Screening and Identification of Rice Yellow Mottle Virus -tolerant Genotypes for Enhanced Breeding

Screening and Identification of Rice Yellow Mottle Virus -tolerant Genotypes for Enhanced Breeding | 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 Screening and Identification of Rice Yellow Mottle Virus -tolerant Genotypes for Enhanced Breeding Mavis Owusuaa Osei-Wusu, Leila Isabelle Bande, Joseph Honger, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7811299/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of Crop Science and Biotechnology → Version 1 posted You are reading this latest preprint version Abstract One serious disease that affects rice production in Africa is Rice yellow mottle virus (RYMV). Finding novel sources of RYMV resistance for breeding purposes and evaluating the impact of RYMV disease on selected yield-related characteristics of rice in Ghana were the goals of this study. Two highly tolerant and two susceptible checks were among the seventeen (17) rice genotypes used in this study. Completely randomized design with four replications was used in the experiment, with uninoculated genotypes serving as the control (Rep 4). Compared to the other attributes, RYMV considerably reduced the grain yield, panicle number, fresh biomass, tiller number, and dry biomass. Tiller number, panicle number, plant height, flowering days, maturity days, fresh biomass, and grain yield showed high heritability in addition to high genetic advance. Grain yield, dry biomass, and grain width were the three primary areas of variation in the germplasm of the first three principal components. According to the biplot created from the data, the disease had the least effect on genotypes FL478, NERICA 4, IR29, FARO 67, CRI-Amankwatia, ART35-49-D1-1, and CRI-Enapa, along with Gigante and Tog5674 (resistant checks). The hierarchical cluster analysis revealed two primary groupings. Three moderately resistant genotypes and two highly resistant genotypes were found from the study. The recently discovered tolerant genotypes can be employed in RYMV disease resistance breeding in the Africa. Rice yellow mottle virus (RYMV) disease severity and incidence heritability disease resistance genetic variability Figures Figure 1 Figure 2 Figure 3 1. Introduction Rice [ Oryza sativa (L.), 2n = 2x = 24 ] is a major food crop that supports almost half the world's population. It is primarily consumed as cooked food but also serves as the raw material for other industrial goods such as cosmetics, rice cakes, oils, and wine. In West Africa, paddy rice production was estimated at 20,095,603 tonnes on an area of 11,001,892 hectares, representing an average yield of 1.9 t/ha (FAOSTAT 2021 ). The consumption of rice, a basic crop in Ghana, is steadily rising because of urbanization and shifting consumer tastes (Ragasa and Chapoto 2017 ). In Ghana, rice ranks second to maize as a source of food and income for many rural homes (MoFA 2017 ). It is cultivated over an area of 0.33 million hectares, achieving a productivity rate of 2.93 tons per hectare (FAOSTAT 2021 ). The demand for rice is estimated to exceed that of maize in the coming years, positioning rice as not only a staple crop but also a crucial food security crop. Consequently, increasing rice production is essential for enhancing food security in the country (MiDA 2010 ; FAO 2014 ). Rice is produced in all agro-ecological zones of Ghana, ranging from semi-deciduous areas to regions with high rainfall (Issaka et al. 2012 ; Buri and Issaka 2019 ). In the past decade, the cultivation of rice has experienced consistent growth, with an average annual increase of approximately 5.3%, primarily driven by the expansion of cultivated land (Ouédraogo et al. 2021 ). Despite this progress, Ghana remains only 48% self-sufficient in rice production, relying on imports for the remaining 52%. According to the Business and Financial Times, Ghana’s rice import bill in 2022 stood at 560 million USD, and this was expected to increase in the coming years (B and FT 2023). This situation poses a threat to Ghana's rice self-sufficiency, food security, and overall economy. Various biotic and abiotic stresses contribute to the limited ability of farmers to fully capitalize on the prospect of rice production systems and market opportunities throughout the country. Numerous environmental effects such as drought, flooding, inconsistent rainfall, extreme temperatures, salinity, soil acidity/alkalinity, poor soil quality, soil erosion, and high phosphorus fixation, as well as biotic challenges like weeds, rice blast disease, Rice yellow mottle virus (RYMV), and the African rice gall midge (AfRGM), hinder rice production (Balasubramanian et al. 2007 ). Among all rice biotic stresses, the Rice yellow mottle virus (RYMV), first identified in Kenya by Bakker in 1966, is the most harmful rice pathogen in Africa (Okioma and Sarkarung 1983 ; Kouassi et al. 2005 ; Traoré et al. 2009 ). RYMV is found in all major rice-producing regions of sub-Saharan Africa, including East, West, Central, and Southern Africa, as well as in Madagascar. Its first appearance in Ghana was recorded in 1980 (Kouassi et al. 2005 ). The severity of RYMV disease, including its effects on yield, can differ considerably depending on factors such as the rice variety, period of disease infection, and the viral strain involved (Bakker 1970 ). Most rice accessions, including the O. glaberrima species, have demonstrated susceptibility to RYMV (Sereme et al. 2016 ). Interestingly, previous studies have identified most sources of RYMV resistance within this species (Traore et al. 2015 ). The significant genetic diversity of RYMV has also accelerated the development of new and virulent strains that can overcome the resistance of cultivated rice plants (Traoré et al. 2006 ; Hébrard et al. 2018 ). As a result, no commercial rice variety has been developed that simultaneously possesses strong resistance to RYMV along with other favorable agronomic traits (Kouassi et al. 2005 ) and in Ghana, popular aromatic rice varieties are highly susceptible to RYMV (Asante et al. 2013 ; Traore et al. 2015 ; Omiat et al. 2023 ). Finally, RYMV can reduce grain yield by 10–100% and in severe cases death of plants can occur (Kouassi et al. 2005 ). Moreover, recent findings by (Omiat et al. 2023 ) have revealed the presence of RYMV disease in all rice cultivation regions across Ghana, with the highest incidence observed in the Ashanti region. Given these challenges, it is crucial to identify high-yielding rice varieties tolerant to RYMV disease to improve existing farmer and consumer-preferred rice varieties like Jasmine 85, CRI-Agra Rice and Legon Rice 1. It is in this context that this study aimed to identify high-yielding rice varieties with tolerance to RYMV that are adapted to Ghana’s environment. With the increasing population, rice production must be doubled to meet Ghanaian’s current demand to enhance self-sufficiency. Addressing this problem is essential not only for ensuring food security and reducing economic reliance on rice importation, but also for contributing to sustainable agricultural development and livelihood improvement in the concerned regions. 2. Materials and methods 2.1. Plant material and agronomic practices Seventeen (17) rice lines were obtained from AfricaRice (Ivory Coast) and Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI), Ghana. Among the varieties were 13 genotypes and 4 controls. The 4 controls were Tog5674 and Gigante which are highly resistant genotypes and have been shown to be efficient against different strains of RYMV in Africa (Traoré et al. 2006 ; Hubert et al. 2017 ), and Bouake 189 plus Jasmine 85 which were the susceptible varieties sourced from CSIR-CRI. The experiment was laid out in a completely randomized design (CRD) with four (4) replications (Rep). Rep 1, Rep 2 and Rep 3 (Inoculated with RYMV) were spaced 1 m apart, and Rep 4 (uninoculated) was spaced 2 m apart to avoid any contact between the inoculated and uninoculated plants. Replicated pots were spaced 25 cm apart with a planting distance of 20 cm × 20 cm within each replicate. Seeds were nursed for each genotype and transplanted after 14 days into pots filled with 10 kg of soil as the planting media. Two seedlings/hill were sown. Each genotype in each Rep had two pots representing one plot and each pot contained two seedlings or plants of the respective genotype at a spacing of 20 cm. Three grams of NPK 15: 15: 15 was applied as basal and then later top dressed with 3 g of urea at 35–65 days after sowing. Weeds were controlled manually. The plants were irrigated as and when necessary. 2.2. Inoculation of the genotypes The virus isolates were obtained from an experimental field in Sokwai, in the Ashanti Region of Ghana. They were then multiplied by infecting Jasmine 85 (a susceptible variety) with extracts from infected leaves prepared by following the procedure of (Thiémélé et al. 2010 ). The addition of carborundum (600 mesh) to the extracts facilitated the virus's entry into the plant tissue. Inoculation was done at 21 days after planting using isolates from the infected Jasmine 85 by first rubbing sandpaper on the leaf surface followed by inoculum application onto the entire plant's leaves from the base to the tip. To prevent any escapes, the inoculation was repeated a week later. All plants were artificially inoculated while Rep 4 was left uninoculated thereby serving as the control. 2.3. DAS-ELISA procedure RYMV viral load was assessed using samples from infected plants. Leaves were collected on day 28 after the first inoculation and stored at -18°C. These samples underwent diagnosis using DMSZ’s DAS-ELISA procedure as described by (Clark and Adams 1977 ). 2.4. Data collection Data collected included days to symptoms appearance (DTSA), disease incidence and severity, tiller number, panicle number, plant height, days to 50% flowering, days to maturity, 1000 grain weight (TGW), fresh biomass, dry biomass and seed discoloration. To obtain data on the severity of the disease, scoring for RYMV was done using the scale in IRRI’s standard evaluation system for rice (Fig. 1 ) (IRRI 2013 ). Scoring on disease severity began 7 days post inoculation (dpi) and was done respectively at 8 dpi, 11 dpi, 15 dpi, 18 dpi, 21 dpi, 25 dpi, 32 dpi, 39 dpi, and 46 dpi. The disease incidence was estimated using the mathematical formula by (Asante et al. 2013 ). I % = PI/PT x 100, where: I % = disease incidence; PI = number of infected or dead plants; and PT = All plants inoculated. The other above-mentioned data were recorded in both inoculated and uninoculated tests. Grain length and width were measured after harvest on five grains of each genotype. Maturity days were recorded, i.e. when 80% to 90% of the grains on the panicle turned straw brown. Fresh biomass per plant was measured by cutting the plants from the bottom and weighing on a digital scale. Dry biomass per plant was measured by cutting the plant from the bottom and drying it, and grain weight was measured with a digital scale. Grain yield per genotype/plant was measured by using the grain weight of the given genotype/plant with 11% moisture content. 