Fodder and Grain Yield Potential of Little Millet, Panicum sumatrense race robusta and trait-specific genetic resources for crop improvement

Fodder and Grain Yield Potential of Little Millet, Panicum sumatrense race robusta and trait-specific genetic resources for crop improvement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fodder and Grain Yield Potential of Little Millet, Panicum sumatrense race robusta and trait-specific genetic resources for crop improvement Mani Vetriventhan, Kommineni Jagadeesh, D Naresh, Kuldeep Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7373619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 7 You are reading this latest preprint version Abstract Little millet is one of the small millets native to India, used for both food and fodder purposes. Among the two races ( nana and robusta ) of little millet, race robusta has a robust growth habit and produces high biomass and grain yield, but matures late compared to race nana . In this study, 126 accessions belonging to the race robusta conserved in the ICRISAT Genebank were evaluated during two rainy seasons for fodder and grain yield potential, for the identification of trait-specific germplasm for little millet improvement. Residual maximum likelihood (REML) analysis revealed significant variability for all traits evaluated in the individual seasons and pooled for the two seasons. High heritability was observed for most traits in the individual seasons and the pooled data for the two seasons. Principal component analysis revealed the existence of genetic diversity and identified the key traits that contributed to maximum variability. Cluster analysis revealed two major clusters that could form the basis for selecting diverse genotypes for breeding programs. Based on the pooled data, the top 20 accessions each for high grain yield and fodder yield (stover dry weight) were identified, of which accessions IPmr 880, IPmr 871, IPmr 875, IPmr 896 and IPmr 907 were identified for dual purposes (grain and fodder), whereas accessions IPmr 702, IPmr 873, IPmr 876, IPmr 913, IPmr 1056 were the top five in terms of stover dry weight yield. Multi-location testing of identified sources for yield and adaptation and their use in breeding programs aids in little millet improvement and variety release. Little millet Landraces Germplasm Biomass Grain Yield Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The rapid increase in global population and the increasing impact of climate change on various crops pose a significant threat to global food and nutritional security. Global crop cultivation is dominated by cereals such as rice, wheat, and maize, which account for around 40% of the total area under cultivation ( https://www.fao.org/faostat/en/#home , Accessed on 02 Aug 2025). Crop diversification, which can result in better soil health and the development of sustainable agroecosystems (Yang et al. 2020 ), therefore, it is a need of the day. Diverse crops with better climate resilience and nutritional richness can help combat threats to food and nutritional security. Small millets are crops with greater endurance to biotic and abiotic stresses and nutritional superiority, but these crops are currently considered underutilized. To transform food systems into diverse, sustainable, climate-resilient, and able to meet our nutritional requirements, there is a need for the cultivation of underutilized but climate-resilient and nutrient-dense crops, making them an integral part of our food basket. Little millet ( Panicum sumatrense Roth ex Roem. & Schult.) is one of the small millets, belonging to the family Poaceae. It is cultivated as a cereal crop in India and is widely distributed as a weed or wild plant in many Asian countries, including Nepal, Pakistan, Sri Lanka, Indonesia, and Myanmar (Vetriventhan et al. 2020 ). India is the largest producer of the little millet with an area of 0.26 million ha and a productivity of 0.12 million tons (Bhat et al. 2018 ). In India, it is grown primarily in the tribal belts of Madhya Pradesh, Chhattisgarh, and Andhra Pradesh and Tamil Nadu (Nandini et al. 2019 ). Little millet has high water-use efficiency and salt and waterlogging tolerance, and is less prone to insect pests and diseases (Upadhyaya et al. 2016 ; Ganapathy 2017 ; Vetriventhan et al. 2021 ). Several studies have revealed the nutraceutical richness of little millet in terms of carbohydrates, protein, and phenols, and it is particularly rich in iron (18–58 mg/kg), zinc (19–40 mg/kg), calcium (92–390 mg/kg), protein (6–16%), and dietary fibre (7.7%) (Saleh et al. 2013 ; Longvah 2017 ; Vetriventhan et al. 2021 ). It can easily substitute for rice-based recipes and is considered a functional and nutritional food choice for the management of diet-related metabolic disorders (Mannuramath et al. 2015 ). The acceptance of little millet-based products, such as dosa , and Pongal and more by people and their preference over meals prepared from polished white rice envisages future scope (Anitha et al. 2024 ). The productivity of little millet remains low because it is largely cultivated on marginal lands under rainfed conditions with minimal inputs and crop improvement efforts. Furthermore, rice and wheat straw (21.9% and 15.1%, respectively) are the dominant dry feed stocks for the ruminants in India (Duncan et al. 2020 ). Identifying crops with better forage potential, in addition to grain yield, is essential for promoting crop diversification. Identification of little millet with high grain yield and greater fodder potential can establish it as a better candidate for crop diversification. A focus on genetic diversity and its estimation is essential for the success of plant breeding programs. Among the two races of little millet, race robusta is a late maturing, erect culm with a large, strongly branched robust growth habit, and inflorescences are either erect and open (subrace laxa ), or compact and curved (subrace compacta ). While accessions belonging to the race nana produce plants with decumbent to almost all prostate culms that become erect at flowering, produce erect, open strongly branched inflorescences (subrace laxa ) or inflorescence branches that sometimes clump at the time of maturity (subrace compacta ) (De Wet et al. 1983 ). Overall, race nana is early maturing, produces relatively low grain and biomass yields, and is prone to lodging, whereas race robusta is late maturing, mostly non-lodging, and produces higher grain and fodder yields compared to race nana . The greater yield potential of race robusta over nana and the trait-specific sources identified provided a greater scope for yield improvement (Vetriventhan et al. 2021 ). However, the forage potential of little millet remains largely unexplored. The study on the identification of trait-specific germplasm for high biomass revealed their potential (Vetriventhan et al. 2021 ) and emphasized the need for further studies in this direction. There is a need for little millet cultivars with early maturity, non-lodging, high grain, and biomass yield. Currently, the ICRISAT Genebank conserves 473 accessions of little millet, including 126 accessions belonging to the race robusta , while the remaining accessions are of the race nana ( https://genebank.icrisat.org/IND/Passport?Crop=Little%20millet ). The current study focused on the evaluation of a complete set of 126 little millet accessions of the race robusta and the identification of trait-specific sources for grain and fodder yield. This can strengthen the little millet breeding programs and dietary diversification for food and fodder security and nutrition. Materials and Methods Experimental material and details The experimental material consisted of 126 accessions of race robusta and four checks. The experiment was carried out in red soils during the 2021 and 2022 rainy seasons at the ICRISAT, Hyderabad, Telangana, India, using an alpha-lattice design with three replications. Sowing was done in a single row of 4 m length, row-to-row spacing of 60 cm, and plant-to-plant spacing of approximately 10 cm. Standard agronomic practices were followed during the crop growth period. The rainfall of 829 mm and 849 mm has been received during the cropping period, i.e., July to December in 2021 and 2022, respectively. The maximum temperature during the growing period ranged from 30.21°C to 31.23°C in 2021 and 28.78°C to 30.76°C in 2022, and the minimum temperature ranged from 13.92°C to 22.18°C in 2021 and 14.63°C to 20.62°C in 2022 during the period from July to December (data accessed from ICRISAT Intranet https://intranet.icrisat.org/ ). Data collection Data were collected for 20 quantitative traits during both seasons. The traits, namely, days to 50% flowering, days to maturity, grain yield, stover fresh weight, and stover dry weight, were recorded on a plot basis, and grain yield, stover fresh weight, and stover dry weight were converted into kg/ha. The traits, namely plant height (cm), basal tillers per plant, flag leaf blade length (mm), flag leaf blade width (mm), flag leaf sheath length (mm), peduncle length (mm), panicle exertion (mm), inflorescence length (mm), inflorescence width (mm), inflorescence lowest primary branch length (mm), nodes per plant on main stalk, leaves per plant on main stalk, culm thickness (mm) and 100 seed weight (g) were recorded on five randomly selected plants, and average values were calculated. The harvest index was calculated as the ratio of grain yield to biological yield (= grain yield + stover dry weight). Statistical analysis The data collected for 20 quantitative traits during both seasons were analysed individually and pooled over two seasons using the residual maximum likelihood (REML) approach in the GenStat 20th edition, considering genotypes as random and seasons as fixed ( https://vsni.co.uk/software/genstat/ ). The significance of seasons was tested using Wald’s statistics. The variance components of genotype ( σ 2 g ), genotype × season ( σ 2 gs ), and error variance were estimated and used to calculate genetic parameters, such as heritability ( h 2 b ), genotypic and phenotypic coefficients of variation (GCV and PCV), genetic advance, and genetic advance as a percentage of the mean. The h 2 b was categorized as low ( 0.60); while GCV and PCV values were categorized as low ( 20%) (Deshmukh et al. 1986 ). The genetic advance as % mean was categorised low ( 20%) (Johnson et al. 1955 ). The best linear unbiased predictors (BLUPs) were estimated for various agronomic traits for individual and pooled seasons and used for further analysis. The distribution of individual seasons was plotted using kernel density plots using ggplot2 package (version 3.5.1) of R software ( https://www.r-project.org/ ). Correlation coefficients were estimated using the metan package (version: 1.18.0), and principal component analysis was performed using the package factoextra (version: 1.0.7) in R software. Hierarchical clustering was performed based on Euclidean distance and Ward’s method using the libraries Pandas (McKinney 2011 ), NumPy (Walt et al. 2011 ), Scikit-learn (Pedregosa et al. 2011 ), SciPy (Jones et al. 2001 ) and Matplotlib (Hunter 2007 ) of the Python software. The trait-specific accessions for grain yield (kg/ha), stover fresh weight (kg/ha), and stover dry weight (kg/ha) were identified through comparison with better-performing checks (based on pooled BLUPs) for respective traits using LSD (5%). Results The REML analysis indicated significant σ 2 g and σ 2 gs among the germplasm tested in the individual seasons and the pooled data of both seasons (Table 1 ). Wald’s statistic for the season was highly significant for most traits, except plant height, flag leaf sheath length, inflorescence width and 100 seed weight. Overall, this study indicates the presence of significant variability among the accessions and the profound impact of the environment on the performance of accessions. Among the various traits, high heritability (> 0.60) was observed for many traits during the 2021 and 2022 individual seasons and the pooled data of both seasons. In the case of individual seasons, flag leaf blade length and flag leaf sheath length showed medium heritability (0.30 to 0.60), which was high when pooled, flag leaf blade width was low in 2021, medium in 2022 and high in pooled, peduncle length was medium in both individual seasons and also in pooled. Basal tillers per plant showed high heritability in both individual seasons but medium when pooled. Inflorescence length, nodes per plant on main stalk, leaves per plant on main stalk and 100 seed weight showed high and medium heritability in 2021 and 2022 respectively and it was high when pooled. Medium heritability was observed in case of inflorescence width during 2021 and high in 2022 and pooled. The remaining traits showed high heritability in individual seasons and pooled data of both seasons, with days to 50% flowering having the highest heritability among all traits, being 0.98 in 2021, 0.97 in both 2022 and the pooled data. The heritability of grain yield (kg/ha) was high in 2021 (0.62) and 2022 (0.75), but medium in the pooled case (0.48). Table 1 Variance components due to genotype ( σ 2 g ), genotype × season interaction ( σ 2 gs ), and broad-sense heritability ( h 2 b ) for agronomic traits of little millet accessions evaluated in two rainy seasons (2021 and 2022) in ICRISAT, Patancheru, India. Trait Individual season Combined over two seasons 2021 2022 σ 2 g σ 2 gs Wald statistic season h 2 b σ 2 g h 2 b σ 2 g h 2 b Days to 50% flowering 88.29*** 0.98 62.75*** 0.97 71.76*** 3.74*** 153.90*** 0.97 Days to maturity 93.67*** 0.97 67.91*** 0.97 76.16*** 4.62*** 171.85*** 0.97 Plant height (cm) 366.50*** 0.91 249.85*** 0.79 292.53*** 16.42*** 0.63 NS 0.95 Basal tillers per plant 1.87*** 0.69 1.46*** 0.69 0.73*** 0.94*** 10.57* 0.55 Flag leaf blade length (mm) 319.87*** 0.46 237.80*** 0.37 217.30*** 59.80*** 101.83*** 0.70 Flag leaf blade width (mm) 0.32*** 0.28 0.54*** 0.34 0.37*** 0.06*** 7.14* 0.67 Flag leaf sheath length (mm) 81.80** 0.56 76.85*** 0.46 54.08*** 25.03*** 77.85 NS 0.81 Peduncle length (mm) 144.76*** 0.41 100.17*** 0.43 49.00*** 73.40*** 16.65* 0.43 Panicle exertion (mm) 207.19*** 0.62 156.31*** 0.82 105.18*** 76.67*** 20.02*** 0.67 Inflorescence length (mm) 557.70*** 0.65 272.25*** 0.47 305.80*** 107.00*** 21.96*** 0.75 Inflorescence width (mm) 305.63*** 0.48 663.96*** 0.69 298.90*** 187.20*** 1.06 NS 0.67 Inflorescence primary branch length (mm) 532.97*** 0.71 624.85*** 0.74 421.50*** 159.50*** 51.25*** 0.78 Nodes per plant on main stalk 0.67*** 0.73 0.19*** 0.42 0.33*** 0.10*** 61.90*** 0.78 Leaves per plant on main stalk 