Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: Implications for breeding high-yielding nutrient-dense hybrids

Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: Implications for breeding high-yielding nutrient-dense hybrids | 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 Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: Implications for breeding high-yielding nutrient-dense hybrids Monika Punia, LD Sharma, DK Gothwal, Sohan Lal Kajla, Lalit Kumar Rolaniya, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8583609/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Micronutrient malnutrition, particularly iron and zinc deficiency, affects over two billion people globally, with women and children in developing countries being the most vulnerable. Pearl millet [ Pennisetum glaucum (L.) R. Br.], a climate-resilient cereal in arid and semi-arid regions, presents a very good opportunity for biofortification as it has an inherently high micronutrient level and genetic variability. The present investigation aimed to estimate combining ability effects, establish gene action, and identify superior parents and hybrids for the simultaneous improvement of grain yield and biofortification traits. Ten genetically diverse inbred lines were crossed in a half diallel mating design, following Griffing's Method 2, Model 1. The resulting 55 entries (45 F₁ hybrids and 10 parents) were evaluated across two environments in a randomized complete block design with three replicates. The analysis revealed high broad-sense heritability for iron (0.94), zinc (0.90), and protein (0.90) contents. Among parents, RIB-9205 had the highest GCA for iron content (6.65***), RIB-9184 for zinc (3.85***), and protein (0.78***), and RIB-15131 was a balanced multi-trait combiner. RIB-9184 x RIB-15131 showed the best hybrid with the highest multi-trait selection index value of 1.31, which produced good grain yield (18.84 g/plant) with improved iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. The strong positive correlation between iron and zinc (rg = 0.82**) enables an efficient simultaneous improvement. The results suggest hybrid breeding for yield maximization and population improvement approaches for biofortification traits to develop high-yielding, high-nutrient pearl millet cultivars. Pearl millet Biofortification Combining ability Diallel analysis Iron Zinc Heterosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Key message Hybrid RIB-9184 × RIB-15131 combines high yield (18.84 g/plant) with enhanced iron (46.16 ppm) and zinc (38.86 ppm); strong Fe-Zn correlation (r = 0.82**) enables simultaneous biofortification improvement. 1. Introduction Micronutrient malnutrition, known as 'hidden hunger,' is a global phenomenon that affects over two billion people and represents one of the most serious health challenges worldwide (Muthayya et al. 2013 ; WHO 2021 ). Iron (Fe) and zinc (Zn) deficiencies are especially widespread, causing anaemia, poor cognitive development, poor immune function, and high mortality rates, especially among women and children in developing nations (Anitha et al., 2021 ). In India, the National Family Health Survey (NFHS-5) found that 57% of women and 67% of children under 5 years of age suffer from anaemia, while the Global Hidden Hunger Index places India among the countries with severe micronutrient deficiencies (Nguyen et al., 2021 ). Although traditional interventions, such as dietary supplementation and food fortification, have been shown to be effective, they are limited by cost, infrastructure requirements, and accessibility in resource-poor settings (Bouis et al., 2011 ). Biofortification, the genetic improvement of staple food crops with increased micronutrient densities, has therefore emerged as a sustainable, cost-effective, and farmer-friendly approach to combat hidden hunger at the root (Govindaraj et al. 2024 ). Pearl millet [ Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone] is the sixth most important cereal crop globally and is a dietary staple for more than 90 million people in the arid and semi-arid tropical regions of Asia and Africa (Taylor 2016 ). This climate-resilient crop grows on marginal soils with limited moisture availability, where other cereals are unable to yield economically. Pearl millet has an intrinsically superior nutritional profile, partly with 8.5–15% protein and a well-balanced amino acid composition, which has enhanced levels of iron (31–61 mg kg − 1 ) and zinc (32–54 mg kg − 1 ) compared to rice and wheat (Burton et al., 1972 ; Govindaraj et al., 2011 ). These attributes have made pearl millet a priority crop for HarvestPlus biofortification initiatives, with breeding goals set at 77 mg kg − 1 Fe (Govindaraj et al. 2024 ). Bioavailability studies have established that the consumption of biofortified pearl millet can meet up to 80% of the daily Fe requirements in target populations, establishing the practical impact of this breeding approach (Kodkany et al. 2013 ). India is the leading producer of pearl millet in the world, with a production of 10.72 million tonnes on 7.38 million hectares, of which Rajasthan has a share of 4.38 million hectares (Indiastat, 2024a, b). However, productivity in arid Rajasthan is still lower than the national average because of environmental constraints, which explains the need for region-specific biofortified cultivars. Effective crop improvement programs require in-depth knowledge of the genetic basis of target traits. Diallel mating designs, especially the half-diallel cross, serve as a systematic procedure for the dissection of the inheritance of quantitative characters, as measured by the estimation of combining ability effects (Griffing 1956 ). General combining ability (GCA) is the average performance of a parent in all combinations of hybrids and is due to additive genetic effects, whereas specific combining ability (SCA) is the difference in the performance of a hybrid from the expectation based on the paternal GCA and is due to non-additive (dominance and epistatic) gene action (Sprague and Tatum, 1942 ). The relative size of these genetic components, as measured by Baker's ratio [2σ²GCA/(2σ²GCA + σ²SCA)], provides important information for the choice of breeding strategy: values close to unity indicate the predominance of additive effects, in favour of population improvement methods, whereas lower values indicate an important non-additive variance for which hybrid breeding methods should be considered (Baker 1978 ). Complementary information on the action of genes is presented by Hayman's (1954b) graphical analysis, which explains the extent of dominance, occurrence of epistasis, and distribution of dominant and recessive alleles between parents using variance-covariance (Vr-Wr) regression. Pearl millet is a highly cross-pollinated (< 85% outcrossing) plant with protogynous floral traits and well-defined cytoplasmic male sterility (CMS) mechanisms, making it an ideal crop for exploiting heterosis in commercial hybrid production (Burton 1983 ; Bashir et al. 2014 ). The first single-cross pearl millet hybrid (HB-1) was released in India in 1965, and successive decades have seen progressive genetic gains in yield and adaptability from the effects of systematic hybrid breeding (Yadav et al., 2024 ). Considerable heterosis for grain yield has been well documented, ranging from 24% panmictic mid-parent heterosis in West African population crosses (Dutta et al. 2021 ) to more than 100% in elite Indian germplasm (Maheswari et al. 2024 ). Importantly, exploitable heterosis has also been shown to be true for biofortification traits, with reports of 18–27% for Fe and 13–14% for Zn (Ladumor et al. 2018 ; Rani et al. 2019 ). Recent studies on combining abilities have shown that both additive and non-additive gene effects are involved in Fe and Zn inheritance, and the effects are mostly additive for micronutrients and show significant non-additive effects on grain yield (Thribhuvan et al. 2023 ; Kanatti et al. 2014 ). Despite leading progress in pearl millet biofortification, several critical knowledge gaps limit the breeding process, especially under difficult arid conditions in Rajasthan. First, the analyses of combining ability involving both yield and biofortification traits in multiple environments are limited, with most studies focusing on either trait category in isolation (Velu et al. 2011 ; Govindaraj et al. 2013 ). Second, potential trade-offs between grain yield and micronutrient density must be systematically characterized because conflicting reports exist regarding the nature and magnitude of these correlations (Kanatti et al. 2014 ; Govindaraj et al. 2022 ). Third, multi-trait selection approaches that allow the simultaneous improvement of yield and nutritional quality are significantly lacking. Fourth, genotype by environment (GxE) interaction effects on biofortification trait expressions need to be investigated, as there are reports indicating that different environments significantly influence not only the grain yield but also the micronutrient composition (Reddy et al. 2021 ; Pawar et al. 2018 ). Addressing these gaps is necessary to produce biofortified cultivars suited to resource-constrained environments, where the prevalence of malnutrition is the highest. Based on the foregoing considerations, we hypothesize that (i) biofortification traits (Fe, Zn, and protein) are governed predominantly by additive gene action, enabling effective selection in early generations, whereas grain yield exhibits significant non-additive variance necessitating hybrid breeding; (ii) the positive correlation between Fe and Zn content will facilitate their simultaneous improvement without severe trade-offs with grain yield; and (iii) superior hybrid combinations can be identified that combine high yield potential with enhanced nutritional quality. To test these hypotheses, the present investigation was undertaken using a half diallel mating design involving 10 diverse pearl millet inbred lines evaluated across two environments. The specific objectives were as follows: estimation of GCA and SCA effects and establishment of the relative importance of the additive versus non-additive gene action for grain yield and biofortification traits using Baker's ratio and Hayman's graphical analysis; identification of superior parents with favorable GCA and promising hybrid combinations with high SCA for both yield and nutritional enhancement; characterization of trait correlations at the genetic and phenotypic levels and feasibility of simultaneous improvement; development of a multi-trait selection index combining yield and biofortification objectives; and development of evidence-based breeding recommendations for pearl millet biofortification programs targeting the arid environment. The outcomes of this study are expected to provide valuable genetic resources and strategic guidance for addressing micronutrient malnutrition through crop improvement. 2. Materials and methods 2.1 Plant material The experimental material consisted of 10 different lines of pearl millet that were chosen based on their differences in grain iron (Fe) and zinc (Zn) and protein content, agronomic performance, and adaptation to arid climates (Table 1 ). The source of the parental lines was the Rajasthan Agricultural Research Institute, Durgapura, Rajasthan, India, which had a great diversity of variation for use in bio-fortification and yield traits. Table 1 Parental inbred lines used in the half-diallel study S.No. Parent Source Salient Features 1 RIB-9178 RARI, Durgapura Early maturity 2 RIB-9184 RARI, Durgapura Highest Zn, high protein, highest biomass 3 RIB-9185 RARI, Durgapura High Fe, highest test weight 4 RIB-9205 RARI, Durgapura High Fe 5 RIB-15131 RARI, Durgapura Highest Fe, good combiner 6 RIB-16300 RARI, Durgapura Drought tolerant 7 RIB-16324 RARI, Durgapura High yield potential 8 J-2340 Junagadh, Gujarat Medium plant height, normal days to flowering, late maturity and low number of effective tillers 9 20K86 RARI, Durgapura Highest grain yield, highest panicle girth 10 RIB-192 RARI, Durgapura Medium plant height, high number of effective tillers, highest panicle length and small ear girth 2.2 Mating design and crossing program The 10 parental inbred lines were crossed in a half diallel mating design following Griffing's (1956) Method 2 (parents and one set of F 1 s, excluding reciprocals), Model 1 (fixed effects) during summer 2019 at ICRISAT, Patancheru, Hyderabad, India. The number of F 1 hybrid combinations generated was as follows: Number of F 1 crosses = p(p − 1)/2 = 10(10 − 1)/2 = 45 ..(1) where p is the number of parents. Crosses were made by hand emasculation and controlled pollination to produce sufficient seeds for replicated evaluation. 2.3 Experimental design and field evaluation The experimental material comprising 55 entries (45 F1 hybrids + 10 parents) was evaluated during the kharif season of 2019 at the Agricultural Research Farm, Rajasthan Agricultural Research Institute (RARI), Durgapura, Jaipur, Rajasthan, India (26.49°N latitude, 75.48°E longitude, 390 m altitude). Two environments were established by staggered sowing dates to assess genotype × environment (GxE) interaction: E 1 (normal sowing: 25 July 2019) and E 2 (late sowing: 08 August, 2019). In each environment, the experiment was designed using a randomized complete block design (RCBD) with three replicates. To reduce intergenotypic competition among genotypes between vigorous hybrids and parental inbreds, the parents and hybrids were blocked separately within each replication (Panse and Sukhatme 1985 ). Each entry was sown in a two-row plot of 4 m length with inter-row spacing of 50 cm and intra-row spacing of 15 cm (maintained by thinning at 20 DAS). Non-experimental border rows were planted around the field to eliminate edge effects from the study. Standard agronomic practices recommended for pearl millet cultivation in Rajasthan were uniformly followed. 2.4 Observations recorded Data were recorded for 13 quantitative traits which were grouped into four categories (Table 2 ). Five competitive plants were randomly tagged in each plot before flowering for observations. Days to 50% flowering and maturity were determined on a whole-plot basis. Table 2 Traits recorded with abbreviations, units, and measurement methods Category Trait Abbr. Unit Method Phenological Days to 50% flowering DF days Plot basis Days to maturity DM days Plot basis Morphological Plant height PH cm 5-plant mean Productive tillers/plant Tillers no. 5-plant mean Panicle length PL cm 5-plant mean Panicle girth PG cm Vernier caliper Yield Test weight TW g 1000-grain wt. Dry fodder yield/plant DFY g 5-plant mean Grain yield/plant GY g 5-plant mean Harvest index HI % Calculated Biofortification Iron content Fe mg kg⁻¹ XRF Zinc content Zn mg kg⁻¹ XRF Protein content Protein % Kjeldahl 2.5 Biofortification trait analysis Grain samples were cleaned, dried at 60°C for 6 h in a hot air oven, and stored in butter paper covers. Care was taken to avoid contamination with dust and metal particles. Fe and Zn concentrations were determined by Energy-Dispersive X-Ray Fluorescence Spectrometry (ED-XRF) at the Central Analytical Services Laboratory, ICRISAT, Patancheru, following standardised protocols (Paltridge et al. 2012 ; Govindaraj et al. 2016 ). For protein analysis, the nitrogen content was determined using the micro-Kjeldahl method (AOAC 1990 , Official Method 950.48) with a KEL PLUS distillation unit (Pelican Equipment, Chennai, India). A 200 mg flour sample was digested with concentrated H 2 SO 4 in the presence of a catalyst at 350°C. Protein was calculated as follows: Protein (%) = Nitrogen (%) × 6.25 ..(2) 2.5 Statistical analysis 2.5.1 Pooled analysis of variance A pooled analysis of variance (ANOVA) across environments was used to examine the mean for each trait, following Panse and Sukhatme ( 1985 ). The structure of the pooled ANOVA is presented in Table 3 . Table 3 Structure of pooled ANOVA for half-diallel analysis Source of Variation df Expected MS Environments (E) e − 1 = 1 σ²e + rσ²ge + rgσ²E Replications/E e(r − 1) = 4 σ²e + gσ²r Genotypes (G) g − 1 = 54 σ²e + rσ²ge + reσ²g Parents (P) p − 1 = 9 σ²e + rσ²pe + reσ²p Crosses (C) c − 1 = 44 σ²e + rσ²ce + reσ²c P vs C 1 σ²e + reσ²(PvsC) G × E (g − 1)(e − 1) = 54 σ²e + rσ²ge P × E (p − 1)(e − 1) = 9 σ²e + rσ²pe C × E (c − 1)(e − 1) = 44 σ²e + rσ²ce (P vs C) × E e − 1 = 1 σ²e + rσ²(PvsC)e Pooled Error e(r − 1)(g − 1) = 216 σ²e where e = environments (2), r = replications (3), g = genotypes (55), p = parents (10), and c = crosses (45). Significance was tested using F−tests at P ≤ 0.05 (*) and P ≤ 0.01 (**) . 2.5.2 Variance Components and Genetic Parameters Variance components were estimated from expected mean squares (EMS) and used to calculate the genetic parameters (Burton and DeVane 1953 ; Johnson et al. 1955 ): σ²g = (MSG − MSG×E) / re ..(3) σ²g×e = (MSG×E − MSE) / r ..(4) σ²p = σ²g + σ²g×e/e + σ²e/re ..(5) Broad-sense heritability: H² = σ²g / σ²p ..(6) GCV (%) = (√σ²g / X̄) × 100 ..(7) PCV (%) = (√σ²p / X̄) × 100 ..(8) Genetic advance: GA = k × √σ²p × H² ..(9) where k = 2.06 (selection differential at 5% intensity) and X̄ = the grand mean. GAM (%) = (GA / X̄) × 100 ..(10) 2.5.3 Combining ability analysis Combining ability analysis was performed following Griffing ’s(1956) Method 2, Model 1. The linear model is: Xij = µ + gi + gj + sij + eij ..(11) where Xij = mean of cross between i th and j th parents; µ = population mean; gi , gj = GCA effects; sij = SCA effect; and eij = error. GCA effects: gi = [1/(p + 2)] × [Xi. + Xii − (2/p) × X..] ..(12) SCA effects: sij = Xij − [(Xi. + Xj. + 2Xii + 2Xjj)/(p + 2)] + [2X../(p + 1)(p + 2)] ..(13) where Xi. = sum of array involving i th parent; Xii = parental value; X.. = grand total; p = number of parents. Standard errors for significance testing. SE(gi) = √[(p − 1)MSe / p(p + 2)] ..(14) SE(sij) = √[p(p − 1)MSe / (p + 1)(p + 2)] ..(15) Variance components and Baker's ratio σ²GCA = (MSGCA − MSe) / (p + 2) ..(16) σ²SCA = MSSCA − MSe ..(17) Baker's ratio = 2σ²GCA / (2σ²GCA + σ²SCA) ..(18) Baker's ratio approaching 1.0 indicates predominance of additive gene action; lower values indicate significant non-additive effects (Baker 1978 ). Proportional contribution (%). GCA contribution (%) = (SSGCA / SSCrosses) × 100 ..(19) SCA contribution (%) = (SSSCA / SSCrosses) × 100 ..(20) 2.5.4 Hayman's diallel analysis Hayman's (1954a, 1954b) approach was employed to estimate the genetic components of variance. The analysis assumes (i) diploid segregation, (ii) homozygous parents, (iii) no reciprocal differences, (iv) no epistasis, (v) no multiple allelism, and (vi) independent gene assortment. The model validity was tested using the regression of Wr (covariance of r th array with non-recurrent parent) on Vr (variance of r th array): b = Cov(Wr, Vr) / Var(Vr) ..(21) The additive dominance model is valid when b is significantly different from zero but not from unity (tested by t -test at n − 2 df). Genetic components were estimated as follows: D = additive variance; H₁ = dominance variance; H₂ = H₁[1−(u − v)²], where u and v are frequencies of positive and negative alleles, respectively; F = covariance of additive and dominance effects; h² = dominance effect summed over loci; and E = environmental variance. Derived parameters: Average degree of dominance = √(H₁/D) ..(22) Proportion of genes with +/− effects = H₂/4H₁ ..(23) KD/KR = [(4DH₁)^0.5 + F] / [(4DH₁)^0.5 − F] ..(24) h²ns = (0.5D) / (0.5D + 0.25H₁ − 0.25F + E) ..(25) V r -W r graphical analysis: Graphs were constructed according to Hayman ( 1954b ) and Singh and Singh ( 1984 ). The position of array points on the regression line defines the genetic constitution: array points close to the origin - parents with mostly dominant alleles; array points far from the origin - parents with mostly recessive alleles. The intercept of the regression line on the Wr axis indicates the following: positive intercept = partial dominance; intercept at origin = complete dominance; negative intercept = overdominance. 2.5.5 Heterosis estimation Heterosis was calculated as described by Fonseca and Patterson ( 1968 ). Mid-parent heterosis: MPH (%) = [(F₁ − MP) / MP] × 100 ..(26) Better-parent heterosis: BPH (%) = [(F₁ − BP) / BP] × 100 ..(27) where F₁ = hybrid mean, MP = (P₁ + P₂)/2, and BP = better parent mean. Significance testing: SE(MPH) = √(3MSe / 2r) ..(28) SE(BPH) = √(2MSe / r) ..(29) The t -values were compared against the table values at the error degrees of freedom. 2.5.6 Correlation analysis Phenotypic correlation coefficients (rp) were calculated using Pearson's product-moment correlation. GCA level correlations (rGCA) were derived from parental GCA effects to test the genetic relationships among traits. Significance was tested at P ≤ 0.05 and P ≤ 0.01 using t-test with n-2 df. 2.5.7 GGE biplot analysis Genotype + Genotype × Environment (GGE) biplot analysis was performed following Yan and Kang ( 2002 ) to visualize the genotype performance across traits and identify ideal genotypes. The model is: Yij − µ − βj = λ₁ξi1ηj1 + λ₂ξi2ηj2 + εij ..(30) 2.5.8 Multi-trait selection index A modified Smith-Hazel selection index (Smith 1936 ; Hazel 1943 ) was constructed to identify superior hybrids combining yield and biofortification. I = Σ(wi × zi) ..(31) where wi = economic weight for trait i ; zi = standardized value = (Xi − X̄)/SD. Five weighting schemes were evaluated for GY, Fe, Zn, and Protein: (i) Equal (0.25 each); (ii) Yield priority (0.40, 0.20, 0.20, 0.20); (iii) Biofortification priority (0.20, 0.30, 0.30, 0.20); (iv) Fe-Zn focus (0.25, 0.30, 0.30, 0.15); (v) Balanced (0.30, 0.25, 0.25, 0.20). 2.5.9 Cluster analysis Hierarchical cluster analysis was performed on parental GCA effects using Ward's minimum variance method (Ward 1963 ) with squared Euclidean distance to identify genetically similar groups and potential heterotic pools. 2.5.10 Statistical software All statistical analyses were performed using R version 4.2.0 (R Core Team 2022 ) with the following packages: agricolae (De Mendiburu 2021 ) for ANOVA and combining ability; lme4 (Bates et al. 2015 ) for mixed models; GGEBiplots (Dumble 2022 ) for GGE analysis; and corrplot (Wei and Simko 2021 ) for correlation visualization. Data visualization was performed using Python 3.10 with the Matplotlib , Seaborn , and SciPy libraries. 3. Results 3.1 Phenotypic variation and mean performance The 55 genotypes (10 parents and 45 F1 hybrids) exhibited wide phenotypic variability across the two environments (Table S1 ; Fig. 1 ; Supplementary Fig. S1 ). Grain yield ranged from 18.42 to 52.67 g plant − 1 , with a mean of 34.28 g. The mean hybrid yield (36.84 g) exceeded the parental mean (28.15 g) by 30.9%. For biofortification traits, grain Fe concentration varied from 24.72 to 63.29 mg kg − 1 (mean: 42.15 mg kg − 1 ), Zn ranged from 21.87 to 39.06 mg kg − 1 (mean: 31.24 mg kg − 1 ), and protein content spanned 7.90–11.91% (mean: 9.84%). Among parents, RIB-9205 showed highest Fe (56.24 mg kg − 1 ), RIB-9184 exhibited maximum Zn (36.82 mg kg − 1 ) and protein (11.15%), whereas RIB-15131 was the best yielding parent (32.45 g plant − 1 ). Transgressive segregation was observed in several hybrids for yield and biofortification traits in this study. 