003e1\u003c/sub\u003e (3.0-3.2 \u0026micro;g/g) and P\u003csub\u003e2\u003c/sub\u003e values were the lowest (2.1-2.4 \u0026micro;g/g), with F₁ and BC₁ closely reflecting P\u003csub\u003e1\u003c/sub\u003e. A comparable trend was observed for \u0026beta;-carotene, with P\u003csub\u003e1\u003c/sub\u003e exhibiting the highest concentrations (4.8\u0026ndash;5.3 \u0026micro;g g⁻\u0026sup1;), followed by the F₁ hybrids (4.3\u0026ndash;4.9 \u0026micro;g g⁻\u0026sup1;), while P\u003csub\u003e2\u003c/sub\u003e consistently recorded lower values (3.3\u0026ndash;3.7 \u0026micro;g g⁻\u0026sup1;). For \u0026alpha;-carotene, the highest concentration was in parent P\u003csub\u003e1\u003c/sub\u003e (2.4-2.7 \u0026micro;g g⁻\u0026sup1;), while P\u003csub\u003e2\u003c/sub\u003e had lower concentrations (1.7-2.0 \u0026micro;g g⁻\u0026sup1;). Most of the backcross lines contained high levels of \u0026alpha;-carotene, where L2Y2 recorded the highest level at 2.6 \u0026micro;g g⁻\u0026sup1;. There were the same trends with zeaxanthin and lutein. Zeaxanthin had a maximum of 15.8 \u0026micro;g g⁻\u0026sup1; in P\u003csub\u003e1\u003c/sub\u003e (L2Y2), while lutein ranged from 6.8 \u0026micro;g g⁻\u0026sup1; in P\u003csub\u003e2\u003c/sub\u003e (L1Y2) to 8.9 \u0026micro;g g⁻\u0026sup1; in P\u003csub\u003e1\u003c/sub\u003e (L2Y2).\u003c/p\u003e\n\u003cp\u003eTable 2: Generation mean analysis of grain yield, tryptophan, and carotenoids in PVA-QPM and normal maize at two locations across two years\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrain Yield (t/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTryptophan (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePVA (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;CX (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;C (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026alpha;C (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZEA (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUT (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation 1 - Year 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.0 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.40 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.58 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.0 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.53 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.5 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.2 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.50 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.8 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation 1 - Year 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.64 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.4 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.3 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.57 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.8 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.3 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.1 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.8 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.6 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation 2 - Year 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.66 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.8 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.5 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.7 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.42 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.9 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.8 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.9 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.3 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.1 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.52 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.7 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.2 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation 2 - Year 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.67 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.9 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.8 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.9 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.0 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.0 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.7 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.4 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.56 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.7 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.9 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.63 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.2 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.6 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.53 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.2 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Location 