0.73*** 0.73 0.18*** 0.40 0.33*** 0.12*** 67.96*** 0.76 Culm thickness (mm) 1.28*** 0.74 0.58*** 0.64 0.79*** 0.14*** 17.05* 0.86 100 Seed weight (g) 0.00043*** 0.84 0.00041*** 0.59 0.00038*** 0.00004** 0.18 NS 0.88 Grain yield (Kg/ha) 77044*** 0.62 115653*** 0.75 35096.00** 61652.00*** 281.13*** 0.48 Stover fresh weight (Kg/ha) 38892452*** 0.86 39234430*** 0.95 34444783*** 4638021*** 16.77*** 0.92 Stover dry weight (Kg/ha) 6437895*** 0.83 4161279*** 0.89 4627927*** 690204*** 45.87*** 0.91 Harvest Index 0.0045*** 0.89 0.0034*** 0.84 0.0033*** 0.0006*** 138.95*** 0.89 ⁎ - Significant at P ≤ 0.05, ⁎⁎ - Significant at P ≤ 0.01 and ⁎⁎⁎- Significant at P ≤ 0.001; ns – non-significant Mean, range and genetic variability parameters The mean and range values of various traits were estimated from the pooled BLUPs, and the genetic parameters estimated using the variances are presented in Table 2 . The distribution of genotypes for various traits during 2021 and 2022 can be observed from the kernel density (KDE) plot (Fig. 1 ). The greater values of PCV over GCV for all traits indicate the existence of an environmental influence, but the low margin between PCV and GCV shows that the influence was minimal. A high genetic advance as % mean (> 20%) was observed for most traits including days to 50% flowering, plant height (cm), basal tiller per plant, panicle exertion (mm), inflorescence primary branch length (mm), culm thickness (mm), stover fresh weight (kg/ha) and stover dry weight (kg/ha), harvest index, while low (< 10%) for flag leaf blade length and width (mm), and peduncle length (mm), and medium (10–20%) for days to maturity, flag leaf sheath length (mm), inflorescence length (mm), inflorescence width (mm), nodes per plant on main stalk, leaves per plant on main stalk, grain yield (kg/ha) and 100 seed weight. Table 2 Pooled range, mean, and genetic variability parameters of agronomic traits in little millet germplasm evaluated in two rainy seasons (2021 and 2022) at ICRISAT, Patancheru, India. S. No Mean Range h 2 b GCV (%) PCV (%) Genetic Advance (%) GA as % Mean LSD (5%) CV (%) Min Max Days to 50% flowering 70.59 41 107 0.97 12.00 12.18 17.19 24.36 2.16 1.89 Days to maturity 97.63 67 134 0.97 8.94 9.10 17.66 18.09 2.66 1.68 Plant height (cm) 168.73 108.11 200.75 0.95 10.14 10.42 34.27 20.31 11.55 4.23 Basal tillers per plant 6 5 11 0.55 13.17 17.75 1.31 20.14 1.40 13.37 Flag leaf blade length (mm) 270.53 230.42 299.72 0.70 5.45 6.53 25.33 9.36 31.99 7.30 Flag leaf blade width (mm) 11.14 9.31 12.38 0.67 5.45 6.67 1.02 9.16 1.55 8.61 Flag leaf sheath length (mm) 103.00 84.01 129.49 0.81 7.14 7.93 13.64 13.24 1.41 0.85 Peduncle length (mm) 127.32 111.05 154.53 0.43 5.50 8.38 9.46 7.43 21.09 10.23 Panicle exertion (mm) 24.34 3.57 63.35 0.67 42.13 51.42 17.31 71.11 14.40 36.51 Inflorescence length (mm) 308.56 246.40 349.39 0.75 5.67 6.56 31.12 10.09 28.17 5.64 Inflorescence width (mm) 248.94 187.09 286.91 0.67 6.94 8.46 29.23 11.74 28.43 7.05 Inflorescence primary branch length (mm) 167.31 108.50 224.53 0.78 12.27 13.87 37.42 22.37 24.20 8.93 Nodes per plant on main stalk 8 6 9 0.78 7.12 8.06 1.04 12.97 0.82 6.32 Leaves per plant on main stalk 9 7 11 0.76 6.31 7.25 1.03 11.30 0.84 5.71 Culm thickness (mm) 7.95 4.58 9.43 0.86 11.22 12.13 1.70 21.38 1.01 7.83 100 Seed weight (g) 0.19 0.14 0.25 0.88 10.31 10.99 0.04 19.94 0.02 7.24 Grain yield (Kg/ha) 2216 1521 2768 0.48 8.45 12.20 267.41 12.07 336.10 9.36 Stover fresh weight (Kg/ha) 17764 3863 37521 0.92 33.04 34.40 2786.85 65.37 722.96 10.47 Stover dry weight (Kg/ha) 6701 1800 17836 0.91 32.10 33.66 1014.49 63.08 320.98 12.32 Harvest Index 0.27 0.09 0.50 0.89 21.73 23.07 0.11 42.00 0.04 9.42 Correlation and trait contributions Grain yield was positively correlated with days to 50% flowering, days to maturity, plant height (cm), flag leaf blade width (mm), peduncle length (mm), panicle exertion (mm), nodes per plant on main stalk, leaves per plant on main stalk, culm thickness (mm), stover fresh weight (kg/ha), and stover dry weight (kg/ha) (Fig. 2 ). Furthermore, a negative association was observed between grain yield (kg/ha) and traits such as basal tiller per plant, flag leaf sheath length (cm), inflorescence primary branch length (mm), and harvest index. The association with grain yield was non-significant for flag leaf blade length (mm), inflorescence length (mm), inflorescence width (mm), and 100 seed weight (g). The direct and indirect effects of various traits on grain yield, estimated using path analysis, are presented in Supplementary Table 1 . The positive correlations between grain yield and days to 50% flowering, plant height, panicle exertion, nodes per plant on main stalk and stover fresh weight were due to their positive direct effects. While positive associations between grain yield and days to maturity, culm thickness, leaves per plant on main stalk, flag leaf sheath length, peduncle length, and stover dry weight were observed due to the positive indirect effect. The negative correlations between grain yield, basal tiller per plant and 100 seed weight were due to direct negative effects. Indirect effects also contributed to the negative direct effects on inflorescence primary branch length. Principal component analysis PCA analysis revealed that the first four PCs (eigenvalue > 1) together explained a variation of approximately 79.75% (Table 3 ). PC1 explained a large portion of the variability (45.11%), with an eigenvalue of 9.02. The traits, namely, nodes per plant on main stalk, leaves per plant on main stalk, stover fresh weight, plant height, days to 50% flowering, days to maturity, culm thickness, stover dry weight and harvest index are the traits contributing majorly to the variance explained by PC1. Similarly, inflorescence length, inflorescence width, flag leaf sheath length, flag leaf blade length and inflorescence primary branch length contributed significantly to the variability explained by PC2. From the biplot (Fig. 3 ), the vector length of the trait indicates its contribution to the total diversity of the germplasm. Longer the length of the trait vectors, are major contributor to the total diversity in the germplasm. Furthermore, the angle between the trait vectors is a measure of their correlation. The acute angle between grain yield and days to maturity, days to 50% flowering, stover fresh weight, stover dry weight, 100 seed weight, and nodes per plant on main stalk and leaves per plant on main stalk indicates positive correlations among them. The obtuse angle between the vectors of grain yield and the traits of the harvest index and basal tillers per plant is a measure of the negative correlation between them. Table 3 Eigenvalue, variance (%) and cumulative variance (%) of major principal components Principal component PC1 PC2 PC3 PC4 Eigen value 9.02 3.69 1.69 1.54 Variance % 45.11 18.48 8.45 7.71 Cumulative variance % 45.11 63.59 72.04 79.75 Individual Trait contributions (%) towards PCs Traits PC1 PC2 PC3 PC4 Days to 50% flowering 8.48 0.40 2.93 0.52 Days to maturity 8.38 0.39 2.94 0.54 Plant height (cm) 8.88 2.08 0.47 0.01 Basal tillers per plant 3.87 2.09 12.16 7.16 Flag leaf blade length(mm) 1.52 14.73 0.62 0.07 Flag leaf blade width (mm) 2.81 2.76 0.24 2.47 Flag leaf sheath length (mm) 0.55 17.61 1.59 0.48 Peduncle length (mm) 1.02 0.81 13.00 36.50 Panicle exertion (mm) 1.95 5.00 15.86 21.06 Inflorescence length (mm) 0.29 19.89 0.91 0.01 Inflorescence width (mm) 1.00 16.29 3.98 0.33 Inflorescence primary branch length (mm) 2.51 15.63 0.77 1.40 Nodes per plant on main stalk 9.43 0.36 0.17 1.12 Leaves per plant on main stalk 9.76 0.31 0.11 1.86 Culm thickness (mm) 8.65 1.28 1.23 1.09 100 seed weight (g) 0.05 0.00 34.99 15.51 Stover fresh weight (kg/ha) 9.34 0.00 3.38 3.23 Stover dry weight (kg/ha) 8.89 0.00 2.41 3.13 Harvest Index 8.92 0.35 2.22 1.73 Grain yield (kg/ha) 3.72 0.03 0.00 1.76 Cluster analysis The Euclidean distance matrix calculated based on 20 quantitative traits of 130 little millet accessions, including checks, was used for clustering based on Ward’s method, resulting in two major clusters with 91 genotypes in cluster 1 and 39 genotypes in cluster 2 (Fig. 4 ). Compared to cluster 2, which has lower mean values of 10,266 kg/ha for stover fresh weight and 4,177 kg/ha for stover dry weight, cluster 1 exhibits significantly higher means of 20,977 kg/ha for stover fresh weight and 7,7835 kg/ha for stover dry weight. Similarly, the mean grain yield (kg/ha) of Cluster 1 (2303 Kg/ha) was higher than that of Cluster 2 (2014 kg/ha). Trait-specific accessions For grain yield, the top 20 accessions were identified based on pooled means, of which 10 are statistically superior to Phule Ekadashi (grain yield of 2210 kg/ha) (Table 4 ). Among the top 20 accessions, IPmr 723, IPmr 1042 and IPmr 990 with a pooled mean grain yield of 2674 kg/ha, 2635 kg/ha and 2603 kg/ha are identified as the top yielding accession which are statistically superior to the check in pooled and also in individual seasons. The pooled grain yields of the top 10 accessions ranged from 2546 kg/ha to 2768 kg/ha. These high-grain-yielding accessions matured 95 to 105 days after sowing. Based on the pooled BLUPs of stover dry weight, 87 accessions based on pooled data, 79 accessions in 2021, and 88 accessions in 2022 were statistically better performers compared to the check GV2. Similarly, in the case of stover fresh weight, most accessions (95 accessions) were statistically superior to BL 6 (11234 kg/ha) in the pooled, as well as in the 2021 (99 accessions) and 2022 (91 accessions). For fodder yield, the top 20 accessions were identified based on the pooled BLUPs of stover dry weight. Stover dry weight of these identified top 20 accessions ranges from 8,648 to 17,386 kg/ha and matured in 95 to 134 days after sowing (Table 5 ). There are 5 accessions, namely IPmr 880, IPmr 871, IPmr 875, IPmr 896 and IPmr 907, which were identified as dual-purpose producing significantly high grain and stover yields. Table 4 Little millet germplasm accessions identified for high grain yield and their agronomic characteristics Accessions Grain Yield (kg/ha) Days to 50% Flowering Days to Maturity Plant Height (cm) Pooled 2021 2022 Pooled 2021 2022 Pooled 2021 2022 Pooled 2021 2022 IPmr 714 2768* 2438* 3039 71 72 70 98 99 97 175 175 175 IPmr 723 2674* 2176* 3138* 72 75 68 98 101 95 180 182 178 IPmr 1050 2643* 2504* 2705 71 74 67 99 102 95 169 170 168 IPmr 1042 2635* 2071* 3180* 77 80 74 105 109 100 178 176 180 IPmr 896 2615* 2221* 2975 70 72 68 97 98 95 176 178 174 IPmr 990 2603* 2056* 3134* 75 74 77 102 101 104 176 169 183 IPmr 891 2572* 1912 3223* 72 75 68 98 101 96 178 184 172 IPmr 989 2564* 2056* 3057 76 81 71 103 108 97 180 178 181 IPmr 875 2556* 1640 3497* 75 81 70 103 110 97 172 168 176 IPmr 880 2546* 2129* 2933 74 77 70 101 106 97 181 183 179 IPmr 1040 2524 2254* 2759 72 74 70 101 104 98 176 180 172 IPmr 999 2518 2007* 3009 77 80 74 104 108 100 174 174 173 IPmr 871 2518 2322* 2661 70 72 68 96 98 95 184 192 176 IPmr 1003 2487 2064* 2899 70 74 67 98 102 95 165 163 167 IPmr 722 2482 1997* 2939 72 75 69 99 102 96 167 174 161 IPmr 1049 2480 2062* 2897 68 70 66 95 96 94 176 179 174 IPmr 1032 2464 2188* 2713 76 75 76 102 101 104 168 164 172 IPmr 1053 2456 1930 2981 78 80 76 104 106 103 173 174 173 IPmr 996 2456 2026* 2873 73 74 71 99 100 98 172 172 171 IPmr 907 2452 2060* 2801 74 77 70 101 105 98 188 189 185 Controls BL 6 1928 1585 2290 52 50 54 80 80 81 152 149 157 GNV 3 2185 1851 2525 69 72 67 97 99 94 155 150 160 GV 2 1905 1664 2179 69 72 67 96 98 94 149 151 147 Phule Ekadashi 2210 1628 2810 51 52 51 77 78 76 146 136 158 Trial mean 2216 1821 2612 71 73 68 98 170 95 169 170 168 Minimum 1521 1143 1747 41 41 41 67 103 67 108 103 111 Maximum 2768 2504 3497 107 110 103 134 199 129 201 199 201 LSD (5%) 336 350 321 2 2 2 3 3 2 12 10 13 CV (%) 9.36 11.90 7.59 1.89 1.76 2.01 1.68 1.77 1.58 4.23 3.61 4.79 * Indicates statistical superiority over the check (Phule Ekadashi) at 5% level of significance Table 5 Trait-specific accessions of little millet germplasm identified for stover fresh weight (Kg/ha) and dry weight (kg/ha) Accessions Stover dry weight (Kg/ha) Stover fresh weight (Kg/ha) Plant Height (cm) Days to maturity Grain Yield (kg/ha) Pooled 2021 2022 Pooled 2021 2022 Pooled 2021 2022 Pooled 2021 2022 Pooled 2021 2022 IPmr 913 17386* 17482* 16836* 37521* 35913* 38490* 165 168 162 134 138 129 1701 1191 2254 IPmr 876 11322* 13792* 8627* 27203* 31390* 22537* 178 177 179 104 109 99 2259 1797 2711 IPmr 873 10324* 11969* 8516* 24513* 27353* 21317* 179 181 175 100 101 99 1988 1482 2520 IPmr 1056 10266* 9690* 10690* 29236* 27444* 30808* 201 199 201 111 115 107 2111 1404 2851 IPmr 702 10028* 11267* 8652* 25732* 26531* 24806* 177 176 179 102 105 100 2322 1823 2822 IPmr 1054 9775* 10243* 9187* 25596* 26271* 24710* 189 195 182 111 114 108 2284 1826 2745 IPmr 703 9712* 10742* 8567* 22952* 25478* 20284* 178 186 171 100 101 98 2234 1792 2684 IPmr 882 9614* 10702* 8332* 23376* 25596* 20817* 190 191 188 106 110 102 2315 1615 3033 IPmr 1069 9541* 11357* 7554* 31312* 30030* 32204* 191 192 188 119 120 117 1957 1702 2213 IPmr 907 9435* 10167* 8563* 26543* 27401* 25325* 188 189 185 101 105 98 2452 2060 2801 IPmr 874 9286* 9677* 8811* 22597* 22071* 23047* 176 181 172 95 98 93 2114 1400 2871 IPmr 878 9282* 10043* 8355* 23090* 25449* 20397* 190 191 189 103 106 100 2174 1868 2489 IPmr 1051 9193* 10108* 8102* 25048* 26632* 23033* 184 192 176 106 109 104 2417 1739 3087 IPmr 877 9133* 9684* 8524* 24209* 23972* 24458* 182 185 178 101 106 96 2325 1647 3027 IPmr 871 9061* 9521* 8433* 23019* 25947* 19668* 184 192 176 96 98 95 2518 2322 2661 IPmr 701 9055* 8688* 9291* 23178* 21166* 25114* 190 187 192 102 107 98 2190 1816 2561 IPmr 700 9000* 8647* 9162* 22238* 19022* 25277* 182 178 184 99 101 98 2413 1713 3111 IPmr 875 8918* 8780* 9012* 24071* 22405* 25730* 172 168 176 103 110 97 2556 1640 3497 IPmr 896 8706* 9401* 7934* 24396* 25375* 23150* 176 178 174 97 98 95 2615 2221 2975 IPmr 880 8648* 10123* 7037* 22747* 23791* 21503* 181 183 179 101 106 97 2546 2129 2933 Controls BL 6 3925 3742 4227 11234 10543 12145 152 149 157 80 80 81 1928 1585 2290 GNV 3 4510 5432 3715 10772 13181 8578 155 150 160 97 99 94 2185 1851 2525 GV 2 4684 5372 4135 9598 12104 7309 149 151 147 96 98 94 1905 1664 2179 Phule Ekadashi 4010 3752 4432 10940 10486 11773 146 136 158 77 78 76 2210 1628 2810 Trial mean 6701 7284 6113 17764 18800 16709 169 170 168 98 170 95 2216 1821 2612 Minimum 1800 1565 2023 3863 3245 3988 108 103 111 67 103 67 1521 1143 1747 Maximum 17386 17482 16836 37521 35913 38490 201 199 201 134 199 129 2768 2504 3497 LSD (5%) 1337 1489 1158 3012 3609 2223 201 199 201 3 3 2 336 350 321 CV (%) 12.32 12.64 11.72 10.47 