3.2 Parental diversity assessment Hierarchical cluster analysis based on standardized values of 13 agronomic and biofortification traits using Ward's minimum variance method grouped the 10 parental inbred lines into three distinct clusters with a Euclidean distance threshold of 3.75 (Fig. 2 ). Cluster I (orange), which included RIB-9205, RIB-9178, 20K86, and J-2340; Cluster II (green), which included RIB-16324, RIB-16300, and RIB-192; and Cluster III (red), which included RIB-15131, RIB-9184 and RIB-9185. The large degree of genetic divergence between clusters was the basis for choosing these diverse parents for the half diallel mating design. Inter-cluster crosses tended to have greater levels of heterosis than intra-cluster combinations, supporting the classical relationship between genetic distance and hybrid vigour. This clustering pattern provided a rational framework for interpreting the effect of combining ability and identifying complementary parental combinations for the simultaneous improvement of grain yield and biofortification traits. 3.2 Pooled analysis of variance The results of the pooled analysis of variance showed highly significant (P ≤ 0.01) differences among genotypes for all 13 traits (Table 4 ). Significant variation was observed among parents, crosses, and parents versus crosses for all traits. The G × environment (G × E) interaction was significant for grain yield, dry fodder yield, harvest index, days to flowering, and days to maturity. However, GxE interaction was not significant for biofortification traits (Fe, Zn, and protein content). The parents versus crosses contrast was highly significant for all traits, with the crosses having superior mean performance than the parents. This was especially true for grain yield, productive tillers per plant, and panicle length. 3.4 Genetic parameters and heritability The results of the estimation of genetic parameters are presented in Table 4 . Broad-sense heritability (H 2 ) was high for biofortification traits: for protein (0.81), Fe (0.78), and Zn (0.74). Moderate heritability was recorded for grain yield (0.62), days to flowering (0.68), and days to maturity (0.71). The genotypic coefficient of variation (GCV) was maximum for the grain yield (28.45%), productive tillers per plant (25.32%), and dry fodder yield (22.18%). For biofortification traits, GCV varied from 12.38% (protein) to 18.64% (Fe) content. The phenotypic coefficient of variation (PCV) was slightly higher than the GCV for all traits. Genetic advance as a percentage of the mean (GAM) was high (> 20%) for grain yield (35.67%), productive tillers (29.84%), and dry fodder yield (26.92%). Moderate GAM (10–20%) was found for Fe (18.45%), Zn (15.62%) and protein (13.78%). Table 4 Pooled analysis of variance, variance components, and genetic parameters for yield and biofortification traits in pearl millet (10 parents × 45 F₁ hybrids) across two environments Source of Variation df DF DM PH Tillers PL PG TW DFY GY HI Fe Zn Protein Mean Squares (MS) Environment (E) 1 590.33 ** 901.45 ** 3118.07 2.03 ** 149.66 ** 13.86 ** 14.53 * 12061.56 ** 1182.57 *** 250.94 ** 977.89 ** 603.66 ** 2.39 Replication/E 4 9.84 31.36 440.22 0.07 4.50 0.28 0.87 240.05 6.41 10.02 16.04 12.19 0.44 Genotypes (G) 54 150.74 *** 213.11 *** 2919.47 *** 1.44 *** 93.93 *** 3.83 *** 5.98 *** 7401.13 *** 147.21 *** 168.94 *** 349.42 *** 116.47 *** 5.17 *** Parents (P) 9 87.92 ** 97.04 4520.09 *** 0.64 * 152.08 *** 3.61 ** 9.41 ** 2569.84 *** 11.51 125.66 *** 451.74 *** 118.85 ** 1.98 *** Crosses (C) 44 143.34 *** 211.67 *** 1752.45 *** 1.59 *** 70.36 *** 3.80 *** 4.43 * 7246.54 *** 137.22 *** 180.65 *** 320.40 *** 100.68 *** 5.90 *** P vs C 1 1041.47 * 1320.91 * 39862.97 * 2.41 607.49 * 7.14 43.17 * 57684.31 1808.43 43.23 * 705.18 789.64 1.76 G × E 54 14.25 *** 35.15 ** 275.58 * 0.37 *** 2.06 0.48 *** 1.98 *** 186.10 ** 8.72 *** 11.53 *** 20.51 *** 11.26 * 0.07 P × E 9 15.88 * 51.62 * 32.00 0.14 ** 8.57 ** 0.64 ** 1.09 ** 126.64 5.56 * 4.19 26.79 ** 19.08 ** 0.12 C × E 44 14.18 ** 32.49 * 326.33 ** 0.42 *** 0.75 0.46 ** 2.20 *** 192.59 ** 8.32 *** 13.29 *** 19.31 *** 9.77 0.06 (P vs C) × E 1 3.06 4.19 235.11 0.20 * 1.42 0.10 0.19 435.59 * 54.85 *** 0.27 16.54 6.69 0.30 Pooled Error 216 7.45 21.55 186.99 0.04 2.66 0.25 0.41 101.10 2.66 3.78 9.30 7.63 0.56 Variance Components σ²g (Genotypic) 22.75 29.66 440.65 0.18 15.31 0.56 0.67 1202.51 23.08 26.24 54.82 17.53 0.85 σ²gxe (G×E) 2.27 4.53 29.53 0.11 0.00 0.08 0.52 28.33 2.02 2.58 3.74 1.21 0.00 σ²e (Error) 7.45 21.55 186.99 0.04 2.66 0.25 0.41 101.10 2.66 3.78 9.30 7.63 0.56 σ²p (Phenotypic) 25.12 35.52 486.58 0.24 15.75 0.64 1.00 1233.52 24.54 28.16 58.24 19.41 0.94 Genetic Parameters H² (Broad-sense) 0.91 0.84 0.91 0.75 0.97 0.87 0.67 0.97 0.94 0.93 0.94 0.90 0.90 GCV (%) 9.49 7.03 11.12 22.13 16.94 10.34 10.69 38.22 32.06 33.91 17.99 13.14 9.75 PCV (%) 9.97 7.69 11.68 25.61 17.18 11.07 13.07 38.71 33.06 35.13 18.54 13.82 10.27 GA (5%) 9.35 10.25 41.15 0.75 7.95 1.44 1.38 70.53 9.60 10.19 14.80 8.20 1.80 GAM (%) 18.60 13.23 21.79 39.37 34.40 19.92 18.01 77.73 64.06 67.43 35.95 25.72 19.05 Grand Mean 50.26 77.50 188.83 1.91 23.10 7.22 7.64 90.74 14.99 15.10 41.16 31.88 9.46 CV (%) 5.43 5.99 7.24 10.82 7.05 6.90 8.42 11.08 10.89 12.87 7.41 8.66 7.93 *, **, *** Significant at P<0.05, P<0.01, and P<0.001, respectively H² = σ²g / (σ²g + σ²gxe/e + σ²e/re); GCV = genotypic coefficient of variation; PCV = phenotypic coefficient of variation; GA = genetic advance at 5% selection intensity; GAM = genetic advance as percent of mean DF = days to 50% flowering; DM = days to maturity; PH = plant height (cm); Tillers = productive tillers/plant; PL = panicle length (cm); PG = panicle girth (cm); TW = test weight (g); DFY = dry fodder yield/plant (g); GY = grain yield/plant (g); HI = harvest index (%); Fe = iron content (ppm); Zn = zinc content (ppm); Protein = protein content (%) 3.5 Combining ability analysis Analysis of variance for combining ability showed highly significant (P < 0.001) differences between genotypes for both general combining ability (GCA) and specific combining ability (SCA) effects for all 13 traits (Table 5 ). Table 5 Combining ability analysis (Griffing's Method 2, Model 1) with variance components and Baker's ratio for yield and biofortification traits in pearl millet Source of Variation df DF DM PH Tillers PL PG TW Mean Squares (MS) GCA 9 464.24 *** 553.50 * 5346.77 *** 3.49 ** 383.38 *** 13.23 *** 13.50 ** SCA 45 88.04 *** 145.03 *** 2434.01 *** 1.03 *** 36.04 *** 1.95 *** 4.47 ** GCA × E 9 31.87 *** 109.18 *** 190.29 0.53 *** 4.88 0.80 ** 1.76 *** SCA × E 45 10.73 * 20.35 292.64 * 0.33 *** 1.50 0.42 ** 2.02 *** Error 216 7.45 21.55 186.99 0.04 2.66 0.25 0.41 Variance Components σ²GCA 60.05 61.71 716.18 0.41 52.57 1.73 1.63 σ²SCA 12.88 20.78 356.90 0.12 5.76 0.26 0.41 σ²A (Additive) 120.10 123.42 1432.36 0.82 105.14 3.45 3.26 σ²D (Dominance) 12.88 20.78 356.90 0.12 5.76 0.26 0.41 Genetic Parameters Baker's Ratio 0.90 0.86 0.80 0.88 0.95 0.93 0.89 σ²GCA/σ²SCA 4.66 2.97 2.01 3.52 9.13 6.76 4.00 Avg. Degree of Dominance 0.46 0.58 0.71 0.53 0.33 0.38 0.50 % Contribution GCA 51.33 43.29 30.52 40.33 68.03 57.53 37.65 % Contribution SCA 48.67 56.71 69.48 59.67 31.97 42.47 62.35 Source of Variation df DFY GY HI Fe Zn Protein Mean Squares (MS) GCA 9 7478.61 *** 110.80 *** 177.29 *** 1118.95 *** 323.47 *** 6.99 *** SCA 45 7385.63 *** 154.50 *** 167.28 *** 195.51 *** 75.07 *** 4.81 *** GCA × E 9 245.18 * 6.92 ** 5.36 39.79 *** 20.47 ** 0.11 SCA × E 45 174.28 ** 9.08 *** 12.76 *** 16.65 ** 9.42 0.06 Error 216 101.10 2.66 3.78 9.30 7.63 0.56 Variance Components σ²GCA 1004.64 14.43 23.88 149.88 42.08 0.96 σ²SCA 1201.89 24.24 25.75 29.81 10.94 0.79 σ²A (Additive) 2009.29 28.86 47.76 299.77 84.17 1.91 σ²D (Dominance) 1201.89 24.24 25.75 29.81 10.94 0.79 Genetic Parameters Baker's Ratio 0.63 0.54 0.65 0.91 0.88 0.71 σ²GCA/σ²SCA 0.84 0.60 0.93 5.03 3.85 1.21 Avg. Degree of Dominance 1.09 1.30 1.04 0.45 0.51 0.91 % Contribution GCA 16.84 12.54 17.49 53.37 46.29 22.54 % Contribution SCA 83.16 87.46 82.51 46.63 53.71 77.46 *, **, *** Significant at P<0.05, P<0.01, and P<0.001, respectively Baker's Ratio = 2σ²GCA/(2σ²GCA + σ²SCA); values closer to 1 indicate predominance of additive gene action σ²A = 2σ²GCA; σ²D = σ²SCA; Avg. Degree of Dominance = √(2σ²D/σ²A); 1 = overdominance GCA tested against GCA×E; SCA tested against SCA×E The GCA C environment and SCA Χ environment interactions were significant for most traits and indicated differential parental and hybrid performance in the two environments. Baker's ratio, a measure of the relative importance of additive and non-additive gene action, varied between 0.54 for grain yield and 0.95 for panicle length. Biofortification traits showed predominantly additive gene action, with Baker ratios of 0.91 for iron, 0.88 for zinc, and 0.71 for protein content. The GCA effects identified superior parents for targeted trait improvement (Table 6 ; Fig. 3 ). For iron content, RIB-9205 showed the highest positive GCA effect (6.65, P < 0.001), followed by RIB-9184 (3.20, P < 0.001), making the two lines elite donors for iron biofortification. RIB-9184 was the superior general combiner for Zn (3.85, P < 0.001) and protein (0.78, P < 0.001) contents, and RIB-9185 ranked second for Zn content (2.07, P < 0.001). For grain yield, RIB-9185 (1.39, P < 0.001) and RIB-16324 (1.34, P < 0.001) had the greatest positive GCA effects. The GCA x environment stability analysis showed that RIB-9184 and RIB-9185 showed similar combining ability in all environments for biofortification traits (Fig. 4 ). Table 6 General combining ability (GCA) effects and GCA×E stability for yield and biofortification traits in pearl millet (pooled over environments) Parent DF DM PH Tillers PL PG TW DFY GY HI Fe Zn Protein GCA Effects RIB-9178 -0.84 ** -1.29 * 5.29 *** -0.32 *** -0.34 0.58 *** 0.30 *** 1.92 -0.45 * -0.11 -0.58 0.70 * -0.25 ** RIB-9184 -2.74 *** -3.93 *** 3.30 * -0.12 *** 2.75 *** -0.74 *** 0.59 *** 12.34 *** -1.72 *** -3.18 *** 3.20 *** 3.85 *** 0.78 *** RIB-9185 -1.12 *** -1.28 * 4.63 ** -0.03 2.11 *** -0.09 0.84 *** 2.27 * 1.39 *** 0.84 *** 2.22 *** 2.07 *** -0.22 * RIB-9205 -4.44 *** -3.91 *** -22.00 *** 0.45 *** -1.99 *** -0.47 *** -0.27 *** -16.98 *** -2.17 *** 0.82 *** 6.65 *** 1.82 *** -0.05 RIB-15131 4.09 *** 3.88 *** 6.22 *** -0.07 ** -2.54 *** 0.38 *** -0.21 ** -6.17 *** -0.30 0.60 ** 2.31 *** -0.18 0.12 RIB-16300 -1.31 *** -1.63 ** -1.69 0.05 * -1.18 *** 0.03 0.01 -7.80 *** 0.82 *** 1.46 *** -1.51 *** -1.47 *** 0.17 * RIB-16324 1.35 *** 1.73 ** -4.78 ** 0.13 *** -2.41 *** -0.18 ** -0.26 *** 10.38 *** 1.34 *** -0.80 *** -2.08 *** -1.08 *** -0.22 * J-2340 1.82 *** 2.80 *** 0.10 -0.26 *** 0.90 *** 0.34 *** -0.39 *** -3.35 ** 0.24 1.23 *** -4.21 *** -1.51 *** 0.00 20K86 1.28 *** 1.74 *** 1.34 0.01 -1.28 *** 0.42 *** -0.38 *** -7.26 *** 1.15 *** 1.25 *** 1.03 ** -0.87 ** -0.22 ** RIB-192 1.90 *** 1.89 *** 7.59 *** 0.16 *** 3.97 *** -0.27 *** -0.23 ** 14.66 *** -0.30 -2.11 *** -7.03 *** -3.34 *** -0.11 SE(gi) 0.305 0.519 1.529 0.023 0.182 0.056 0.072 1.124 0.182 0.217 0.341 0.309 0.084 SE(gi-gj) 0.455 0.774 2.279 0.035 0.272 0.083 0.107 1.676 0.272 0.324 0.508 0.460 0.125 GCA×E Stability (σ²) RIB-9178 0.020 0.178 1.020 0.002 0.253 0.013 0.030 0.138 0.006 0.015 0.057 0.422 0.001 RIB-9184 1.054 0.220 0.089 0.022 0.006 0.039 0.163 0.483 0.003 0.020 0.183 0.524 0.006 RIB-9185 0.004 0.037 2.291 0.017 0.159 0.031 0.029 7.387 0.159 0.000 1.867 0.719 0.002 RIB-9205 1.054 12.148 23.172 0.003 0.000 0.009 0.001 19.061 0.019 0.593 2.724 0.758 0.001 RIB-15131 1.666 9.611 0.005 0.013 0.000 0.054 0.061 3.884 0.354 0.000 1.217 1.066 0.004 RIB-16300 1.746 3.618 0.488 0.002 0.035 0.000 0.002 0.065 0.036 0.025 0.148 0.794 0.000 RIB-16324 0.008 0.008 1.562 0.018 0.007 0.004 0.013 4.941 0.999 0.409 0.185 0.084 0.000 J-2340 2.119 0.573 6.040 0.012 0.095 0.041 0.033 0.392 0.107 0.161 0.245 0.169 0.005 20K86 0.217 0.216 11.398 0.023 0.350 0.003 0.027 5.105 0.012 0.040 0.007 0.153 0.008 RIB-192 0.078 0.686 1.506 0.019 0.315 0.004 0.079 19.839 0.032 0.077 3.316 0.429 0.000 *, **, *** Significant at P<0.05, P<0.01, and P<0.001, respectively. SE(gi) = Standard error of GCA effect; SE(gi−gj) = Standard error of difference between two GCA effects, GCA×E Stability (σ²) = Variance of GCA effects across environments; Lower values indicate stable GCA Specific combining ability effects identified promising hybrid combinations beyond parental GCA predictions (Table 7 , Fig. 5 , and Supplementary Table S2 ). For grain yield, RIB-16324 × 20K86 (SCA = 12.09, P < 0.001) and RIB-16300 × 20K86 (SCA = 11.73, P < 0.001) had the maximum positive SCA effects, both of which involved high × high GCA parent combination. Of interest, RIB-16300 × RIB-192 exhibited exceptional SCA effects for iron (11.54, P < 0.001) and zinc (11.44, P < 0.001), even though both parents had negative GCA for these traits, indicating complementary gene action. For protein content, RIB-9205 × J-2340 (1.71, P < 0.001) and RIB-9184 × RIB-15131 (1.55, P < 0.001) showed the highest SCA effects, representing crosses between high GCA parents, respectively. Table 7 Top 5 specific combining ability (SCA) effects for grain yield and biofortification traits in pearl millet with parental GCA classification Rank Cross SCA Effect GCA Class GCA (P1) GCA (P2) Grain Yield (g/plant) 1 RIB-16324 × 20K86 12.09 *** H×H 1.34 *** 1.15 *** 2 RIB-16300 × 20K86 11.73 *** H×H 0.82 *** 1.15 *** 3 RIB-9184 × RIB-192 7.07 *** L×L -1.72 *** -0.30 4 RIB-16324 × RIB-192 6.89 *** H×L 1.34 *** -0.30 5 RIB-9178 × RIB-15131 6.42 *** L×L -0.45 * -0.30 Iron Content (ppm) 1 RIB-9205 × 20K86 14.46 *** H×H 6.65 *** 1.03 ** 2 RIB-16300 × RIB-192 11.54 *** L×L -1.51 *** -7.03 *** 3 RIB-9205 × J-2340 10.70 *** H×L 6.65 *** -4.21 *** 4 RIB-9184 × J-2340 10.13 *** H×L 3.20 *** -4.21 *** 5 RIB-16300 × RIB-16324 9.04 *** L×L -1.51 *** -2.08 *** Zinc Content (ppm) 1 RIB-16300 × RIB-192 11.44 *** L×L -1.47 *** -3.34 *** 2 RIB-9185 × RIB-16324 6.18 *** H×L 2.07 *** -1.08 *** 3 RIB-9178 × J-2340 5.56 *** H×L 0.70 * -1.51 *** 4 RIB-9205 × RIB-15131 4.59 *** H×H 1.82 *** -0.18 5 RIB-9178 × RIB-16324 3.88 *** H×L 0.70 * -1.08 *** Protein Content (%) 1 RIB-9205 × J-2340 1.71 *** H×H -0.05 0.00 2 RIB-9184 × RIB-15131 1.55 *** H×H 0.78 *** 0.12 3 J-2340 × 20K86 1.44 *** H×L 0.00 -0.22 ** 4 RIB-9184 × RIB-192 1.41 *** H×L 0.78 *** -0.11 5 RIB-16300 × RIB-192 1.38 *** H×L 0.17 * -0.11 *, **, *** Significant at P<0.05, P<0.01, and P<0.001, respectively GCA Class: H = High GCA (≥ median), L = Low GCA (< median) H×H = both parents high combiner; H×L = high × low combiner; L×L = both parents low combiner 3.6 Hayman’s diallel analysis Hayman's genetic component analysis provided detailed information on the nature of gene action for grain yield and biofortification traits (Table 8 ; Supplementary Table S3). The dominance variance components (H 1 and H 2 ) were significantly higher than the additive component (D) for GY, with H 1 = 92.67 (P < 0.01) versus D = 1.11 (non-significant), confirming the predominance of non-additive gene action. The average degree of dominance (√H₁/D) was 9.15 for grain yield, indicating substantial overdominance. In contrast, iron content exhibited significant additive variance (D = 72.88, P < 0.05) and dominance effects (H₁ = 145.41, P < 0.01), with √H₁/D = 1.41 suggesting slight overdominance. Zinc content (√H₁/D = 1.55) and protein content (√H₁/D = 3.64) also exhibited overdominance, although to varying degrees. The H₂/4H₁ ratios varied from 0.18 to 0.24, which was away from the maximum of 0.25, indicating an asymmetric distribution of dominant alleles between parents. Vr-Wr regression analysis was used to measure the fit of the additive-dominance model (Fig. 6 ; Supplementary Fig. S2 ; Supplementary Table S4). Regression coefficients that were significantly different from zero but not unity indicated the adequacy of the model for panicle length and zinc content. Graphical analysis placed the parents on the regression line, with the parents close to the origin having more dominant alleles and the parents further away having more recessive alleles. Table 8 Estimates of Hayman's genetic components for grain yield and biofortification traits in pearl millet (Pooled over environments) Components Grain yield (g/plant) Iron content (ppm) Zinc content (ppm) Protein content (%) D 1.11 ± 20.65 72.88 ± 30.08 * 18.06 ± 8.44 0.23 ± 0.42 H₁ 92.67 ± 18.58 ** 145.41 ± 27.08 ** 43.27 ± 7.59 ** 3.11 ± 0.38 ** H₂ 87.32 ± 10.51 ** 107.56 ± 15.32 ** 39.60 ± 4.30 ** 2.91 ± 0.21 ** F -1.38 ± 12.39 45.05 ± 18.05 * 3.63 ± 5.06 0.01 ± 0.25 h² 119.06 ± 3.72 ** 45.67 ± 5.42 ** 51.49 ± 1.52 ** 0.08 ± 0.08 E 0.81 ± 10.32 2.41 ± 15.04 1.75 ± 4.22 0.10 ± 0.21 √(H₁/D) 9.15 1.41 1.55 3.64 H₂/4H₁ 0.24 0.18 0.23 0.23 KD/KR 0.87 1.56 1.14 1.01 h²(ns) 0.02 0.37 0.30 0.10 h²(bs) 0.98 0.98 0.96 0.95 *, ** Significant at P<0.05 and P<0.01, respectively D = Additive genetic variance; H₁, H₂ = Dominance variance components; F = Covariance of additive and dominance effects h² = Dominance effect over all loci; E = Environmental variance; √(H₁/D) = Average degree of dominance H₂/4H₁ = Proportion of genes with positive and negative effects (0.25 = symmetrical distribution) KD/KR = Ratio of dominant to recessive alleles; h²(ns) = Narrow sense heritability; h²(bs) = Broad sense heritability Interpretation: √(H₁/D) 1 = Over−dominance 3.7 Heterosis estimation Mid-parent heterosis (MPH) and better-parent heterosis (BPH) varied considerably across the trait categories (Fig. 7 ). Yield traits showed a higher degree of heterosis, and the grain yield MPH ranged from − 19.97 to 167.02% (mean 61.04%) and BPH ranged from − 25.70 to 163.20% (mean 47.82%), of which 41 of 45 crosses had a positive MPH (Table 9 ). RIB-16324 × 20K86 had the highest MPH value for grain yield (167.02%), followed by RIB-16300 × 20K86 (153.99%) and RIB-16324 × RIB-192 (151.46%). Biofortification traits showed moderate but commercially significant levels of heterosis (Supplementary Tables S5a and S5b). For iron content, MPH ranged from − 27.80 to 68.30%, and BPH ranged from − 41.86 to 60.90%, with RIB-16300 × RIB-192 showing maximum positive values for both MPH and BPH, and 33 crosses showed positive values for MPH (Table 9 ). Table 9 Summary of mid-parent heterosis (MPH) and top 5 crosses for grain yield and biofortification traits in pearl millet (Pooled over environments) Trait Heterosis summary Top 5 crosses based on MPH Range (%) Mean (%) +ve -ve Rank Cross MPH (%) BPH (%) Grain yield (g/plant) -15.72 to 167.02 61.04 41.00 4.00 1.00 RIB-16324 x 20K86 167.02 159.09 2.00 RIB-16300 x 20K86 153.99 151.33 3.00 RIB-16324 x RIB-192 151.46 113.50 4.00 J-2340 x RIB-192 145.30 118.16 5.00 RIB-16300 x RIB-192 123.00 86.28 Iron content (ppm) -25.70 to 68.30 11.14 33.00 12.00 1.00 RIB-16300 x RIB-192 68.30 61.98 2.00 RIB-9205 x J-2340 54.77 20.42 3.00 RIB-9184 x J-2340 52.42 22.95 4.00 RIB-9205 x 20K86 46.50 40.39 5.00 RIB-9205 x RIB-16300 40.32 12.59 Zinc content (ppm) -14.27 to 63.04 14.37 40.00 5.00 1.00 RIB-16300 x RIB-192 63.04 58.76 2.00 RIB-9185 x RIB-16324 37.21 20.59 3.00 RIB-9178 x J-2340 33.50 16.91 4.00 RIB-9205 x RIB-16324 29.53 15.02 5.00 RIB-9184 x J-2340 26.74 5.93 Protein content (%) -19.23 to 19.82 -1.92 17.00 28.00 1.00 RIB-9205 x J-2340 19.82 18.55 2.00 RIB-9184 x RIB-192 19.01 10.38 3.00 RIB-16300 x RIB-192 14.33 7.64 4.00 J-2340 x 20K86 13.62 13.38 5.00 RIB-9185 x RIB-9205 13.24 12.45 MPH = Mid−parent heterosis = [(F₁ − MP)/MP] × 100; BPH = Better−parent heterosis = [(F₁ − BP)/BP] × 100 +ve = Number of crosses with positive heterosis; −ve = Number of crosses with negative heterosis; Total crosses = 45 Similarly, RIB-16300 × RIB-192 was ranked 1st for Zn content, with an MPH of 63.04% and BPH of 68.38%, with 40 crosses showing positive MPH values. Protein content had a comparatively low magnitude of heterosis (MPH = -20.08% − 19.82%; BPH = -27.45% − 20.24%), and RIB-9205 × J-2340 showed the highest MPH (19.82%) and BPH (20.24%). Phenological traits showed negative heterosis for days to flowering (MPH:-29.00% to 4.09%; BPH:-35.00% to -2.04%) and maturity (MPH:-24.00% to 3.88%; BPH:-28.00% to -1.53%) which is desirable as it represents dominance for earliness, a vital trait for drought escape in arid environments. The violin-box plots showed that yield traits had the broadest heterosis distribution with many outliers with values higher than 150% MPH, while biofortification traits showed narrower but generally positive distributions (Fig. 7 ), which corroborates the potential for the simultaneous improvement of yield and nutritional quality through hybrid breeding. 3.8 Trait correlations GCA-level and phenotypic correlations showed significant relationships with breeding decisions (Table 10 ; Fig. 8 ; Supplementary Fig. S3). A strong positive correlation between iron and zinc (rg = 0.82, P < 0.01), with a corresponding phenotypic correlation of rp = 0.56, demonstrated the possibility of simultaneous improvement of the two micronutrients through selection. Table 10 GCA-level and phenotypic correlations between grain yield and biofortification traits for assessing simultaneous improvement feasibility in pearl millet S. No. Trait Pair GCA Correlation (rg) Phenotypic Correlation (rp) Inference 1 Grain yield × Iron content -0.434 -0.222 Negative; trade-off, overcome by SCA 2 Grain yield × Zinc content -0.465 -0.035 Negative; trade-off, overcome by SCA 3 Grain yield × Protein content -0.526 0.041 Negative; trade-off, overcome by SCA 4 Iron content × Zinc content 0.819 ** 0.560 Positive; simultaneous improvement favorable 5 Iron content × Protein content 0.251 0.132 Independent; simultaneous improvement possible 6 Zinc content × Protein content 0.469 0.237 Positive; simultaneous improvement favorable ** Significant at 1% probability level (df = 8) rg = GCA−level correlation (additive genetic); rp = Phenotypic correlation Note: Moderate negative GCA correlations between yield and biofortification traits can be overcome through favourable SCA effects, as evidenced by superior hybrids (for example, RIB−9184 × RIB−15131) that combine high yield with enhanced nutritional quality . Zinc and protein content also showed a positive GCA association (rg = 0.47, rp = 0.24), in favour of combined nutritional enhancement. Iron and protein content showed a weak positive correlation (rg = 0.25, rp = 0.13), suggesting that the two traits are inherited independently. Grain yield showed weak negative GCA correlations with iron (rg = -0.43, rp = -0.22), zinc (rg = -0.47, rp = -0.04), and protein (rg = -0.53, rp = 0.04) contents which were not significant at the 1% probability level. The phenotypic correlations were significantly lower than the GCA correlations, suggesting environmental effects on trait expression. These moderate negative associations between yield and biofortification traits can be overcome by favourable SCA effects on the yield. 