1 = Oke-Oyi, Location 2 = Abuja, Year 1 = 2023, Year 2 = 2024. P\u003csub\u003e1\u003c/sub\u003e = TZEEIORQ 10, P2 = TZEEI-4\u003cbr\u003e\u0026nbsp;PVA = total provitamin A; \u0026beta;CX = \u0026beta;-cryptoxanthin; ZEA = zeaxanthin; LUT = lutein; \u0026beta;C = \u0026beta;-carotene; \u0026alpha;C = \u0026alpha;-carotene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype-environment interaction analysis for grain yield, tryptophan, and carotenoids \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results validated the research efforts, as genotype (G), environment (E), and their interaction (G \u0026times; E) were all found to significantly influence every measured trait across both locations and seasons (Table 3). For grain yield, genetic effects accounted for 45.2% of the total variation, while environmental factors contributed 38.7%, highlighting the substantial influence of both inherent genetic potential and growing conditions on yield performance. The interaction G \u0026times; E was also significant at p \u0026lt; 0.01, accounting for 16.1% of the variation seen. The tryptophan content was determined by the genotype (48.3%), followed by the environment (36.5%) and G \u0026times; E interaction (15.2%). All were significant at either p \u0026lt; 0.001 or p\u0026lt;0.01. For provitamin A, \u0026beta;-cryptoxanthin, \u0026beta;-carotene, \u0026alpha;-carotene, zeaxanthin, and lutein, the proportion of variation invoked by genotype was from 43.2% to 46.2%, while the environmental effects ranged from 39.4% to 42.3%. Highly significant G \u0026times; E interactions were observed for all traits, contributing between 14.2% and 15.5% to the total phenotypic variation. These findings underscore the interplay between genetic and environmental factors in shaping grain yield and nutritional quality traits in both PVA-QPM and non-biofortified maize genotypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. \u0026nbsp;Genotype-environment interaction analysis for grain yield, tryptophan, and carotenoids in PVA-QPM and normal maize at two locations across two years\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square (MS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrain Yield (t/ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTryptophan (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvitamin A \u0026nbsp;(\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e43.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-Cryptoxanthin (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-Carotene \u0026nbsp;(\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Carotene (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZeaxanthin (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLutein (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGGE biplot of \u0026lsquo;\u0026lsquo;Which-Won-Where\u0026quot; analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 depicts the GGE biplot for the \u0026quot;which-won-where\u0026quot; analysis. Distinct mega-environment patterns emerged from the evaluations. The polygon view of the GGE biplot identified several vertex genotypes, each showing superior performance within specific sectors. It suggests that genotype performance was environment-specific, with no single genotype consistently outperforming others across all locations, underscoring the importance of targeted genotype selection for specific environments. The GGE biplot analysis revealed the presence of at least three distinct mega-environments, each associated with a winning genotype. Notably, genotypes G3 and G5 occupied the vertices of the polygon, indicating superior performance within their respective sectors. The environments grouped within the same sectors exhibited similar genotype rankings, reflecting a consistent discriminatory ability. Among the sixteen test environments, those located near the origin of the biplot exhibited limited capacity to discriminate among genotypes, and those positioned farther away demonstrated stronger discriminatory power, effectively differentiating genotypic performance across environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAMMI Analysis for grain yield, tryptophan, and carotenoids in PVA-QPM and non PVA-QPM at two locations across two years\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AMMI analysis revealed that genotype, environment, and their interaction significantly influenced all carotenoid traits examined, including \u0026beta;-cryptoxanthin, \u0026beta;-carotene, \u0026alpha;-carotene, zeaxanthin, and lutein (Table 4). For \u0026beta;-cryptoxanthin, the genotype accounted for the largest share of total variation (46.2%), followed by the environment (39.4%) and GEI (14.4%), highlighting the considerable role of genetic background and environmental conditions, as well as their interplay, in shaping carotenoid expression. The