11.88 8.23 11.55 9.9 12.99 1.68 1.77 1.58 9.36 11.90 7.59 * Indicates statistical superiority over the check (GV 2 for Stover dry weight (kg/ha) and BL 6 for Stover fresh weight (kg/ha)) at 5% level of significance Discussion Crops like small millets, which have received little breeding push, are, in general, low-yielding compared to those that have received global systematic breeding support from the public and private sectors; hence, these crops need basic information on the potential of the germplasm that could be used for further improving their productivity. Unveiling genetic diversity through the evaluation of germplasm resources and the identification of trait-specific sources is essential for exploring untapped genetic potential. This could also enhance the utility of the existing germplasm resources for their improvement. The current study on little millet revealed the existence of high variability for yield and biomass contributing traits, which will be an asset to its breeding program. The high heritability of most traits indicates a greater potential for successful selection for the improvement of various traits. Among the various traits, higher heritability was observed for important traits such as days to 50% flowering, days to maturity, plant height, inflorescence length and width, 100 seed weight, and stover fresh and dry weight in the pooled data of two seasons. Traits with high heritability and high genetic advance are mostly governed by additive gene action, for which selection is much more effective. Further, significant improvement is also possible for the traits having medium heritability and high genetic advance (% mean). Non-additive gene action was predominant for traits with low heritability and low genetic advance as % mean, for which recombination breeding would be the most suitable method of trait improvement. Grain yield showed medium heritability and medium genetic advance as % mean, while some studies reported high heritability and high genetic advance (Behera et al. 2024 ). This proves that improvement in grain yield is possible through hybridization followed by selection. Traits such as stover fresh and dry weights showed high heritability and high genetic advance as % the mean. Therefore, the fodder yield can be improved through direct selection from the identified trait-specific sources. Medium heritability and genetic advance for grain yield highlight the value of indirect selection for genotypes based on positively associated traits. Traits such as days to 50% flowering, plant height, and days to maturity were positively associated with grain yield in this study and negatively associated with basal tillers per plant. A similar correlation was observed when both races of little millet were assessed in our previous study (Vetriventhan et al. 2021 ). The association is caused by both direct and indirect effects; consideration of direct effects alone can enhance the scope of success of trait improvement (Kumar et al. 2024 ). In the case of days to maturity, peduncle length, leaves per plant on main stalk, culm thickness and stover dry weight, the positive association was due to larger positive indirect effects of various traits dominating their direct negative association with grain yield (kg/ha). A positive association due to the dominance of direct effects on grain yield was observed in the case of days to 50% flowering, plant height, panicle exertion, nodes per plant on main stalk, and the stover fresh weight. Correlation between grain yield and harvest index was negligible in 2021, negative in 2022, and in the pooled data of both seasons. Some studies reported a significant positive relationship (Kinal Patel et al. 2023 ; Behera et al. 2024 ), while our previous study indicated a negligible relationship when both races were studied together (Vetriventhan et al. 2021 ). The negative correlation in the pooled data of both seasons in the current study was due to the greater indirect negative effects of stover fresh weight on the positive direct effects of harvest index. In a previous study (Kinal Patel et al. 2023 ), the dominance of the direct positive effects of the harvest index over the negative indirect effects of fodder yield resulted in a positive correlation in little millet. The dominance of the indirect effects of stover fresh weight on grain yield is a measure of the greater fodder potential of robusta over the grain yield potential, which agrees with a previous study (Vetriventhan et al. 2021 ). Moreover, as the positive direct effects are much higher in the case of days to 50% flowering, panicle exertion, and stover fresh weight, these are the best indices for indirect selection for the improvement of grain yield. The first four principal components together accounted for 79.75% of the total variation, and the traits, namely days to 50% flowering, days to maturity, plant height, nodes per plant on main stalk, leaves per plant on main stalk, culm thickness, stower fresh weight, and stover dry weight and harvest index were the traits contributed significantly to total variance. Cluster analysis divided the 130 genotypes into two different clusters, and all four cultivars used as checks grouped under a single cluster, indicating a similarity in performance based on overall traits. Hybridization between the most diverse accessions, resulting in heterosis followed by selection, can be beneficial for improving grain yield and other traits governed by non-additive gene action. Thus, clustering can provide a basis for selecting the most diverse genotype. The identification of trait-specific sources, resulting in a major focus on these genetic resources, can improve breeding programs. The top 10 trait-specific sources identified for grain yield based on the pooled data are superior to Phule Ekadashi. Accession IPmr 723 was among the top 10 performing accessions in pooled (2674 kg/ha) and in both individual seasons (2176 kg/ha during 2021 and 3138 kg/ha during 2022). Most of the high-yielding accessions identified were of medium duration, with a maturity duration of 95 to 105 days. There is a need to attempt to transfer high-yielding potential from identified trait-specific sources into the genetic background of short-duration accessions, as variability among the germplasm screened was large for days to maturity, ranging from 67 days to 134 days. This study also identified the top 20 accessions for stover dry weight yield. As days to maturity were highly positively correlated with stover fresh weight and dry weight, most of the trait-specific accessions for high biomass were 95 to 134 days to maturity. Accessions, namely IPmr 913, IPmr 876, IPmr 873, IPmr 1056 and IPmr 702 are among the top five. In addition to the fodder potential, consideration of the fodder quality can improve its utility to a much greater extent. Our attempts to study the fodder quality of 20 little millet accessions (unpublished) indicated a mean IVMOD of 47.33% (range 46.08–48.82%), which is comparable to finger millet (Backiyalakshmi et al. 2021 ) and wheat (mean ~ 48%)(Blümmel et al. 2019 ) while higher than the reported value in rice (mean 42%, range 38.2 to 45.6%) (Subudhi et al. 2020 ). Similarly, crude protein content in little millet ranged from 5.44 to 8.33% with a mean of 6.78% (unpublished), which is similar to finger millet (mean 7.23%, range 6.47 to 8.15%) (Backiyalakshmi et al. 2021 ) and higher than that of rice (mean 5.56%, range 4.23 to 7.88%) (Subudhi et al. 2020 ). This indicates the greater potential of little millet as a fodder crop. Among small millet crops, the stover dry yield of finger millet ranged from 2890–10779 kg/ha, and accessions with multiple fodder quality aspects have been reported (Backiyalakshmi et al. 2021 ). Similarly, the stover dry yield in the case of kodo millet ranged from 1156–8777 kg/ha(Vetriventhan and Upadhyaya 2019 ) and the dry biomass in the proso millet germplasm ranged from 2909 to 9775 kg/ha (Calamai et al. 2020 ). The proso millet variety TNAU 151 has a fodder potential of 4130 kg/ha with 72 days to maturing (Nirmalakumari et al. 2011 ). The mean dry matter yield in sweet sorghum is about 20.18 t/ha, and 18.49 t/ha and 20.62 t/ha in dual-purpose and forage type, respectively (Singh and Chauhan 2017 ). The average dry straw yield of commercial rice varieties is around 9.7 t/ha(Matías et al. 2019 ) and in the case of pearl millet under rainfed conditions, it ranges from 11.8 to 16.4 t/ha (Dhedhi et al. 2016 ). The stover dry weight of little millet germplasm, evaluated in this study, ranged from 1.8 to 17.8 t/ha. Further, the top accessions identified for stover dry weight ranged from 8.6 t/ha to 17.8 t/ha, highlighting the importance of genetic variability and the potential for selecting and breeding high-yielding varieties for improved grain and fodder production. Conclusion This study's findings open up significant opportunities for little millet breeding programs focused on forage or dual-purpose cultivation. The potential for developing varieties with high grain and fodder yield having early maturity is particularly promising, as it could address the growing demand for grain and fodder requirement while maintaining efficient crop cycles. There are 5 accessions identified as dual purpose (IPmr 871, IPmr 875, IPmr 880, IPmr 896 and IPmr 907), which could be explored through multilocation testing and further use in the breeding program. This dual-purpose approach could enhance the economic viability of little millet cultivation, making it more attractive to farmers and potentially increasing its adoption in diverse agricultural systems. Furthermore, the exploration of little millet as a bioenergy crop represents an exciting new frontier in its utilization. Drawing parallels with switch grass ( Panicum virgatum L.), which has already established itself as a valuable bioenergy source, little millet's potential in this area could be substantial. This multifaceted utility - encompassing grain yield, fodder production, and bioenergy potential - positions little millet as a highly versatile crop. Such versatility could play a crucial role in promoting crop diversification, enhancing agricultural sustainability, and contributing to food, feed, and energy security in various regions. As research in this area progresses, it may lead to the development of specialized little millet varieties optimized for specific end-uses, further expanding its agricultural and industrial applications. Declarations Funding Statement: This study was undertaken as part of the CGIAR Genebank Initiative and is part of the Research Program on Accelerated Crop Improvement, ICRISAT. Data Access Statement: All the supporting data are presented in the manuscript and in the supplementary tables. Conflict of Interest declaration: The authors declare that they have no conflict of interest. Author Contributions: MV and KS contributed to the design and implementation of the research; MV and DN conducted the experiment; JK and DN analysed the data; JK & MV wrote the first draft of the manuscript. All authors reviewed the manuscript. References Anitha S, Arjun P, Palli NC, Sreekanth N, Miruthika Devi SA, Pandey S, Krishnan S, Prasad S, Sharma S, Chidambara Murthy KN, Botha R, Upadhyay S, Kane-Potaka J (2024) Sensory and nutritional evaluation of nine types of millet substituted for polished white rice in select Indian meal preparations. Front Sustain Food Syst 8. https://doi.org/10.3389/fsufs.2024.1331260 Backiyalakshmi C, Babu C, Reddy DN, Padmakumar VP, Prasad KVSV, Azevedo VCR, Vetriventhan M (2021) Assessing forage potential of the global collection of finger millet (Eleusine coracana (l.) gaertn.) conserved at the icrisat genebank. Agronomy 11. https://doi.org/10.3390/agronomy11091706 Behera R, Singamsetti A, Rout S, Nanda S, Sharma SS (2024) Genetic variability and character associations for grain yield and other secondary traits in little millet (Panicum sumatrense L.) at Eastern Ghats Zone of Odisha. Electronic Journal of Plant Breeding 15:239–245. https://doi.org/10.37992/2024.1501.028 Bhat B V, Tonapi VA, Rao BD, Singode A, Santra D, Johnson J (2018) Production and utilization of millets in India. In: International millet symposium on 3rd international symposium on broomcorn millet (3rd ISBM). pp 24–36 Blümmel M, Updahyay SR, Gautam N, Barma NCD, Abdul Hakim M, Hussain M, Yaqub Mujahid M, Chatrath R, Sohu VS, Mavi G, Mishra VK, Kalappanavar IK, Naik R, Biradar S, Prasad SVS, Singh RP, Joshi AK (2019) Comparative assessment of food-fodder traits in a wide range of wheat germplasm for diverse biophysical target domains in South Asia. Field Crops Res 236:68–74. https://doi.org/10.1016/j.fcr.2019.03.001 Calamai A, Masoni A, Marini L, Dell’acqua M, Ganugi P, Boukail S, Benedettelli S, Palchetti E (2020) Evaluation of the agronomic traits of 80 accessions of proso millet (Panicum miliaceum l.) under mediterranean pedoclimatic conditions. Agriculture (Switzerland) 10:1–15. https://doi.org/10.3390/agriculture10120578 De Wet JMJ, Prasada Rao KE, Brink DE (1983) Systematics and domestication of Panicum sumatrense (Graminae). Journal d’agriculture traditionnelle et de botanique appliquée 30:159–168 Deshmukh SN, Basu MS, Reddy PS (1986) Genetic variability, character association and path coefficients of quantitative traits in Virginia bunch varieties of groundnut. Dhedhi KK, Ansodariya V V., Chaudhari NN, Sorathiya JS (2016) Study of green fodder yield potential of pearl millet genotypes under rainfed conditions of Gujarat, India. Agricultural Science Digest - A Research Journal 36. https://doi.org/10.18805/asd.v36i2.10631 Duncan AJ, Samaddar A, Blümmel M (2020) Rice and wheat straw fodder trading in India: Possible lessons for rice and wheat improvement. Field Crops Res 246. https://doi.org/10.1016/j.fcr.2019.107680 Ganapathy KN (2017) Genetic improvement in little millet. In: Millets and sorghum: biology and genetic improvement. Wiley Online Library, pp 170–183 Hunter JD (2007) Matplotlib: A 2D graphics environment. Comput Sci Eng 9:90–95 Johnson HW, Robinson HF, Comstock RE (1955) Estimates of genetic and environmental variability in soybeans. Jones E, Oliphant T, Peterson P (2001) SciPy: Open source scientific tools for Python Kinal Patel, Arna Das, Dhrumi Dalsaniya, Arvind D. Kalola, Ghanshyam B. Patil, Rumit Patel, Dipak A. Patel, Harshal E. Patil (2023) Study on character association and path analysis in little millet (Panicum sumatrense L.). Electronic Journal of Plant Breeding 14. https://doi.org/10.37992/2023.1401.005 Kumar A, Sharma NK, Kumar R, Sanadya SK, Sahoo S (2024) Correlation and Path Analysis Studies in Mungbean [Vigna radiata (L.) Wilczek] under Arid Environment