3.9 GGE biplot analysis The GGE biplot analysis, based on the main effects of genotypes and genotype × environment interaction, was used to visualize the relationship among the 13 traits and determine trait groupings among the 55 genotypes (Fig. 9 ). The first two principal components accounted for 45.3% of the total variation, with 25.6% and 19.7% of the variation explained by PC1 and PC2, respectively (Supplementary Fig. S4). The scree plot showed that five principal components were needed to account for more than 75% of the total variances. The PCA loadings showed different patterns of trait clustering (Supplementary Fig. S5) with the biofortification traits (Fe, Zn, Protein) clustering in one quadrant, the yield-related traits (GY, DFY, HI) in a different sector, and the morphological traits (PH, PL, PG) and phenological traits (DF, DM) in individual groups. Parents and hybrids were scattered throughout all quadrants, with high-performing biofortified hybrids located towards the biofortification trait vectors. This pattern of trait clustering supported the correlation analysis and provided a graphical framework for understanding trait interrelationships in the breeding population. 3.10 Multi-trait selection index The Smith-Hazel multi-trait selection index was used to track superior hybrids for the simultaneous improvement of grain yield and biofortification traits using five different weighting schemes (Table 11 ; Fig. 10 ). RIB-9184 × RIB-15131 was the best hybrid with the highest selection index value of 1.31 and ranked as the first hybrid in all five weighting schemes, including equal weights, yield priority, biofortification priority, Fe-Zn focus, and balanced approaches. Table 11 Top 5 superior hybrids for simultaneous improvement of grain yield and biofortification traits based on Smith-Hazel multi-trait selection index in pearl millet (Pooled over environments) Rank Cross Mean Performance Selection Index Rank under different weighting schemes Consistency GY Fe Zn Protein Equal Yield Priority Biofort. Priority Fe-Zn Focus Balanced Top 5 Count Avg. Rank 1 RIB-9184 × RIB-15131 18.84 46.16 38.86 11.91 1.314 1 1 1 1 1 5/5 1.0 2 RIB-16300 × RIB-192 20.81 44.17 38.51 10.90 1.070 2 2 2 2 2 5/5 2.0 3 RIB-9205 × J-2340 13.33 54.29 33.43 11.11 0.767 3 10 4 4 5 4/5 5.2 4 RIB-9205 × 20K86 14.12 63.29 36.19 8.96 0.740 4 9 3 3 3 4/5 4.4 5 RIB-9185 × RIB-9205 19.17 47.53 34.23 10.46 0.725 5 5 6 5 4 4/5 5.0 GY = Grain yield (g/plant); Fe = Iron content (mg/kg); Zn = Zinc content (mg/kg); Protein = Protein content (%) Selection Index (Smith−Hazel): I = Σ(bi × zi) where bi = economic weight and zi = standardized value; z−score = (X − Mean)/SD Weighting schemes: Equal (0.25 each), Yield Priority (GY=0.40, others=0.20), Priority (GY=0.20, Fe=Zn=0.30, Protein=0.20); Fe−Zn Focus (GY=0.25, Fe=Zn=0.30, Protein=0.15); Balanced (GY=0.30, Fe=Zn=0.25, Protein=0.20) Top 5 Count = Number of schemes in which hybrid ranked in top 5; Avg. Rank = Mean rank across all 5 schemes (lower = more consistent) This hybrid showed a good mean performance for grain yield (18.84 g/plant), iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. RIB-16300 × RIB-192 ranked second consistently (index = 1.07) with superior grain yield (20.81 g/plant), iron (44.17 ppm), zinc (38.51 ppm), and protein (10.90%) contents. RIB-9205 x J-2340 with index = 0.77 was found to have the highest iron content (54.29 ppm) among the top hybrids, followed by RIB-9205 × 20K86 with the highest iron (63.29 ppm) and zinc (36.19 ppm) combination. These hybrids are good candidates for developing biofortified pearl millet. 4. Discussion 4.1 Genetic variability and selection potential The pooled analysis of variance showed highly significant (P < 0.001) differences between genotypes for all 13 traits, suggesting that there is a large amount of genetic diversity between the parental inbred lines and the F 1 hybrids (Table 4 ). The significant genotype × environment interaction for most traits highlighted the need for a multi-environment evaluation in pearl millet breeding programs. The significance of the parents vs. crosses component for most traits indicated heterosis. Broad-sense heritability (H 2 ) ranged from 0.67 (test weight) to 0.97 (panicle length, dry fodder yield), and the biofortification traits showed high heritability; iron (0.94), zinc (0.90), and protein (0.90) content. These values are similar to those reported by Govindaraj et al. ( 2019 ) and Singhal et al. (2021) for pearl millet biofortification traits. High heritability values reveal that a high proportion of phenotypic variance is due to genetic factors and that reliable selection can be expected in the early generations. The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) indicated a high degree of variation for yield traits, with grain yield having GCV and PCV of 32.06% and 33.06%, respectively. For biofortification traits, iron content showed moderate GCV (17.99%) and PCV (18.54%), while the values for zinc content were 13.14% and 13.82%, respectively. The low GCV-PCV for iron (0.55%) and zinc (0.68%) suggests minimal environmental effects on these traits (Yadav et al., 2023 ). Genetic advance as a percentage of the mean (GAM) was high (> 20%) for grain yield (64.06%), Fe (35.95%), and Zn (25.72%) content. The combination of high heritability and high GAM for Fe and Zn contents suggest the predominance of additive gene action, in which effective improvement may be achieved through direct selection (Johnson et al., 1955 ). 4.2 Gene action and breeding Strategy The nature of gene action in trait expression is fundamental to the design of appropriate breeding strategies. Baker's ratio, which provides a measure of relative importance of additive versus non-additive variance components of genetic variance, indicated contrasting patterns of gene action between biofortification and yield traits in the present study (Table 5 ). Biofortification traits showed mostly additive gene action, with Baker's ratios of 0.91, 0.88, and 0.71 for iron, zinc, and protein content, respectively. Values close to unity suggest additive genetic effects, which account for the majority of genetic variance, in agreement with the results of Kanatti et al. ( 2014 ) and Govindaraj et al. ( 2019 ). The preponderance of additive gene action implies that population improvement methods, including recurrent selection, progeny testing, and development of open-pollinated varieties (OPVs), would be effective in accumulating favourable alleles for enhanced micronutrient content. This genetic architecture is responsible for the success of conventional breeding in the development of biofortified varieties, such as Dhanashakti and ICTP 8203 Fe (Rai et al., 2014 ). In contrast, the Baker's ratio of grain yield was 0.54, indicating a considerable contribution of non-additive genetic variance, including dominance and epistatic effects. Similar results were obtained by Davda and Dangaria ( 2018 ) and Patel et al. ( 2025 ) in pearl millet. Such genetic architecture provides strong support for hybrid breeding for yield improvement and thus for the exploitation of heterosis through the use of superior parental combinations. The significant SCA effects recorded for grain yield, with the top crosses such as RIB-16324 × 20K86 (12.09) and RIB-16300 × 20K86 (11.73), confirm the possibility of heterosis exploitation. The different patterns of gene action require an integrated approach to breeding that includes population improvement for biofortification and hybrid development for yield maximization. 4.3 Superior parents for hybridization The identification of superior general combiners is important for the development of high-yielding biofortified hybrids. Parental differences in GCA effects for different categories of traits allowed for strategic selection in hybridization programs (Table 6 ; Figs. 3 and 4 ). RIB-9205 was the winner when it came to the highest positive GCA effect (6.65) of all parents. This parent also contributed positively to zinc content (1.82) and seemed to have earliness for flowering (-4.44) and maturity (-3.91) which makes it ideal for biofortification breeding under arid environments where early maturity is desired for drought escape. Similar high Fe donors were identified by Rai et al. ( 2014 ) and Govindaraj et al. ( 2019 ) in pearl millet germplasms. RIB-9184 was identified as a multi-trait combiner with significantly positive GCA effects for zinc (3.85), protein (0.78), iron (3.20), panicle length (2.75), and test weight (0.59***). The combination of good GCA for multiple biofortification traits makes this parent valuable for pyramiding nutritional quality genes. Additionally, RIB-9184 showed a stable GCA×E interaction for grain yield (σ² = 0.003), indicating consistent performance across environments. RIB-15131 was a balanced combiner for positive GCA of iron (2.31), harvest index (0.60), plant height (6.22), and panicle girth (0.38). Although it showed a late-flowering tendency (GCA = 4.09), its contribution to multiple agronomic and quality traits provides breeding flexibility. RIB-9185 showed positive GCA for grain yield (1.39), iron (2.22), zinc (2.07), and test weight (0.84**), which represents another valuable parent to simultaneously improve yield and biofortification. These identified superior combiners can be strategically used in hybridization programs based on the recommendations of Kanatti et al. ( 2014 ) and Choudhary et al. (2012). 4.4 Promising hybrids for yield and biofortification The identification of improved hybrids with high grain yields and increased micronutrient content is the ultimate goal of biofortification breeding. Based on the multi-trait selection index, SCA impact, and heterosis estimates, a number of promising hybrids were identified (Tables 7 , 9 , and 11 ; Figs. 5 and 7 ). RIB-9184 × RIB-15131 appeared to be the best of all hybrids, with the highest rankings in all five weighting schemes and the highest selection index (1.31). This cross showed better performance in terms of mean grain yield (18.84 g/plant), iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. The hybrid was the product of the combination of a multi-trait combiner (RIB-9184) and a balanced combiner (RIB-15131), which demonstrated effective complementation of parental strengths. Significant positive SCA for protein (1.55***) and heterosis for grain yield (102.43% BPH) further prove its breeding potential (Yadav et al., 2023 ). RIB-16300 × RIB-192 showed outstanding heterosis for biofortification traits with 68.30% MPH for iron and 63.04% MPH for zinc which was the highest among the 45 crosses. This cross had significantly positive SCA effects on Fe (11.54) and Zn (11.44). Interestingly, this combination of L × L (both parents with negative GCA for Fe) implies the involvement of complementary gene action and epistatic interactions in transgressive segregation (Govindaraj et al., 2013 ; Kanatti et al., 2014 ; Vetriventhan and Upadhyaya, 2018 ). RIB-16324 × 20K86 showed maximum grain yield heterosis (167.02% MPH) with the maximum SCA effect (12.09***), which was the best hybrid for yield improvement. These results confirm the usefulness of diallel analysis for identifying promising combinations in pearl millet biofortification programs (Manwaring et al., 2016 ; Pujar et al., 2020 ). 4.5 Simultaneous improvement feasibility The correlation study between grain yield and biofortification traits has critical implications for simultaneous improvement (Table 10 ; Fig. 8 ). The high positive GCA correlation between Fe and Zn content (rg = 0.82**) suggests that selection for either micronutrient would lead to a concomitant improvement in the other (Velu et al. 2007 ). This favourable association, consistent with the outcomes of Govindaraj et al. ( 2013 ) and Pujar et al. ( 2020 ), allows for the efficient joint selection of both micronutrients in biofortification breeding programs. The positive correlation of zinc with protein (rg = 0.47) also supports the need for combined adrenaline nutritional enhancement which makes the selection of multi-traits very effective. The weak negative GCA correlations between grain yield and biofortification traits (GY × Fe: rg = -0.43; GY × Zn: rg = -0.47; GY × Protein: rg = -0.53) were non-significant, indicating that there was no severe genetic trade-off. According to Kanatti et al. ( 2014 ), such moderate negative correlations can be avoided by the favourable effects of SCA. Phenotypic correlations were significantly weaker (rp = -0.22 to 0.04), suggesting environmental masking of genetic associations (Jain et al., 2025 ). Superior hybrids, such as RIB-9184 × RIB-15131, with high yield and improved nutritional quality, show that improving both parameters can be achieved simultaneously by the strategic selection of parents and the exploitation of non-additive gene action (Manwaring et al., 2016 ). 4.6 Hayman’s analysis interpretation Hayman's diallel analysis revealed information regarding the genetic structure of the yield and biofortification traits (Table 8 ). Following the theoretical framework of Hayman (1954), the average degree of dominance (√H₁/D) indicated contrasting patterns of inheritance, showing high values of overdominance in grain yield (9.15), while biofortification traits showed slight overdominance values for Fe (1.41) and Zn (1.55) with moderate values of overdominance for Pr (3.64). The significance of H 1 and H 2 for all traits proved the importance of dominance variance. The high overdominance for grain yield supports the importance of exploiting heterosis (Athoni et al., 2016 ). The ratio of H 2 /4H 1 was 0.18–0.24, which deviated from the theoretical maximum of 0.25, suggesting an asymmetrical distribution of alleles between parents (Jinks, 1954 ). The KD/KR ratio greater than unity for iron (1.56) and zinc (1.14) is suggestive of the preponderance of dominant alleles for the biofortification traits in favour of the accumulation of high-nutrient alleles (Patil and Gupta, 2022 ). Narrow-sense heritability was moderate for iron (0.37) and zinc (0.30) and low for grain yield (0.02), supporting hybrid breeding for yield and population improvement for micronutrients (Izge et al. 2007 ). 4.7 G×E Interaction and stability Significant genotype × environment interactions require stability considerations when breeding decisions are made. According to Comstock and Moll ( 1963 ), such interactions can lead to biases in genetic estimates if they are not considered correctly taken into account. The GCA×E variance revealed differential stability among the parents (Table 6 ). RIB-9184 exhibited stable GCA effects for iron (σ² = 0.183) and zinc (σ² = 0.524), making it reliable for multi-environmental programs (Yadav and Rai, 2013 ). RIB-9185 exhibited consistent GCA for grain yield (σ² = 0.159), ensuring predictable hybrid performance across diverse environments. Parents with low GCA×E variance should be prioritized for developing stable biofortified cultivars. The SCA C E interaction was significant for grain yield suggesting environment-specific hybrid performance (Jain et al., 2025 ). However, RIB-9184 × RIB-15131 showed a better ranking in all environments, indicating both high mean performance and stability. Such stable superior combinations are crucial for the development of widely adaptable biofortified hybrids for various agro-climatic conditions of arid and semi-arid lands, where pearl millet is grown largely (Satyavathi et al., 2021 ). 4.8 Breeding recommendations Based on comprehensive genetic analyses, trait-specific breeding strategies are recommended for developing high-yielding biofortified pearl millet (Table 12 ). Table 12 Breeding recommendations for pearl millet improvement programs based on combining ability analysis S. No. Breeding Program Objective Recommended Parents/Crosses Remarks 1 OPV Development High GCA for all traits RIB-9184, RIB-9185, RIB-15131 Best combiners for population improvement 2 Hybrid Development Superior F₁ performance RIB-9184 × RIB-15131; RIB-16300 × RIB-192; RIB-9205 × J-2340 High yield + biofortification 3 Fe Biofortification High GCA for Fe RIB-9205, RIB-9184, RIB-15131 For Fe-enriched varieties 4 Zn Biofortification High GCA for Zn RIB-9184, RIB-9185, RIB-9205 For Zn-enriched varieties 5 Yield Improvement High GCA for GY RIB-9185, RIB-16324, 20K86 For high-yielding varieties 6 Protein Enhancement High GCA for Protein RIB-9184, RIB-16300, RIB-15131 For protein-rich varieties 7 Population Improvement Recurrent selection base RIB-9184, RIB-9185, RIB-15131, RIB-9205 Diverse genetic base with high GCA GCA = General combining ability; GY = Grain yield; Fe = Iron content; Zn = Zinc content Short-term (1–3 years): Superior hybrids RIB-9184 × RIB-15131 and RIB-16300 × RIB-192 should be advanced for multi-location testing and release evaluation. According to Manwaring et al. ( 2016 ), these hybrids achieve HarvestPlus biofortification targets with grain yield > 18 g/plant, iron > 44ppm and zinc > 38ppm. RIB-16324 × 20K86 should be tested in high-yielding environments based on its outstanding levels of heterosis (167% MPH). The conversion of elite parents (RIB-9205 and RIB-9184) to CMS lines would enable commercial seed production (Jain et al., 2025 ). Medium-term (3–5 years): OPV development through recurrent selection utilizing additive variance for biofortification (Baker’s ratio > 0.70) is recommended (Govindaraj et al., 2013 ). Parents with high GCA for iron (RIB-9205 and RIB-9184) and zinc (RIB-9184 and RIB-9185) should be used to form base populations. OPVs offer farmer-saved seed advantages to resource-poor farmers in arid regions (Yadav et al., 2021 ). Long-term (> 5 years): Reciprocal recurrent selection programs for sustained gains in yield (exploiting non-additive variance) and biofortification (accumulating additive variance) are recommended (Hallauer et al. 2010 ). The integration of genomic selection with Fe-Zn QTL markers would help to speed up progress (Kumar et al., 2018 ; Kanatti et al., 2019 ). The excellent Fe-Zn correlation (rg = 0.82) allows for an efficient simultaneous improvement via index selection (Singhal et al., 2021). Maintaining genetic diversity through the inclusion of diverse sources of germplasm would ensure climate resilience and long-term sustainability of biofortification gains in the pearl millet crop in arid and semi-arid regions of India and Africa. 5. Conclusions The present investigation on diallel analysis of 10 pearl millet inbred lines and their 45 F 1 hybrids showed contrasting gene action patterns for yield and biofortification traits. Baker's ratio was indicative of predominant additive gene action for iron (0.91), zinc (0.88), and protein (0.71) content and grain yield was affected by non-additive gene action (0.54). This differential genetic structure requires different breeding approaches for each trait: hybrid breeding for yield maximisation and population improvement strategies for biofortification traits. Among parents, RIB-9205 was found to be a wider champion donor for iron (GCA = 6.65), RIB-9184 was a better combiner for multi-traits for zinc (3.85) and protein (0.78*), and RIB-15131 was a better combiner with the contribution of multiple agronomic traits. The high positive correlation between iron and zinc content (rg = 0.82) allows for efficient joint selection for the simultaneous improvement of both micronutrients. RIB-9184 x RIB-15131 was found to be the best overall hybrid with a high multi-trait selection index (1.31), which was first in rank in all the weighting schemes used, though combined with superior grain yield (18.84 g/plant) and improved iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) contents. This hybrid, along with RIB-16300 × RIB-192 with outstanding heterosis for biofortification traits (Fe: 68.30%, Zn: 63.04% MPH), are promising candidates for developing high-yielding biofortified pearl millet cultivars for nutritional security in arid regions. Statements & Declarations Acknowledgements The first author gratefully acknowledges Sri Karan Narendra Agriculture University, for providing support and guidance during the PhD program. Sincere thanks are extended to Rajasthan Agricultural Research Institute (RARI), Durgapura, Jaipur for providing field experimental facilities. The author expresses gratitude to the advisory committee members and faculty of the Department of Genetics and Plant Breeding for their valuable suggestions and critical evaluation throughout the research work. Technical assistance rendered by the field staff during the crossing program and data recording is duly acknowledged. The first author also thanks the Director, ICAR-Indian Institute of Pulses Research, Kanpur for sanctioning earned leave to complete thesis submission formalities. Author Contributions Conceptualization: Monika Punia, L D Sharma, D K Gothwal; Methodology: Monika Punia, L D Sharma, Lalit Kumar Rolaniya; Formal analysis and investigation: Monika Punia, Lalit Kumar Rolaniya; Writing - original draft preparation: Monika Punia, Vaibhav Sharma, Sohan Lal Kajla; Writing - review and editing: Ram Lal Jat, Lalit Kumar Rolaniya; Supervision: L D Sharma. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors declare no competing interests. This work is part of the first author's PhD research conducted at SKN Agriculture University, Jobner, completed before joining the current employer. Data Availability Statement: All data generated or analysed during this study are included in this published article and its supplementary information files. Additional raw data are available from the corresponding author on reasonable request. References Anitha S, Kane-Potaka J, Botha R, Givens DI, Sulaiman NLB, Upadhyay S, Vetriventhan M, Tsusaka TW, Parasannanavar DJ, Longvah T, Rajendran A (2021) Millets can have a major impact on improving iron status, hemoglobin level, and in reducing iron deficiency anemia-a systematic review and meta-analysis. 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Front Plant Sci 12:645038. https://doi.org/10.3389/fpls.2021.645038 Yadav OP, Rai KN (2013) Genetic improvement of pearl millet in India. Agric Res 2(4):275-292. https://doi.org/10.1007/s40003-013-0089-z Yadav OP, Singh DV, Kumari V, Prasad M, Seni S, Singh RK, Sood S, Kant L, Dayakar Rao B, Madhusudhana R, Venkatesh Bhat B, Gupta SK, Yadava DK, Mohapatra T (2024) Production and cultivation dynamics of millets in India. Crop Sci 64(1):1-26. https://doi.org/10.1002/csc2.21207 Yadav S, Singh SP, Singhal T, Sankar SM, Mahendru-Singh A, Bhargavi HA, Aavula N, Sonu, Goswami S, Satyavathi CT (2023) Genetic elucidations of grain iron, zinc and agronomic traits by generation mean analysis in pearl millet [ Pennisetum glaucum (L.) R. Br.]. J Cereal Sci 113:103751. https://doi.org/10.1016/j.jcs.2023.103751 Yan W, Kang MS (2002) GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists, 1st edn. 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07:59:27","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":365442,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/f0815f340dffeff9df67589a.html"},{"id":100657049,"identity":"35e64767-3dd2-4307-93fa-42e91b777d11","added_by":"auto","created_at":"2026-01-20 07:59:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319618,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic distribution of grain yield and biofortification traits across environments in pearl millet. Violin-box plots showing the distribution of (a) grain yield (g/plant), (b) iron content (ppm), (c) zinc content (ppm), and (d) protein content (%) for 55 genotypes (10 parents + 45 F₁ hybrids) evaluated under E1 (normal sowing), E2 (late sowing), and pooled environments. Box plots within violins indicate median (red line), interquartile range, and whiskers extending to 1.5× IQR. Individual data points are represented by grey dots.