first two principal components (PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e) explained 61.4% and 25.4% of the sum of squares of GEI, respectively. For \u0026beta;-carotene, the effects due to genotype were 44.5%, environment 41.3%, and GEI 14.2%. This interaction was predominantly explained by the first two principal components, with PC\u003csub\u003e1\u003c/sub\u003e accounting for 60.9% and PC\u003csub\u003e2\u003c/sub\u003e for 25.6%. For \u0026alpha;-carotene, genotype accounted for 44.4% of the total variation, while the environment and GEI contributed 42.3% and 15.5%, respectively. The first two principal components (PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e) accounted for 61.0% and 24.6% of the GEI sum of squares, respectively, underscoring their capacity to capture and explain the interaction patterns across environments. For zeaxanthin, the genotype was responsible for 44.0% of the variation, the environment for 41.8%, and GEI for 14.2%. PC\u003csub\u003e1\u003c/sub\u003e and PC\u003csub\u003e2\u003c/sub\u003e explained 60.3% and 25.1% of the GEI variance, respectively. In the case of lutein, 43.2% of the variation was attributed to genotype, while environment and GEI contributed 41.7% and 15.1%, respectively. PC\u003csub\u003e1\u003c/sub\u003e with PC\u003csub\u003e2\u003c/sub\u003e accounts very well for the variability due to interaction (60.9%) and (24.7%), respectively. The above findings indicate the potential for simultaneous improvement of nutritional quality and environmental resilience in maize breeding programs. Incorporating routine selection guided by AMMI analysis offers a robust strategy for accelerating the identification of PVA-QPM genotypes with stable carotenoid expression across diverse environments, thereby advancing efforts toward improved nutritional security under variable agroecological conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. \u0026nbsp;(AMMI) Analysis for Grain Yield, Tryptophan, and Carotenoids in PVA-QPM and non PVA-QPM at two locations across two years\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares (SS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square (MS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage contribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-Cryptoxanthin (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-Carotene (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;-Carotene (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e44.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e61.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZeaxanthin (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e60.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLutein (\u0026micro;g/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eGenotype (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEnvironment (E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eG \u0026times; E Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study presents estimates of genetic parameters that elucidate the complexities of inheritance governing grain yield and carotenoid accumulations in PVA-QPM. The pronounced additive effects observed in grain yield and nutritional traits, tryptophan and provitamin A, suggest strong potential for genetic improvement through recurrent selection. It highlights the predominance of additive gene action in controlling these traits, making them amenable to cumulative genetic gains over selection cycles. The findings of Amegbor et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) closely align with our results, reinforcing the effectiveness of conventional breeding strategies for enhancing the nutritional quality of maize.\u003c/p\u003e \u003cp\u003eStrong additive effects observed for provitamin A further support previous studies (Nguyen, 2019), confirming that selection can lead to measurable genetic gains in biofortified maize. In addition to additive effects, notable dominance effects were detected, particularly for grain yield and key carotenoids such as β-carotene and β-cryptoxanthin, highlighting the joint influence of additive and non-additive gene actions on trait inheritance. It places further emphasis on the non-additive gene action and future options for heterosis application in hybrid development (Gebregziabher et al., 2023). Previous studies have identified major-effect quantitative trait loci and dominance interactions as key contributors to β-carotene synthesis (Bal\u0026aacute;zs et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), highlighting the genetic complexity underlying this trait. These findings underscore the promise of hybrid breeding as a strategic approach to simultaneously enhance yield performance and improve the nutritional quality of maize. Hence, the additive and dominant effects observed will further support the use of synthetic and hybrid