Combining Blast Resistance and High Yield in F1 Aromatic Rice Through Classical Hybrid Breeding Approaches

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Mamunur Rashid, Md. Mominur Rahman, Md. Mamunur Rashid, Md. Arifuzzaman, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8276066/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 22 You are reading this latest preprint version Abstract Rice blast caused by the outbreak rapidly in Bangladesh brings about an imminent threat to the production of rice causing an average 40% yield loss in boro rice with infection rates ranging from 20 to 80%. Fewer high-yielding aromatic rice are available due to limiting genetic potential for high yields. To address these problems a study was conducted from July 2021 to December 2022 at the Jute Research Institute, Nashipur, Dinajpur, to develop high-yielding, blast-resistant F aromatic rice employing conventional breeding methods. The site at 24.000° N, 90.250° E, and 34 meters above sea level, is part of the Old Himalayan Piedmont Plain in Agro-Ecological Zone-1 (AEZ-1). The study shows that the F generation often exhibits hybrid vigor, characterised by greater resilience and performance compared to parent lines. This is promising for agriculture, especially in developing high-yielding, blast-resistant rice varieties that support sustainable farming and food security. The F genotype AR08 exhibited the maximum 2-acetyl-1-pyrroline (2AP) content, while the Munni genotype showed the minimum. Nine promising F aromatic rice lines with moderate to high blast resistance were developed through targeted parental crosses. Among them, AR03, AR04, and AR09 showed the best resistance to blast and yield potential, while other F genotypes like AR02, AR06, AR08, AR10, and AR11 outperformed their parents in yield traits with moderate blast resistance. AR01, AR03, AR04, AR05 and AR09 are recommended for developing blast-resistant, high-yielding rice varieties, offering significant potential for sustainable farming and preserving the cultural heritage of aromatic rice. Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Plant sciences Classical breeding Rice blast Molecular markers Oryza sativa Rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Rice ( Oryza sativa L., 2n = 2x = 24) is the leading staple food in Asia, leading in both production and productivity. It is essential to the livelihoods of 160 million people in Bangladesh, which ranks as the third-largest producer and consumer of rice, with an annual output of 37 million metric tons [ 1 , 2 ]. As of 2023/2024, global rice production is reached 520.65 million metric tons, China leads global production with 144.62 million metric tons (28% of the total), followed by India with 137 million metric tons (26%). Bangladesh contributes approximately 7% of the world's rice production [ 3 , 4 ]. Rice production in Bangladesh is significantly affected by various diseases, including blast, sheath rot, sheath blight, bacterial blight, and brown spot. Both biotic and abiotic factors contribute to low yields, with diseases being the most common biotic stress [ 5 , 6 ]. These diseases cause 36 fungal, 21 bacterial, and several nematode infections. Currently, 31 rice diseases have been identified in Bangladesh, with ten considered major [ 7 , 8 ]. Among these, rice blast, caused by the fungus Pyricularia oryzae ( Magnaporthe oryzae ), is the most destructive, reducing grain yield and quality significantly [ 9 ]. Documented first in China in 1637 and later in Japan, Italy, the USA, and India, the rice blast causes a 30% yield loss in Bangladesh, particularly severe in the northern and central regions considered as the hotspot for blast disease, with up to 40% loss in Dinajpur [ 10 , 11 ]. According to a relevant research during the Boro season, the incidences of blast disease were as high as 58.2% in the high-yielding rice cultivar BRRI dhan47, 39.8% in BRRI dhan29, and 20.3% in BRRI dhan28, and 59.8% in BRRI dhan34 [ 12 , 13 ]. Currently, 267 races of rice blast have been identified, and the disease pressure is increasing, posing a significant threat globally. Rice blasts can affect all growth stages, from seedling to pre-maturity, with