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/a01c7253ddb018d2e9f7c9b6.png"},{"id":100657052,"identity":"7b2aa748-c42f-4049-b708-633e48d74ea7","added_by":"auto","created_at":"2026-01-20 08:00:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":376651,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering dendrogram of 10 pearl millet parental inbred lines based on 13 agronomic and biofortification traits using Ward's minimum variance method and Euclidean distance. The red dashed line indicates the cluster threshold (Euclidean distance = 3.75), delineating three distinct clusters: Cluster I (orange: RIB-9205, RIB-9178, 20K86, and J-2340), Cluster II (green: RIB-16324, RIB-16300, and RIB-192), and Cluster III (red: RIB-15131, RIB-9184, RIB-9185).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/32cbbc5f9433f2906f28fe62.png"},{"id":100656999,"identity":"aadb954c-701f-47f5-863d-e92460ff9990","added_by":"auto","created_at":"2026-01-20 07:57:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167869,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of standardized general combining ability (GCA) effects for 10 pearl millet parents across 13 traits with hierarchical clustering (Ward's method). Colour intensity represents standardized GCA values: green indicates positive effects (desirable for most traits), and red indicates negative effects. Dendrograms show clustering patterns for both parents (rows) and trait (columns). Traits: Tillers (productive tillers/plant), DFY (dry fodder yield), PL (panicle length), PH (plant height), TW (test weight), DF (days to flowering), DM (days to maturity), PG (panicle girth), HI (harvest index), Fe (iron content), Zn (zinc content), GY (grain yield), and protein (protein content).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/69653a6b8da5c391b40c464e.png"},{"id":100657007,"identity":"5bdbfdad-aa22-4248-a0a5-757e398bce55","added_by":"auto","created_at":"2026-01-20 07:58:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127165,"visible":true,"origin":"","legend":"\u003cp\u003eGCA × environment interaction plots for grain yield and biofortification traits in 10 pearl millet parents across two environments (E1: normal sowing; E2: late sowing). (a) Grain yield (g/plant), (b) iron content (ppm), (c) zinc content (ppm), and (d) protein content (%). Non-parallel lines indicate differential parental responses across environments (crossover interactions), whereas parallel lines suggest stable GCA effects. Parents with minimal rank changes across environments (e.g. RIB-9184 and RIB-9185 for biofortification traits) were considered stable combiners.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/6eaf1c2760bb9d5175f63e0a.png"},{"id":100657054,"identity":"4c3eaf6c-587d-4f21-94f7-a63351682d71","added_by":"auto","created_at":"2026-01-20 08:00:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200321,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot displaying specific combining ability (SCA) effects for 45 F₁ hybrids based on composite scores of grain yield, iron, zinc, and protein content. The outer arcs represent the 10 parental lines, with the arc colours indicating the overall parental contribution to the hybrid performance. Ribbons connecting parents represent individual cross combinations: green ribbons indicate positive (desirable) SCA effects, and red ribbons indicate negative (undesirable) SCA effects. The ribbon thickness corresponds to the magnitude of the SCA effect (thick = strong effect; thin = weak effect). The color scale bar shows standardized composite SCA values\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/4c0e35eff807c0b70d764986.png"},{"id":100657001,"identity":"897088df-a89c-4f41-94ef-55d82bf5b53d","added_by":"auto","created_at":"2026-01-20 07:57:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230051,"visible":true,"origin":"","legend":"\u003cp\u003eVr-Wr regression analysis (Hayman's graphical approach) for grain yield and biofortification traits in pearl millet (pooled data). (a) Grain yield (b = 0.045, r² = 0.090), (b) iron content (b = 0.421, r² = 0.456), (c) zinc content (b = 0.607, r² = 0.545), and (d) protein content (b = 0.377, r² = 0.231). Solid lines represent the regression of Wr (array covariance) on Vr (array variance); dashed curves indicate the limiting parabola. Parents positioned near the origin possess dominant alleles, whereas those farther from the origin carry recessive alleles. The regression line intercepting above the origin indicates partial dominance, and below the origin indicates overdominance.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/78edb715a6a00bd1367135fc.png"},{"id":100657045,"identity":"895a8eb4-af3c-46a6-9173-ae049bccc120","added_by":"auto","created_at":"2026-01-20 07:59:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":73689,"visible":true,"origin":"","legend":"\u003cp\u003eHeterosis distribution by trait category for 45 F₁ pearl millet hybrids (pooled data). (a) Mid-parent heterosis (MPH) and (b) better-parent heterosis (BPH) across four trait categories: phenological (days to flowering, maturity), morphological (plant height, tillers, panicle length, panicle girth), yield (grain yield, dry fodder yield, harvest index, test weight), and biofortification (iron, zinc, protein content). Violin plots show data distribution; embedded box plots display the median (red line) and interquartile range. The dashed horizontal line indicates zero heterosis. Yield traits exhibited the widest positive heterosis distribution, with outliers exceeding 250% MPH, whereas phenological traits showed predominantly negative heterosis (desirable for earliness).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/9ea16080eec99f5ae404ec5a.png"},{"id":100657047,"identity":"9d891760-907a-45b8-b43c-1bc62053b0c3","added_by":"auto","created_at":"2026-01-20 07:59:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":277020,"visible":true,"origin":"","legend":"\u003cp\u003eSplit heatmap displaying genetic (upper triangle) and phenotypic (lower triangle) correlations among 13 traits in pearl millet. Traits were grouped into four categories: phenological (DF: days to flowering, DM: days to maturity), morphological (PH: plant height, Tillers: productive tillers, PL: panicle length, PG: panicle girth), yield (TW: test weight, DFY: dry fodder yield, GY: grain yield, HI: harvest index), and biofortification (Fe: iron content, Zn: zinc content, Protein: protein content). The ellipse shape indicates the correlation strength (narrow = strong; circular = weak), the and colour indicates the direction (green = positive; red = negative). Border thickness denotes significance level: thick = P\u0026lt;0.01, thin = P\u0026lt;0.05, faded = non-significant\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/c6c9c8040ac6dd66c6a228c8.png"},{"id":100657048,"identity":"0e2f07ab-8344-4b0e-9a05-482ce618be05","added_by":"auto","created_at":"2026-01-20 07:59:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":133122,"visible":true,"origin":"","legend":"\u003cp\u003eGGE biplot displaying relationships among 55 pearl millet genotypes (10 parents + 45 F₁ hybrids) and 13 traits based on pooled data. PC1 and PC2 explained 25.6% and 19.7% of the total variation, respectively. Blue squares represent parents; gray circles represent hybrids. Trait vectors are colour-coded by category: phenological (blue: DF, DM), morphological (green: PH, Tillers, PL, PG), yield (red: TW, DFY, GY, HI), and biofortification (orange: Fe, Zn, Protein). Acute angles between vectors indicate positive correlations, and obtuse angles indicate negative correlations. Genotypes positioned in the direction of the trait vectors exhibited superior performance for those traits.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/d508203e9d74ea245f9c3f12.png"},{"id":100657028,"identity":"89fe22d6-d37d-4d64-9f03-3be50b7c8310","added_by":"auto","created_at":"2026-01-20 07:58:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":138449,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-trait selection plot displaying grain yield versus composite biofortification score (Fe + Zn + Protein) for 55 pearl millet genotypes. Squares represent parents (n=10); circles represent F₁ hybrids (n=45). The colour gradient indicates the Smith-Hazel selection index values (green = high; red = low). Dashed lines demarcate four quadrants: upper-right (high yield + high biofortification) represents ideal genotypes for simultaneous improvement; upper-left (low yield + high biofortification); lower-right (high yield + low biofortification); and lower-left (undesirable for both traits). RIB-9184 × RIB-15131 positioned in the ideal quadrant with highest selection index, followed by RIB-16300 × RIB-192 and RIB-9185 × RIB-9205\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/874f0652c41fddcfb3d5066b.png"},{"id":100658439,"identity":"124bb6b7-2b20-4a49-b726-a204a6e4a022","added_by":"auto","created_at":"2026-01-20 08:18:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4578073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/74e070de-a07b-42bb-91f7-cc273db1b92c.pdf"},{"id":100657126,"identity":"999fccf0-cb11-4a66-9206-7cd75c3f22bf","added_by":"auto","created_at":"2026-01-20 08:00:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":145363,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/cdf55888c1760ea78bd661e9.docx"},{"id":100657008,"identity":"8e7b9529-2f86-40f8-830d-26eb88394e91","added_by":"auto","created_at":"2026-01-20 07:58:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2770842,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8583609/v1/d721f3e3ff6f58903921333d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: Implications for breeding high-yielding nutrient-dense hybrids","fulltext":[{"header":"Key message","content":"\u003cp\u003eHybrid RIB-9184 \u0026times; RIB-15131 combines high yield (18.84 g/plant) with enhanced iron (46.16 ppm) and zinc (38.86 ppm); strong Fe-Zn correlation (r\u0026thinsp;=\u0026thinsp;0.82**) enables simultaneous biofortification improvement.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eMicronutrient malnutrition, known as 'hidden hunger,' is a global phenomenon that affects over two billion people and represents one of the most serious health challenges worldwide (Muthayya et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; WHO \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Iron (Fe) and zinc (Zn) deficiencies are especially widespread, causing anaemia, poor cognitive development, poor immune function, and high mortality rates, especially among women and children in developing nations (Anitha et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In India, the National Family Health Survey (NFHS-5) found that 57% of women and 67% of children under 5 years of age suffer from anaemia, while the Global Hidden Hunger Index places India among the countries with severe micronutrient deficiencies (Nguyen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although traditional interventions, such as dietary supplementation and food fortification, have been shown to be effective, they are limited by cost, infrastructure requirements, and accessibility in resource-poor settings (Bouis et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Biofortification, the genetic improvement of staple food crops with increased micronutrient densities, has therefore emerged as a sustainable, cost-effective, and farmer-friendly approach to combat hidden hunger at the root (Govindaraj et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePearl millet [\u003cem\u003ePennisetum glaucum\u003c/em\u003e (L.) R. Br., syn. \u003cem\u003eCenchrus americanus\u003c/em\u003e (L.) Morrone] is the sixth most important cereal crop globally and is a dietary staple for more than 90\u0026nbsp;million people in the arid and semi-arid tropical regions of Asia and Africa (Taylor \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This climate-resilient crop grows on marginal soils with limited moisture availability, where other cereals are unable to yield economically. Pearl millet has an intrinsically superior nutritional profile, partly with 8.5\u0026ndash;15% protein and a well-balanced amino acid composition, which has enhanced levels of iron (31\u0026ndash;61 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and zinc (32\u0026ndash;54 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to rice and wheat (Burton et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Govindaraj et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These attributes have made pearl millet a priority crop for HarvestPlus biofortification initiatives, with breeding goals set at 77 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Fe (Govindaraj et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Bioavailability studies have established that the consumption of biofortified pearl millet can meet up to 80% of the daily Fe requirements in target populations, establishing the practical impact of this breeding approach (Kodkany et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). India is the leading producer of pearl millet in the world, with a production of 10.72\u0026nbsp;million tonnes on 7.38\u0026nbsp;million hectares, of which Rajasthan has a share of 4.38\u0026nbsp;million hectares (Indiastat, 2024a, b). However, productivity in arid Rajasthan is still lower than the national average because of environmental constraints, which explains the need for region-specific biofortified cultivars.\u003c/p\u003e \u003cp\u003eEffective crop improvement programs require in-depth knowledge of the genetic basis of target traits. Diallel mating designs, especially the half-diallel cross, serve as a systematic procedure for the dissection of the inheritance of quantitative characters, as measured by the estimation of combining ability effects (Griffing \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). General combining ability (GCA) is the average performance of a parent in all combinations of hybrids and is due to additive genetic effects, whereas specific combining ability (SCA) is the difference in the performance of a hybrid from the expectation based on the paternal GCA and is due to non-additive (dominance and epistatic) gene action (Sprague and Tatum, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1942\u003c/span\u003e). The relative size of these genetic components, as measured by Baker's ratio [2σ\u0026sup2;GCA/(2σ\u0026sup2;GCA\u0026thinsp;+\u0026thinsp;σ\u0026sup2;SCA)], provides important information for the choice of breeding strategy: values close to unity indicate the predominance of additive effects, in favour of population improvement methods, whereas lower values indicate an important non-additive variance for which hybrid breeding methods should be considered (Baker \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). Complementary information on the action of genes is presented by Hayman's (1954b) graphical analysis, which explains the extent of dominance, occurrence of epistasis, and distribution of dominant and recessive alleles between parents using variance-covariance (Vr-Wr) regression.\u003c/p\u003e \u003cp\u003ePearl millet is a highly cross-pollinated (\u0026lt;\u0026thinsp;85% outcrossing) plant with protogynous floral traits and well-defined cytoplasmic male sterility (CMS) mechanisms, making it an ideal crop for exploiting heterosis in commercial hybrid production (Burton \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Bashir et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The first single-cross pearl millet hybrid (HB-1) was released in India in 1965, and successive decades have seen progressive genetic gains in yield and adaptability from the effects of systematic hybrid breeding (Yadav et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Considerable heterosis for grain yield has been well documented, ranging from 24% panmictic mid-parent heterosis in West African population crosses (Dutta et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to more than 100% in elite Indian germplasm (Maheswari et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, exploitable heterosis has also been shown to be true for biofortification traits, with reports of 18\u0026ndash;27% for Fe and 13\u0026ndash;14% for Zn (Ladumor et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rani et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent studies on combining abilities have shown that both additive and non-additive gene effects are involved in Fe and Zn inheritance, and the effects are mostly additive for micronutrients and show significant non-additive effects on grain yield (Thribhuvan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kanatti et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite leading progress in pearl millet biofortification, several critical knowledge gaps limit the breeding process, especially under difficult arid conditions in Rajasthan. First, the analyses of combining ability involving both yield and biofortification traits in multiple environments are limited, with most studies focusing on either trait category in isolation (Velu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Govindaraj et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Second, potential trade-offs between grain yield and micronutrient density must be systematically characterized because conflicting reports exist regarding the nature and magnitude of these correlations (Kanatti et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Govindaraj et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Third, multi-trait selection approaches that allow the simultaneous improvement of yield and nutritional quality are significantly lacking. Fourth, genotype by environment (GxE) interaction effects on biofortification trait expressions need to be investigated, as there are reports indicating that different environments significantly influence not only the grain yield but also the micronutrient composition (Reddy et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pawar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Addressing these gaps is necessary to produce biofortified cultivars suited to resource-constrained environments, where the prevalence of malnutrition is the highest.\u003c/p\u003e \u003cp\u003eBased on the foregoing considerations, we hypothesize that (i) biofortification traits (Fe, Zn, and protein) are governed predominantly by additive gene action, enabling effective selection in early generations, whereas grain yield exhibits significant non-additive variance necessitating hybrid breeding; (ii) the positive correlation between Fe and Zn content will facilitate their simultaneous improvement without severe trade-offs with grain yield; and (iii) superior hybrid combinations can be identified that combine high yield potential with enhanced nutritional quality. To test these hypotheses, the present investigation was undertaken using a half diallel mating design involving 10 diverse pearl millet inbred lines evaluated across two environments. The specific objectives were as follows: estimation of GCA and SCA effects and establishment of the relative importance of the additive versus non-additive gene action for grain yield and biofortification traits using Baker's ratio and Hayman's graphical analysis; identification of superior parents with favorable GCA and promising hybrid combinations with high SCA for both yield and nutritional enhancement; characterization of trait correlations at the genetic and phenotypic levels and feasibility of simultaneous improvement; development of a multi-trait selection index combining yield and biofortification objectives; and development of evidence-based breeding recommendations for pearl millet biofortification programs targeting the arid environment. The outcomes of this study are expected to provide valuable genetic resources and strategic guidance for addressing micronutrient malnutrition through crop improvement.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant material\u003c/h2\u003e \u003cp\u003eThe experimental material consisted of 10 different lines of pearl millet that were chosen based on their differences in grain iron (Fe) and zinc (Zn) and protein content, agronomic performance, and adaptation to arid climates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The source of the parental lines was the Rajasthan Agricultural Research Institute, Durgapura, Rajasthan, India, which had a great diversity of variation for use in bio-fortification and yield traits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParental inbred lines used in the half-diallel study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSalient Features\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEarly maturity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest Zn, high protein, highest biomass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Fe, highest test weight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Fe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest Fe, good combiner\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrought tolerant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh yield potential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJunagadh, Gujarat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium plant height, normal days to flowering, late maturity and low number of effective tillers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest grain yield, highest panicle girth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRARI, Durgapura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium plant height, high number of effective tillers, highest panicle length and small ear girth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Mating design and crossing program\u003c/h2\u003e \u003cp\u003eThe 10 parental inbred lines were crossed in a half diallel mating design following Griffing's (1956) Method 2 (parents and one set of F\u003csub\u003e1\u003c/sub\u003es, excluding reciprocals), Model 1 (fixed effects) during summer 2019 at ICRISAT, Patancheru, Hyderabad, India. The number of F\u003csub\u003e1\u003c/sub\u003e hybrid combinations generated was as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eNumber of F\u003c/em\u003e \u003csub\u003e \u003cem\u003e1\u003c/em\u003e \u003c/sub\u003e \u003cem\u003ecrosses\u0026thinsp;=\u0026thinsp;p(p\u0026thinsp;\u0026minus;\u0026thinsp;1)/2\u0026thinsp;=\u0026thinsp;10(10\u0026thinsp;\u0026minus;\u0026thinsp;1)/2\u0026thinsp;=\u0026thinsp;45\u003c/em\u003e ..