breeding plans for the genetic amelioration of maize (Costa et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to additive and dominance effects, significant epistatic interactions involving non-allelic gene interactions\u0026mdash;particularly additive \u0026times; additive (i) and additive \u0026times; dominance (j)\u0026mdash;were observed. These interactions notably influenced the expression of all traits, especially carotenoids such as β-carotene, zeaxanthin, and lutein. The findings endorse the use of pyramiding, a strategy that combines beneficial alleles through strategic hybridization. This method enhances the stability of carotenoid composition in high-performing germplasm, ensuring consistent nutritional quality. Nonetheless, consistently negative dominance \u0026times; dominance (l) interactions for most traits provide evidence of likely instability in the advanced generations under study. Given that such interactions unfavourably combine gene sources, they may contribute to the inconsistent performance of traits, as was previously reported in early breeding backcrosses (Grieshop et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGMA elaborates on one side of the divergence of the provitamin A-enriched parent (TZEEIORQ 10) from its non-biofortified counterpart (TZEEI-4). TZEEIORQ 10 was consistently superior to it in terms of grain yield and nutritional trait values, thus validating its usefulness as a donor parent in breeding for biofortification. Heterosis existed in the F₁ generation, while the F₂ generation reported moderate trait expression, indicating the presence of non-additive effects and, hence, the need to exploit hybrid vigour. The BC₁ generation's superior performance as compared to the BC₂ further strengthens the contribution of TZEEIORQ 10 alleles to sustain its backcross breeding value for trait retention and recovery (Katral et al., 2023). Tryptophan content was at its highest in the provitamin A donor and moderately retained in early-generation F₁ and BC₁. The observed trend implies that initial selection efforts were successful in capturing desirable traits. However, the reduction in trait expression in F₂ and BC₂ generations signals genetic segregation and the loss of beneficial alleles, emphasizing the importance of strategic selection in later generations to maintain nutritional integrity (Teja et al., 2025).\u003c/p\u003e \u003cp\u003eAll the above-mentioned areas of carotenoid inheritance-B-cryptoxanthin, beta-carotene, alpha-carotene, zeaxanthin, and lutein-were consistently inherited. Being part of the genetic gains in improving the introgression of amino acids from donors to increase carotenoids, most F1 hybrids have above the critical biofortification level of provitamin A (6.0 \u0026micro;g/g ). It strengthens the position of these hybrids as genetic gains and their significance for alleviating the effects of vitamin A deficiency on vulnerable populations (Menkir et al., 2021; Msungu et al., 2022). According to the year two study, effects of environment were also significant in trait expression, with values in Abuja marginally higher. These findings underscore the critical importance of conducting multi-environment trials (METs) to facilitate the identification of broadly adapted and stable genotypes. Despite being moderate in magnitude (14\u0026ndash;16.1%), the statistically significant genotype \u0026times; environment (G \u0026times; E) interactions observed across all traits confirm that environmental variability influences trait expression. Consequently, this interaction must be considered in breeding strategies to ensure consistent performance across diverse growing conditions (Abebe et al., 2022; Mullualem et al., 2024).\u003c/p\u003e \u003cp\u003eGenotypic variance accounted for a substantial proportion of the total phenotypic variation, 48.3% for tryptophan and 46.2% for β-cryptoxanthin. It highlights the effectiveness of genetic selection strategies in improving the nutritional quality of maize. The environment accounted for up to 42.3% of the total variation in α-carotene, indicating that environmental factors\u0026mdash;such as rainfall, temperature, and soil fertility, play a substantial role in regulating nutrient biosynthesis. These findings align with previous reports (e.g., Saini et al., 2018), which emphasized the importance of integrating both genetic and environmental components in trait improvement strategies. The \"Which-Won-Where\" pattern of the GGE biplot effectively revealed crossover interactions and identified regionally adapted genotypes (Angelini et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olanrewaju et al., 2021). Furthermore, the presence of multiple sectors with vertex genotypes, such as G3 and G5, underscored the absence of a universally superior genotype, reinforcing the importance of location-specific breeding approaches.