of Western Rajasthan. Indian J Agric Res. https://doi.org/10.18805/ijare.a-6195 Longvah T (2017) Indian Food Composition Tables. National Institute of Nutrition: xxxi Mannuramath M, Yenagi N, Orsat V (2015) Quality evaluation of little millet (Panicum miliare) incorporated functional bread. J Food Sci Technol 52:8357–8363. https://doi.org/10.1007/s13197-015-1932-y Matías J, Cruz V, García A, González D (2019) Evaluation of rice straw yield, fibre composition and collection under mediterranean conditions. Acta Technologica Agriculturae 22:43–47. https://doi.org/10.2478/ata-2019-0008 McKinney W (2011) pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing 14:1–9 Nandini C, Bhat S, Srinathareddy, Jayramegowda, Prabhakar (2019) Modified crossing (SMUASB) method for artificial hybridization in proso millet (Panicum miliaceum L.) and Little millet (Panicum sumatrense). Electronic Journal of Plant Breeding 10:1161–1170. https://doi.org/10.5958/0975-928X.2019.00147.9 Nirmalakumari, Subramanian A, Manoharan S, Raguchander T, Muthiah AR, Raveendran TS (2011) A High Yielding Prosomillet National Variety TNAU 151. Madras Agricultural Journal 98:15–17. https://doi.org/10.29321/MAJ.10.100231 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12:2825–2830 Saleh ASM, Zhang Q, Chen J, Shen Q (2013) Millet grains: Nutritional quality, processing, and potential health benefits. Compr Rev Food Sci Food Saf 12:281–295. https://doi.org/10.1111/1541-4337.12012 Singh D, Chauhan A (2017) Fodder yield, quality and nutrients uptake potential of different types of sorghum (sorghum bicolor) varieties in central Gujarat. Forage Res 43:121–128 Subudhi HN, Prasad KVSV, Ramakrishna C, Rameswar PS, Pathak H, Ravi D, Khan AA, Padmakumar V, Blümmel M (2020) Genetic variation for grain yield, straw yield and straw quality traits in 132 diverse rice varieties released for different ecologies such as upland, lowland, irrigated and salinity prone areas in India. Field Crops Res 245. https://doi.org/10.1016/j.fcr.2019.107626 Upadhyaya HD, Vetriventhan M, Dwivedi SL, Pattanashetti SK, Singh SK (2016) Proso, barnyard, little, and kodo millets. In: Genetic and genomic resources for grain cereals improvement. Elsevier, pp 321–343 Vetriventhan M, Azevedo VCR, Upadhyaya HD, Nirmalakumari A, Kane-Potaka J, Anitha S, Ceasar SA, Muthamilarasan M, Bhat BV, Hariprasanna K, Bellundagi A, Cheruku D, Backiyalakshmi C, Santra D, Vanniarajan C, Tonapi VA (2020) Genetic and genomic resources, and breeding for accelerating improvement of small millets: current status and future interventions. Nucleus (India) 63:217–239 Vetriventhan M, Upadhyaya HD (2019) Variability for productivity and nutritional traits in germplasm of kodo millet, an underutilized nutrient-rich climate smart crop. Crop Sci 59:1095–1106. https://doi.org/10.2135/cropsci2018.07.0450 Vetriventhan M, Upadhyaya HD, Azevedo VCR, Allan V, Anitha S (2021) Variability and trait-specific accessions for grain yield and nutritional traits in germplasm of little millet (Panicum sumatrense Roth. Ex. Roem. & Schult.). Crop Sci 61:2658–2679. https://doi.org/10.1002/csc2.20527 Walt S van der, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22–30 Yang T, Siddique KHM, Liu K (2020) Cropping systems in agriculture and their impact on soil health-A review. Glob Ecol Conserv 23 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 23 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 14 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7373619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501296653,"identity":"5978b667-95c0-4899-ac55-931a8a151ec7","order_by":0,"name":"Mani Vetriventhan","email":"data:image/png;base64,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","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":true,"prefix":"","firstName":"Mani","middleName":"","lastName":"Vetriventhan","suffix":""},{"id":501296656,"identity":"19c064bd-6ce3-4746-a6b1-4709f85cb75c","order_by":1,"name":"Kommineni Jagadeesh","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Kommineni","middleName":"","lastName":"Jagadeesh","suffix":""},{"id":501296657,"identity":"b5348047-b51d-47bb-9d90-52f809655a9e","order_by":2,"name":"D Naresh","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"D","middleName":"","lastName":"Naresh","suffix":""},{"id":501296658,"identity":"92e9f74a-7ce1-4a92-bbdc-e6fac187ac64","order_by":3,"name":"Kuldeep Singh","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Kuldeep","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-08-14 11:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7373619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7373619/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-025-02691-7","type":"published","date":"2025-12-12T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89661371,"identity":"96446a8a-c5de-4540-a006-d2fc2c94cb10","added_by":"auto","created_at":"2025-08-22 11:11:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176945,"visible":true,"origin":"","legend":"\u003cp\u003eKernel Density plots of grain yield and other quantitative traits of little millet germplasm evaluated during the rainy 2021 and 2022 at ICRISAT Hyderabad. \u003cstrong\u003eNote:\u003c/strong\u003eDFL-days to 50% flowering, DM- days to maturity, PHT– plant height (cm), BT- basal tillers per plant, FLBL– flag leaf blade length (mm), FLBW - flag leaf blade width (mm), \u0026nbsp;FLSL– flag leaf sheath length (mm), PedL– peduncle length (mm), PE– panicle exertion (mm), INFL– inflorescence length (mm), INFW– inflorescence width (mm), IPBL - inflorescence primary branch length (mm), NPP-nodes per plant on main stalk, LPP – leaves per plant on main stalk, CT– culm thickness (mm), 100_SWT– 100 seed weight (g), GY grain yield (kg/ha)-, SDWT– stower dry weight (kg/ha), SFWT– stower fresh weight (kg/ha), HI – harvest Index. Red and blue vertical lines indicate mean values of the respective trait during rainy 2021 and 2022).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/1054f8a1191e0626e337fe44.png"},{"id":89662050,"identity":"e9a45c63-6600-42b5-b46f-f0e934a6cbf3","added_by":"auto","created_at":"2025-08-22 11:19:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231038,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between grain yield and various other agro-morphological characteristics of little millet germplasm evaluated at ICRISAT, Hyderabad, during rainy 2021 and 2022. \u003cstrong\u003eNote:\u003c/strong\u003e DFL-days to 50% flowering, DM- days to maturity, PHT– plant height (cm), BT- basal tillers per plant, FLBL– flag leaf blade length (mm), FLBW - flag leaf blade width (mm), FLSL– flag leaf sheath length (mm), PedL– peduncle length (mm), PE– panicle exertion (mm), INFL– inflorescence length (mm), INFW– inflorescence width (mm), IPBL - inflorescence primary branch length (mm), NPP-nodes per plant on main stalk, LPP – leaves per plant on main stalk, CT– culm thickness (mm), 100_SWT– 100 seed weight (g), GY grain yield (kg/ha)-, SDWT– stower dry weight (kg/ha), SFWT– stower fresh weight (kg/ha), HI – harvest Index.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/20f0592b7d29dbf4c7eba4ad.png"},{"id":89661373,"identity":"4294ee17-601e-4fb8-bd0a-a27fa84a43b2","added_by":"auto","created_at":"2025-08-22 11:11:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71061,"visible":true,"origin":"","legend":"\u003cp\u003eBi-plot of principal component analysis of various agro-morphological traits of little millet germplasm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003eDFL-days to 50% flowering, DM- days to maturity, PHT– plant height (cm), BT- basal tillers per plant, FLBL– flag leaf blade length (mm), FLBW - flag leaf blade width (mm), FLSL– flag leaf sheath length (mm), PedL– peduncle length (mm), PE– panicle exertion (mm), INFL– inflorescence length (mm), INFW– inflorescence width (mm), IPBL - inflorescence primary branch length (mm), NPP-nodes per plant on main stalk, LPP – leaves per plant on main stalk, CT– culm thickness (mm), 100_SWT– 100 seed weight (g), GY grain yield (kg/ha)-, SDWT– stower dry weight (kg/ha), SFWT– stower fresh weight (kg/ha), HI – harvest Index.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/c0efedc4a6044d1eed41e095.png"},{"id":89663236,"identity":"72c91cc9-0dc3-4308-9193-7ec62cd296af","added_by":"auto","created_at":"2025-08-22 11:27:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119166,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering of 130 little millet germplasm accessions based on Ward’s method and Euclidean distance\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/3877c26170cfbfd75fba308a.png"},{"id":98243870,"identity":"000ad293-3e05-4f97-bff2-458ec96695a8","added_by":"auto","created_at":"2025-12-15 16:10:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2172135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/b687a9b5-995d-4a02-87b3-1970488e4123.pdf"},{"id":89662052,"identity":"c667ee6f-aca5-4f06-bd04-4911449cb2a2","added_by":"auto","created_at":"2025-08-22 11:19:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23842,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7373619/v1/57bb6b6e34a2f50d26cf1e18.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fodder and Grain Yield Potential of Little Millet, Panicum sumatrense race robusta and trait-specific genetic resources for crop improvement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid increase in global population and the increasing impact of climate change on various crops pose a significant threat to global food and nutritional security. Global crop cultivation is dominated by cereals such as rice, wheat, and maize, which account for around 40% of the total area under cultivation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#home\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed on 02 Aug 2025). Crop diversification, which can result in better soil health and the development of sustainable agroecosystems (Yang et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), therefore, it is a need of the day. Diverse crops with better climate resilience and nutritional richness can help combat threats to food and nutritional security. Small millets are crops with greater endurance to biotic and abiotic stresses and nutritional superiority, but these crops are currently considered underutilized. To transform food systems into diverse, sustainable, climate-resilient, and able to meet our nutritional requirements, there is a need for the cultivation of underutilized but climate-resilient and nutrient-dense crops, making them an integral part of our food basket.\u003c/p\u003e\u003cp\u003eLittle millet (\u003cem\u003ePanicum sumatrense\u003c/em\u003e Roth ex Roem. \u0026amp; Schult.) is one of the small millets, belonging to the family Poaceae. It is cultivated as a cereal crop in India and is widely distributed as a weed or wild plant in many Asian countries, including Nepal, Pakistan, Sri Lanka, Indonesia, and Myanmar (Vetriventhan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). India is the largest producer of the little millet with an area of 0.26\u0026nbsp;million ha and a productivity of 0.12\u0026nbsp;million tons (Bhat et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In India, it is grown primarily in the tribal belts of Madhya Pradesh, Chhattisgarh, and Andhra Pradesh and Tamil Nadu (Nandini et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Little millet has high water-use efficiency and salt and waterlogging tolerance, and is less prone to insect pests and diseases (Upadhyaya et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ganapathy \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Several studies have revealed the nutraceutical richness of little millet in terms of carbohydrates, protein, and phenols, and it is particularly rich in iron (18\u0026ndash;58 mg/kg), zinc (19\u0026ndash;40 mg/kg), calcium (92\u0026ndash;390 mg/kg), protein (6\u0026ndash;16%), and dietary fibre (7.7%) (Saleh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Longvah \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It can easily substitute for rice-based recipes and is considered a functional and nutritional food choice for the management of diet-related metabolic disorders (Mannuramath et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The acceptance of little millet-based products, such as \u003cem\u003edosa\u003c/em\u003e, and \u003cem\u003ePongal\u003c/em\u003e and more by people and their preference over meals prepared from polished white rice envisages future scope (Anitha et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe productivity of little millet remains low because it is largely cultivated on marginal lands under rainfed conditions with minimal inputs and crop improvement efforts. Furthermore, rice and wheat straw (21.9% and 15.1%, respectively) are the dominant dry feed stocks for the ruminants in India (Duncan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Identifying crops with better forage potential, in addition to grain yield, is essential for promoting crop diversification. Identification of little millet with high grain yield and greater fodder potential can establish it as a better candidate for crop diversification. A focus on genetic diversity and its estimation is essential for the success of plant breeding programs. Among the two races of little millet, race \u003cem\u003erobusta\u003c/em\u003e is a late maturing, erect culm with a large, strongly branched robust growth habit, and inflorescences are either erect and open (subrace \u003cem\u003elaxa\u003c/em\u003e), or compact and curved (subrace \u003cem\u003ecompacta\u003c/em\u003e). While accessions belonging to the race \u003cem\u003enana\u003c/em\u003e produce plants with decumbent to almost all prostate culms that become erect at flowering, produce erect, open strongly branched inflorescences (subrace \u003cem\u003elaxa\u003c/em\u003e) or inflorescence branches that sometimes clump at the time of maturity (subrace \u003cem\u003ecompacta\u003c/em\u003e) (De Wet et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Overall, race \u003cem\u003enana\u003c/em\u003e is early maturing, produces relatively low grain and biomass yields, and is prone to lodging, whereas race \u003cem\u003erobusta\u003c/em\u003e is late maturing, mostly non-lodging, and produces higher grain and fodder yields compared to race \u003cem\u003enana\u003c/em\u003e. The greater yield potential of race \u003cem\u003erobusta\u003c/em\u003e over \u003cem\u003enana\u003c/em\u003e and the trait-specific sources identified provided a greater scope for yield improvement (Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the forage potential of little millet remains largely unexplored. The study on the identification of trait-specific germplasm for high biomass revealed their potential (Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and emphasized the need for further studies in this direction. There is a need for little millet cultivars with early maturity, non-lodging, high grain, and biomass yield. Currently, the ICRISAT Genebank conserves 473 accessions of little millet, including 126 accessions belonging to the race \u003cem\u003erobusta\u003c/em\u003e, while the remaining accessions are of the race \u003cem\u003enana\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genebank.icrisat.org/IND/Passport?Crop=Little%20millet\u003c/span\u003e\u003cspan address=\"https://genebank.icrisat.org/IND/Passport?Crop=Little%20millet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The current study focused on the evaluation of a complete set of 126 little millet accessions of the race \u003cem\u003erobusta\u003c/em\u003e and the identification of trait-specific sources for grain and fodder yield. This can strengthen the little millet breeding programs and dietary diversification for food and fodder security and nutrition.