2.5. Data analysis The data collected for each of the 17 genotypes were used to determine the resistance or susceptibility of the genotypes, in reference to the disease scale and ELISA results. The recorded mean scores were used to evaluate the severity of the RYMV disease. Average severity ranges according to (Sereme et al. 2016 ) were further rated as follows. Highly resistant (HR): 1.0–1.5 = 1; Resistant (R): 1.6–3.5 = 3; Moderately resistant (MR): 3.6–5.5 = 5; Susceptible (S): 5.6–7.5 = 7; and Highly susceptible (HS): 7.6–9.0 = 9 Using the scoring by visual observation (Fig. 1 ) and the ELISA test, frequency distribution and a bar chart was plotted for the three classes (HR, MR and S) to determine the predominant reaction group in the population. The percentage reduction of the RYMV disease on each rice genotype for some parameters such as tiller number, panicle number, plant height, panicle length, grain length, grain width, flowering days, maturity days, fresh biomass weight, dry biomass weight, grain yield/plant and the disease severity was estimated between the control and the inoculated trials as described below; % Percentage reduction = \(\:\frac{Cv-Iv}{Cv}\) x 100 where; Cv = value of the control trial Iv = value of the inoculated trial ANOVA was performed using R software (version 4.4.1). RYMV disease effect data (estimated using the above equation) were transformed using logx + 1 to fit the assumptions of an ANOVA model. Correlation coefficients were determined using the “metan” package in R and interpreted according to the method proposed by (Taylor 1990 ). Genotypic coefficient of variation, phenotypic coefficient of variation, broad sense heritability, Genetic Advance, and Genetic Advance as percentage of means were carried out using the variability package in R statistical software. Broad sense heritability was computed using the formula given by Allard ( 1960 ). H 2 (%) = \(\:\frac{{\delta\:}2\:\text{G}}{{\delta\:}2\:\text{P}}\text{*}100\) Where; H 2 = heritability in the broad sense, δ 2 G = genotypic variance δ 2 P = phenotypic variance heritability in broad sense was categorized as; low = 0–30%, intermediate (medium) = 30–60% and high = above 60%, according to (Johnson et al. 1955 ). Genetic Advance (GA) was calculated as; GA = \(\:\frac{{\delta\:}2\text{G}}{{\delta\:}2\:\text{P}}\) \(\:\text{*}\) K \(\:\text{*}\) \(\:{\delta\:}\text{p}\) Where; δ 2 g = genotypic variance δ 2 p = phenotypic variance δp = phenotypic standard deviation 𝐾 = selection differential at 5% selection intensity = 2.06 Genetic Advance as percentage of Mean (GAM) = \(\:\frac{\text{G}\text{A}}{\text{X}}\) \(\:\text{*}\) 100 GA = genetic advance X = population mean GAM can be classified as low = less than 10%, intermediate = 10–20%, and high = more than 20%, according to Johnson et al. ( 1955 ). 3. Results 3.1. Mean pathological parameters The results obtained through the scoring by visual observation and the ELISA test showed that the genotypes were classified into three groups: highly resistant (HR), moderately resistant (MR) and susceptible (S). Among the seventeen (17) genotypes, two (2) were classified as highly resistant aside the two (2) resistant controls (Gigante and Tog5674), three (3) as moderately resistant and ten (10) as susceptible including the two (2) susceptible controls (Bouake 189 and Jasmine 85) (Table 1 ). Table 1 Mean values for pathological indices for assessing RYMV inoculated rice genotypes Genotype RYMV severity (1–9) Class Elisa score Elisa reaction Class DTSA RYMV incidence Discolored seed FL478 6.8 S 0.416 +++ S 8 100.00% 1 NERICA-L23 6.3 S 0.589 +++ S 8 100.00% 1 NERICA 4 1.0 HR 0.078 - HR NS 0.00% 0 IR29 6.0 S 0.428 +++ S 8 100.00% 1 FARO 66 6.3 S 0.467 +++ S 8 100.00% 1 FARO 67 4.9 MR 0.219 ++ MR 15 100.00% 1 Legon Rice 1 6.3 S 0.477 +++ S 8 100.00% 1 CRI-Amankwatia 4.9 MR 0.230 ++ MR 15 100.00% 1 CSIR-MALIMALI 6.6 S 0.568 +++ S 11 100.00% 1 CSIR-SAVANNA 6.3 S 0.466 +++ S 11 100.00% 1 Gigante 1.0 HR 0.077 - HR NS 0.00% 0 Jasmine 85 6.6 S 0.576 +++ S 8 100.00% 1 Bouake 189 6.3 S 0.461 +++ S 8 100.00% 1 ART35-49-D1-1 1.0 HR 0.079 - HR NS 0.00% 0 Tog5674 1.0 HR 0.076 - HR NS 0.00% 0 NERICA L 27 6.3 S 0.417 +++ S 11 100.00% 1 CRI-Enapa 4.4 MR 0.187 ++ MR 15 100.00% 1 DTSA = Days to Symptom Appearance, HR = highly resistant, R = resistant, MR = moderately resistant, S = susceptible, HS = highly susceptible, NS = no symptom, Elisa score (+ ≥ 0.392, buffer = 0.076, H 2 O = 0.080), (-) = negative reaction; (+) = weak reaction; (++) = weak, but clear reaction; (+++) = strong reaction. 3.2. Mean squares from analysis of variance (ANOVA) The performed ANOVA for both uninoculated and inoculated genotypes showed the effect of the disease on the various traits (Tables 2 and 3 ). The ANOVA showed significant differences among the inoculated and uninoculated genotypes (p 0.05) within the inoculated group (Table 3 ). Table 2 Mean square from analysis of variance of agronomic parameters of uninoculated plants Source of variance Df Mean Square TN PN PH PL GL GW FL MD FB DB GY Replicate 2 19.94 15.06 49.46 2.54 0.01 0.02 324.70*** 108.31* 97.10 15.24 14.15 Genotype 16 162.18*** 190.34*** 603.03*** 11.76*** 0.22*** 0.08*** 1073.75*** 1078.13*** 3266.70*** 893.62*** 904.29*** Residuals 32 11.16 13.21 62.93 1.51 0.03 0.01 24.60 24.77 428.10 133.76 23.18 *Significant at p < 0.05; **significant at p < 0.01; *** significant at p < 0.001; 0 not Significant Table 3 Mean square from analysis of variance of agronomic parameters of inoculated plants. Source of variance Df Mean Square TN PN PH PL GL GW FL MD FB DB GY Replicate 2 166.98 162.34 24.09 33.18 1.470 46.44 439.09*** 85.11* 27.72 21.72 61.39 Genotype 16 1042.80*** 1459.21*** 279.54*** 73.89** 27.23** 22.18 389.79*** 618.37*** 1076.90*** 480.40** 2681.32*** Residuals 32 97.73 144.52 46.97 20.67 8.00 21.01 34.82 19.48 103.60 176.59 77.88 *Significant at p < 0.05; **significant at p < 0.01; *** significant at p < 0.001; 0 not Significant 3.3. Mean agronomic values of rice lines. Tiller number was counted in the uninoculated and inoculated genotypes to find the effect of RYMV on tillering. The average number of tillers in the uninoculated genotypes was 31 whilst the average tiller number in the inoculated lines was 22 (Appendix 1 and Appendix 2). This gave a percentage reduction ranging from 0.4% to 55.2% with an average value of 27.2% per genotype. The highest percentage reduction in tiller number due to RYMV occurred in CSIR-MALIMALI (55.2%). In contrast, the lowest effect on tiller numbers was observed in Tog5674 (0.4%, Table 4 ). To determine the effect of RYMV disease on the selected traits, the number of panicles in the uninoculated and inoculated genotypes were counted. The average number of panicles in the uninoculated genotypes was 28 while that of the inoculated was 20 (Appendix 1 and Appendix 2). The percentage reduction of panicle number among the genotypes ranged from 3.5% to 76.9% with a mean value of 29.6%. The highest effect of the RYMV disease on the panicle number occurred in CSIR-MALIMALI (76.9%). In contrast, the lowest effect on panicle numbers was observed in FARO 67 (3.5%, Table 4 ). Both the inoculated and uninoculated genotypes were assessed to determine the effect of RYMV disease on plant height. The average plant height for the uninoculated genotypes was 116 cm, while the average plant height for inoculated genotypes was 99 cm (Appendix 1 and Appendix 2). The percentage reduction of plant height among the genotypes ranged from 0.9% to 35.0% with a mean value of 14.6%. The highest reduction in plant height was observed in NERICA L 27 (35.0%) and the lowest reduction was recorded in NERICA 4 (0.9%, Table 4 ). Panicle length was measured for both the inoculated and uninoculated genotypes, to assess the effect of RYMV disease on the trait. The average panicle length for genotypes that were not inoculated was 27.1 cm, whereas the average panicle length for inoculated genotypes was 24.8cm (Appendix 1 and Appendix 2). The percentage reduction of panicle length among the genotypes ranged from 0.7% to 18.8% with a mean value of 9.3%. The disease highly affected the panicle length in Enapa (18.75%), whereas the weak reduction on panicle length was observed in NERICA 4 (0.75%, Table 4 ). The length of the grains was measured in both the inoculated and uninoculated genotypes to determine how the disease affects this trait. The average grain length for genotypes that were not inoculated was 6.39 mm, whereas the average grain length for inoculated genotypes was 6.26 mm (Appendix 1 and Appendix 2). The percentage reduction in grain length, which varied from 0.4% to 11.6% had a mean value of 3.6%. The highest reduction of grain length due to RYMV disease was observed in CSIR-SAVANNA (11.6%). The lowest effect of the disease on grain length was observed in ART35-49-D1-1(0.4%, Table 4 ). The effect of RYMV disease on grain width was determined. Genotypes that were not inoculated had an average grain width of 2.09 mm, while the average grain width for inoculated genotypes was 2.04 mm (Appendix 1 and Appendix 2). The percentage reduction in grain width ranged from 0.6% to 9.9% with an average reduction of 4.5%. The highest reduction of grain width due to RYMV disease was observed in CRI-Amankwatia (9.9%). The lowest effect of the disease on grain width was observed in Tog5674 (0.6%, Table 4 ). With reference to days flowering, the genotypes that were not inoculated showed an average of 81 days to flowering, while the inoculated genotypes displayed an average of 92 days to flowering (Appendix 1 and Appendix 2). The percentage reduction on flowering days ranged from 0.9% to 42.1% with a mean value of 13.9%. The days to flowering were highly delayed in CSIR-SAVANNA (42.1%). Tog5674 (0.9%) had the least reduction in days to flowering due to RYMV and was the resistant check (Table 4 ). To assess how the disease affected days to maturity, both the inoculated and uninoculated genotypes were examined. The genotypes that were not inoculated showed an average of 110 days to maturity, while the inoculated genotypes exhibited an average of 128 days to maturity (Appendix 1 and Appendix 2). The percentage reduction in days to maturity ranged from 0% to 44.6% with a mean value of 16.3%. The days to maturity were highly delayed in CSIR-SAVANNA (44.6%). The resistant check Gigante showed the least reduction in days to maturity (0%, Table 4 ). The fresh biomass was weighed in both treatments, with and without inoculation, to determine the effect of the disease on the trait. Genotypes that were not inoculated exhibited an average of 196.3 g of fresh biomass, whereas the inoculated genotypes showed an average of 142.7 g of fresh biomass (Appendix 1 and Appendix 2). The mean value of the percentage reduction on fresh biomass was 26.6%, with a range of 3.4% to 67.5%. The highest reduction in fresh biomass was observed in Bouake 189 (67.5%) while the lowest was observed in Tog5674 (3.4%, Table 4 ). The dry biomass was weighed for the various genotypes, with and without inoculation, to identify the effect of RYMV disease on this character. Genotypes that were not inoculated exhibited an average of 73.4 g of dry biomass, whereas the inoculated genotypes showed an average of 62.6 g of dry biomass (Appendix 1 and Appendix 2). The mean value of the percentage reduction in dry biomass was 19.7%, with a range from 3.1% to 47.8%. The highest percentage reduction in dry biomass was observed in Legon Rice 1 (47.8%), while the lowest effect of the disease on dry biomass was observed in Tog5674 (3.1%, Table 4 ). Grain yield was assessed in the uninoculated and inoculated genotypes to find the effect of RYMV on the trait. The average of grain yield in the uninoculated genotypes was 53.17 g while the inoculated genotypes had an average yield of 34.09 g (Appendix 1 and Appendix 2). The percentage reduction of grain yields due to RYMV disease ranged from 4.6% to 84.9% with a mean value of 36.9%. The highest reduction in grain yield was observed in NERICA-L23 (84.9%) and the least reduction in yield due to RYMV was recorded in the resistant check Gigante (4.6%, Table 4 ). Table 4 Percentage reduction of RYMV disease on agronomic traits. Genotypes TN PN PH PL GL GW FL MD FB DB GY Severity FL478 5.9 6.8 6.5 1.7 2.2 2.2 8.2 6.3 8.7 16.8 11.1 6.8 NERICA-L23 50.2 53.9 10.1 9.1 8.8 4.5 16.5 36.8 21.6 14.8 84.9 6.3 NERICA 4 20.0 26.1 0.9 0.7 3.0 7.1 1.6 11.6 15.2 12.2 15.5 1.0 IR29 20.9 22.0 10.6 16.2 3.3 2.8 16.2 22.7 9.8 12.0 45.8 6.0 FARO 66 34.6 22.7 24.9 9.7 2.3 2.2 22.9 24.4 38.2 11.7 72.3 6.3 FARO 67 28.1 3.5 19.4 8.5 7.8 6.9 19.2 11.6 8.9 8.9 18.0 4.9 Legon Rice 1 48.6 46.4 19.3 5.4 4.3 6.9 10.2 12.6 44.3 47.8 48.3 6.3 CRI-Amankwatia 4.2 17.5 18.4 8.8 2.0 9.9 6.7 9.5 24.7 13.2 11.7 4.9 CSIR-MALIMALI 55.2 76.9 15.3 14.1 3.6 3.3 36.9 44.6 57.1 32.2 74.0 6.6 CSIR-SAVANNA 46.4 51.4 20.2 14.7 11.6 4.0 42.1 44.6 30.9 25.4 84.3 6.3 Gigante 4.4 6.8 2.0 6.3 1.7 1.8 6.9 0.0 11.4 18.8 4.6 1.0 Jasmine 85 47.4 61.3 19.7 9.3 1.7 4.9 7.7 8.5 42.3 43.7 65.7 6.6 Bouake 189 36.5 35.9 25.0 13.4 1.2 1.2 13.3 6.8 67.5 34.7 29.1 6.3 ART35-49-D1-1 6.1 7.5 2.5 5.1 0.4 9.2 2.8 0.5 10.5 14.9 5.2 1.0 Tog5674 0.4 4.7 2.5 5.7 1.8 0.6 0.9 1.0 3.4 3.1 4.9 1.0 NERICA L 27 34.4 35.1 35.0 10.4 2.2 4.4 12.0 23.4 41.3 13.2 42.5 6.3 Enapa 19.7 24.3 15.1 18.8 2.4 4.4 12.9 13.1 16.2 11.1 8.9 4.4 Average 27.2 29.6 14.6 9.3 3.6 4.5 13.9 16.3 26.6 19.7 36.9 4.8 Min 0.4 3.5 0.9 0.7 0.4 0.6 0.9 0.0 3.4 3.1 4.6 1.0 Max 55.2 76.9 35.0 18.8 11.6 9.9 42.1 44.6 67.5 47.8 84.9 6.8 SED 4.3 5.3 2.3 1.2 0.8 0.7 2.8 3.4 4.6 3.1 6.8 0.5 TN = Tiller number, PN = Panicle number, PH = Plant height, PL = Panicle length, GL = Grain length, GW = Grain width, FL = Flowering days, MD = Maturity days, FB = Fresh biomass weight, DB = Dry biomass weight, GY = Grain yield/plant. 