(1)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ep\u003c/em\u003e is the number of parents. Crosses were made by hand emasculation and controlled pollination to produce sufficient seeds for replicated evaluation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental design and field evaluation\u003c/h2\u003e \u003cp\u003eThe experimental material comprising 55 entries (45 F1 hybrids\u0026thinsp;+\u0026thinsp;10 parents) was evaluated during \u003cem\u003ethe kharif season of\u003c/em\u003e 2019 at the Agricultural Research Farm, Rajasthan Agricultural Research Institute (RARI), Durgapura, Jaipur, Rajasthan, India (26.49\u0026deg;N latitude, 75.48\u0026deg;E longitude, 390 m altitude). Two environments were established by staggered sowing dates to assess genotype \u0026times; environment (GxE) interaction: E\u003csub\u003e1\u003c/sub\u003e (normal sowing: 25 July 2019) and E\u003csub\u003e2\u003c/sub\u003e (late sowing: 08 August, 2019). In each environment, the experiment was designed using a randomized complete block design (RCBD) with three replicates. To reduce intergenotypic competition among genotypes between vigorous hybrids and parental inbreds, the parents and hybrids were blocked separately within each replication (Panse and Sukhatme \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Each entry was sown in a two-row plot of 4 m length with inter-row spacing of 50 cm and intra-row spacing of 15 cm (maintained by thinning at 20 DAS). Non-experimental border rows were planted around the field to eliminate edge effects from the study. Standard agronomic practices recommended for pearl millet cultivation in Rajasthan were uniformly followed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Observations recorded\u003c/h2\u003e \u003cp\u003eData were recorded for 13 quantitative traits which were grouped into four categories (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Five competitive plants were randomly tagged in each plot before flowering for observations. Days to 50% flowering and maturity were determined on a whole-plot basis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTraits recorded with abbreviations, units, and measurement methods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhenological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays to 50% flowering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlot basis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays to maturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePlot basis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMorphological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5-plant mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProductive tillers/plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTillers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5-plant mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanicle length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5-plant mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePanicle girth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVernier caliper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1000-grain wt.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry fodder yield/plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDFY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5-plant mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield/plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5-plant mean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarvest index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCalculated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiofortification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emg kg⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXRF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emg kg⁻\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXRF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKjeldahl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Biofortification trait analysis\u003c/h2\u003e \u003cp\u003eGrain samples were cleaned, dried at 60\u0026deg;C for 6 h in a hot air oven, and stored in butter paper covers. Care was taken to avoid contamination with dust and metal particles. Fe and Zn concentrations were determined by Energy-Dispersive X-Ray Fluorescence Spectrometry (ED-XRF) at the Central Analytical Services Laboratory, ICRISAT, Patancheru, following standardised protocols (Paltridge et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Govindaraj et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For protein analysis, the nitrogen content was determined using the micro-Kjeldahl method (AOAC \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1990\u003c/span\u003e, Official Method 950.48) with a KEL PLUS distillation unit (Pelican Equipment, Chennai, India). A 200 mg flour sample was digested with concentrated H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e in the presence of a catalyst at 350\u0026deg;C. Protein was calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eProtein (%)\u0026thinsp;=\u0026thinsp;Nitrogen (%) \u0026times; 6.25\u003c/em\u003e ..(2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Pooled analysis of variance\u003c/h2\u003e \u003cp\u003eA pooled analysis of variance (ANOVA) across environments was used to examine the mean for each trait, following Panse and Sukhatme (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The structure of the pooled ANOVA is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructure of pooled ANOVA for half-diallel analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected MS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironments (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;ge\u0026thinsp;+\u0026thinsp;rgσ\u0026sup2;E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplications/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee(r\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;gσ\u0026sup2;r\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes (G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;ge\u0026thinsp;+\u0026thinsp;reσ\u0026sup2;g\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;pe\u0026thinsp;+\u0026thinsp;reσ\u0026sup2;p\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrosses (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;ce\u0026thinsp;+\u0026thinsp;reσ\u0026sup2;c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;reσ\u0026sup2;(PvsC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(g\u0026thinsp;\u0026minus;\u0026thinsp;1)(e\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;ge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(p\u0026thinsp;\u0026minus;\u0026thinsp;1)(e\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;pe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(c\u0026thinsp;\u0026minus;\u0026thinsp;1)(e\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;ce\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(P vs C) \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u0026thinsp;+\u0026thinsp;rσ\u0026sup2;(PvsC)e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePooled Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ee(r\u0026thinsp;\u0026minus;\u0026thinsp;1)(g\u0026thinsp;\u0026minus;\u0026thinsp;1)\u0026thinsp;=\u0026thinsp;216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u0026sup2;e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ewhere \u003cem\u003ee\u003c/em\u003e = environments (2), \u003cem\u003er\u003c/em\u003e = replications (3), \u003cem\u003eg\u003c/em\u003e = genotypes (55), \u003cem\u003ep\u003c/em\u003e = parents (10), \u003cem\u003eand c\u003c/em\u003e = crosses (45). Significance was tested using F\u0026minus;tests at P \u0026le; 0.05 (*) and P \u0026le; 0.01 (**)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Variance Components and Genetic Parameters\u003c/h2\u003e \u003cp\u003eVariance components were estimated from expected mean squares (EMS) and used to calculate the genetic parameters (Burton and DeVane \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1953\u003c/span\u003e; Johnson et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1955\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cem\u003eσ\u0026sup2;g = (MSG\u0026thinsp;\u0026minus;\u0026thinsp;MSG\u0026times;E) / re\u003c/em\u003e ..(3)\u003c/p\u003e \u003cp\u003e \u003cem\u003eσ\u0026sup2;g\u0026times;e = (MSG\u0026times;E\u0026thinsp;\u0026minus;\u0026thinsp;MSE) / r\u003c/em\u003e ..(4)\u003c/p\u003e \u003cp\u003e \u003cem\u003eσ\u0026sup2;p\u0026thinsp;=\u0026thinsp;σ\u0026sup2;g\u0026thinsp;+\u0026thinsp;σ\u0026sup2;g\u0026times;e/e\u0026thinsp;+\u0026thinsp;σ\u0026sup2;e/re\u003c/em\u003e ..(5)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBroad-sense heritability: H\u0026sup2; = σ\u0026sup2;g / σ\u0026sup2;p\u003c/em\u003e ..(6)\u003c/p\u003e \u003cp\u003e \u003cem\u003eGCV (%) = (\u0026radic;σ\u0026sup2;g / X̄) \u0026times; 100\u003c/em\u003e ..(7)\u003c/p\u003e \u003cp\u003e \u003cem\u003ePCV (%) = (\u0026radic;σ\u0026sup2;p / X̄) \u0026times; 100\u003c/em\u003e ..(8)\u003c/p\u003e \u003cp\u003e \u003cem\u003eGenetic advance: GA\u0026thinsp;=\u0026thinsp;k \u0026times; \u0026radic;σ\u0026sup2;p \u0026times; H\u0026sup2;\u003c/em\u003e ..(9)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.06 (selection differential at 5% intensity) and \u003cem\u003eX̄\u003c/em\u003e = the grand mean.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGAM (%) = (GA / X̄) \u0026times; 100\u003c/em\u003e ..(10)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Combining ability analysis\u003c/h2\u003e \u003cp\u003eCombining ability analysis was performed following Griffing \u0026rsquo;s(1956) Method 2, Model 1. The linear model is:\u003c/p\u003e \u003cp\u003e \u003cem\u003eXij\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;gi\u0026thinsp;+\u0026thinsp;gj\u0026thinsp;+\u0026thinsp;sij\u0026thinsp;+\u0026thinsp;eij\u003c/em\u003e ..(11)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eXij\u003c/em\u003e\u0026thinsp;=\u0026thinsp;mean of cross between \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e and \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e parents; \u0026micro;\u0026thinsp;=\u0026thinsp;population mean; \u003cem\u003egi\u003c/em\u003e, \u003cem\u003egj\u003c/em\u003e\u0026thinsp;=\u0026thinsp;GCA effects; \u003cem\u003esij\u003c/em\u003e\u0026thinsp;=\u0026thinsp;SCA effect; \u003cem\u003eand eij\u003c/em\u003e\u0026thinsp;=\u0026thinsp;error.\u003c/p\u003e \u003cp\u003eGCA effects:\u003c/p\u003e \u003cp\u003e \u003cem\u003egi = [1/(p\u0026thinsp;+\u0026thinsp;2)] \u0026times; [Xi. + Xii \u0026minus; (2/p) \u0026times; X..]\u003c/em\u003e ..(12)\u003c/p\u003e \u003cp\u003eSCA effects:\u003c/p\u003e \u003cp\u003e \u003cem\u003esij\u0026thinsp;=\u0026thinsp;Xij \u0026minus; [(Xi. + Xj. + 2Xii\u0026thinsp;+\u0026thinsp;2Xjj)/(p\u0026thinsp;+\u0026thinsp;2)] + [2X../(p\u0026thinsp;+\u0026thinsp;1)(p\u0026thinsp;+\u0026thinsp;2)]\u003c/em\u003e ..(13)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eXi.\u003c/em\u003e = sum of array involving \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e parent; \u003cem\u003eXii\u003c/em\u003e\u0026thinsp;=\u0026thinsp;parental value; \u003cem\u003eX..\u003c/em\u003e = grand total; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;number of parents.\u003c/p\u003e \u003cp\u003eStandard errors for significance testing.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSE(gi) = \u0026radic;[(p\u0026thinsp;\u0026minus;\u0026thinsp;1)MSe / p(p\u0026thinsp;+\u0026thinsp;2)]\u003c/em\u003e ..(14)\u003c/p\u003e \u003cp\u003e \u003cem\u003eSE(sij) = \u0026radic;[p(p\u0026thinsp;\u0026minus;\u0026thinsp;1)MSe / (p\u0026thinsp;+\u0026thinsp;1)(p\u0026thinsp;+\u0026thinsp;2)]\u003c/em\u003e ..(15)\u003c/p\u003e \u003cp\u003eVariance components and Baker's ratio\u003c/p\u003e \u003cp\u003e \u003cem\u003eσ\u0026sup2;GCA = (MSGCA\u0026thinsp;\u0026minus;\u0026thinsp;MSe) / (p\u0026thinsp;+\u0026thinsp;2)\u003c/em\u003e ..(16)\u003c/p\u003e \u003cp\u003e \u003cem\u003eσ\u0026sup2;SCA\u0026thinsp;=\u0026thinsp;MSSCA\u0026thinsp;\u0026minus;\u0026thinsp;MSe\u003c/em\u003e ..(17)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBaker's ratio\u0026thinsp;=\u0026thinsp;2σ\u0026sup2;GCA / (2σ\u0026sup2;GCA\u0026thinsp;+\u0026thinsp;σ\u0026sup2;SCA)\u003c/em\u003e ..(18)\u003c/p\u003e \u003cp\u003eBaker's ratio approaching 1.0 indicates predominance of additive gene action; lower values indicate significant non-additive effects (Baker \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProportional contribution (%).\u003c/p\u003e \u003cp\u003e \u003cem\u003eGCA contribution (%) = (SSGCA / SSCrosses) \u0026times; 100\u003c/em\u003e ..(19)\u003c/p\u003e \u003cp\u003e \u003cem\u003eSCA contribution (%) = (SSSCA / SSCrosses) \u0026times; 100\u003c/em\u003e ..(20)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Hayman's diallel analysis\u003c/h2\u003e \u003cp\u003eHayman's (1954a, 1954b) approach was employed to estimate the genetic components of variance. The analysis assumes (i) diploid segregation, (ii) homozygous parents, (iii) no reciprocal differences, (iv) no epistasis, (v) no multiple allelism, and (vi) independent gene assortment. The model validity was tested using the regression of Wr (covariance of \u003cem\u003er\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e array with non-recurrent parent) on Vr (variance of \u003cem\u003er\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e array):\u003c/p\u003e \u003cp\u003e \u003cem\u003eb\u0026thinsp;=\u0026thinsp;Cov(Wr, Vr) / Var(Vr)\u003c/em\u003e ..(21)\u003c/p\u003e \u003cp\u003eThe additive dominance model is valid when \u003cem\u003eb\u003c/em\u003e is significantly different from zero but not from unity (tested by \u003cem\u003et\u003c/em\u003e-test at \u003cem\u003en\u003c/em\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2 df).\u003c/p\u003e \u003cp\u003eGenetic components were estimated as follows:\u003c/p\u003e \u003cp\u003eD\u0026thinsp;=\u0026thinsp;additive variance; H₁ = dominance variance; H₂ = H₁[1\u0026minus;(u\u0026thinsp;\u0026minus;\u0026thinsp;v)\u0026sup2;], where \u003cem\u003eu\u003c/em\u003e and \u003cem\u003ev\u003c/em\u003e are frequencies of positive and negative alleles, respectively; F\u0026thinsp;=\u0026thinsp;covariance of additive and dominance effects; h\u0026sup2; = dominance effect summed over loci; and E\u0026thinsp;=\u0026thinsp;environmental variance.\u003c/p\u003e \u003cp\u003eDerived parameters:\u003c/p\u003e \u003cp\u003e \u003cem\u003eAverage degree of dominance = \u0026radic;(H₁/D)\u003c/em\u003e ..(22)\u003c/p\u003e \u003cp\u003e \u003cem\u003eProportion of genes with +/\u0026minus; effects\u0026thinsp;=\u0026thinsp;H₂/4H₁\u003c/em\u003e ..(23)\u003c/p\u003e \u003cp\u003e \u003cem\u003eKD/KR = [(4DH₁)^0.5\u0026thinsp;+\u0026thinsp;F] / [(4DH₁)^0.5\u0026thinsp;\u0026minus;\u0026thinsp;F]\u003c/em\u003e ..(24)\u003c/p\u003e \u003cp\u003e \u003cem\u003eh\u0026sup2;ns = (0.5D) / (0.5D\u0026thinsp;+\u0026thinsp;0.25H₁ \u0026minus; 0.25F\u0026thinsp;+\u0026thinsp;E)\u003c/em\u003e ..(25)\u003c/p\u003e \u003cp\u003e \u003cem\u003eV\u003c/em\u003er\u003cem\u003e-W\u003c/em\u003er graphical analysis: Graphs were constructed according to Hayman (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1954b\u003c/span\u003e) and Singh and Singh (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The position of array points on the regression line defines the genetic constitution: array points close to the origin - parents with mostly dominant alleles; array points far from the origin - parents with mostly recessive alleles. The intercept of the regression line on the Wr axis indicates the following: positive intercept\u0026thinsp;=\u0026thinsp;partial dominance; intercept at origin\u0026thinsp;=\u0026thinsp;complete dominance; negative intercept\u0026thinsp;=\u0026thinsp;overdominance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5 Heterosis estimation\u003c/h2\u003e \u003cp\u003eHeterosis was calculated as described by Fonseca and Patterson (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1968\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMid-parent heterosis: MPH (%) = [(F₁ \u0026minus; MP) / MP] \u0026times; 100\u003c/em\u003e ..(26)\u003c/p\u003e \u003cp\u003e \u003cem\u003eBetter-parent heterosis: BPH (%) = [(F₁ \u0026minus; BP) / BP] \u0026times; 100\u003c/em\u003e ..(27)\u003c/p\u003e \u003cp\u003ewhere F₁ = hybrid mean, MP = (P₁ + P₂)/2, and BP\u0026thinsp;=\u0026thinsp;better parent mean.\u003c/p\u003e \u003cp\u003eSignificance testing:\u003c/p\u003e \u003cp\u003e \u003cem\u003eSE(MPH) = \u0026radic;(3MSe / 2r)\u003c/em\u003e ..(28)\u003c/p\u003e \u003cp\u003e \u003cem\u003eSE(BPH) = \u0026radic;(2MSe / r)\u003c/em\u003e ..(29)\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe t\u003c/em\u003e-values were compared against the table values at the error degrees of freedom.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.6 Correlation analysis\u003c/h2\u003e \u003cp\u003ePhenotypic correlation coefficients (rp) were calculated using Pearson's product-moment correlation. GCA level correlations (rGCA) were derived from parental GCA effects to test the genetic relationships among traits. Significance was tested at P\u0026thinsp;\u0026le;\u0026thinsp;0.05 and P\u0026thinsp;\u0026le;\u0026thinsp;0.01 using t-test with n-2 df.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.5.7 GGE biplot analysis\u003c/h2\u003e \u003cp\u003eGenotype\u0026thinsp;+\u0026thinsp;Genotype \u0026times; Environment (GGE) biplot analysis was performed following Yan and Kang (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) to visualize the genotype performance across traits and identify ideal genotypes. The model is:\u003c/p\u003e \u003cp\u003e \u003cem\u003eYij\u0026thinsp;\u0026minus;\u0026thinsp;\u0026micro;\u0026thinsp;\u0026minus;\u0026thinsp;βj\u0026thinsp;=\u0026thinsp;λ₁ξi1ηj1\u0026thinsp;+\u0026thinsp;λ₂ξi2ηj2\u0026thinsp;+\u0026thinsp;εij\u003c/em\u003e ..(30)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.5.8 Multi-trait selection index\u003c/h2\u003e \u003cp\u003eA modified Smith-Hazel selection index (Smith \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1936\u003c/span\u003e; Hazel \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1943\u003c/span\u003e) was constructed to identify superior hybrids combining yield and biofortification.\u003c/p\u003e \u003cp\u003e \u003cem\u003eI\u0026thinsp;=\u0026thinsp;Σ(wi \u0026times; zi)\u003c/em\u003e ..(31)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ewi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;economic weight for trait \u003cem\u003ei\u003c/em\u003e; \u003cem\u003ezi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standardized value = (Xi\u0026thinsp;\u0026minus;\u0026thinsp;X̄)/SD. Five weighting schemes were evaluated for GY, Fe, Zn, and Protein: (i) Equal (0.25 each); (ii) Yield priority (0.40, 0.20, 0.20, 0.20); (iii) Biofortification priority (0.20, 0.30, 0.30, 0.20); (iv) Fe-Zn focus (0.25, 0.30, 0.30, 0.15); (v) Balanced (0.30, 0.25, 0.25, 0.20).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.9 Cluster analysis\u003c/h2\u003e \u003cp\u003eHierarchical cluster analysis was performed on parental GCA effects using Ward's minimum variance method (Ward \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) with squared Euclidean distance to identify genetically similar groups and potential heterotic pools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.10 Statistical software\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.2.0 (R Core Team \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the following packages: \u003cem\u003eagricolae\u003c/em\u003e (De Mendiburu \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for ANOVA and combining ability; \u003cem\u003elme4\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for mixed models; \u003cem\u003eGGEBiplots\u003c/em\u003e (Dumble \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for GGE analysis; and \u003cem\u003ecorrplot\u003c/em\u003e (Wei and Simko \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for correlation visualization. Data visualization was performed using Python 3.10 with \u003cem\u003ethe Matplotlib\u003c/em\u003e, \u003cem\u003eSeaborn\u003c/em\u003e, and \u003cem\u003eSciPy\u003c/em\u003e libraries.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phenotypic variation and mean performance\u003c/h2\u003e \u003cp\u003eThe 55 genotypes (10 parents and 45 F1 hybrids) exhibited wide phenotypic variability across the two environments (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Grain yield ranged from 18.42 to 52.67 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean of 34.28 g. The mean hybrid yield (36.84 g) exceeded the parental mean (28.15 g) by 30.9%. For biofortification traits, grain Fe concentration varied from 24.72 to 63.29 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean: 42.15 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Zn ranged from 21.87 to 39.06 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean: 31.24 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and protein content spanned 7.90\u0026ndash;11.91% (mean: 9.84%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong parents, RIB-9205 showed highest Fe (56.24 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), RIB-9184 exhibited maximum Zn (36.82 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and protein (11.15%), whereas RIB-15131 was the best yielding parent (32.45 g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Transgressive segregation was observed in several hybrids for yield and biofortification traits in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Parental diversity assessment\u003c/h2\u003e \u003cp\u003eHierarchical cluster analysis based on standardized values of 13 agronomic and biofortification traits using Ward's minimum variance method grouped the 10 parental inbred lines into three distinct clusters with a Euclidean distance threshold of 3.75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cluster I (orange), which included RIB-9205, RIB-9178, 20K86, and J-2340; Cluster II (green), which included RIB-16324, RIB-16300, and RIB-192; and Cluster III (red), which included RIB-15131, RIB-9184 and RIB-9185. The large degree of genetic divergence between clusters was the basis for choosing these diverse parents for the half diallel mating design. Inter-cluster crosses tended to have greater levels of heterosis than intra-cluster combinations, supporting the classical relationship between genetic distance and hybrid vigour. This clustering pattern provided a rational framework for interpreting the effect of combining ability and identifying complementary parental combinations for the simultaneous improvement of grain yield and biofortification traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pooled analysis of variance\u003c/h2\u003e \u003cp\u003eThe results of the pooled analysis of variance showed highly significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) differences among genotypes for all 13 traits (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Significant variation was observed among parents, crosses, and parents versus crosses for all traits. The G \u0026times; environment (G \u0026times; E) interaction was significant for grain yield, dry fodder yield, harvest index, days to flowering, and days to maturity. However, GxE interaction was not significant for biofortification traits (Fe, Zn, and protein content). The parents versus crosses contrast was highly significant for all traits, with the crosses having superior mean performance than the parents. This was especially true for grain yield, productive tillers per plant, and panicle length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Genetic parameters and heritability\u003c/h2\u003e \u003cp\u003eThe results of the estimation of genetic parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) was high for biofortification traits: for protein (0.81), Fe (0.78), and Zn (0.74). Moderate heritability was recorded for grain yield (0.62), days to flowering (0.68), and days to maturity (0.71). The genotypic coefficient of variation (GCV) was maximum for the grain yield (28.45%), productive tillers per plant (25.32%), and dry fodder yield (22.18%). For biofortification traits, GCV varied from 12.38% (protein) to 18.64% (Fe) content. The phenotypic coefficient of variation (PCV) was slightly higher than the GCV for all traits. Genetic advance as a percentage of the mean (GAM) was high (\u0026gt;\u0026thinsp;20%) for grain yield (35.67%), productive tillers (29.84%), and dry fodder yield (26.92%). Moderate GAM (10\u0026ndash;20%) was found for Fe (18.45%), Zn (15.62%) and protein (13.78%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePooled analysis of variance, variance components, and genetic parameters for yield and biofortification traits in pearl millet (10 parents \u0026times; 45 F₁ hybrids) across two environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTillers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDFY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Squares (MS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e590.