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental material and details\u003c/h2\u003e\u003cp\u003eThe experimental material consisted of 126 accessions of race \u003cem\u003erobusta\u003c/em\u003e and four checks. The experiment was carried out in red soils during the 2021 and 2022 rainy seasons at the ICRISAT, Hyderabad, Telangana, India, using an alpha-lattice design with three replications. Sowing was done in a single row of 4 m length, row-to-row spacing of 60 cm, and plant-to-plant spacing of approximately 10 cm. Standard agronomic practices were followed during the crop growth period. The rainfall of 829 mm and 849 mm has been received during the cropping period, i.e., July to December in 2021 and 2022, respectively. The maximum temperature during the growing period ranged from 30.21\u0026deg;C to 31.23\u0026deg;C in 2021 and 28.78\u0026deg;C to 30.76\u0026deg;C in 2022, and the minimum temperature ranged from 13.92\u0026deg;C to 22.18\u0026deg;C in 2021 and 14.63\u0026deg;C to 20.62\u0026deg;C in 2022 during the period from July to December (data accessed from ICRISAT Intranet \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://intranet.icrisat.org/\u003c/span\u003e\u003cspan address=\"https://intranet.icrisat.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData were collected for 20 quantitative traits during both seasons. The traits, namely, days to 50% flowering, days to maturity, grain yield, stover fresh weight, and stover dry weight, were recorded on a plot basis, and grain yield, stover fresh weight, and stover dry weight were converted into kg/ha. The traits, namely plant height (cm), basal tillers per plant, flag leaf blade length (mm), flag leaf blade width (mm), flag leaf sheath length (mm), peduncle length (mm), panicle exertion (mm), inflorescence length (mm), inflorescence width (mm), inflorescence lowest primary branch length (mm), nodes per plant on main stalk, leaves per plant on main stalk, culm thickness (mm) and 100 seed weight (g) were recorded on five randomly selected plants, and average values were calculated. The harvest index was calculated as the ratio of grain yield to biological yield (=\u0026thinsp;grain yield\u0026thinsp;+\u0026thinsp;stover dry weight).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe data collected for 20 quantitative traits during both seasons were analysed individually and pooled over two seasons using the residual maximum likelihood (REML) approach in the GenStat 20th edition, considering genotypes as random and seasons as fixed (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vsni.co.uk/software/genstat/\u003c/span\u003e\u003cspan address=\"https://vsni.co.uk/software/genstat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The significance of seasons was tested using Wald\u0026rsquo;s statistics. The variance components of genotype (\u003cb\u003eσ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003csub\u003e\u003cb\u003eg\u003c/b\u003e\u003c/sub\u003e), genotype \u0026times; season (\u003cb\u003eσ\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003csub\u003e\u003cb\u003egs\u003c/b\u003e\u003c/sub\u003e), and error variance were estimated and used to calculate genetic parameters, such as heritability (\u003cb\u003eh\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003csub\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sub\u003e), genotypic and phenotypic coefficients of variation (GCV and PCV), genetic advance, and genetic advance as a percentage of the mean. The \u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eb\u003c/sub\u003e was categorized as low (\u0026lt;\u0026thinsp;0.30), medium (0.30\u0026ndash;0.60), and high (\u0026gt;\u0026thinsp;0.60); while GCV and PCV values were categorized as low (\u0026lt;\u0026thinsp;10%), medium (10\u0026ndash;20%) and high (\u0026gt;\u0026thinsp;20%) (Deshmukh et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The genetic advance as % mean was categorised low (\u0026lt;\u0026thinsp;10%), (10\u0026ndash;20%) and high (\u0026gt;\u0026thinsp;20%) (Johnson et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1955\u003c/span\u003e). The best linear unbiased predictors (BLUPs) were estimated for various agronomic traits for individual and pooled seasons and used for further analysis. The distribution of individual seasons was plotted using kernel density plots using \u003cem\u003eggplot2 package\u003c/em\u003e (version 3.5.1) of R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Correlation coefficients were estimated using \u003cem\u003ethe metan\u003c/em\u003e package (version: 1.18.0), and principal component analysis was performed using the package \u003cem\u003efactoextra\u003c/em\u003e (version: 1.0.7) in R software. Hierarchical clustering was performed based on Euclidean distance and Ward\u0026rsquo;s method using the libraries \u003cem\u003ePandas\u003c/em\u003e (McKinney \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), \u003cem\u003eNumPy\u003c/em\u003e (Walt et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), \u003cem\u003eScikit-learn\u003c/em\u003e (Pedregosa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), \u003cem\u003eSciPy\u003c/em\u003e(Jones et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and \u003cem\u003eMatplotlib\u003c/em\u003e(Hunter \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) of the Python software. The trait-specific accessions for grain yield (kg/ha), stover fresh weight (kg/ha), and stover dry weight (kg/ha) were identified through comparison with better-performing checks (based on pooled BLUPs) for respective traits using LSD (5%).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe REML analysis indicated significant \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e and \u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003egs\u003c/sub\u003e among the germplasm tested in the individual seasons and the pooled data of both seasons (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Wald\u0026rsquo;s statistic for the season was highly significant for most traits, except plant height, flag leaf sheath length, inflorescence width and 100 seed weight. Overall, this study indicates the presence of significant variability among the accessions and the profound impact of the environment on the performance of accessions. Among the various traits, high heritability (\u0026gt;\u0026thinsp;0.60) was observed for many traits during the 2021 and 2022 individual seasons and the pooled data of both seasons. In the case of individual seasons, flag leaf blade length and flag leaf sheath length showed medium heritability (0.30 to 0.60), which was high when pooled, flag leaf blade width was low in 2021, medium in 2022 and high in pooled, peduncle length was medium in both individual seasons and also in pooled. Basal tillers per plant showed high heritability in both individual seasons but medium when pooled. Inflorescence length, nodes per plant on main stalk, leaves per plant on main stalk and 100 seed weight showed high and medium heritability in 2021 and 2022 respectively and it was high when pooled. Medium heritability was observed in case of inflorescence width during 2021 and high in 2022 and pooled. The remaining traits showed high heritability in individual seasons and pooled data of both seasons, with days to 50% flowering having the highest heritability among all traits, being 0.98 in 2021, 0.97 in both 2022 and the pooled data. The heritability of grain yield (kg/ha) was high in 2021 (0.62) and 2022 (0.75), but medium in the pooled case (0.48).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariance components due to genotype (\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eg\u003c/sub\u003e), genotype \u0026times; season interaction (\u003cem\u003e\u0026sigma;\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003egs\u003c/sub\u003e), and broad-sense heritability (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eb\u003c/sub\u003e) for agronomic traits of little millet accessions evaluated in two rainy seasons (2021 and 2022) in ICRISAT, Patancheru, India.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eIndividual season\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCombined over two seasons\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003egs\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWald statistic season\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003csub\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 50% flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.29***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.75***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.91***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal tillers per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.57*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319.87***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217.30***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.83***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf sheath length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.80**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.08***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.03***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.85\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeduncle length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.00***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.40***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.65*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle exertion (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.02***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557.70***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e272.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.00***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.96***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.63***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e663.96***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence primary branch length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e532.97***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e624.85***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.25***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodes per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.90***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaves per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.96***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCulm thickness (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.05*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 Seed weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00043***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00041***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00038***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77044***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115653***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35096.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61652.00***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e281.13***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover fresh weight (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38892452***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39234430***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34444783***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4638021***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover dry weight (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6437895***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4161279***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4627927***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e690204***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.87***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarvest Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0045***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0034***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0033***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.95***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e⁎ - Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, ⁎⁎ - Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01 and ⁎⁎⁎- Significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001; ns \u0026ndash; non-significant\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eMean, range and genetic variability parameters\u003c/h3\u003e\n\u003cp\u003eThe mean and range values of various traits were estimated from the pooled BLUPs, and the genetic parameters estimated using the variances are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The distribution of genotypes for various traits during 2021 and 2022 can be observed from the kernel density (KDE) plot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The greater values of PCV over GCV for all traits indicate the existence of an environmental influence, but the low margin between PCV and GCV shows that the influence was minimal. A high genetic advance as % mean (\u0026gt;\u0026thinsp;20%) was observed for most traits including days to 50% flowering, plant height (cm), basal tiller per plant, panicle exertion (mm), inflorescence primary branch length (mm), culm thickness (mm), stover fresh weight (kg/ha) and stover dry weight (kg/ha), harvest index, while low (\u0026lt;\u0026thinsp;10%) for flag leaf blade length and width (mm), and peduncle length (mm), and medium (10\u0026ndash;20%) for days to maturity, flag leaf sheath length (mm), inflorescence length (mm), inflorescence width (mm), nodes per plant on main stalk, leaves per plant on main stalk, grain yield (kg/ha) and 100 seed weight.