3.4. Genetic estimates of agronomic traits of rice lines. Heritability and genetic advance were estimated for the characters present in Table 5 . Number of tillers, number of panicles, plant height, days to flowering, days to maturity, fresh biomass and yield had high heritability in addition to high genetic advance as percentage of mean. However, panicle length, grain length and dry biomass showed a moderate heritability with high genetic advance as percentage of mean. Only grain width showed low heritability with low genetic advance as percentage of mean (Table 5 ). Table 5 Broad sense heritability, genetic advance and genetic advance as percentage of mean of agronomic traits of rice genotypes. Traits H2 (%) Genetic Advance (GA) GAM TN 76.32 31.94 117.25 PN 75.20 37.40 126.40 PH 62.27 14.31 98.33 PL 46.18 5.90 63.53 GL 44.48 3.48 97.96 GW 1.82 0.17 3.86 FL 77.27 19.70 141.32 MD 91.11 27.78 169.92 FB 75.80 32.30 121.55 DB 36.45 12.52 63.65 GY 91.76 58.13 157.67 H 2 = Broad Sense Heritability, GA = Genetic Advance, GAM = Genetic Advance as Percentage of Mean, TN = Tiller number, PN = Panicle number, PH = Plant height, PL = Panicle length, GL = Grain length, GW = Grain width, FL = Flowering days, MD = Maturity days, FB = Fresh biomass weight, DB = Dry biomass weight, GY = Grain yield/plant. 3.5. Principal component analysis (PCA) among the traits. PCA was performed using yield and attributing components on rice genotypes. Out of twelve principal components (PCs), three exhibited more than 1 Eigen value and showed about 80.99% total variability (Table 6 ). PC1 showed 57.69% total variation while, PC2 and PC3 accounted for 12.58% and 10.71% variability, respectively. The traits contributed differently to the variability, some contributed more than the others. The first principal component (PC1) was more influenced by important traits such as GY, TN, Severity and MD. The second principal component (PC2) was dominated by DB, GL, FB and PL. The traits that explained the greatest variation among the genotypes under the third principal component (PC3) were GW, GL, PL and MD. The highest sources of variation under PC1, PC2 and PC3 were GY, DB and GW respectively. Table 6 Principal component analysis of RYMV severity on agronomic traits of rice. Characters PC 1 PC 2 PC 3 DB 0.23 0.52 0.13 FL 0.33 -0.27 0.05 FB 0.31 0.37 0.21 GL 0.20 -0.38 -0.53 GY 0.35 -0.01 -0.10 GW 0.06 0.32 -0.56 MD 0.33 -0.18 -0.28 PH 0.31 -0.15 0.24 PL 0.22 -0.33 0.41 PN 0.31 0.29 0.00 Severity 0.33 -0.14 0.10 TN 0.34 0.13 -0.15 Eigen value 6.92 1.51 1.29 Proportion of Variance (%) 57.69% 12.58% 10.71% Cumulative proportion (%) 57.69% 70.28% 80.99% PC = principal component, DB = dry biomass weight, FL = Flowering days, FB = fresh biomass weight, GL = Grain length, GY = Grain yield/plant, GW = Grain width, MD = Maturity days, PH = Plant height, PL = Panicle length, PN = Panicle number, TN = Tiller number. 3.6. Biplot for the evaluated genotypes One of the most informative graphical representations of a multivariate dataset is biplot. Biplots are graphical representations of the PCA. Figure 2 shows two components, classifying the sources of variation into groups. Component 1 and Component 2 are the first two principal components, which explain the majority (57.69% and 12.58%) of the variance in the data. Traits like GY, TN, disease severity and MD were the major contributors to the variability for Comp. 1 while DB, GL, FB and PL are close to the y-axis. The varieties numbered 1 to 17 under conditions of RYMV infection and non-infection are plotted on the biplot. GY, TN, disease severity and maturity days had vectors pointing in the same direction with the susceptible genotypes like genotype 2 (NERICA-L23), genotype 5 (IR29) ,10 (CSIR-SAVANNA), 16 (NERICA L 27), 9 (CSIR-MALIMALI), 13 (Bouake 189), 7 (Legon Rice 1) and 12 (Jasmine 85). Conversely, genotypes in the opposite direction to the vectors may be more resistant to the RYMV disease i.e genotype 1 (FL478), 3 (NERICA 4), 4 (IR29), 6 (FARO 67), 8 (CRI-Amankwatia), 11 (Gigante), 14 (ART35-49-D1-1), 15 (Tog5674) and 17 (CRI-Enapa). 3.7. Grouping of genotypes into various clusters. The hierarchical clustering (Fig. 3 ) was done in reference to the measured parameters, and it aligned with the PCA-biplot (Fig. 2 ). It categorized the lines into two main clusters in reference to the measured traits and the plants’ defense response. Cluster 1 had eight (8) genotypes including two known susceptible genotypes (Jasmine 85 and Bouake 189) and Cluster 2 had nine (9) genotypes, which includes the two resistant checks (Tog5674 and Gigante). The susceptible group (Cluster 1) which contained the 8 genotypes were represented by (CSIR-MALIMALI, NERICA-L23, CSIR-SAVANNA, Legon Rice 1, Jasmine 85, Bouake 189, FARO 66, and NERICA L 27) while the resistant group (Cluster 2) which contained the best 9 genotypes were represented by Tog5674, FARO 67, IR29, CRI-Enapa, NERICA 4, FL478, Gigante, CRI-Amankwatia and ART35-49-D1-1. 4. Discussion The research presents fresh perspectives into the opportunities and challenges of sub-Saharan Africa's (SSA) breeding for RYMV-resistant rice varieties, indicating the urgent need for genetic solutions to curb the devastating effects of RYMV. In addition to lending support to existing evidence on the limited recovery of RYMV-resistant genotypes (Sereme et al. 2016 ; Longue et al. 2018 ), this research introduces new pathways of resistance and brings in important genetic diversity to the breeding gene pool. The benefit of this study is the application of DAS-ELISA with visual assessments to ensure proper disease impact assessment to provide a full understanding of how virus-host interaction operates. Employing this two-pronged approach guarantees that resistant and asymptomatic lines are distinctly characterized, enabling one to isolate the causative genetic factors for further research. This result confirms the findings of Asante et al. ( 2020 ), who found no asymptomatic plants, and that the ELISA test result had a strong correlation with the visual scoring result (r = 0.99). Asante et al. ( 2020 ) found two resistant varieties, 8261112 and 8261119 upon studying the incidence of rice lines subjected to RYMV infection in Ghana. Results from our work additionally suggests NERICA 4 and ART35-49-D1-1 are RYMV resistant lines since they exhibited no symptoms of RYMV just as the controls (Gigante and Tog7291). It is worth noting that this is the first time ART35-49-D1-1, a breeding line from AfricaRice has been evaluated in Ghana and it will be interesting to see its performance in the field. In the case of NERICA 4, the presence of O. glaberrima in its background suggests that its resistance could be due to the presence of RYMV2 or RYMV3 . Further research needs to be conducted to ascertain the type of RYMV resistance found in this line. Furthermore, the degree of susceptibility or resistance of each line to RYMV determined how long the symptoms of RYMV took to develop. The onset of symptoms was accelerated in susceptible genotypes and delayed in resistant genotypes. This supports the work of Bakker ( 1970 ) who reported that typical symptoms of the disease appeared after 8–20 days post inoculation. Furthermore, the seed discoloration (Table 1 ) as observed due to RYMV disease speaks of the negative effect of the disease on seed quality. The finding supports the work of Soko et al. ( 2015 ) who reported seed discoloration as one of the symptoms of RYMV infection in addition to stunted growth, reduction in tillering, and panicle exertion. The effect of the disease on plant growth and development was also found to be linked to the severity in some varieties, while in others the effect of the disease varied according to the agronomic parameter considered. In this study, FL478 and IR29 recorded high severity but recovered and did not experience much impact of RYMV on yield. These observations show that the severity of symptoms on leaves is not always a determining criterion in the evaluation of varieties as resistant or susceptible, as found by Issaka et al. ( 2012 ). However, it remains a valuable tool for diagnosing the disease. According to N’Guessan et al. ( 2001 ), the severity of leaf symptoms should be associated with production losses and abnormal vegetative development of plants, for pathogenic characterization of isolates and for assessing the susceptibility of varieties to RYMV. Findings from this work showed significant difference between agronomic traits among the various genotypes. This is in line with the work of Traore et al. ( 2015 ) and Longue et al. ( 2018 ) who both recorded significant differences among accessions used especially for days to symptom appearance. Moreover, the significant difference of measured agronomic parameters among the genotypes studied suggests some traits like tiller number that contribute to high yield as vital for good gain and for selecting genotypes as potential parents for breeding programs. Furthermore, the work establishes the sharp yield loss and adverse effects on agronomic traits such as tiller number and biomass and panicle number, grain yield, thus highlighting the economic importance of RYMV infections. The findings of the research regarding differential impact of RYMV on tiller number, panicle number, plant height, panicle length, grain length, grain width, flowering days, maturity days, fresh biomass weigh, dry biomass weigh and grain yield are indicative of breeding complexity in terms of resistance as well as that reaction of the aforementioned traits to infection by the virus is a major selection criterion for genotypes. The range of yield losses reported in this study is in line with the work of Onwughalu et al. ( 2010 ) who reported 94.4% as the highest yield losses due to RYMV. The low yield reduction recorded by line/genotype ART35-49-D1-1 (5.2%) which is comparable to that of the two resistant checks implies that ART35-49-D1-1 possesses high resistance to RYMV and would not experience low yield if grown in RYMV prone field. A Work by Salaudeen ( 2014 ), indicated a low yield loss of 7.1% found in Gigante but the highest one came from FARO 29 (28.4%). The genetic analysis revealed high heritability and genetic advance as percentage of mean for key traits like tiller number, panicle number, plant height, flowering days, maturity days, fresh biomass and grain yield indicating the potential for effective selection in breeding programs. This result agrees with earlier genetic studies (Lingaiah 2015 ; Abebe et al. 2017 ) but offers new information regarding the role of additive gene action for RYMV resistance. In contrast, low heritability in grain width emphasizes the role of environmental effect, suggesting that certain traits would require alternative breeding strategies. Principal component analysis (PCA) and genotype-trait biplot use provide a new selection framework for key traits responsible for variation among rice genotypes. The results of the present study involving grain yield, tiller number, disease severity and maturity days as primary factors responsible for genetic variation provide new directions for future breeding programs. In addition, the cluster analysis confirms the genetic similarity of genotypes, which also confirms the application of hierarchical clustering in the classification of rice varieties based on resistance and agronomic performance. This finding is consistent with the research of Amadu et al. ( 2024 ) who identified 5 highly resistant and 10 resistant genotypes from the hierarchical cluster analysis. 