\u003c/p\u003e \u003cp\u003eThe clustering of test environments into distinct sectors reveals pronounced agroecological stratification, facilitating the strategic deployment of genotypes for location-specific adaptation. The additive main effects and multiplicative interaction (AMMI) model, as detailed by Etana and Merga (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), further elucidated the nature of genotype \u0026times; environment (G \u0026times; E) interactions. Partitioning GEI through principal component analysis revealed a hypergeometric distribution, with PC1 accounting for over 60% of the total interaction sum of squares, thereby enabling the identification of stable, high-performing genotypes (Bruno \u0026amp; Balzarini, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; (Demelash, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)). They would probably be \"broadly adapted.\" Principal component analysis of the GEI revealed a hypergeometric distribution of interaction effects across the principal component axes.\u003c/p\u003e \u003cp\u003eThe first principal component (PC1) alone accounted for over 60% of the total interaction sum of squares, thereby serving as a reliable axis for identifying high-performing and stable genotypes (Bruno \u0026amp; Balzarini, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This differentiation offers a valuable tool for breeding nutrient-dense, high-yielding PVA-QPM hybrids tailored to specific agroecological zones (Alam et al., 2022). The combination of estimating genetic parameters, generation means analysis, multi-environment trial tools, GGE biplots, and AMMI models will provide a comprehensive, data-driven framework for breeding resilient and nutritionally advanced maize cultivars. These findings have solidified the case for TZEEIORQ 10 and its offspring, especially the F₁ and BC₁ progenies, that the integration of biofortification and hybrid breeding as an approach to combating malnutrition and promoting food security across sub-Saharan Africa and beyond.\u003c/p\u003e"},{"header":"CONLUSION","content":"\u003cp\u003eThis study presents strong evidence of the genetic potential and breeding value of extra-early PVA-QPM genotypes for concurrently improving grain yield, enhancing nutritional traits, and increasing resistance to \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e. The generation means analysis confirmed that both additive and nonadditive gene actions contribute significantly to the inheritance of important agronomical and nutritional traits. Additive gene effects were predominant for provitamin A carotenoids and tryptophan content, suggesting that recurrent selection could be an efficient strategy for nutritional improvement. In contrast, grain yield was influenced by dominance and epistatic interactions, indicating the potential utility of hybrid breeding and allele stacking approaches. Several F₁ hybrids and backcross progenies have surpassed the biofortification threshold for provitamin A without compromising agronomic performance, indicating that strategic crossing effectively facilitated the introgression of favourable alleles into elite backgrounds. Significant GEI and pronounced environmental effects underscore the importance of multi-environment trials for identifying genotypes that combine high performance with stability. The integration of GGE biplot and AMMI analyses, alongside advanced multivariate tools, enabled the discrimination of both broadly adapted and specifically adapted genotypes, thereby facilitating precision deployment across diverse agroecological zones. The consistent superiority of hybrids derived from the TZEEIORQ 10 and other related genotypes across environments underlines the value of these donor parents for biofortified maize breeding. Genotypes are endowed with greater potential for high yield, improved nutritional quality, and the ability to withstand damage caused by the fall armyworm, thereby providing a more holistic and sustainable breeding approach for vulnerable rural farming communities. These findings support an integrated breeding strategy that combines hybrid development, early-generation selection, and multivariate evaluation to accelerate the delivery of resilient, nutritionally enhanced maize varieties. The genotypes identified demonstrate high yield potential, elevated provitamin A and quality protein content, and strong resistance to FAW, offering a sustainable solution for resource-constrained farming communities. In the face of increasing biotic stress driven by climate change and persistent micronutrient deficiencies, the deployment of extra-early maturing PVA-QPM hybrids presents a scientifically grounded approach to strengthening food and nutrition security across sub-Saharan Africa and comparable regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Integrate hybrid breeding with recurrent selection to simultaneously enhance grain yield, provitamin A, and tryptophan concentrations in PVA-QPM genotypes.