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePooled range, mean, and genetic variability parameters of agronomic traits in little millet germplasm evaluated in two rainy seasons (2021 and 2022) at ICRISAT, Patancheru, India.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eh\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGCV\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePCV\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGenetic Advance\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003cp\u003eas % Mean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLSD (5%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 50% flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal tillers per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf sheath length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeduncle length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle exertion (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence primary branch length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodes per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaves per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCulm thickness (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 Seed weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e267.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e336.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover fresh weight (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2786.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e722.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover dry weight (Kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1014.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarvest Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation and trait contributions\u003c/h2\u003e\n \u003cp\u003eGrain yield was positively correlated with days to 50% flowering, days to maturity, plant height (cm), flag leaf blade width (mm), peduncle length (mm), panicle exertion (mm), nodes per plant on main stalk, leaves per plant on main stalk, culm thickness (mm), stover fresh weight (kg/ha), and stover dry weight (kg/ha) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, a negative association was observed between grain yield (kg/ha) and traits such as basal tiller per plant, flag leaf sheath length (cm), inflorescence primary branch length (mm), and harvest index. The association with grain yield was non-significant for flag leaf blade length (mm), inflorescence length (mm), inflorescence width (mm), and 100 seed weight (g).\u003c/p\u003e\n \u003cp\u003eThe direct and indirect effects of various traits on grain yield, estimated using path analysis, are presented in \u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e. The positive correlations between grain yield and days to 50% flowering, plant height, panicle exertion, nodes per plant on main stalk and stover fresh weight were due to their positive direct effects. While positive associations between grain yield and days to maturity, culm thickness, leaves per plant on main stalk, flag leaf sheath length, peduncle length, and stover dry weight were observed due to the positive indirect effect. The negative correlations between grain yield, basal tiller per plant and 100 seed weight were due to direct negative effects. Indirect effects also contributed to the negative direct effects on inflorescence primary branch length.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrincipal component analysis\u003c/h3\u003e\n\u003cp\u003ePCA analysis revealed that the first four PCs (eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1) together explained a variation of approximately 79.75% (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). PC1 explained a large portion of the variability (45.11%), with an eigenvalue of 9.02. The traits, namely, nodes per plant on main stalk, leaves per plant on main stalk, stover fresh weight, plant height, days to 50% flowering, days to maturity, culm thickness, stover dry weight and harvest index are the traits contributing majorly to the variance explained by PC1. Similarly, inflorescence length, inflorescence width, flag leaf sheath length, flag leaf blade length and inflorescence primary branch length contributed significantly to the variability explained by PC2. From the biplot (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the vector length of the trait indicates its contribution to the total diversity of the germplasm. Longer the length of the trait vectors, are major contributor to the total diversity in the germplasm. Furthermore, the angle between the trait vectors is a measure of their correlation. The acute angle between grain yield and days to maturity, days to 50% flowering, stover fresh weight, stover dry weight, 100 seed weight, and nodes per plant on main stalk and leaves per plant on main stalk indicates positive correlations among them. The obtuse angle between the vectors of grain yield and the traits of the harvest index and basal tillers per plant is a measure of the negative correlation between them.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEigenvalue, variance (%) and cumulative variance (%) of major principal components\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrincipal component\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePC4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEigen value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariance %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCumulative variance %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eIndividual Trait contributions (%) towards PCs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 50% flowering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal tillers per plant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade length(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf blade width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlag leaf sheath length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeduncle length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle exertion (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence width (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInflorescence primary branch length (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodes per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeaves per plant on main stalk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCulm thickness (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 seed weight (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover fresh weight (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStover dry weight (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHarvest Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield (kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eCluster analysis\u003c/h3\u003e\n\u003cp\u003eThe Euclidean distance matrix calculated based on 20 quantitative traits of 130 little millet accessions, including checks, was used for clustering based on Ward\u0026rsquo;s method, resulting in two major clusters with 91 genotypes in cluster 1 and 39 genotypes in cluster 2 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared to cluster 2, which has lower mean values of 10,266 kg/ha for stover fresh weight and 4,177 kg/ha for stover dry weight, cluster 1 exhibits significantly higher means of 20,977 kg/ha for stover fresh weight and 7,7835 kg/ha for stover dry weight. Similarly, the mean grain yield (kg/ha) of Cluster 1 (2303 Kg/ha) was higher than that of Cluster 2 (2014 kg/ha).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eTrait-specific accessions\u003c/h2\u003e\n \u003cp\u003eFor grain yield, the top 20 accessions were identified based on pooled means, of which 10 are statistically superior to Phule Ekadashi (grain yield of 2210 kg/ha) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the top 20 accessions, IPmr 723, IPmr 1042 and IPmr 990 with a pooled mean grain yield of 2674 kg/ha, 2635 kg/ha and 2603 kg/ha are identified as the top yielding accession which are statistically superior to the check in pooled and also in individual seasons. The pooled grain yields of the top 10 accessions ranged from 2546 kg/ha to 2768 kg/ha. These high-grain-yielding accessions matured 95 to 105 days after sowing. Based on the pooled BLUPs of stover dry weight, 87 accessions based on pooled data, 79 accessions in 2021, and 88 accessions in 2022 were statistically better performers compared to the check GV2. Similarly, in the case of stover fresh weight, most accessions (95 accessions) were statistically superior to BL 6 (11234 kg/ha) in the pooled, as well as in the 2021 (99 accessions) and 2022 (91 accessions). For fodder yield, the top 20 accessions were identified based on the pooled BLUPs of stover dry weight. Stover dry weight of these identified top 20 accessions ranges from 8,648 to 17,386 kg/ha and matured in 95 to 134 days after sowing (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). There are 5 accessions, namely IPmr 880, IPmr 871, IPmr 875, IPmr 896 and IPmr 907, which were identified as dual-purpose producing significantly high grain and stover yields.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLittle millet germplasm accessions identified for high grain yield and their agronomic characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAccessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGrain Yield (kg/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDays to 50% Flowering\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDays to Maturity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePlant Height (cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2768*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2438*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2674*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2176*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3138*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2643*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2504*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2635*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2071*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3180*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2615*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2221*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2603*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2056*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3134*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2572*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3223*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2564*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2056*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2556*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3497*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2546*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2129*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2254*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2007*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2322*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2064*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1997*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2062*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2188*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2026*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2060*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"13\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGNV 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhule Ekadashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrial mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e* Indicates statistical superiority over the check (Phule Ekadashi) at 5% level of significance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTrait-specific accessions of little millet germplasm identified for stover fresh weight (Kg/ha) and dry weight (kg/ha)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAccessions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStover dry weight (Kg/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eStover fresh weight (Kg/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePlant Height (cm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDays to maturity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGrain Yield (kg/ha)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17386*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17482*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16836*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37521*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35913*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38490*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11322*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13792*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8627*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27203*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31390*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22537*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10324*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11969*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8516*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24513*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27353*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21317*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10266*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9690*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10690*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29236*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27444*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30808*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11267*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8652*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25732*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26531*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24806*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9775*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10243*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9187*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25596*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26271*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24710*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9712*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10742*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8567*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22952*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25478*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20284*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9614*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10702*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8332*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23376*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25596*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20817*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9541*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11357*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7554*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31312*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30030*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32204*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9435*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10167*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8563*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26543*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27401*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25325*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9286*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9677*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8811*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22597*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22071*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23047*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9282*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10043*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8355*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23090*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20397*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 1051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9193*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10108*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8102*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25048*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26632*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23033*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9133*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9684*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8524*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24209*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23972*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24458*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9061*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9521*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8433*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23019*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25947*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19668*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9055*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8688*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9291*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23178*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21166*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25114*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8647*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9162*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22238*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19022*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25277*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8918*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8780*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24071*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22405*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25730*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8706*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9401*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7934*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24396*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25375*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIPmr 880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8648*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10123*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7037*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22747*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23791*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21503*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"16\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBL 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGNV 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGV 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhule Ekadashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2810\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrial mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003cstrong\u003e*\u003c/strong\u003e Indicates statistical superiority over the check (GV 2 for Stover dry weight (kg/ha) and BL 6 for Stover fresh weight (kg/ha)) at 5% level of significance\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCrops like small millets, which have received little breeding push, are, in general, low-yielding compared to those that have received global systematic breeding support from the public and private sectors; hence, these crops need basic information on the potential of the germplasm that could be used for further improving their productivity. Unveiling genetic diversity through the evaluation of germplasm resources and the identification of trait-specific sources is essential for exploring untapped genetic potential. This could also enhance the utility of the existing germplasm resources for their improvement. The current study on little millet revealed the existence of high variability for yield and biomass contributing traits, which will be an asset to its breeding program. The high heritability of most traits indicates a greater potential for successful selection for the improvement of various traits. Among the various traits, higher heritability was observed for important traits such as days to 50% flowering, days to maturity, plant height, inflorescence length and width, 100 seed weight, and stover fresh and dry weight in the pooled data of two seasons. Traits with high heritability and high genetic advance are mostly governed by additive gene action, for which selection is much more effective. Further, significant improvement is also possible for the traits having medium heritability and high genetic advance (% mean). Non-additive gene action was predominant for traits with low heritability and low genetic advance as % mean, for which recombination breeding would be the most suitable method of trait improvement. Grain yield showed medium heritability and medium genetic advance as % mean, while some studies reported high heritability and high genetic advance (Behera et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This proves that improvement in grain yield is possible through hybridization followed by selection. Traits such as stover fresh and dry weights showed high heritability and high genetic advance as % the mean. Therefore, the fodder yield can be improved through direct selection from the identified trait-specific sources. Medium heritability and genetic advance for grain yield highlight the value of indirect selection for genotypes based on positively associated traits. Traits such as days to 50% flowering, plant height, and days to maturity were positively associated with grain yield in this study and negatively associated with basal tillers per plant. A similar correlation was observed when both races of little millet were assessed in our previous study (Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe association is caused by both direct and indirect effects; consideration of direct effects alone can enhance the scope of success of trait improvement (Kumar et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the case of days to maturity, peduncle length, leaves per plant on main stalk, culm thickness and stover dry weight, the positive association was due to larger positive indirect effects of various traits dominating their direct negative association with grain yield (kg/ha). A positive association due to the dominance of direct effects on grain yield was observed in the case of days to 50% flowering, plant height, panicle exertion, nodes per plant on main stalk, and the stover fresh weight. Correlation between grain yield and harvest index was negligible in 2021, negative in 2022, and in the pooled data of both seasons. Some studies reported a significant positive relationship (Kinal Patel et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Behera et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while our previous study indicated a negligible relationship when both races were studied together (Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The negative correlation in the pooled data of both seasons in the current study was due to the greater indirect negative effects of stover fresh weight on the positive direct effects of harvest index. In a previous study (Kinal Patel et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the dominance of the direct positive effects of the harvest index over the negative indirect effects of fodder yield resulted in a positive correlation in little millet. The dominance of the indirect effects of stover fresh weight on grain yield is a measure of the greater fodder potential of \u003cem\u003erobusta\u003c/em\u003e over the grain yield potential, which agrees with a previous study (Vetriventhan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, as the positive direct effects are much higher in the case of days to 50% flowering, panicle exertion, and stover fresh weight, these are the best indices for indirect selection for the improvement of grain yield.\u003c/p\u003e\u003cp\u003eThe first four principal components together accounted for 79.75% of the total variation, and the traits, namely days to 50% flowering, days to maturity, plant height, nodes per plant on main stalk, leaves per plant on main stalk, culm thickness, stower fresh weight, and stover dry weight and harvest index were the traits contributed significantly to total variance. Cluster analysis divided the 130 genotypes into two different clusters, and all four cultivars used as checks grouped under a single cluster, indicating a similarity in performance based on overall traits. Hybridization between the most diverse accessions, resulting in heterosis followed by selection, can be beneficial for improving grain yield and other traits governed by non-additive gene action. Thus, clustering can provide a basis for selecting the most diverse genotype.\u003c/p\u003e\u003cp\u003eThe identification of trait-specific sources, resulting in a major focus on these genetic resources, can improve breeding programs. The top 10 trait-specific sources identified for grain yield based on the pooled data are superior to Phule Ekadashi. Accession IPmr 723 was among the top 10 performing accessions in pooled (2674 kg/ha) and in both individual seasons (2176 kg/ha during 2021 and 3138 kg/ha during 2022). Most of the high-yielding accessions identified were of medium duration, with a maturity duration of 95 to 105 days. There is a need to attempt to transfer high-yielding potential from identified trait-specific sources into the genetic background of short-duration accessions, as variability among the germplasm screened was large for days to maturity, ranging from 67 days to 134 days.\u003c/p\u003e\u003cp\u003eThis study also identified the top 20 accessions for stover dry weight yield. As days to maturity were highly positively correlated with stover fresh weight and dry weight, most of the trait-specific accessions for high biomass were 95 to 134 days to maturity. Accessions, namely IPmr 913, IPmr 876, IPmr 873, IPmr 1056 and IPmr 702 are among the top five. In addition to the fodder potential, consideration of the fodder quality can improve its utility to a much greater extent. Our attempts to study the fodder quality of 20 little millet accessions (unpublished) indicated a mean IVMOD of 47.33% (range 46.08\u0026ndash;48.82%), which is comparable to finger millet (Backiyalakshmi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and wheat (mean\u0026thinsp;~\u0026thinsp;48%)(Bl\u0026uuml;mmel et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) while higher than the reported value in rice (mean 42%, range 38.2 to 45.6%) (Subudhi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, crude protein content in little millet ranged from 5.44 to 8.33% with a mean of 6.78% (unpublished), which is similar to finger millet (mean 7.23%, range 6.47 to 8.15%) (Backiyalakshmi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and higher than that of rice (mean 5.56%, range 4.23 to 7.88%) (Subudhi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This indicates the greater potential of little millet as a fodder crop.\u003c/p\u003e\u003cp\u003eAmong small millet crops, the stover dry yield of finger millet ranged from 2890\u0026ndash;10779 kg/ha, and accessions with multiple fodder quality aspects have been reported (Backiyalakshmi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, the stover dry yield in the case of kodo millet ranged from 1156\u0026ndash;8777 kg/ha(Vetriventhan and Upadhyaya \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the dry biomass in the proso millet germplasm ranged from 2909 to 9775 kg/ha (Calamai et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The proso millet variety TNAU 151 has a fodder potential of 4130 kg/ha with 72 days to maturing (Nirmalakumari et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The mean dry matter yield in sweet sorghum is about 20.18 t/ha, and 18.49 t/ha and 20.62 t/ha in dual-purpose and forage type, respectively (Singh and Chauhan \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The average dry straw yield of commercial rice varieties is around 9.7 t/ha(Mat\u0026iacute;as et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and in the case of pearl millet under rainfed conditions, it ranges from 11.8 to 16.4 t/ha (Dhedhi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The stover dry weight of little millet germplasm, evaluated in this study, ranged from 1.8 to 17.8 t/ha. Further, the top accessions identified for stover dry weight ranged from 8.6 t/ha to 17.8 t/ha, highlighting the importance of genetic variability and the potential for selecting and breeding high-yielding varieties for improved grain and fodder production.