5. Conclusion Two useful rice lines with possible resistance to RYMV have been identified in this study. In this research, the serious impact of RYMV on vital traits such as tiller number, fresh biomass, panicle number, and grain yield is emphasized, buttressing the importance of the development of resistant cultivars to minimize loss in yield. By integrating severity scoring with DAS-ELISA, a strong selection basis has been established related to observed trait decline in susceptible genotypes. The research laid the foundation of genetic diversity of the genotypes, with resistance in one variety (NERICA 4) and one line from AfricaRice and (ART35-49-D1-1) and moderate resistance in other varieties (FARO 67, CRI-Amankwatia and CRI-Enapa). The research also sheds light on breeding effort on the traits like grain yield, number of tillers, disease score and days to maturity, indicated by the principal component analysis (PCA). Furthermore, the favorable estimates of genetic advance and heritability suggest that selection for increased resistance and performance is promising. Lastly, the study provides a basis for breeding and employing the identified RYMV-resistant rice varieties to help develop more resistant and productive rice varieties for the affected regions. The identified resistant and moderately resistant lines are valuable resources for future breeding programs to enhance food security as well as agricultural sustainability. NERICA 4 and ART35-49-D1-1 are recommended to be the first crop improvement program in the future to concentrate on selecting genes or alleles conferring resistance to RYMV. In addition, the newly identified genotypes of this study also need to be reevaluated with other breeding lines under lowland or upland. In this subsequent screening, the criteria used in assessing the severity of RYMV disease should be reexamined and upgraded to make accurate and uniform determinations. Declarations Ethics Approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare no conflict of interest. Funding This research was supported by the West Africa Centre for Crop Improvement (WACCI), University of Ghana. Author Contribution MOW and JH conceptualized and supervised the work; LIB, SA and MOW performed experiment; LIB collected data and wrote the first draft of the paper; BA analysed the data; KAO and LIB interpreted analyzed data, and the paper was reviewed by all authors. Acknowledgement We gratefully acknowledge Shadrach Asiedu Coffie, Benjamin Owusu Ottu and Peter Basaking for their assistance in carrying out the laboratory experiments Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Abebe T, Alamerew S, Tulu L (2017) Genetic Variability, Heritability and Genetic Advance for Yield and its Related Traits in Rainfed Lowland Rice (Oryza sativa L.) Genotypes at Fogera and Pawe, Ethiopia. Adv Crop Sci Tech 05. https://doi.org/10.4172/2329-8863.1000272 Allard I (1960) Principles of Plant Breeding. Chapter 6 through Chap. 9. University of California, Davis Amadu B, Asante MD, Oppong A et al (2024) Genetic variations of rice yellow mottle virus disease on selected rice ( Oryza sativa L ) genotypes and their effects on yield and yield-related traits. 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Virus Res 141:258–267. https://doi.org/10.1016/j.virusres.2009.01.011 Traore V, Asante M, Gracen V et al (2015) Screening of Rice Accessions For Resistance to Rice Yellow Mottle Virus. AJEA 9:1–12. https://doi.org/10.9734/AJEA/2015/19897 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Journal of Crop Science and Biotechnology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7811299","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533678512,"identity":"298524d4-07cc-4730-a79d-2553949f609b","order_by":0,"name":"Mavis Owusuaa Osei-Wusu","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Mavis","middleName":"Owusuaa","lastName":"Osei-Wusu","suffix":""},{"id":533678515,"identity":"a92ec301-25c7-4c6e-b625-48d18f2c02d8","order_by":1,"name":"Leila Isabelle Bande","email":"","orcid":"","institution":"University of 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1","display":"","copyAsset":false,"role":"figure","size":52065,"visible":true,"origin":"","legend":"\u003cp\u003eRYMV severity scoring scale (adopted from IRRI 2002), where 1 = no symptoms observed; 3 = leaves green but with spare dots or streaks plus less than 5% of height reduction; 5 = leaves green or pale green with mottling plus 6 to 25% of height reduction, slight delayed in flowering; 7 = leaves pale yellow or yellow plus 26–75% of height reduction, delayed flowering; and 9 = leaves turning yellow or yellow orange, and more than 75% height reduction, no flowering or some dead plants (John and Thottappilly 1987).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7811299/v1/4622d32c69e06022cc17abfd.jpg"},{"id":94463372,"identity":"4e501f1e-024c-4333-954c-39733131cff3","added_by":"auto","created_at":"2025-10-27 15:07:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10043,"visible":true,"origin":"","legend":"\u003cp\u003eA biplot visualization of the relationship between different rice varieties (numbered 1 to 17) and the measured traits under conditions of RYMV (\u003cem\u003eRice yellow mottle virus\u003c/em\u003e) infection and non-infection. TN (shown as TILL) = Tiller number; PN = Panicle number; PH = Plant height; PL = Panicle length; GL = Grain length; GW = Grain width; FL = Flowering days; MD = Maturity days; FB (shown as FRESH.WGT) = Fresh biomass weight; DB (shown as DRY.WGT) = Dry biomass weight; GY (shown as GrYld.wgh) = Grain yield/plant.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7811299/v1/58a640510fc8e133cd9f1fd9.jpg"},{"id":94463373,"identity":"b7d09dac-f8d3-47c5-be7b-2a42b3fb67d1","added_by":"auto","created_at":"2025-10-27 15:07:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22681,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram representing the distribution of 17 rice genotypes based on RYMV severity and agronomic traits of rice.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7811299/v1/73d7fe6759a00e857b14dd93.jpg"},{"id":107352612,"identity":"8a042acc-f4ca-4ae4-a037-7fe14fbfdeca","added_by":"auto","created_at":"2026-04-20 16:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":910266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7811299/v1/25dcbe5a-b877-476e-9e77-698c17c54e14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eScreening and Identification of Rice Yellow Mottle Virus -tolerant Genotypes for Enhanced Breeding\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice [\u003cem\u003eOryza sativa\u003c/em\u003e (L.), \u003cem\u003e2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;24\u003c/em\u003e] is a major food crop that supports almost half the world's population. It is primarily consumed as cooked food but also serves as the raw material for other industrial goods such as cosmetics, rice cakes, oils, and wine. In West Africa, paddy rice production was estimated at 20,095,603 tonnes on an area of 11,001,892 hectares, representing an average yield of 1.9 t/ha (FAOSTAT \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The consumption of rice, a basic crop in Ghana, is steadily rising because of urbanization and shifting consumer tastes (Ragasa and Chapoto \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Ghana, rice ranks second to maize as a source of food and income for many rural homes (MoFA \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is cultivated over an area of 0.33\u0026nbsp;million hectares, achieving a productivity rate of 2.93 tons per hectare (FAOSTAT \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The demand for rice is estimated to exceed that of maize in the coming years, positioning rice as not only a staple crop but also a crucial food security crop. Consequently, increasing rice production is essential for enhancing food security in the country (MiDA \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; FAO \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rice is produced in all agro-ecological zones of Ghana, ranging from semi-deciduous areas to regions with high rainfall (Issaka et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Buri and Issaka \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the past decade, the cultivation of rice has experienced consistent growth, with an average annual increase of approximately 5.3%, primarily driven by the expansion of cultivated land (Ou\u0026eacute;draogo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite this progress, Ghana remains only 48% self-sufficient in rice production, relying on imports for the remaining 52%. According to the Business and Financial Times, Ghana\u0026rsquo;s rice import bill in 2022 stood at 560\u0026nbsp;million USD, and this was expected to increase in the coming years (B and FT 2023). This situation poses a threat to Ghana's rice self-sufficiency, food security, and overall economy.\u003c/p\u003e\u003cp\u003eVarious biotic and abiotic stresses contribute to the limited ability of farmers to fully capitalize on the prospect of rice production systems and market opportunities throughout the country. Numerous environmental effects such as drought, flooding, inconsistent rainfall, extreme temperatures, salinity, soil acidity/alkalinity, poor soil quality, soil erosion, and high phosphorus fixation, as well as biotic challenges like weeds, rice blast disease, \u003cem\u003eRice yellow mottle virus\u003c/em\u003e (RYMV), and the African rice gall midge (AfRGM), hinder rice production (Balasubramanian et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Among all rice biotic stresses, the \u003cem\u003eRice yellow mottle virus\u003c/em\u003e (RYMV), first identified in Kenya by Bakker in 1966, is the most harmful rice pathogen in Africa (Okioma and Sarkarung \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Kouassi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Traor\u0026eacute; et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRYMV is found in all major rice-producing regions of sub-Saharan Africa, including East, West, Central, and Southern Africa, as well as in Madagascar. Its first appearance in Ghana was recorded in 1980 (Kouassi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The severity of RYMV disease, including its effects on yield, can differ considerably depending on factors such as the rice variety, period of disease infection, and the viral strain involved (Bakker \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1970\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost rice accessions, including the \u003cem\u003eO. glaberrima\u003c/em\u003e species, have demonstrated susceptibility to RYMV (Sereme et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Interestingly, previous studies have identified most sources of RYMV resistance within this species (Traore et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The significant genetic diversity of RYMV has also accelerated the development of new and virulent strains that can overcome the resistance of cultivated rice plants (Traor\u0026eacute; et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; H\u0026eacute;brard et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result, no commercial rice variety has been developed that simultaneously possesses strong resistance to RYMV along with other favorable agronomic traits (Kouassi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and in Ghana, popular aromatic rice varieties are highly susceptible to RYMV (Asante et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Traore et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Omiat et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, RYMV can reduce grain yield by 10\u0026ndash;100% and in severe cases death of plants can occur (Kouassi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, recent findings by (Omiat et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have revealed the presence of RYMV disease in all rice cultivation regions across Ghana, with the highest incidence observed in the Ashanti region. Given these challenges, it is crucial to identify high-yielding rice varieties tolerant to RYMV disease to improve existing farmer and consumer-preferred rice varieties like Jasmine 85, CRI-Agra Rice and Legon Rice 1. It is in this context that this study aimed to identify high-yielding rice varieties with tolerance to RYMV that are adapted to Ghana\u0026rsquo;s environment. With the increasing population, rice production must be doubled to meet Ghanaian\u0026rsquo;s current demand to enhance self-sufficiency. Addressing this problem is essential not only for ensuring food security and reducing economic reliance on rice importation, but also for contributing to sustainable agricultural development and livelihood improvement in the concerned regions.