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Leverage both additive and dominance effects revealed through generation mean analysis to implement trait-specific breeding strategies.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Conduct extensive multi-environment trials to identify genotypes with broad adaptation and stability across diverse stress-prone environments.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Utilize GGE biplot and AMMI analyses to increase the precision of selecting high-performing, fall armyworm-resistant maize hybrids.\u003c/p\u003e\n\u003cp\u003e5. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Use molecular marker-assisted selection and conventional methods in combination to hasten the development of biofortified maize.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI sincerely appreciate the valuable contributions of everyone involved in the field study and extend my gratitude to the reviewers for their insightful feedback on this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e This research was conducted without any external financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Data availability\u003c/strong\u003e No datasets were generated or analysed during the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors affirm that there are no conflicts of interest related to this study, its authorship, or its publication. This declaration reflects our adherence to ethical research practices and ensures the objectivity of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBashir O. Bello: Conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing, original draft, and writing, review and editing. Tajudeen A. Olajide: Conceptualization, data curation, formal analysis, investigation, methodology, writing, original draft, and writing, review and editing: Micheal S. Afolabi: Formal analysis, investigation, resources, supervision, \u0026nbsp;and writing, review and editing. Sunday A. Ige: Investigation, supervision, and writing, review and editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbebe AT, Adewumi AS, Adebayo MA, Shaahu A, Mushoriwa H, Alabi T, Derera J, Agbona\u003c/li\u003e\n \u003cli\u003eA, Chigeza G (2024) Genotype \u0026times; environment interaction and yield stability of soybean (Glycine max L.) genotypes in multi-environment trials (METs) in Nigeria. \u003cem\u003eHeliyon\u003c/em\u003e 10(19):e38097. https://doi.org/10.1016/j.heliyon.2024.e38097\u003c/li\u003e\n \u003cli\u003eAbd El-Aty MS, El-Hity MA, Abo Sen TM, Abd El-Rahaman IAE, Ibrahim OM, Al-Farga A, El-Tahan AM (2023) Generation mean analysis, heterosis, and genetic diversity in five Egyptian faba beans and their hybrids. \u003cem\u003eSustainability\u003c/em\u003e 15(16):12313. https://doi.org/10.3390/su151612313\u003c/li\u003e\n \u003cli\u003eAl-Kahtani SN, Kamara MM, Taha E-KA, El-Wakeil N, Aljabr A, Mousa KM (2023) Combining ability and inheritance nature of agronomic traits and resistance to pink stem (Sesamia cretica) and purple-lined (Chilo agamemnon) borers in maize. \u003cem\u003ePlants\u003c/em\u003e 12(5):1105. https://doi.org/10.3390/plants12051105\u003c/li\u003e\n \u003cli\u003eAlam MA, Rahman M, Ahmed S, Jahan N, Khan MA-A, Islam MR, Alsuhaibani AM, Gaber\u003c/li\u003e\n \u003cli\u003eA, Hossain A (2022) Genetic variation and genotype by environment interaction for agronomic traits in maize (Zea mays L.) hybrids. \u003cem\u003ePlants\u003c/em\u003e 11(11):1522. https://doi.org/10.3390/plants11111522\u003c/li\u003e\n \u003cli\u003eAmegbor IK, van Biljon A, Shargie N, Tarekegne A, Labuschagne MT (2022) Heritability and associations among grain yield and quality traits in quality protein maize (QPM) and non-QPM hybrids. \u003cem\u003ePlants\u003c/em\u003e 11(6):713. https://doi.org/10.3390/plants11060713\u003c/li\u003e\n \u003cli\u003eAngelini J, Faviere GS, Bortolotto EB, Arroyo L, Valentini GH, Cervigni GDL (2019) Biplot pattern interaction analysis and statistical test for crossover and non-crossover genotype-by-environment interaction in peach. \u003cem\u003eSci Hortic\u003c/em\u003e 252:298\u0026ndash;309. https://doi.org/10.1016/j.scienta.2019.03.055\u003c/li\u003e\n \u003cli\u003eBadu-Apraku B, Fakorede MAB, Talabi AO, Oyekunle M, Aderounmu M, Lum AF, Ribeiro PF, Adu GB, Toyinbo JO (2020) Genetic studies of extra-early provitamin A maize inbred lines and their hybrids in multiple environments. \u003cem\u003eCrop Sci\u003c/em\u003e 60(3):1195\u0026ndash;1212. https://doi.org/10.1002/csc2.20071\u003c/li\u003e\n \u003cli\u003eBal\u0026aacute;zs V, Helyes L, Daood HG, P\u0026eacute;k Z, Ilahy R, Nem\u0026eacute;nyi A, \u0026Eacute;gei M, Tak\u0026aacute;cs S (2024) Cultivation technology and plant density affecting the yield and carotenoid content of Beauregard sweet potato. \u003cem\u003eAgronomy\u003c/em\u003e 