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study's findings open up significant opportunities for little millet breeding programs focused on forage or dual-purpose cultivation. The potential for developing varieties with high grain and fodder yield having early maturity is particularly promising, as it could address the growing demand for grain and fodder requirement while maintaining efficient crop cycles. There are 5 accessions identified as dual purpose (IPmr 871, IPmr 875, IPmr 880, IPmr 896 and IPmr 907), which could be explored through multilocation testing and further use in the breeding program. This dual-purpose approach could enhance the economic viability of little millet cultivation, making it more attractive to farmers and potentially increasing its adoption in diverse agricultural systems. Furthermore, the exploration of little millet as a bioenergy crop represents an exciting new frontier in its utilization. Drawing parallels with switch grass (\u003cem\u003ePanicum virgatum\u003c/em\u003e L.), which has already established itself as a valuable bioenergy source, little millet's potential in this area could be substantial. This multifaceted utility - encompassing grain yield, fodder production, and bioenergy potential - positions little millet as a highly versatile crop. Such versatility could play a crucial role in promoting crop diversification, enhancing agricultural sustainability, and contributing to food, feed, and energy security in various regions. As research in this area progresses, it may lead to the development of specialized little millet varieties optimized for specific end-uses, further expanding its agricultural and industrial applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e This study was undertaken as part of the CGIAR Genebank Initiative and is part of the Research Program on Accelerated Crop Improvement, ICRISAT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement:\u003c/strong\u003e All the supporting data are presented in the manuscript and in the supplementary tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest declaration:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e MV and KS contributed to the design and implementation of the research; MV and DN conducted the experiment; JK and DN analysed the data; JK \u0026amp; MV wrote the first draft of the manuscript. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnitha S, Arjun P, Palli NC, Sreekanth N, Miruthika Devi SA, Pandey S, Krishnan S, Prasad S, Sharma S, Chidambara Murthy KN, Botha R, Upadhyay S, Kane-Potaka J (2024) Sensory and nutritional evaluation of nine types of millet substituted for polished white rice in select Indian meal preparations. Front Sustain Food Syst 8. https://doi.org/10.3389/fsufs.2024.1331260\u003c/li\u003e\n \u003cli\u003eBackiyalakshmi C, Babu C, Reddy DN, Padmakumar VP, Prasad KVSV, Azevedo VCR, Vetriventhan M (2021) Assessing forage potential of the global collection of finger millet (Eleusine coracana (l.) gaertn.) conserved at the icrisat genebank. Agronomy 11. https://doi.org/10.3390/agronomy11091706\u003c/li\u003e\n \u003cli\u003eBehera R, Singamsetti A, Rout S, Nanda S, Sharma SS (2024) Genetic variability and character associations for grain yield and other secondary traits in little millet (Panicum sumatrense L.) at Eastern Ghats Zone of Odisha. Electronic Journal of Plant Breeding 15:239\u0026ndash;245. https://doi.org/10.37992/2024.1501.028\u003c/li\u003e\n \u003cli\u003eBhat B V, Tonapi VA, Rao BD, Singode A, Santra D, Johnson J (2018) Production and utilization of millets in India. In: International millet symposium on 3rd international symposium on broomcorn millet (3rd ISBM). pp 24\u0026ndash;36\u003c/li\u003e\n \u003cli\u003eBl\u0026uuml;mmel M, Updahyay SR, Gautam N, Barma NCD, Abdul Hakim M, Hussain M, Yaqub Mujahid M, Chatrath R, Sohu VS, Mavi G, Mishra VK, Kalappanavar IK, Naik R, Biradar S, Prasad SVS, Singh RP, Joshi AK (2019) Comparative assessment of food-fodder traits in a wide range of wheat germplasm for diverse biophysical target domains in South Asia. Field Crops Res 236:68\u0026ndash;74. https://doi.org/10.1016/j.fcr.2019.03.001\u003c/li\u003e\n \u003cli\u003eCalamai A, Masoni A, Marini L, Dell\u0026rsquo;acqua M, Ganugi P, Boukail S, Benedettelli S, Palchetti E (2020) Evaluation of the agronomic traits of 80 accessions of proso millet (Panicum miliaceum l.) under mediterranean pedoclimatic conditions. Agriculture (Switzerland) 10:1\u0026ndash;15. https://doi.org/10.3390/agriculture10120578\u003c/li\u003e\n \u003cli\u003eDe Wet JMJ, Prasada Rao KE, Brink DE (1983) Systematics and domestication of Panicum sumatrense (Graminae). Journal d\u0026rsquo;agriculture traditionnelle et de botanique appliqu\u0026eacute;e 30:159\u0026ndash;168\u003c/li\u003e\n \u003cli\u003eDeshmukh SN, Basu MS, Reddy PS (1986) Genetic variability, character association and path coefficients of quantitative traits in Virginia bunch varieties of groundnut.\u003c/li\u003e\n \u003cli\u003eDhedhi KK, Ansodariya V V., Chaudhari NN, Sorathiya JS (2016) Study of green fodder yield potential of pearl millet genotypes under rainfed conditions of Gujarat, India. Agricultural Science Digest - A Research Journal 36. https://doi.org/10.18805/asd.v36i2.10631\u003c/li\u003e\n \u003cli\u003eDuncan AJ, Samaddar A, Bl\u0026uuml;mmel M (2020) Rice and wheat straw fodder trading in India: Possible lessons for rice and wheat improvement. Field Crops Res 246. https://doi.org/10.1016/j.fcr.2019.107680\u003c/li\u003e\n \u003cli\u003eGanapathy KN (2017) Genetic improvement in little millet. In: Millets and sorghum: biology and genetic improvement. Wiley Online Library, pp 170\u0026ndash;183\u003c/li\u003e\n \u003cli\u003eHunter JD (2007) Matplotlib: A 2D graphics environment. Comput Sci Eng 9:90\u0026ndash;95\u003c/li\u003e\n \u003cli\u003eJohnson HW, Robinson HF, Comstock RE (1955) Estimates of genetic and environmental variability in soybeans.\u003c/li\u003e\n \u003cli\u003eJones E, Oliphant T, Peterson P (2001) SciPy: Open source scientific tools for Python\u003c/li\u003e\n \u003cli\u003eKinal Patel, Arna Das, Dhrumi Dalsaniya, Arvind D. Kalola, Ghanshyam B. Patil, Rumit Patel, Dipak A. Patel, Harshal E. Patil (2023) Study on character association and path analysis in little millet (Panicum sumatrense L.). Electronic Journal of Plant Breeding 14. https://doi.org/10.37992/2023.1401.005\u003c/li\u003e\n \u003cli\u003eKumar A, Sharma NK, Kumar R, Sanadya SK, Sahoo S (2024) Correlation and Path Analysis Studies in Mungbean [Vigna radiata (L.) Wilczek] under Arid Environment of Western Rajasthan. Indian J Agric Res. https://doi.org/10.18805/ijare.a-6195\u003c/li\u003e\n \u003cli\u003eLongvah T (2017) Indian Food Composition Tables. National Institute of Nutrition: xxxi\u003c/li\u003e\n \u003cli\u003eMannuramath M, Yenagi N, Orsat V (2015) Quality evaluation of little millet (Panicum miliare) incorporated functional bread. J Food Sci Technol 52:8357\u0026ndash;8363. https://doi.org/10.1007/s13197-015-1932-y\u003c/li\u003e\n \u003cli\u003eMat\u0026iacute;as J, Cruz V, Garc\u0026iacute;a A, Gonz\u0026aacute;lez D (2019) Evaluation of rice straw yield, fibre composition and collection under mediterranean conditions. Acta Technologica Agriculturae 22:43\u0026ndash;47. https://doi.org/10.2478/ata-2019-0008\u003c/li\u003e\n \u003cli\u003eMcKinney W (2011) pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing 14:1\u0026ndash;9\u003c/li\u003e\n \u003cli\u003eNandini C, Bhat S, Srinathareddy, Jayramegowda, Prabhakar (2019) Modified crossing (SMUASB) method for artificial hybridization in proso millet (Panicum miliaceum L.) and Little millet (Panicum sumatrense). Electronic Journal of Plant Breeding 10:1161\u0026ndash;1170. https://doi.org/10.5958/0975-928X.2019.00147.9\u003c/li\u003e\n \u003cli\u003eNirmalakumari, Subramanian A, Manoharan S, Raguchander T, Muthiah AR, Raveendran TS (2011) A High Yielding Prosomillet National Variety TNAU 151. Madras Agricultural Journal 98:15\u0026ndash;17. https://doi.org/10.29321/MAJ.10.100231\u003c/li\u003e\n \u003cli\u003ePedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V (2011) Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12:2825\u0026ndash;2830\u003c/li\u003e\n \u003cli\u003eSaleh ASM, Zhang Q, Chen J, Shen Q (2013) Millet grains: Nutritional quality, processing, and potential health benefits. Compr Rev Food Sci Food Saf 12:281\u0026ndash;295. https://doi.org/10.1111/1541-4337.12012\u003c/li\u003e\n \u003cli\u003eSingh D, Chauhan A (2017) Fodder yield, quality and nutrients uptake potential of different types of sorghum (sorghum bicolor) varieties in central Gujarat. Forage Res 43:121\u0026ndash;128\u003c/li\u003e\n \u003cli\u003eSubudhi HN, Prasad KVSV, Ramakrishna C, Rameswar PS, Pathak H, Ravi D, Khan AA, Padmakumar V, Bl\u0026uuml;mmel M (2020) Genetic variation for grain yield, straw yield and straw quality traits in 132 diverse rice varieties released for different ecologies such as upland, lowland, irrigated and salinity prone areas in India. Field Crops Res 245. https://doi.org/10.1016/j.fcr.2019.107626\u003c/li\u003e\n \u003cli\u003eUpadhyaya HD, Vetriventhan M, Dwivedi SL, Pattanashetti SK, Singh SK (2016) Proso, barnyard, little, and kodo millets. In: Genetic and genomic resources for grain cereals improvement. Elsevier, pp 321\u0026ndash;343\u003c/li\u003e\n \u003cli\u003eVetriventhan M, Azevedo VCR, Upadhyaya HD, Nirmalakumari A, Kane-Potaka J, Anitha S, Ceasar SA, Muthamilarasan M, Bhat BV, Hariprasanna K, Bellundagi A, Cheruku D, Backiyalakshmi C, Santra D, Vanniarajan C, Tonapi VA (2020) Genetic and genomic resources, and breeding for accelerating improvement of small millets: current status and future interventions. Nucleus (India) 63:217\u0026ndash;239\u003c/li\u003e\n \u003cli\u003eVetriventhan M, Upadhyaya HD (2019) Variability for productivity and nutritional traits in germplasm of kodo millet, an underutilized nutrient-rich climate smart crop. Crop Sci 59:1095\u0026ndash;1106. https://doi.org/10.2135/cropsci2018.07.0450\u003c/li\u003e\n \u003cli\u003eVetriventhan M, Upadhyaya HD, Azevedo VCR, Allan V, Anitha S (2021) Variability and trait-specific accessions for grain yield and nutritional traits in germplasm of little millet (Panicum sumatrense Roth. Ex. Roem. \u0026amp; Schult.). Crop Sci 61:2658\u0026ndash;2679. https://doi.org/10.1002/csc2.20527\u003c/li\u003e\n \u003cli\u003eWalt S van der, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22\u0026ndash;30\u003c/li\u003e\n \u003cli\u003eYang T, Siddique KHM, Liu K (2020) Cropping systems in agriculture and their impact on soil health-A review. Glob Ecol Conserv 23\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Little millet, Landraces, Germplasm, Biomass, Grain Yield","lastPublishedDoi":"10.21203/rs.3.rs-7373619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7373619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLittle millet is one of the small millets native to India, used for both food and fodder purposes. Among the two races (\u003cem\u003enana\u003c/em\u003e and \u003cem\u003erobusta\u003c/em\u003e) of little millet, race \u003cem\u003erobusta\u003c/em\u003e has a robust growth habit and produces high biomass and grain yield, but matures late compared to race \u003cem\u003enana\u003c/em\u003e. In this study, 126 accessions belonging to the race \u003cem\u003erobusta\u003c/em\u003e conserved in the ICRISAT Genebank were evaluated during two rainy seasons for fodder and grain yield potential, for the identification of trait-specific germplasm for little millet improvement. Residual maximum likelihood (REML) analysis revealed significant variability for all traits evaluated in the individual seasons and pooled for the two seasons. High heritability was observed for most traits in the individual seasons and the pooled data for the two seasons. Principal component analysis revealed the existence of genetic diversity and identified the key traits that contributed to maximum variability. Cluster analysis revealed two major clusters that could form the basis for selecting diverse genotypes for breeding programs. Based on the pooled data, the top 20 accessions each for high grain yield and fodder yield (stover dry weight) were identified, of which accessions IPmr 880, IPmr 871, IPmr 875, IPmr 896 and IPmr 907 were identified for dual purposes (grain and fodder), whereas accessions IPmr 702, IPmr 873, IPmr 876, IPmr 913, IPmr 1056 were the top five in terms of stover dry weight yield. Multi-location testing of identified sources for yield and adaptation and their use in breeding programs aids in little millet improvement and variety release.\u003c/p\u003e","manuscriptTitle":"Fodder and Grain Yield Potential of Little Millet, Panicum sumatrense race robusta and trait-specific genetic resources for crop improvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 11:11:25","doi":"10.21203/rs.3.rs-7373619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-23T15:17:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-23T07:08:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299152937745634403843364712393258971830","date":"2025-08-15T05:54:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T14:07:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T13:43:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-14T13:40:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2025-08-14T11:50:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"97e08117-ae43-4f18-b6bc-faadee744922","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-7373619","link":"https://doi.org/10.1007/s10722-025-02691-7","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2025-12-12 15:57:20","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-08-22 11:11:25","video":"","vorDoi":"10.1007/s10722-025-02691-7","vorDoiUrl":"https://doi.org/10.1007/s10722-025-02691-7","workflowStages":[]},"version":"v1","identity":"rs-7373619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7373619","identity":"rs-7373619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}