severe conditions causing 70–90% yield losses. Favorable conditions for epidemic development include high temperatures, high humidity, dew, drought stress, and excessive nitrogenous fertilization [ 14 ]. The disease manifests in forms such as leaf, node, neck, and panicle blast, each with specific symptoms causing severe damage. SSR markers in the selection process for blast resistance have revolutionized rice efforts to enhance. Their specificity, effectiveness, and simplicity in Marker-Assisted Selection (MAS) make it an essential tool in developing blast-resistant aromatic rice genotypes [ 15 – 17 ]. As molecular marker technology continues to progress, rice breeding is gradually more accurate and effective in treating the challenges resulting from rice blast disease [ 18 , 19 ]. Developing blast-resistant aromatic rice is critical for enhancing Bangladesh’s agricultural productivity and ensuring long-term food security [ 20 , 21 ]. Aromatic varieties such as BRRI dhan34 and Sadabahar are widely prized for their fragrance and superior grain quality [ 22 , 23 ], yet their susceptibility to blast results in significant yield losses in major growing regions. Improving disease resistance in these cultivars will promote production stability, support farmers in blast-affected areas, and enhance the economic value of aromatic rice [ 19 , 24 ]. Moreover, resilient aromatic varieties hold strong potential for boosting export opportunities and strengthening Bangladesh’s position in the global rice market. Therefore, integrating blast resistance into aromatic rice is essential for maintaining crop resilience, sustaining food supply, and promoting national economic growth. Despite notable advancements in blast epidemiology and the development of resistant rice varieties, a substantial research gap persists in the improvement of aromatic rice in Bangladesh. Most previous study has focused on non-aromatic cultivars with very little effort to combine blast resistance with high yield potential in this premium rice group. Although molecular markers such as SSRs have aided in more accurate resistance breeding, their integration with traditional hybridization methods of developing new aromatic genotypes adapted to blast-prone environments remains limited. Consequently, comprehensive evaluations of parental genotypes and their F 1 hybrids for blast resistance, yield performance, and agronomic traits under local field conditions are still lacking. Addressing this gap, the present study develops and assesses F 1 aromatic rice hybrids that combine blast resistance with improved yield through classical hybrid breeding, providing valuable insights for breeding resilient, high-yielding aromatic cultivars suited to the evolving challenges of blast disease in Bangladesh. 2. Materials and Methods 2.1 Experimental location and duration The study was carried out in the BJRI Research Field in Nashipur, Dinajpur, and consisted of two phases: a preparatory phase and an experimental phase. The preparatory phase (July 2021–December 2022) involved the development of F 1 hybrid seeds. The experimental phase (July 2022-December 2022) included a field evaluation of these F 1 hybrids to determine agronomic traits, yield performance, and blast resistance. The location is in the Old Himalayan Piedmont Plain (AEZ-1) at 24.000° N latitude and 90.250° E longitude, 34 m above sea level. 2.2 Soil conditions Soil samples were collected from a depth of 0 to 15 cm at the experimental field. After being dried, ground, and passed through a 20-mesh sieve, the samples were analyzed at the Soil Resources Development Institute (SRDI), Nashipur, Dinajpur. The experimental pot soil was sandy loam, with 65% sand, 30% silt, 5% clay, had an acidic pH of 4.70 and low water retention capacity. Initial soil analysis showed 0.514% organic carbon, 0.04% total nitrogen, 55.85 ppm available phosphorus, 0.26 meq/100 g soil exchangeable potassium, and 9.93 ppm available sulfur (Table 1 ). Table 1 Physicochemical characteristics of the surface soil (0–15 cm depth) at the experimental site Characteristics Value (%) Critical level Interpretation Extraction methods Sand 65 - - - Silt 30 - - - Clay 5 - - - Soil type - - - Sandy loam pH (Soil: water = 1: 25) 4.70 - L Glass-electrode pH meter [ 25 ]. Organic carbon 0.514 - L Wet oxidation method [ 26 ]. Organic matter 0.89 - L Calculated by Van Bemmelen factor [ 27 ]. Total N 0.04 0.10 L Micro-Kjeldahl method [ 28 ]. Available P (ppm) 55.85 0.12 M Molybdate blue ascorbic acid method [ 29 ]. Exchangeable K (meq) 0.26 0.12 L Determined by a Flame photometer Available S (ppm) 9.93 10.00 L Turbidity method using BaCl 2 [ 30 ]. Available B (ppm) 0.25 0.20 L Mono-calcium bi-phosphate method, determined by Spectro-photometer following Azomethine H method [ 25 ]. Available Zn (ppm) 1.43 0.60 M AAS [ 31 ]. Note: Critical nutrient levels were adapted from the Bangladesh Agricultural Research Council (BARC) Fertilizer Recommendation Guide 2018, which provides scientifically established threshold values for assessing soil nutrient sufficiency in Bangladesh. These values were used to classify soil nutrient status as low (L), medium (M), or high (H) [ 32 ]. 2.3 Weather conditions of the experimental site The area experiences a subtropical climate with heavy rainfall from May to September and lighter precipitation from October to April. Throughout the experimental period, temperature, rainfall, and relative humidity (RH) data were collected from the BWMRI Meteorological Station in Dinajpur. The weather data of the experimental site is presented in Fig. 1 . 2.4 Experimental treatments and design For the development of F 1 genotypes, seven aromatic rice genotypes were collected from different locations during July and December 2021: Kalovat Sugondhi, BRRI dhan34, Ranashail, Munni, Hazardana, Sadabadsha, and Multioverian (Table 2 ). Different levels of pathogen susceptibility and resistance have been detected by these genotypes. To determine parental lines with desired agronomic attributes and specific resistance to blast disease, the germplasm, which includes the entire genetic makeup of the crop and its associated species, was examined. Genotypes with positive attributes, such as high yield and blast tolerance, were used to select seeds. Table 2 Name and source of experimental parent genotypes Sl. No. Name Source Blast reaction 1 Kalovat Sugondhi Tanor, Rajshahi, Bangladesh R 2 BRRI Dhan34 Sadar, Dinajpur, Bangladesh S 3 Ranashail Tanor, Rajshahi, Bangladesh R 4 Multiovarian Syedpur, Nilphamari, Bangladesh R 5 Shadabadsha Tanor, Rajshahi, Bangladesh S 6 Munni Tanor, Rajshahi, Bangladesh R 7 Hazardana Tanor, Rajshahi, Bangladesh R Here, R = Resistance, S = Susceptible The preparatory phase of the research was to develop F 1 hybrids of aromatic rice that are resistant to blast. Emasculation, or the removal of anthers from bisexual flowers prior to pollination, was the first step in the controlled hybridization process. One day before the anthesis, emasculation was carried out between 4 and 6 p.m. utilizing techniques such as vacuum suction. To avoid accidental pollination, panicles containing treated spikelets were immediately covered with paper bags after emasculation. On the following morning, pollen from designated male parents was transferred to emasculated flowers in order to perform cross-pollination. To keep everything under control, the treated flowers were re-bagged after pollination. The resulting hybrid seeds were ready for harvesting after maturing in 28–34 days. Eleven F 1 hybrids were created as a result of cross-polination between all progenitor genotypes (Table 3 ). F 1 rice seeds were carefully ditched from the spikes using a needle and then stored in a regulated dry atmosphere with relative humidity values below 25% for preservation [ 32 ]. Table 3 Summary of cross-pollination events among selected aromatic rice parental genotypes and the corresponding F₁ hybrids developed SL No. Female parents Male parents F 1 hybrids 1 Kalovat sugondhi Ranashail AR01 2 Kalovat sugondhi BRRI dhan34 AR02 3 BRRI dhan34 Munni AR03 4 Ranashail Hazardana AR04 5 Ranashail BRRI dhan34 AR05 6 Multioverian Ranashail AR06 7 Sadabadsha BRRI dhan34 AR07 8 Ranashail Sadabadsha AR08 9 Sadabadsha Multioverian AR09 10 Multioverian Sadabadsha AR10 11 Multioverian BRRI dhan34 AR11 The 2nd phase of the research was from June to December 2022 and featured 18 