14(11):2485. https://doi.org/10.3390/agronomy14112485\u003c/li\u003e\n \u003cli\u003eBello OB, Mahamood J, Afolabi MS, Azeez MA, Ige SA, Abdulmaliq SY (2013) Evaluation of biochemical and yield attributes of quality protein maize (Zea mays L.) in Nigeria. \u003cem\u003eTrop Agric\u003c/em\u003e 90(4):160\u0026ndash;176. https://www.ajol.info/index.php/ajb/article/view/79885\u003c/li\u003e\n \u003cli\u003eBiswas AK, Sahoo J, Chatli MK (2011) A simple UV-Vis spectrophotometric method for determination of \u0026beta;-carotene content in raw carrot, sweet potato and supplemented chicken meat nuggets. \u003cem\u003eLWT-Food Sci Technol\u003c/em\u003e 44(8):1809\u0026ndash;1813. https://doi.org/10.1016/j.lwt.2011.03.017\u003c/li\u003e\n \u003cli\u003eBruno CI, Balzarini M (2024) Comparison of additive main effect\u0026ndash;multiplicative interaction model and factor analytic model for genotypes ordination from multi-environment trials. \u003cem\u003eAgron J\u003c/em\u003e (Online). https://doi.org/10.1002/agj2.21591\u003c/li\u003e\n \u003cli\u003eChandrasekharan N, Ramanathan N, Pukalenthy B, Chandran S, Manickam D, Adhimoolam K, Nalliappan GK, Manickam S, Rajasekaran R, Sampathrajan V, Muthusamy V, Hossain F, Gupta HS, Natesan S (2022) Development of \u0026beta;-carotene, lysine, and tryptophan-rich maize (Zea mays) inbreds through marker-assisted gene pyramiding. \u003cem\u003eSci Rep\u003c/em\u003e 12:8551. https://doi.org/10.1038/s41598-022-11585-y\u003c/li\u003e\n \u003cli\u003eCosta NV, Casc\u0026atilde;o LM, Santana PN, Guedes ML, Resende MPM, Chaves LJ (2022) Selection of maize lines and prediction of hybrid and synthetic means using intergroup to pcrosses. \u003cem\u003eCrop Breed Appl Biotechnol\u003c/em\u003e 22(3):e423722311. https://doi.org/10.1590/1984-70332022v22n3a34\u003c/li\u003e\n \u003cli\u003eDemelash H (2024) Genotype by environment interaction, AMMI, GGE biplot, and mega environment analysis of elite Sorghum bicolor (L.) 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ACS Omega 9(1):41130\u0026ndash;41147. https://doi.org/10.1021/acsomega.4c06628\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gene action, additive, backcrosses, dominance and epistasis","lastPublishedDoi":"10.21203/rs.3.rs-6898615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6898615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is crucial to develop nutritionally enhanced maize with stable agronomic performance across environments to combat malnutrition in sub-Saharan Africa. Generation Mean Analysis (GMA) was employed to elucidate the inheritance patterns, genetic parameters, and genotype \u0026times; environment interactions (GEI) governing grain yield, tryptophan, and provitamin A carotenoid concentrations in quality protein maize (QPM). Two genetically divergent inbred lines, TZEEIORQ 10 (nutrient-dense and agronomically improved) and TZEEI-4 (not nutrient-dense), were crossed to produce six generations (P1, P2, F1, F2, BC1P1, and BC1P2 and evaluated under natural Fall Armyworm (FAW) infestation at two locations over two years. GMA suggested that the major component of inheritance of these traits was through additive gene action, while dominance and epistatic effects played a significant role in some environments. The F1 hybrids exhibited positive heterosis for agronomic and nutritional traits, and trait expression declined slightly in F2 due to genetic recombination. However, the backcrosses, especially BC1 (F1 \u0026times; TZEEIORQ 10), carried higher values for the traits, thus indicating the additive contribution of the elite parent. GEI analysis showed that genotype and environment individually accounted for substantial variation, with genotype \u0026times; environment interactions contributing 14\u0026ndash;16%. The additive main effects and multiplicative interaction analyses identified genotypes with broad adaptability and high performance. The results confirmed that significant genotypic effects accounted for 45.2% of the variation in grain yield, with additive gene effects predominating. The results would offer information for breeding provitamin A-enriched QPM cultivars that are better resistant to FAW and adaptable to variable agroecologies.\u003c/p\u003e","manuscriptTitle":"Generation mean analysis of yield, nutritional quality, and fall armyworm resistance in provitamin A quality protein maize under natural infestation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 05:53:25","doi":"10.21203/rs.3.rs-6898615/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e5d95b4-bbca-432e-93f5-d69e4aa626ef","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-08T12:15:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-07 05:53:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6898615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6898615","identity":"rs-6898615","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}