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Plant material and agronomic practices\u003c/h2\u003e\u003cp\u003eSeventeen (17) rice lines were obtained from AfricaRice (Ivory Coast) and Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI), Ghana. Among the varieties were 13 genotypes and 4 controls. The 4 controls were Tog5674 and Gigante which are highly resistant genotypes and have been shown to be efficient against different strains of RYMV in Africa (Traor\u0026eacute; et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hubert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Bouake 189 plus Jasmine 85 which were the susceptible varieties sourced from CSIR-CRI. The experiment was laid out in a completely randomized design (CRD) with four (4) replications (Rep). Rep 1, Rep 2 and Rep 3 (Inoculated with RYMV) were spaced 1 m apart, and Rep 4 (uninoculated) was spaced 2 m apart to avoid any contact between the inoculated and uninoculated plants. Replicated pots were spaced 25 cm apart with a planting distance of 20 cm \u0026times; 20 cm within each replicate. Seeds were nursed for each genotype and transplanted after 14 days into pots filled with 10 kg of soil as the planting media. Two seedlings/hill were sown. Each genotype in each Rep had two pots representing one plot and each pot contained two seedlings or plants of the respective genotype at a spacing of 20 cm. Three grams of NPK 15: 15: 15 was applied as basal and then later top dressed with 3 g of urea at 35\u0026ndash;65 days after sowing. Weeds were controlled manually. The plants were irrigated as and when necessary.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Inoculation of the genotypes\u003c/h2\u003e\u003cp\u003eThe virus isolates were obtained from an experimental field in Sokwai, in the Ashanti Region of Ghana. They were then multiplied by infecting Jasmine 85 (a susceptible variety) with extracts from infected leaves prepared by following the procedure of (Thi\u0026eacute;m\u0026eacute;l\u0026eacute; et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The addition of carborundum (600 mesh) to the extracts facilitated the virus's entry into the plant tissue.\u003c/p\u003e\u003cp\u003eInoculation was done at 21 days after planting using isolates from the infected Jasmine 85 by first rubbing sandpaper on the leaf surface followed by inoculum application onto the entire plant's leaves from the base to the tip. To prevent any escapes, the inoculation was repeated a week later. All plants were artificially inoculated while Rep 4 was left uninoculated thereby serving as the control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. DAS-ELISA procedure\u003c/h2\u003e\u003cp\u003eRYMV viral load was assessed using samples from infected plants. Leaves were collected on day 28 after the first inoculation and stored at -18\u0026deg;C. These samples underwent diagnosis using DMSZ\u0026rsquo;s DAS-ELISA procedure as described by (Clark and Adams \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data collection\u003c/h2\u003e\u003cp\u003eData collected included days to symptoms appearance (DTSA), disease incidence and severity, tiller number, panicle number, plant height, days to 50% flowering, days to maturity, 1000 grain weight (TGW), fresh biomass, dry biomass and seed discoloration. To obtain data on the severity of the disease, scoring for RYMV was done using the scale in IRRI\u0026rsquo;s standard evaluation system for rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (IRRI \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Scoring on disease severity began 7 days post inoculation (dpi) and was done respectively at 8 dpi, 11 dpi, 15 dpi, 18 dpi, 21 dpi, 25 dpi, 32 dpi, 39 dpi, and 46 dpi. The disease incidence was estimated using the mathematical formula by (Asante et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eI % = PI/PT x 100, where:\u003c/p\u003e\u003cp\u003eI % = disease incidence; PI\u0026thinsp;=\u0026thinsp;number of infected or dead plants; and PT\u0026thinsp;=\u0026thinsp;All plants inoculated.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe other above-mentioned data were recorded in both inoculated and uninoculated tests. Grain length and width were measured after harvest on five grains of each genotype. Maturity days were recorded, i.e. when 80% to 90% of the grains on the panicle turned straw brown. Fresh biomass per plant was measured by cutting the plants from the bottom and weighing on a digital scale. Dry biomass per plant was measured by cutting the plant from the bottom and drying it, and grain weight was measured with a digital scale. Grain yield per genotype/plant was measured by using the grain weight of the given genotype/plant with 11% moisture content.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data analysis\u003c/h2\u003e\u003cp\u003eThe data collected for each of the 17 genotypes were used to determine the resistance or susceptibility of the genotypes, in reference to the disease scale and ELISA results. The recorded mean scores were used to evaluate the severity of the RYMV disease. Average severity ranges according to (Sereme et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were further rated as follows.\u003c/p\u003e\u003cp\u003eHighly resistant (HR): 1.0\u0026ndash;1.5\u0026thinsp;=\u0026thinsp;1; Resistant (R): 1.6\u0026ndash;3.5\u0026thinsp;=\u0026thinsp;3; Moderately resistant (MR): 3.6\u0026ndash;5.5\u0026thinsp;=\u0026thinsp;5; Susceptible (S): 5.6\u0026ndash;7.5\u0026thinsp;=\u0026thinsp;7; and Highly susceptible (HS): 7.6\u0026ndash;9.0\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e\u003cp\u003eUsing the scoring by visual observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the ELISA test, frequency distribution and a bar chart was plotted for the three classes (HR, MR and S) to determine the predominant reaction group in the population. The percentage reduction of the RYMV disease on each rice genotype for some parameters such as tiller number, panicle number, plant height, panicle length, grain length, grain width, flowering days, maturity days, fresh biomass weight, dry biomass weight, grain yield/plant and the disease severity was estimated between the control and the inoculated trials as described below;\u003c/p\u003e\u003cp\u003e% Percentage reduction = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Cv-Iv}{Cv}\\)\u003c/span\u003e\u003c/span\u003e x 100\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ewhere;\u003c/p\u003e\u003cp\u003e\u003cem\u003eCv\u003c/em\u003e\u0026thinsp;=\u0026thinsp;value of the control trial\u003c/p\u003e\u003cp\u003e\u003cem\u003eIv\u003c/em\u003e\u0026thinsp;=\u0026thinsp;value of the inoculated trial\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eANOVA was performed using R software (version 4.4.1). RYMV disease effect data (estimated using the above equation) were transformed using logx\u0026thinsp;+\u0026thinsp;1 to fit the assumptions of an ANOVA model. Correlation coefficients were determined using the \u0026ldquo;metan\u0026rdquo; package in R and interpreted according to the method proposed by (Taylor \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenotypic coefficient of variation, phenotypic coefficient of variation, broad sense heritability, Genetic Advance, and Genetic Advance as percentage of means were carried out using the variability package in R statistical software.\u003c/p\u003e\u003cp\u003eBroad sense heritability was computed using the formula given by Allard (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\delta\\:}2\\:\\text{G}}{{\\delta\\:}2\\:\\text{P}}\\text{*}100\\)\u003c/span\u003e\u003c/span\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere;\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;heritability in the broad sense,\u003c/p\u003e\u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e G\u0026thinsp;=\u0026thinsp;genotypic variance\u003c/p\u003e\u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e P\u0026thinsp;=\u0026thinsp;phenotypic variance\u003c/p\u003e\u003cp\u003eheritability in broad sense was categorized as; low\u0026thinsp;=\u0026thinsp;0\u0026ndash;30%, intermediate (medium)\u0026thinsp;=\u0026thinsp;30\u0026ndash;60% and high\u0026thinsp;=\u0026thinsp;above 60%, according to (Johnson et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGenetic Advance (GA) was calculated as; GA = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\delta\\:}2\\text{G}}{{\\delta\\:}2\\:\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{*}\\)\u003c/span\u003e\u003c/span\u003e K \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{*}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}\\text{p}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere;\u003c/p\u003e\u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e g\u0026thinsp;=\u0026thinsp;genotypic variance\u003c/p\u003e\u003cp\u003eδ\u003csup\u003e2\u003c/sup\u003e p\u0026thinsp;=\u0026thinsp;phenotypic variance\u003c/p\u003e\u003cp\u003eδp\u0026thinsp;=\u0026thinsp;phenotypic standard deviation\u003c/p\u003e\u003cp\u003e\u0026#119870; = selection differential at 5% selection intensity\u0026thinsp;=\u0026thinsp;2.06\u003c/p\u003e\u003cp\u003eGenetic Advance as percentage of Mean (GAM) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{G}\\text{A}}{\\text{X}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{*}\\)\u003c/span\u003e\u003c/span\u003e 100\u003c/p\u003e\u003cp\u003eGA\u0026thinsp;=\u0026thinsp;genetic advance\u003c/p\u003e\u003cp\u003eX\u0026thinsp;=\u0026thinsp;population mean\u003c/p\u003e\u003cp\u003eGAM can be classified as low\u0026thinsp;=\u0026thinsp;less than 10%, intermediate\u0026thinsp;=\u0026thinsp;10\u0026ndash;20%, and high\u0026thinsp;=\u0026thinsp;more than 20%, according to Johnson et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Mean pathological parameters\u003c/h2\u003e\u003cp\u003eThe results obtained through the scoring by visual observation and the ELISA test showed that the genotypes were classified into three groups: highly resistant (HR), moderately resistant (MR) and susceptible (S). Among the seventeen (17) genotypes, two (2) were classified as highly resistant aside the two (2) resistant controls (Gigante and Tog5674), three (3) as moderately resistant and ten (10) as susceptible including the two (2) susceptible controls (Bouake 189 and Jasmine 85) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean values for pathological indices for assessing RYMV inoculated rice genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRYMV severity\u003c/p\u003e\u003cp\u003e(1\u0026ndash;9)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElisa score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eElisa reaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDTSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRYMV incidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDiscolored\u003c/p\u003e\u003cp\u003eseed\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFL478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO 66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO 67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegon Rice 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRI-Amankwatia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSIR-MALIMALI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSIR-SAVANNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGigante\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJasmine 85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBouake 189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART35-49-D1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTog5674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA L 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRI-Enapa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDTSA\u0026thinsp;=\u0026thinsp;Days to Symptom Appearance, HR\u0026thinsp;=\u0026thinsp;highly resistant, R\u0026thinsp;=\u0026thinsp;resistant, MR\u0026thinsp;=\u0026thinsp;moderately resistant, S\u0026thinsp;=\u0026thinsp;susceptible, HS\u0026thinsp;=\u0026thinsp;highly susceptible, NS\u0026thinsp;=\u0026thinsp;no symptom, Elisa score (+ \u0026ge; 0.392, buffer\u0026thinsp;=\u0026thinsp;0.076, H\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;=\u0026thinsp;0.080), (-)\u0026thinsp;=\u0026thinsp;negative reaction; (+)\u0026thinsp;=\u0026thinsp;weak reaction; (++)\u0026thinsp;=\u0026thinsp;weak, but clear reaction; (+++)\u0026thinsp;=\u0026thinsp;strong reaction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Mean squares from analysis of variance (ANOVA)\u003c/h2\u003e\u003cp\u003eThe performed ANOVA for both uninoculated and inoculated genotypes showed the effect of the disease on the various traits (Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ANOVA showed significant differences among the inoculated and uninoculated genotypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for all the traits except grain width (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) within the inoculated group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean square from analysis of variance of agronomic parameters of uninoculated plants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSource of variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReplicate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e324.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e108.31*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e97.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e15.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e14.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e162.18***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e190.34***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e603.03***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.76***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.22***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.08***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1073.75***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1078.13***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3266.70***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e893.62***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e904.29***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResiduals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e24.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e24.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e428.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e133.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e23.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 0 not Significant\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean square from analysis of variance of agronomic parameters of inoculated plants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSource of variance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReplicate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e166.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e162.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e46.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e439.09***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e85.11*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e27.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e21.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e61.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1042.80***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1459.21***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e279.54***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73.89**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e27.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e389.79***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e618.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1076.90***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e480.40**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e2681.32***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResiduals\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e144.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e34.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e19.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e103.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e176.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e77.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 0 not Significant\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Mean agronomic values of rice lines.\u003c/h2\u003e\u003cp\u003eTiller number was counted in the uninoculated and inoculated genotypes to find the effect of RYMV on tillering. The average number of tillers in the uninoculated genotypes was 31 whilst the average tiller number in the inoculated lines was 22 (Appendix 1 and Appendix 2). This gave a percentage reduction ranging from 0.4% to 55.2% with an average value of 27.2% per genotype. The highest percentage reduction in tiller number due to RYMV occurred in CSIR-MALIMALI (55.2%). In contrast, the lowest effect on tiller numbers was observed in Tog5674 (0.4%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo determine the effect of RYMV disease on the selected traits, the number of panicles in the uninoculated and inoculated genotypes were counted. The average number of panicles in the uninoculated genotypes was 28 while that of the inoculated was 20 (Appendix 1 and Appendix 2). The percentage reduction of panicle number among the genotypes ranged from 3.5% to 76.9% with a mean value of 29.6%. The highest effect of the RYMV disease on the panicle number occurred in CSIR-MALIMALI (76.9%). In contrast, the lowest effect on panicle numbers was observed in FARO 67 (3.5%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBoth the inoculated and uninoculated genotypes were assessed to determine the effect of RYMV disease on plant height. The average plant height for the uninoculated genotypes was 116 cm, while the average plant height for inoculated genotypes was 99 cm (Appendix 1 and Appendix 2). The percentage reduction of plant height among the genotypes ranged from 0.9% to 35.0% with a mean value of 14.6%. The highest reduction in plant height was observed in NERICA L 27 (35.0%) and the lowest reduction was recorded in NERICA 4 (0.9%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePanicle length was measured for both the inoculated and uninoculated genotypes, to assess the effect of RYMV disease on the trait. The average panicle length for genotypes that were not inoculated was 27.1 cm, whereas the average panicle length for inoculated genotypes was 24.8cm (Appendix 1 and Appendix 2). The percentage reduction of panicle length among the genotypes ranged from 0.7% to 18.8% with a mean value of 9.3%. The disease highly affected the panicle length in Enapa (18.75%), whereas the weak reduction on panicle length was observed in NERICA 4 (0.75%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe length of the grains was measured in both the inoculated and uninoculated genotypes to determine how the disease affects this trait. The average grain length for genotypes that were not inoculated was 6.39 mm, whereas the average grain length for inoculated genotypes was 6.26 mm (Appendix 1 and Appendix 2). The percentage reduction in grain length, which varied from 0.4% to 11.6% had a mean value of 3.6%. The highest reduction of grain length due to RYMV disease was observed in CSIR-SAVANNA (11.6%). The lowest effect of the disease on grain length was observed in ART35-49-D1-1(0.4%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe effect of RYMV disease on grain width was determined. Genotypes that were not inoculated had an average grain width of 2.09 mm, while the average grain width for inoculated genotypes was 2.04 mm (Appendix 1 and Appendix 2). The percentage reduction in grain width ranged from 0.6% to 9.9% with an average reduction of 4.5%. The highest reduction of grain width due to RYMV disease was observed in CRI-Amankwatia (9.9%). The lowest effect of the disease on grain width was observed in Tog5674 (0.6%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith reference to days flowering, the genotypes that were not inoculated showed an average of 81 days to flowering, while the inoculated genotypes displayed an average of 92 days to flowering (Appendix 1 and Appendix 2). The percentage reduction on flowering days ranged from 0.9% to 42.1% with a mean value of 13.9%. The days to flowering were highly delayed in CSIR-SAVANNA (42.1%). Tog5674 (0.9%) had the least reduction in days to flowering due to RYMV and was the resistant check (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo assess how the disease affected days to maturity, both the inoculated and uninoculated genotypes were examined. The genotypes that were not inoculated showed an average of 110 days to maturity, while the inoculated genotypes exhibited an average of 128 days to maturity (Appendix 1 and Appendix 2). The percentage reduction in days to maturity ranged from 0% to 44.6% with a mean value of 16.3%. The days to maturity were highly delayed in CSIR-SAVANNA (44.6%). The resistant check Gigante showed the least reduction in days to maturity (0%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe fresh biomass was weighed in both treatments, with and without inoculation, to determine the effect of the disease on the trait. Genotypes that were not inoculated exhibited an average of 196.3 g of fresh biomass, whereas the inoculated genotypes showed an average of 142.7 g of fresh biomass (Appendix 1 and Appendix 2). The mean value of the percentage reduction on fresh biomass was 26.6%, with a range of 3.4% to 67.5%. The highest reduction in fresh biomass was observed in Bouake 189 (67.5%) while the lowest was observed in Tog5674 (3.4%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe dry biomass was weighed for the various genotypes, with and without inoculation, to identify the effect of RYMV disease on this character. Genotypes that were not inoculated exhibited an average of 73.4 g of dry biomass, whereas the inoculated genotypes showed an average of 62.6 g of dry biomass (Appendix 1 and Appendix 2). The mean value of the percentage reduction in dry biomass was 19.7%, with a range from 3.1% to 47.8%. The highest percentage reduction in dry biomass was observed in Legon Rice 1 (47.8%), while the lowest effect of the disease on dry biomass was observed in Tog5674 (3.1%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrain yield was assessed in the uninoculated and inoculated genotypes to find the effect of RYMV on the trait. The average of grain yield in the uninoculated genotypes was 53.17 g while the inoculated genotypes had an average yield of 34.09 g (Appendix 1 and Appendix 2). The percentage reduction of grain yields due to RYMV disease ranged from 4.6% to 84.9% with a mean value of 36.9%. The highest reduction in grain yield was observed in NERICA-L23 (84.9%) and the least reduction in yield due to RYMV was recorded in the resistant check Gigante (4.6%, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage reduction of RYMV disease on agronomic traits.