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e901.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3118.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e149.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.86\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.53\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12061.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1182.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e250.94\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e977.89\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e603.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplication/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e440.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e240.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e16.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e12.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes (G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2919.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.93\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.83\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.98\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7401.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e147.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e168.94\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e349.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e116.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents (P)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.92\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4520.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e152.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.61\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2569.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e125.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e451.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e118.85\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.98\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrosses (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1752.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.36\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.43\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7246.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e137.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e180.65\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e320.40\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e100.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e5.90\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP vs C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1041.47\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1320.91\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39862.97\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e607.49\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e43.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e57684.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1808.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e43.23\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e705.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e789.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e275.58\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e186.10\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e20.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e11.26\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.88\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.62\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e126.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.56\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e26.79\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e19.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.49\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e326.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e192.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e19.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(P vs C) \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e235.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e435.59\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e54.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e16.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePooled Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e101.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance Components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;g (Genotypic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e440.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1202.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e26.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e54.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;gxe (G\u0026times;E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;e (Error)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e186.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e101.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;p (Phenotypic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e486.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1233.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e58.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e19.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenetic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u0026sup2; (Broad-sense)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e33.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e17.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e13.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e35.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e18.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e70.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAM (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e77.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e64.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e67.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e35.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e25.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e19.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrand Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e90.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e41.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e31.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e9.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003e*, **, *** Significant at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001, respectively\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003eH\u0026sup2; = σ\u0026sup2;g / (σ\u0026sup2;g + σ\u0026sup2;gxe/e + σ\u0026sup2;e/re); GCV = genotypic coefficient of variation; PCV = phenotypic coefficient of variation; GA = genetic advance at 5% selection intensity; GAM = genetic advance as percent of mean\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003csup\u003eDF = days to 50% flowering; DM = days to maturity; PH = plant height (cm); Tillers = productive tillers/plant; PL = panicle length (cm); PG = panicle girth (cm); TW = test weight (g); DFY = dry fodder yield/plant (g); GY = grain yield/plant (g); HI = harvest index (%); Fe = iron content (ppm); Zn = zinc content (ppm); Protein = protein content (%)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Combining ability analysis\u003c/h2\u003e \u003cp\u003eAnalysis of variance for combining ability showed highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differences between genotypes for both general combining ability (GCA) and specific combining ability (SCA) effects for all 13 traits (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombining ability analysis (Griffing's Method 2, Model 1) with variance components and Baker's ratio for yield and biofortification traits in pearl millet\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of Variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTillers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMean Squares (MS)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e464.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e553.50\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5346.77\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e383.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.50\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2434.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.95\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCA \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCA \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.73\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e292.64\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance Components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;GCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e716.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;A (Additive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1432.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e105.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;D (Dominance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenetic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaker's Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;GCA/σ\u0026sup2;SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvg. Degree of Dominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Contribution GCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Contribution SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e62.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of Variation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eDFY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eGY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Squares (MS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7478.61\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e177.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1118.95\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e323.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.99\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7385.63\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e195.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.81\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCA \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.92\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCA \u0026times; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.65\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariance Components\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;GCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1004.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e149.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1201.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;A (Additive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2009.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e299.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;D (Dominance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1201.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenetic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaker's Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eσ\u0026sup2;GCA/σ\u0026sup2;SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvg. Degree of Dominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Contribution GCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Contribution SCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e*, **, *** Significant at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001, respectively\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eBaker's Ratio = 2σ\u0026sup2;GCA/(2σ\u0026sup2;GCA + σ\u0026sup2;SCA); values closer to 1 indicate predominance of additive gene action\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eσ\u0026sup2;A = 2σ\u0026sup2;GCA; σ\u0026sup2;D = σ\u0026sup2;SCA; Avg. Degree of Dominance = \u0026radic;(2σ\u0026sup2;D/σ\u0026sup2;A); \u0026lt;1 = partial dominance, =1 = complete dominance, \u0026gt;1 = overdominance\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eGCA tested against GCA\u0026times;E; SCA tested against SCA\u0026times;E\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe GCA C environment and SCA Χ environment interactions were significant for most traits and indicated differential parental and hybrid performance in the two environments. Baker's ratio, a measure of the relative importance of additive and non-additive gene action, varied between 0.54 for grain yield and 0.95 for panicle length. Biofortification traits showed predominantly additive gene action, with Baker ratios of 0.91 for iron, 0.88 for zinc, and 0.71 for protein content.\u003c/p\u003e \u003cp\u003eThe GCA effects identified superior parents for targeted trait improvement (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For iron content, RIB-9205 showed the highest positive GCA effect (6.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by RIB-9184 (3.20, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), making the two lines elite donors for iron biofortification. RIB-9184 was the superior general combiner for Zn (3.85, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and protein (0.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) contents, and RIB-9185 ranked second for Zn content (2.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor grain yield, RIB-9185 (1.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RIB-16324 (1.34, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had the greatest positive GCA effects. The GCA x environment stability analysis showed that RIB-9184 and RIB-9185 showed similar combining ability in all environments for biofortification traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral combining ability (GCA) effects and GCA\u0026times;E stability for yield and biofortification traits in pearl millet (pooled over environments)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"28\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTillers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003ePG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eTW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003eDFY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eHI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c28\" namest=\"c27\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"27\" nameend=\"c27\" namest=\"c1\"\u003e \u003cp\u003eGCA Effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.84\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-1.29\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-0.45\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.70\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.25\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-2.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-3.93\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.30\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-0.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e12.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-1.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e-3.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e3.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e3.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-1.28\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e2.27\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e1.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.84\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e2.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e2.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.22\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-4.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-3.91\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-22.00\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-1.99\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-0.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-16.98\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-2.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e6.65\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e1.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.88\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.22\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.07\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-2.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-6.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e2.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-16300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-1.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-1.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-1.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-7.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e1.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e-1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-1.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-4.78\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-2.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e10.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e1.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e-0.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e-2.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-1.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.22\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.90\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-3.35\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e1.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e-4.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e-1.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-7.26\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e1.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.03\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-0.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.90\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.97\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e-0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e-0.23\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e14.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e-2.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e-7.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e-3.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSE(gi)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSE(gi-gj)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e1.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"27\" nameend=\"c27\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGCA\u0026times;E Stability (σ\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e7.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e19.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e2.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e3.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e1.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-16300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e4.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e11.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e5.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e19.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e3.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c27\" namest=\"c26\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c28\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"28\"\u003e\u003csup\u003e*, **, *** Significant at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001, respectively. SE(gi) = Standard error of GCA effect; SE(gi\u0026minus;gj) = Standard error of difference between two GCA effects, GCA\u0026times;E Stability (σ\u0026sup2;) = Variance of GCA effects across environments; Lower values indicate stable GCA\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpecific combining ability effects identified promising hybrid combinations beyond parental GCA predictions (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor grain yield, RIB-16324 \u0026times; 20K86 (SCA\u0026thinsp;=\u0026thinsp;12.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RIB-16300 \u0026times; 20K86 (SCA\u0026thinsp;=\u0026thinsp;11.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had the maximum positive SCA effects, both of which involved high \u0026times; high GCA parent combination. Of interest, RIB-16300 \u0026times; RIB-192 exhibited exceptional SCA effects for iron (11.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and zinc (11.44, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), even though both parents had negative GCA for these traits, indicating complementary gene action. For protein content, RIB-9205 \u0026times; J-2340 (1.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RIB-9184 \u0026times; RIB-15131 (1.55, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed the highest SCA effects, representing crosses between high GCA parents, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 5 specific combining ability (SCA) effects for grain yield and biofortification traits in pearl millet with parental GCA classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCA Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCA Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCA (P1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGCA (P2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrain Yield (g/plant)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16324 \u0026times; 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16324 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9178 \u0026times; RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.45\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIron Content (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.65\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.70\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.65\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZinc Content (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9185 \u0026times; RIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9178 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.56\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9178 \u0026times; RIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.08\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein Content (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184 \u0026times; RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ-2340 \u0026times; 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e*, **, *** Significant at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001, respectively\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eGCA Class: H = High GCA (\u0026ge; median), L = Low GCA (\u0026lt; median)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eH\u0026times;H = both parents high combiner; H\u0026times;L = high \u0026times; low combiner; L\u0026times;L = both parents low combiner\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Hayman\u0026rsquo;s diallel analysis\u003c/h2\u003e \u003cp\u003eHayman's genetic component analysis provided detailed information on the nature of gene action for grain yield and biofortification traits (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Supplementary Table S3). The dominance variance components (H\u003csub\u003e1\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e) were significantly higher than the additive component (D) for GY, with H\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;92.67 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) versus D\u0026thinsp;=\u0026thinsp;1.11 (non-significant), confirming the predominance of non-additive gene action. The average degree of dominance (\u0026radic;H₁/D) was 9.15 for grain yield, indicating substantial overdominance. In contrast, iron content exhibited significant additive variance (D\u0026thinsp;=\u0026thinsp;72.88, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and dominance effects (H₁ = 145.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with \u0026radic;H₁/D\u0026thinsp;=\u0026thinsp;1.41 suggesting slight overdominance. Zinc content (\u0026radic;H₁/D\u0026thinsp;=\u0026thinsp;1.55) and protein content (\u0026radic;H₁/D\u0026thinsp;=\u0026thinsp;3.64) also exhibited overdominance, although to varying degrees. The H₂/4H₁ ratios varied from 0.18 to 0.24, which was away from the maximum of 0.25, indicating an asymmetric distribution of dominant alleles between parents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVr-Wr regression analysis was used to measure the fit of the additive-dominance model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Supplementary Table S4). Regression coefficients that were significantly different from zero but not unity indicated the adequacy of the model for panicle length and zinc content. Graphical analysis placed the parents on the regression line, with the parents close to the origin having more dominant alleles and the parents further away having more recessive alleles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of Hayman's genetic components for grain yield and biofortification traits in pearl millet (Pooled over environments)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield (g/plant)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIron content (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZinc content (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein content (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;20.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.88\u0026thinsp;\u0026plusmn;\u0026thinsp;30.08\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH₁\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.67\u0026thinsp;\u0026plusmn;\u0026thinsp;18.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.41\u0026thinsp;\u0026plusmn;\u0026thinsp;27.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH₂\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.56\u0026thinsp;\u0026plusmn;\u0026thinsp;15.32\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.60\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.05\u0026thinsp;\u0026plusmn;\u0026thinsp;18.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eh\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026radic;(H₁/D)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH₂/4H₁\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKD/KR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eh\u0026sup2;(ns)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eh\u0026sup2;(bs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e*, ** Significant at P\u0026lt;0.05 and P\u0026lt;0.01, respectively\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eD = Additive genetic variance; H₁, H₂ = Dominance variance components; F = Covariance of additive and dominance effects\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eh\u0026sup2; = Dominance effect over all loci; E = Environmental variance; \u0026radic;(H₁/D) = Average degree of dominance\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eH₂/4H₁ = Proportion of genes with positive and negative effects (0.25 = symmetrical distribution)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eKD/KR = Ratio of dominant to recessive alleles; h\u0026sup2;(ns) = Narrow sense heritability; h\u0026sup2;(bs) = Broad sense heritability\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eInterpretation: \u0026radic;(H₁/D) \u0026lt; 1 = Partial dominance; = 1 = Complete dominance; \u0026gt; 1 = Over\u0026minus;dominance\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Heterosis estimation\u003c/h2\u003e \u003cp\u003eMid-parent heterosis (MPH) and better-parent heterosis (BPH) varied considerably across the trait categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Yield traits showed a higher degree of heterosis, and the grain yield MPH ranged from \u0026minus;\u0026thinsp;19.97 to 167.02% (mean 61.04%) and BPH ranged from \u0026minus;\u0026thinsp;25.70 to 163.20% (mean 47.82%), of which 41 of 45 crosses had a positive MPH (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRIB-16324 \u0026times; 20K86 had the highest MPH value for grain yield (167.02%), followed by RIB-16300 \u0026times; 20K86 (153.99%) and RIB-16324 \u0026times; RIB-192 (151.46%). Biofortification traits showed moderate but commercially significant levels of heterosis (Supplementary Tables S5a and S5b). For iron content, MPH ranged from \u0026minus;\u0026thinsp;27.80 to 68.30%, and BPH ranged from \u0026minus;\u0026thinsp;41.86 to 60.90%, with RIB-16300 \u0026times; RIB-192 showing maximum positive values for both MPH and BPH, and 33 crosses showed positive values for MPH (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of mid-parent heterosis (MPH) and top 5 crosses for grain yield and biofortification traits in pearl millet (Pooled over environments)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHeterosis summary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTop 5 crosses based on MPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-ve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCross\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMPH (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBPH (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eGrain yield (g/plant)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-15.72 to 167.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e61.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e41.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16324 x 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e167.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e159.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16300 x 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e153.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e151.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16324 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e151.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e113.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJ-2340 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e145.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e118.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16300 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e123.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eIron content (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-25.70 to 68.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16300 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9205 x J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9184 x J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9205 x 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9205 x RIB-16300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eZinc content (ppm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-14.27 to 63.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e14.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16300 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9185 x RIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9178 x J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9205 x RIB-16324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9184 x J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eProtein content (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-19.23 to 19.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9205 x J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9184 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-16300 x RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJ-2340 x 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRIB-9185 x RIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eMPH = Mid\u0026minus;parent heterosis = [(F₁ \u0026minus; MP)/MP] \u0026times; 100; BPH = Better\u0026minus;parent heterosis = [(F₁ \u0026minus; BP)/BP] \u0026times; 100\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e+ve = Number of crosses with positive heterosis; \u0026minus;ve = Number of crosses with negative heterosis; Total crosses = 45\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly, RIB-16300 \u0026times; RIB-192 was ranked 1st for Zn content, with an MPH of 63.04% and BPH of 68.38%, with 40 crosses showing positive MPH values. Protein content had a comparatively low magnitude of heterosis (MPH = -20.08% \u0026minus;\u0026thinsp;19.82%; BPH = -27.45% \u0026minus;\u0026thinsp;20.24%), and RIB-9205 \u0026times; J-2340 showed the highest MPH (19.82%) and BPH (20.24%). Phenological traits showed negative heterosis for days to flowering (MPH:-29.00% to 4.09%; BPH:-35.00% to -2.04%) and maturity (MPH:-24.00% to 3.88%; BPH:-28.00% to -1.53%) which is desirable as it represents dominance for earliness, a vital trait for drought escape in arid environments. The violin-box plots showed that yield traits had the broadest heterosis distribution with many outliers with values higher than 150% MPH, while biofortification traits showed narrower but generally positive distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which corroborates the potential for the simultaneous improvement of yield and nutritional quality through hybrid breeding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Trait correlations\u003c/h2\u003e \u003cp\u003eGCA-level and phenotypic correlations showed significant relationships with breeding decisions (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Supplementary Fig. S3). A strong positive correlation between iron and zinc (rg\u0026thinsp;=\u0026thinsp;0.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with a corresponding phenotypic correlation of rp\u0026thinsp;=\u0026thinsp;0.56, demonstrated the possibility of simultaneous improvement of the two micronutrients through selection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGCA-level and phenotypic correlations between grain yield and biofortification traits for assessing simultaneous improvement feasibility in pearl millet\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCA Correlation (rg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhenotypic Correlation (rp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield \u0026times; Iron content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNegative; trade-off, overcome by SCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield \u0026times; Zinc content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNegative; trade-off, overcome by SCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrain yield \u0026times; Protein content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNegative; trade-off, overcome by SCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron content \u0026times; Zinc content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePositive; simultaneous improvement favorable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron content \u0026times; Protein content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eIndependent; simultaneous improvement possible\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc content \u0026times; Protein content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePositive; simultaneous improvement favorable\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e** Significant at 1% probability level (df = 8)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003erg = GCA\u0026minus;level correlation (additive genetic); rp = Phenotypic correlation\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eNote: Moderate negative GCA correlations between yield and biofortification traits can be overcome through favourable SCA effects, as evidenced by superior hybrids (for example, RIB\u0026minus;9184 \u0026times; RIB\u0026minus;15131) that combine high yield with enhanced nutritional quality\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eZinc and protein content also showed a positive GCA association (rg\u0026thinsp;=\u0026thinsp;0.47, rp\u0026thinsp;=\u0026thinsp;0.24), in favour of combined nutritional enhancement. Iron and protein content showed a weak positive correlation (rg\u0026thinsp;=\u0026thinsp;0.25, rp\u0026thinsp;=\u0026thinsp;0.13), suggesting that the two traits are inherited independently. Grain yield showed weak negative GCA correlations with iron (rg = -0.43, rp = -0.22), zinc (rg = -0.47, rp = -0.04), and protein (rg = -0.53, rp\u0026thinsp;=\u0026thinsp;0.04) contents which were not significant at the 1% probability level. The phenotypic correlations were significantly lower than the GCA correlations, suggesting environmental effects on trait expression. These moderate negative associations between yield and biofortification traits can be overcome by favourable SCA effects on the yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.9 GGE biplot analysis\u003c/h2\u003e \u003cp\u003eThe GGE biplot analysis, based on the main effects of genotypes and genotype \u0026times; environment interaction, was used to visualize the relationship among the 13 traits and determine trait groupings among the 55 genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The first two principal components accounted for 45.3% of the total variation, with 25.6% and 19.7% of the variation explained by PC1 and PC2, respectively (Supplementary Fig. S4). The scree plot showed that five principal components were needed to account for more than 75% of the total variances. The PCA loadings showed different patterns of trait clustering (Supplementary Fig. S5) with the biofortification traits (Fe, Zn, Protein) clustering in one quadrant, the yield-related traits (GY, DFY, HI) in a different sector, and the morphological traits (PH, PL, PG) and phenological traits (DF, DM) in individual groups. Parents and hybrids were scattered throughout all quadrants, with high-performing biofortified hybrids located towards the biofortification trait vectors. This pattern of trait clustering supported the correlation analysis and provided a graphical framework for understanding trait interrelationships in the breeding population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Multi-trait selection index\u003c/h2\u003e \u003cp\u003eThe Smith-Hazel multi-trait selection index was used to track superior hybrids for the simultaneous improvement of grain yield and biofortification traits using five different weighting schemes (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). RIB-9184 \u0026times; RIB-15131 was the best hybrid with the highest selection index value of 1.31 and ranked as the first hybrid in all five weighting schemes, including equal weights, yield priority, biofortification priority, Fe-Zn focus, and balanced approaches.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 5 superior hybrids for simultaneous improvement of grain yield and biofortification traits based on Smith-Hazel multi-trait selection index in pearl millet (Pooled over environments)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCross\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMean Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelection Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eRank under different weighting schemes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYield Priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBiofort. Priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFe-Zn Focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTop 5 Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eAvg. Rank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9184 \u0026times; RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9205 \u0026times; 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRIB-9185 \u0026times; RIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eGY = Grain yield (g/plant); Fe = Iron content (mg/kg); Zn = Zinc content (mg/kg); Protein = Protein content (%)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eSelection Index (Smith\u0026minus;Hazel): I = Σ(bi \u0026times; zi) where bi = economic weight and zi = standardized value; z\u0026minus;score = (X \u0026minus; Mean)/SD\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eWeighting schemes: Equal (0.25 each), Yield Priority (GY=0.40, others=0.20), Priority (GY=0.20, Fe=Zn=0.30, Protein=0.20); Fe\u0026minus;Zn Focus (GY=0.25, Fe=Zn=0.30, Protein=0.15); Balanced (GY=0.30, Fe=Zn=0.25, Protein=0.20)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eTop 5 Count = Number of schemes in which hybrid ranked in top 5; Avg. Rank = Mean rank across all 5 schemes (lower = more consistent)\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis hybrid showed a good mean performance for grain yield (18.84 g/plant), iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. RIB-16300 \u0026times; RIB-192 ranked second consistently (index\u0026thinsp;=\u0026thinsp;1.07) with superior grain yield (20.81 g/plant), iron (44.17 ppm), zinc (38.51 ppm), and protein (10.90%) contents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRIB-9205 x J-2340 with index\u0026thinsp;=\u0026thinsp;0.77 was found to have the highest iron content (54.29 ppm) among the top hybrids, followed by RIB-9205 \u0026times; 20K86 with the highest iron (63.29 ppm) and zinc (36.19 ppm) combination. These hybrids are good candidates for developing biofortified pearl millet.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Genetic variability and selection potential\u003c/h2\u003e \u003cp\u003eThe pooled analysis of variance showed highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) differences between genotypes for all 13 traits, suggesting that there is a large amount of genetic diversity between the parental inbred lines and the F\u003csub\u003e1\u003c/sub\u003e hybrids (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The significant genotype \u0026times; environment interaction for most traits highlighted the need for a multi-environment evaluation in pearl millet breeding programs. The significance of the parents vs. crosses component for most traits indicated heterosis. Broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) ranged from 0.67 (test weight) to 0.97 (panicle length, dry fodder yield), and the biofortification traits showed high heritability; iron (0.94), zinc (0.90), and protein (0.90) content. These values are similar to those reported by Govindaraj et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Singhal et al. (2021) for pearl millet biofortification traits. High heritability values reveal that a high proportion of phenotypic variance is due to genetic factors and that reliable selection can be expected in the early generations. The genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) indicated a high degree of variation for yield traits, with grain yield having GCV and PCV of 32.06% and 33.06%, respectively. For biofortification traits, iron content showed moderate GCV (17.99%) and PCV (18.54%), while the values for zinc content were 13.14% and 13.82%, respectively. The low GCV-PCV for iron (0.55%) and zinc (0.68%) suggests minimal environmental effects on these traits (Yadav et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic advance as a percentage of the mean (GAM) was high (\u0026gt;\u0026thinsp;20%) for grain yield (64.06%), Fe (35.95%), and Zn (25.72%) content. The combination of high heritability and high GAM for Fe and Zn contents suggest the predominance of additive gene action, in which effective improvement may be achieved through direct selection (Johnson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Gene action and breeding Strategy\u003c/h2\u003e \u003cp\u003eThe nature of gene action in trait expression is fundamental to the design of appropriate breeding strategies. Baker's ratio, which provides a measure of relative importance of additive versus non-additive variance components of genetic variance, indicated contrasting patterns of gene action between biofortification and yield traits in the present study (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Biofortification traits showed mostly additive gene action, with Baker's ratios of 0.91, 0.88, and 0.71 for iron, zinc, and protein content, respectively. Values close to unity suggest additive genetic effects, which account for the majority of genetic variance, in agreement with the results of Kanatti et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Govindaraj et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The preponderance of additive gene action implies that population improvement methods, including recurrent selection, progeny testing, and development of open-pollinated varieties (OPVs), would be effective in accumulating favourable alleles for enhanced micronutrient content. This genetic architecture is responsible for the success of conventional breeding in the development of biofortified varieties, such as Dhanashakti and ICTP 8203 Fe (Rai et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the Baker's ratio of grain yield was 0.54, indicating a considerable contribution of non-additive genetic variance, including dominance and epistatic effects. Similar results were obtained by Davda and Dangaria (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Patel et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) in pearl millet. Such genetic architecture provides strong support for hybrid breeding for yield improvement and thus for the exploitation of heterosis through the use of superior parental combinations. The significant SCA effects recorded for grain yield, with the top crosses such as RIB-16324 \u0026times; 20K86 (12.09) and RIB-16300 \u0026times; 20K86 (11.73), confirm the possibility of heterosis exploitation. The different patterns of gene action require an integrated approach to breeding that includes population improvement for biofortification and hybrid development for yield maximization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Superior parents for hybridization\u003c/h2\u003e \u003cp\u003eThe identification of superior general combiners is important for the development of high-yielding biofortified hybrids. Parental differences in GCA effects for different categories of traits allowed for strategic selection in hybridization programs (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). RIB-9205 was the winner when it came to the highest positive GCA effect (6.65) of all parents. This parent also contributed positively to zinc content (1.82) and seemed to have earliness for flowering (-4.44) and maturity (-3.91) which makes it ideal for biofortification breeding under arid environments where early maturity is desired for drought escape. Similar high Fe donors were identified by Rai et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Govindaraj et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in pearl millet germplasms.