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eFB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSeverity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFL478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e84.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e45.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO 66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e38.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e72.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO 67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegon Rice 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e47.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRI-Amankwatia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSIR-MALIMALI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e36.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e32.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e74.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSIR-SAVANNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e42.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e44.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e25.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e84.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGigante\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJasmine 85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e42.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e43.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e65.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBouake 189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e67.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e34.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART35-49-D1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTog5674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA L 27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnapa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e27.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e29.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e14.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e9.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e3.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e4.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e13.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e16.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e26.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e19.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e36.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e4.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e3.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e4.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e55.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e76.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e35.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e18.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e11.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e9.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e42.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e44.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e67.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e47.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e84.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e6.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSED\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e4.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e3.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e4.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e6.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTN\u0026thinsp;=\u0026thinsp;Tiller number, PN\u0026thinsp;=\u0026thinsp;Panicle number, PH\u0026thinsp;=\u0026thinsp;Plant height, PL\u0026thinsp;=\u0026thinsp;Panicle length, GL\u0026thinsp;=\u0026thinsp;Grain length, GW\u0026thinsp;=\u0026thinsp;Grain width, FL\u0026thinsp;=\u0026thinsp;Flowering days, MD\u0026thinsp;=\u0026thinsp;Maturity days, FB\u0026thinsp;=\u0026thinsp;Fresh biomass weight, DB\u0026thinsp;=\u0026thinsp;Dry biomass weight, GY\u0026thinsp;=\u0026thinsp;Grain yield/plant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Genetic estimates of agronomic traits of rice lines.\u003c/h2\u003e\u003cp\u003eHeritability and genetic advance were estimated for the characters present in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Number of tillers, number of panicles, plant height, days to flowering, days to maturity, fresh biomass and yield had high heritability in addition to high genetic advance as percentage of mean. However, panicle length, grain length and dry biomass showed a moderate heritability with high genetic advance as percentage of mean. Only grain width showed low heritability with low genetic advance as percentage of mean (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBroad sense heritability, genetic advance and genetic advance as percentage of mean of agronomic traits of rice genotypes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH2 (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenetic Advance (GA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e117.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e126.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e157.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Broad Sense Heritability, GA\u0026thinsp;=\u0026thinsp;Genetic Advance, GAM\u0026thinsp;=\u0026thinsp;Genetic Advance as Percentage of Mean, TN\u0026thinsp;=\u0026thinsp;Tiller number, PN\u0026thinsp;=\u0026thinsp;Panicle number, PH\u0026thinsp;=\u0026thinsp;Plant height, PL\u0026thinsp;=\u0026thinsp;Panicle length, GL\u0026thinsp;=\u0026thinsp;Grain length, GW\u0026thinsp;=\u0026thinsp;Grain width, FL\u0026thinsp;=\u0026thinsp;Flowering days, MD\u0026thinsp;=\u0026thinsp;Maturity days, FB\u0026thinsp;=\u0026thinsp;Fresh biomass weight, DB\u0026thinsp;=\u0026thinsp;Dry biomass weight, GY\u0026thinsp;=\u0026thinsp;Grain yield/plant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Principal component analysis (PCA) among the traits.\u003c/h2\u003e\u003cp\u003ePCA was performed using yield and attributing components on rice genotypes. Out of twelve principal components (PCs), three exhibited more than 1 Eigen value and showed about 80.99% total variability (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). PC1 showed 57.69% total variation while, PC2 and PC3 accounted for 12.58% and 10.71% variability, respectively. The traits contributed differently to the variability, some contributed more than the others. The first principal component (PC1) was more influenced by important traits such as GY, TN, Severity and MD. The second principal component (PC2) was dominated by DB, GL, FB and PL. The traits that explained the greatest variation among the genotypes under the third principal component (PC3) were GW, GL, PL and MD. The highest sources of variation under PC1, PC2 and PC3 were GY, DB and GW respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrincipal component analysis of RYMV severity on agronomic traits of rice.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePC 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePC 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePC 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigen value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProportion of Variance (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.71%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative proportion (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.99%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePC\u0026thinsp;=\u0026thinsp;principal component, DB\u0026thinsp;=\u0026thinsp;dry biomass weight, FL\u0026thinsp;=\u0026thinsp;Flowering days, FB\u0026thinsp;=\u0026thinsp;fresh biomass weight, GL\u0026thinsp;=\u0026thinsp;Grain length, GY\u0026thinsp;=\u0026thinsp;Grain yield/plant, GW\u0026thinsp;=\u0026thinsp;Grain width, MD\u0026thinsp;=\u0026thinsp;Maturity days, PH\u0026thinsp;=\u0026thinsp;Plant height, PL\u0026thinsp;=\u0026thinsp;Panicle length, PN\u0026thinsp;=\u0026thinsp;Panicle number, TN\u0026thinsp;=\u0026thinsp;Tiller number.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Biplot for the evaluated genotypes\u003c/h2\u003e\u003cp\u003eOne of the most informative graphical representations of a multivariate dataset is biplot. Biplots are graphical representations of the PCA. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows two components, classifying the sources of variation into groups. Component 1 and Component 2 are the first two principal components, which explain the majority (57.69% and 12.58%) of the variance in the data. Traits like GY, TN, disease severity and MD were the major contributors to the variability for Comp. 1 while DB, GL, FB and PL are close to the y-axis. The varieties numbered 1 to 17 under conditions of RYMV infection and non-infection are plotted on the biplot. GY, TN, disease severity and maturity days had vectors pointing in the same direction with the susceptible genotypes like genotype 2 (NERICA-L23), genotype 5 (IR29) ,10 (CSIR-SAVANNA), 16 (NERICA L 27), 9 (CSIR-MALIMALI), 13 (Bouake 189), 7 (Legon Rice 1) and 12 (Jasmine 85). Conversely, genotypes in the opposite direction to the vectors may be more resistant to the RYMV disease i.e genotype 1 (FL478), 3 (NERICA 4), 4 (IR29), 6 (FARO 67), 8 (CRI-Amankwatia), 11 (Gigante), 14 (ART35-49-D1-1), 15 (Tog5674) and 17 (CRI-Enapa).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Grouping of genotypes into various clusters.\u003c/h2\u003e\u003cp\u003eThe hierarchical clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was done in reference to the measured parameters, and it aligned with the PCA-biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It categorized the lines into two main clusters in reference to the measured traits and the plants\u0026rsquo; defense response. Cluster 1 had eight (8) genotypes including two known susceptible genotypes (Jasmine 85 and Bouake 189) and Cluster 2 had nine (9) genotypes, which includes the two resistant checks (Tog5674 and Gigante). The susceptible group (Cluster 1) which contained the 8 genotypes were represented by (CSIR-MALIMALI, NERICA-L23, CSIR-SAVANNA, Legon Rice 1, Jasmine 85, Bouake 189, FARO 66, and NERICA L 27) while the resistant group (Cluster 2) which contained the best 9 genotypes were represented by Tog5674, FARO 67, IR29, CRI-Enapa, NERICA 4, FL478, Gigante, CRI-Amankwatia and ART35-49-D1-1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe research presents fresh perspectives into the opportunities and challenges of sub-Saharan Africa's (SSA) breeding for RYMV-resistant rice varieties, indicating the urgent need for genetic solutions to curb the devastating effects of RYMV. In addition to lending support to existing evidence on the limited recovery of RYMV-resistant genotypes (Sereme et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Longue et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this research introduces new pathways of resistance and brings in important genetic diversity to the breeding gene pool.\u003c/p\u003e\u003cp\u003eThe benefit of this study is the application of DAS-ELISA with visual assessments to ensure proper disease impact assessment to provide a full understanding of how virus-host interaction operates. Employing this two-pronged approach guarantees that resistant and asymptomatic lines are distinctly characterized, enabling one to isolate the causative genetic factors for further research. This result confirms the findings of Asante et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who found no asymptomatic plants, and that the ELISA test result had a strong correlation with the visual scoring result (r\u0026thinsp;=\u0026thinsp;0.99).