\u003c/p\u003e \u003cp\u003eRIB-9184 was identified as a multi-trait combiner with significantly positive GCA effects for zinc (3.85), protein (0.78), iron (3.20), panicle length (2.75), and test weight (0.59***). The combination of good GCA for multiple biofortification traits makes this parent valuable for pyramiding nutritional quality genes. Additionally, RIB-9184 showed a stable GCA\u0026times;E interaction for grain yield (σ\u0026sup2; = 0.003), indicating consistent performance across environments. RIB-15131 was a balanced combiner for positive GCA of iron (2.31), harvest index (0.60), plant height (6.22), and panicle girth (0.38). Although it showed a late-flowering tendency (GCA\u0026thinsp;=\u0026thinsp;4.09), its contribution to multiple agronomic and quality traits provides breeding flexibility. RIB-9185 showed positive GCA for grain yield (1.39), iron (2.22), zinc (2.07), and test weight (0.84**), which represents another valuable parent to simultaneously improve yield and biofortification. These identified superior combiners can be strategically used in hybridization programs based on the recommendations of Kanatti et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Choudhary et al. (2012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Promising hybrids for yield and biofortification\u003c/h2\u003e \u003cp\u003eThe identification of improved hybrids with high grain yields and increased micronutrient content is the ultimate goal of biofortification breeding. Based on the multi-trait selection index, SCA impact, and heterosis estimates, a number of promising hybrids were identified (Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, and \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). RIB-9184 \u0026times; RIB-15131 appeared to be the best of all hybrids, with the highest rankings in all five weighting schemes and the highest selection index (1.31). This cross showed better performance in terms of mean grain yield (18.84 g/plant), iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. The hybrid was the product of the combination of a multi-trait combiner (RIB-9184) and a balanced combiner (RIB-15131), which demonstrated effective complementation of parental strengths. Significant positive SCA for protein (1.55***) and heterosis for grain yield (102.43% BPH) further prove its breeding potential (Yadav et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRIB-16300 \u0026times; RIB-192 showed outstanding heterosis for biofortification traits with 68.30% MPH for iron and 63.04% MPH for zinc which was the highest among the 45 crosses. This cross had significantly positive SCA effects on Fe (11.54) and Zn (11.44). Interestingly, this combination of L \u0026times; L (both parents with negative GCA for Fe) implies the involvement of complementary gene action and epistatic interactions in transgressive segregation (Govindaraj et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kanatti et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vetriventhan and Upadhyaya, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). RIB-16324 \u0026times; 20K86 showed maximum grain yield heterosis (167.02% MPH) with the maximum SCA effect (12.09***), which was the best hybrid for yield improvement. These results confirm the usefulness of diallel analysis for identifying promising combinations in pearl millet biofortification programs (Manwaring et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pujar et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Simultaneous improvement feasibility\u003c/h2\u003e \u003cp\u003eThe correlation study between grain yield and biofortification traits has critical implications for simultaneous improvement (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The high positive GCA correlation between Fe and Zn content (rg\u0026thinsp;=\u0026thinsp;0.82**) suggests that selection for either micronutrient would lead to a concomitant improvement in the other (Velu et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This favourable association, consistent with the outcomes of Govindaraj et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Pujar et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), allows for the efficient joint selection of both micronutrients in biofortification breeding programs. The positive correlation of zinc with protein (rg\u0026thinsp;=\u0026thinsp;0.47) also supports the need for combined adrenaline nutritional enhancement which makes the selection of multi-traits very effective.\u003c/p\u003e \u003cp\u003eThe weak negative GCA correlations between grain yield and biofortification traits (GY \u0026times; Fe: rg = -0.43; GY \u0026times; Zn: rg = -0.47; GY \u0026times; Protein: rg = -0.53) were non-significant, indicating that there was no severe genetic trade-off. According to Kanatti et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), such moderate negative correlations can be avoided by the favourable effects of SCA. Phenotypic correlations were significantly weaker (rp = -0.22 to 0.04), suggesting environmental masking of genetic associations (Jain et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Superior hybrids, such as RIB-9184 \u0026times; RIB-15131, with high yield and improved nutritional quality, show that improving both parameters can be achieved simultaneously by the strategic selection of parents and the exploitation of non-additive gene action (Manwaring et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Hayman\u0026rsquo;s analysis interpretation\u003c/h2\u003e \u003cp\u003eHayman's diallel analysis revealed information regarding the genetic structure of the yield and biofortification traits (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Following the theoretical framework of Hayman (1954), the average degree of dominance (\u0026radic;H₁/D) indicated contrasting patterns of inheritance, showing high values of overdominance in grain yield (9.15), while biofortification traits showed slight overdominance values for Fe (1.41) and Zn (1.55) with moderate values of overdominance for Pr (3.64). The significance of H\u003csub\u003e1\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e for all traits proved the importance of dominance variance. The high overdominance for grain yield supports the importance of exploiting heterosis (Athoni et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ratio of H\u003csub\u003e2\u003c/sub\u003e/4H\u003csub\u003e1\u003c/sub\u003e was 0.18\u0026ndash;0.24, which deviated from the theoretical maximum of 0.25, suggesting an asymmetrical distribution of alleles between parents (Jinks, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1954\u003c/span\u003e). The KD/KR ratio greater than unity for iron (1.56) and zinc (1.14) is suggestive of the preponderance of dominant alleles for the biofortification traits in favour of the accumulation of high-nutrient alleles (Patil and Gupta, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Narrow-sense heritability was moderate for iron (0.37) and zinc (0.30) and low for grain yield (0.02), supporting hybrid breeding for yield and population improvement for micronutrients (Izge et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e4.7 G\u0026times;E Interaction and stability\u003c/h2\u003e \u003cp\u003eSignificant genotype \u0026times; environment interactions require stability considerations when breeding decisions are made. According to Comstock and Moll (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1963\u003c/span\u003e), such interactions can lead to biases in genetic estimates if they are not considered correctly taken into account. The GCA\u0026times;E variance revealed differential stability among the parents (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). RIB-9184 exhibited stable GCA effects for iron (σ\u0026sup2; = 0.183) and zinc (σ\u0026sup2; = 0.524), making it reliable for multi-environmental programs (Yadav and Rai, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). RIB-9185 exhibited consistent GCA for grain yield (σ\u0026sup2; = 0.159), ensuring predictable hybrid performance across diverse environments. Parents with low GCA\u0026times;E variance should be prioritized for developing stable biofortified cultivars.\u003c/p\u003e \u003cp\u003eThe SCA C E interaction was significant for grain yield suggesting environment-specific hybrid performance (Jain et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, RIB-9184 \u0026times; RIB-15131 showed a better ranking in all environments, indicating both high mean performance and stability. Such stable superior combinations are crucial for the development of widely adaptable biofortified hybrids for various agro-climatic conditions of arid and semi-arid lands, where pearl millet is grown largely (Satyavathi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec38\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Breeding recommendations\u003c/h2\u003e \u003cp\u003eBased on comprehensive genetic analyses, trait-specific breeding strategies are recommended for developing high-yielding biofortified pearl millet (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBreeding recommendations for pearl millet improvement programs based on combining ability analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreeding Program\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObjective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecommended Parents/Crosses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOPV Development\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh GCA for all traits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9184, RIB-9185, RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBest combiners for population improvement\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHybrid Development\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuperior F₁ performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9184 \u0026times; RIB-15131; RIB-16300 \u0026times; RIB-192; RIB-9205 \u0026times; J-2340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHigh yield\u0026thinsp;+\u0026thinsp;biofortification\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFe Biofortification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh GCA for Fe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9205, RIB-9184, RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFor Fe-enriched varieties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eZn Biofortification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh GCA for Zn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9184, RIB-9185, RIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFor Zn-enriched varieties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYield Improvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh GCA for GY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9185, RIB-16324, 20K86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFor high-yielding varieties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProtein Enhancement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh GCA for Protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9184, RIB-16300, RIB-15131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFor protein-rich varieties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePopulation Improvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecurrent selection base\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRIB-9184, RIB-9185, RIB-15131, RIB-9205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDiverse genetic base with high GCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eGCA\u0026thinsp;=\u0026thinsp;General combining ability; GY\u0026thinsp;=\u0026thinsp;Grain yield; Fe\u0026thinsp;=\u0026thinsp;Iron content; Zn\u0026thinsp;=\u0026thinsp;Zinc content\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eShort-term (1\u0026ndash;3 years): Superior hybrids RIB-9184 \u0026times; RIB-15131 and RIB-16300 \u0026times; RIB-192 should be advanced for multi-location testing and release evaluation. According to Manwaring et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), these hybrids achieve HarvestPlus biofortification targets with grain yield\u0026thinsp;\u0026gt;\u0026thinsp;18 g/plant, iron\u0026thinsp;\u0026gt;\u0026thinsp;44ppm and zinc\u0026thinsp;\u0026gt;\u0026thinsp;38ppm. RIB-16324 \u0026times; 20K86 should be tested in high-yielding environments based on its outstanding levels of heterosis (167% MPH). The conversion of elite parents (RIB-9205 and RIB-9184) to CMS lines would enable commercial seed production (Jain et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMedium-term (3\u0026ndash;5 years): OPV development through recurrent selection utilizing additive variance for biofortification (Baker\u0026rsquo;s ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.70) is recommended (Govindaraj et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Parents with high GCA for iron (RIB-9205 and RIB-9184) and zinc (RIB-9184 and RIB-9185) should be used to form base populations. OPVs offer farmer-saved seed advantages to resource-poor farmers in arid regions (Yadav et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLong-term (\u0026gt;\u0026thinsp;5 years): Reciprocal recurrent selection programs for sustained gains in yield (exploiting non-additive variance) and biofortification (accumulating additive variance) are recommended (Hallauer et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The integration of genomic selection with Fe-Zn QTL markers would help to speed up progress (Kumar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kanatti et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The excellent Fe-Zn correlation (rg\u0026thinsp;=\u0026thinsp;0.82) allows for an efficient simultaneous improvement via index selection (Singhal et al., 2021). Maintaining genetic diversity through the inclusion of diverse sources of germplasm would ensure climate resilience and long-term sustainability of biofortification gains in the pearl millet crop in arid and semi-arid regions of India and Africa.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe present investigation on diallel analysis of 10 pearl millet inbred lines and their 45 F\u003csub\u003e1\u003c/sub\u003e hybrids showed contrasting gene action patterns for yield and biofortification traits. Baker's ratio was indicative of predominant additive gene action for iron (0.91), zinc (0.88), and protein (0.71) content and grain yield was affected by non-additive gene action (0.54). This differential genetic structure requires different breeding approaches for each trait: hybrid breeding for yield maximisation and population improvement strategies for biofortification traits. Among parents, RIB-9205 was found to be a wider champion donor for iron (GCA\u0026thinsp;=\u0026thinsp;6.65), RIB-9184 was a better combiner for multi-traits for zinc (3.85) and protein (0.78*), and RIB-15131 was a better combiner with the contribution of multiple agronomic traits. The high positive correlation between iron and zinc content (rg\u0026thinsp;=\u0026thinsp;0.82) allows for efficient joint selection for the simultaneous improvement of both micronutrients. RIB-9184 x RIB-15131 was found to be the best overall hybrid with a high multi-trait selection index (1.31), which was first in rank in all the weighting schemes used, though combined with superior grain yield (18.84 g/plant) and improved iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) contents. This hybrid, along with RIB-16300 \u0026times; RIB-192 with outstanding heterosis for biofortification traits (Fe: 68.30%, Zn: 63.04% MPH), are promising candidates for developing high-yielding biofortified pearl millet cultivars for nutritional security in arid regions.\u003c/p\u003e"},{"header":"Statements \u0026 Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author gratefully acknowledges Sri Karan Narendra Agriculture University, for providing support and guidance during the PhD program. Sincere thanks are extended to Rajasthan Agricultural Research Institute (RARI), Durgapura, Jaipur for providing field experimental facilities. The author expresses gratitude to the advisory committee members and faculty of the Department of Genetics and Plant Breeding for their valuable suggestions and critical evaluation throughout the research work. Technical assistance rendered by the field staff during the crossing program and data recording is duly acknowledged. The first author also thanks the Director, ICAR-Indian Institute of Pulses Research, Kanpur for sanctioning earned leave to complete thesis submission formalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e Monika Punia, L D Sharma, D K Gothwal; \u003cstrong\u003eMethodology:\u003c/strong\u003e Monika Punia, L D Sharma, Lalit Kumar Rolaniya; \u003cstrong\u003eFormal analysis and investigation:\u0026nbsp;\u003c/strong\u003eMonika Punia, Lalit Kumar Rolaniya; \u003cstrong\u003eWriting - original draft preparation:\u003c/strong\u003e Monika Punia, Vaibhav Sharma, Sohan Lal Kajla; \u003cstrong\u003eWriting - review and editing:\u003c/strong\u003e Ram Lal Jat, Lalit Kumar Rolaniya; \u003cstrong\u003eSupervision:\u003c/strong\u003e L D Sharma. \u003cem\u003eAll authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. This work is part of the first author's PhD research conducted at SKN Agriculture University, Jobner, completed before joining the current employer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files. Additional raw data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnitha S, Kane-Potaka J, Botha R, Givens DI, Sulaiman NLB, Upadhyay S, Vetriventhan M, Tsusaka TW, Parasannanavar DJ, Longvah T, Rajendran A (2021) Millets can have a major impact on improving iron status, hemoglobin level, and in reducing iron deficiency anemia-a systematic review and meta-analysis. 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Agric Res 2(4):275-292. https://doi.org/10.1007/s40003-013-0089-z\u003c/li\u003e\n\u003cli\u003eYadav OP, Singh DV, Kumari V, Prasad M, Seni S, Singh RK, Sood S, Kant L, Dayakar Rao B, Madhusudhana R, Venkatesh Bhat B, Gupta SK, Yadava DK, Mohapatra T (2024) Production and cultivation dynamics of millets in India. Crop Sci 64(1):1-26. https://doi.org/10.1002/csc2.21207\u003c/li\u003e\n\u003cli\u003eYadav S, Singh SP, Singhal T, Sankar SM, Mahendru-Singh A, Bhargavi HA, Aavula N, Sonu, Goswami S, Satyavathi CT (2023) Genetic elucidations of grain iron, zinc and agronomic traits by generation mean analysis in pearl millet [\u003cem\u003ePennisetum glaucum\u003c/em\u003e (L.) R. Br.]. J Cereal Sci 113:103751. https://doi.org/10.1016/j.jcs.2023.103751\u003c/li\u003e\n\u003cli\u003eYan W, Kang MS (2002) GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists, 1st edn. CRC Press, Boca Raton. https://doi.org/10.1201/9781420040371\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pearl millet, Biofortification, Combining ability, Diallel analysis, Iron, Zinc, Heterosis","lastPublishedDoi":"10.21203/rs.3.rs-8583609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8583609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicronutrient malnutrition, particularly iron and zinc deficiency, affects over two billion people globally, with women and children in developing countries being the most vulnerable. Pearl millet [\u003cem\u003ePennisetum glaucum\u003c/em\u003e (L.) R. Br.], a climate-resilient cereal in arid and semi-arid regions, presents a very good opportunity for biofortification as it has an inherently high micronutrient level and genetic variability. The present investigation aimed to estimate combining ability effects, establish gene action, and identify superior parents and hybrids for the simultaneous improvement of grain yield and biofortification traits. Ten genetically diverse inbred lines were crossed in a half diallel mating design, following Griffing's Method 2, Model 1. The resulting 55 entries (45 F₁ hybrids and 10 parents) were evaluated across two environments in a randomized complete block design with three replicates. The analysis revealed high broad-sense heritability for iron (0.94), zinc (0.90), and protein (0.90) contents. Among parents, RIB-9205 had the highest GCA for iron content (6.65***), RIB-9184 for zinc (3.85***), and protein (0.78***), and RIB-15131 was a balanced multi-trait combiner. RIB-9184 x RIB-15131 showed the best hybrid with the highest multi-trait selection index value of 1.31, which produced good grain yield (18.84 g/plant) with improved iron (46.16 ppm), zinc (38.86 ppm), and protein (11.91%) content. The strong positive correlation between iron and zinc (rg = 0.82**) enables an efficient simultaneous improvement. The results suggest hybrid breeding for yield maximization and population improvement approaches for biofortification traits to develop high-yielding, high-nutrient pearl millet cultivars.\u003c/p\u003e","manuscriptTitle":"Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. 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