\u003c/p\u003e\u003cp\u003eAsante et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found two resistant varieties, 8261112 and 8261119 upon studying the incidence of rice lines subjected to RYMV infection in Ghana. Results from our work additionally suggests NERICA 4 and ART35-49-D1-1 are RYMV resistant lines since they exhibited no symptoms of RYMV just as the controls (Gigante and Tog7291). It is worth noting that this is the first time ART35-49-D1-1, a breeding line from AfricaRice has been evaluated in Ghana and it will be interesting to see its performance in the field. In the case of NERICA 4, the presence of \u003cem\u003eO. glaberrima\u003c/em\u003e in its background suggests that its resistance could be due to the presence of \u003cem\u003eRYMV2\u003c/em\u003e or \u003cem\u003eRYMV3\u003c/em\u003e. Further research needs to be conducted to ascertain the type of RYMV resistance found in this line. Furthermore, the degree of susceptibility or resistance of each line to RYMV determined how long the symptoms of RYMV took to develop. The onset of symptoms was accelerated in susceptible genotypes and delayed in resistant genotypes. This supports the work of Bakker (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) who reported that typical symptoms of the disease appeared after 8\u0026ndash;20 days post inoculation. Furthermore, the seed discoloration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as observed due to RYMV disease speaks of the negative effect of the disease on seed quality. The finding supports the work of Soko et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) who reported seed discoloration as one of the symptoms of RYMV infection in addition to stunted growth, reduction in tillering, and panicle exertion.\u003c/p\u003e\u003cp\u003e The effect of the disease on plant growth and development was also found to be linked to the severity in some varieties, while in others the effect of the disease varied according to the agronomic parameter considered. In this study, FL478 and IR29 recorded high severity but recovered and did not experience much impact of RYMV on yield. These observations show that the severity of symptoms on leaves is not always a determining criterion in the evaluation of varieties as resistant or susceptible, as found by Issaka et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, it remains a valuable tool for diagnosing the disease. According to N\u0026rsquo;Guessan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the severity of leaf symptoms should be associated with production losses and abnormal vegetative development of plants, for pathogenic characterization of isolates and for assessing the susceptibility of varieties to RYMV.\u003c/p\u003e\u003cp\u003eFindings from this work showed significant difference between agronomic traits among the various genotypes. This is in line with the work of Traore et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Longue et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) who both recorded significant differences among accessions used especially for days to symptom appearance. Moreover, the significant difference of measured agronomic parameters among the genotypes studied suggests some traits like tiller number that contribute to high yield as vital for good gain and for selecting genotypes as potential parents for breeding programs. Furthermore, the work establishes the sharp yield loss and adverse effects on agronomic traits such as tiller number and biomass and panicle number, grain yield, thus highlighting the economic importance of RYMV infections. The findings of the research regarding differential impact of RYMV on tiller number, panicle number, plant height, panicle length, grain length, grain width, flowering days, maturity days, fresh biomass weigh, dry biomass weigh and grain yield are indicative of breeding complexity in terms of resistance as well as that reaction of the aforementioned traits to infection by the virus is a major selection criterion for genotypes. The range of yield losses reported in this study is in line with the work of Onwughalu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) who reported 94.4% as the highest yield losses due to RYMV. The low yield reduction recorded by line/genotype ART35-49-D1-1 (5.2%) which is comparable to that of the two resistant checks implies that ART35-49-D1-1 possesses high resistance to RYMV and would not experience low yield if grown in RYMV prone field. A Work by Salaudeen (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), indicated a low yield loss of 7.1% found in Gigante but the highest one came from FARO 29 (28.4%).\u003c/p\u003e\u003cp\u003eThe genetic analysis revealed high heritability and genetic advance as percentage of mean for key traits like tiller number, panicle number, plant height, flowering days, maturity days, fresh biomass and grain yield indicating the potential for effective selection in breeding programs. This result agrees with earlier genetic studies (Lingaiah \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Abebe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) but offers new information regarding the role of additive gene action for RYMV resistance. In contrast, low heritability in grain width emphasizes the role of environmental effect, suggesting that certain traits would require alternative breeding strategies.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) and genotype-trait biplot use provide a new selection framework for key traits responsible for variation among rice genotypes. The results of the present study involving grain yield, tiller number, disease severity and maturity days as primary factors responsible for genetic variation provide new directions for future breeding programs. In addition, the cluster analysis confirms the genetic similarity of genotypes, which also confirms the application of hierarchical clustering in the classification of rice varieties based on resistance and agronomic performance. This finding is consistent with the research of Amadu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who identified 5 highly resistant and 10 resistant genotypes from the hierarchical cluster analysis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTwo useful rice lines with possible resistance to RYMV have been identified in this study. In this research, the serious impact of RYMV on vital traits such as tiller number, fresh biomass, panicle number, and grain yield is emphasized, buttressing the importance of the development of resistant cultivars to minimize loss in yield. By integrating severity scoring with DAS-ELISA, a strong selection basis has been established related to observed trait decline in susceptible genotypes.\u003c/p\u003e\u003cp\u003eThe research laid the foundation of genetic diversity of the genotypes, with resistance in one variety (NERICA 4) and one line from AfricaRice and (ART35-49-D1-1) and moderate resistance in other varieties (FARO 67, CRI-Amankwatia and CRI-Enapa). The research also sheds light on breeding effort on the traits like grain yield, number of tillers, disease score and days to maturity, indicated by the principal component analysis (PCA). Furthermore, the favorable estimates of genetic advance and heritability suggest that selection for increased resistance and performance is promising.\u003c/p\u003e\u003cp\u003eLastly, the study provides a basis for breeding and employing the identified RYMV-resistant rice varieties to help develop more resistant and productive rice varieties for the affected regions. The identified resistant and moderately resistant lines are valuable resources for future breeding programs to enhance food security as well as agricultural sustainability. NERICA 4 and ART35-49-D1-1 are recommended to be the first crop improvement program in the future to concentrate on selecting genes or alleles conferring resistance to RYMV. In addition, the newly identified genotypes of this study also need to be reevaluated with other breeding lines under lowland or upland. In this subsequent screening, the criteria used in assessing the severity of RYMV disease should be reexamined and upgraded to make accurate and uniform determinations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the West Africa Centre for Crop Improvement (WACCI), University of Ghana.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMOW and JH conceptualized and supervised the work; LIB, SA and MOW performed experiment; LIB collected data and wrote the first draft of the paper; BA analysed the data; KAO and LIB interpreted analyzed data, and the paper was reviewed by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully acknowledge Shadrach Asiedu Coffie, Benjamin Owusu Ottu and Peter Basaking for their assistance in carrying out the laboratory experiments\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbebe T, Alamerew S, Tulu L (2017) Genetic Variability, Heritability and Genetic Advance for Yield and its Related Traits in Rainfed Lowland Rice (Oryza sativa L.) 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AJEA 9:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/AJEA/2015/19897\u003c/span\u003e\u003cspan address=\"10.9734/AJEA/2015/19897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rice yellow mottle virus (RYMV), disease severity and incidence, heritability, disease resistance, genetic variability","lastPublishedDoi":"10.21203/rs.3.rs-7811299/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7811299/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne serious disease that affects rice production in Africa is Rice yellow mottle virus (RYMV). Finding novel sources of RYMV resistance for breeding purposes and evaluating the impact of RYMV disease on selected yield-related characteristics of rice in Ghana were the goals of this study. Two highly tolerant and two susceptible checks were among the seventeen (17) rice genotypes used in this study. Completely randomized design with four replications was used in the experiment, with uninoculated genotypes serving as the control (Rep 4). Compared to the other attributes, RYMV considerably reduced the grain yield, panicle number, fresh biomass, tiller number, and dry biomass. Tiller number, panicle number, plant height, flowering days, maturity days, fresh biomass, and grain yield showed high heritability in addition to high genetic advance. Grain yield, dry biomass, and grain width were the three primary areas of variation in the germplasm of the first three principal components. According to the biplot created from the data, the disease had the least effect on genotypes FL478, NERICA 4, IR29, FARO 67, CRI-Amankwatia, ART35-49-D1-1, and CRI-Enapa, along with Gigante and Tog5674 (resistant checks). The hierarchical cluster analysis revealed two primary groupings. Three moderately resistant genotypes and two highly resistant genotypes were found from the study. The recently discovered tolerant genotypes can be employed in RYMV disease resistance breeding in the Africa.\u003c/p\u003e","manuscriptTitle":"Screening and Identification of Rice Yellow Mottle Virus -tolerant Genotypes for Enhanced Breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 12:47:19","doi":"10.21203/rs.3.rs-7811299/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cd36de3-a356-4c07-ba6c-ad0e3f133458","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:13:00+00:00","versionOfRecord":{"articleIdentity":"rs-7811299","link":"https://doi.org/10.1007/s12892-026-00350-6","journal":{"identity":"journal-of-crop-science-and-biotechnology","isVorOnly":false,"title":"Journal of Crop Science and Biotechnology"},"publishedOn":"2026-04-13 15:59:14","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-10-27 12:47:19","video":"","vorDoi":"10.1007/s12892-026-00350-6","vorDoiUrl":"https://doi.org/10.1007/s12892-026-00350-6","workflowStages":[]},"version":"v1","identity":"rs-7811299","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7811299","identity":"rs-7811299","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}