genotypes in total, including 7 parental lines and 11 F 1 hybrids were selected for field trial, selection of suitable genotypes, and to make an overall comparison. Field trials were carried out to assess the genotypic performance of both parental lines and the associated F 1 hybrids. For direct comparison, the parental genotypes were planted in plots adjacent to the central plots containing the F 1 hybrids. To avoid accidental cross-pollination, the F 1 hybrid plots were covered with nets. The randomized complete block design (RCBD) was used, with three replications of each genotype. Each plot had three raised planting rows, ensuring a thorough evaluation across replicates. 2.5 Experimentation In late June 2022, the experimental plot was prepared by ploughing and laddering before transplanting aromatic rice seedlings aged 28–30 days. The parental seeds were pre-treated by soaking in water and sprouting, then placed on the seedbed while, the F 1 was allowed to grow in a petri dish under close supervision (Fig. 2 ). Fertilization followed the Bangladesh Agricultural Research Council's (BARC) recommendations, with cow dung, triple superphosphate (TSP), muriate of potash (MOP), gypsum, and zinc sulfate applied during field preparation. Urea was used in three divided doses at 15, 30, and 50 days following transplantation. Gap filling, weeding, watering, pest management, and harvesting were all standard intercultural operations during the cropping season. Gaps were filled rapidly after transplanting with extra seedlings, and weeds were manually controlled, supplemented by urea top-dressing. Due to low rainfall, timely irrigation was essential for optimal crop growth. Pest management practices were undertaken to reduce infestations. For artificial pathogen inoculum, Magnaporthe oryzae isolates were initially isolated from naturally infected plant parts and purified on potato dextrose agar (PDA) at 25°C for 7 days to promote sporulation. Conidial spores were extracted by pouring each PDA plate with sterile distilled water containing 0.02% Tween 20 and adjusting the suspension to 1 × 10 5 conidia mL⁻¹. During the rice tillering stage, plants were uniformly sprayed with this spore suspension till runoff. Harvesting targeted mature seeds for future study and yield assessment. Meteorological data for the research period were collected from the Bangladesh Jute Research Institute (BJRI) at Nashipur, Dinajpur, Bangladesh. 2.6 Collection of data 2.6.1 Morphology, phenology, growth, yield characteristics and yield Plant height was measured with a measuring scale from the plant's base to the tip of the tallest leaf and recorded in centimetres (cm). Flag leaf lengths were also measured in centimetres (cm) by a measuring scale. Effective tillers were those having at least one-grain panicle. The total number of panicle-bearing tillers on each sample hill was counted, and an average was determined. Five randomly selected hills plot − 1 , each with three replications, were counted for fully grown tillers. By measuring the days from transplanting till 50% of the spikes became apparent as determined visually, the number of days to 50% flowering was calculated. Ten randomly selected panicles were utilized to calculate averages of the panicle length, which was measured from the rachis's basal node to the panicle apex. The total grains panicle − 1 includes all completely mature grains, fertilized and unfertilized. These were calculated from five plants selected at random from each plot. Filled grains (fertilized and completely grown) and unfilled grains (sterile or partially formed) were counted individually in the panicles of five randomly chosen plants plot − 1 . A thousand dry, clean grains were randomly selected from the harvested seed harvest and weighed with a digital scale. Grain yield hill − 1 was calculated by sun-drying and weighing the grains. After the plant stover had completely dried, the weigh hill − 1 was measured. The harvest index (HI) was determined as the ratio of grain yield to total shoot dry matter, which served as an indicator of reproductive efficiency. The harvest index was measured by following the formula: 2.6.2 Synthesis of 2-Acetyl-1-pyrroline 2-Acetyl-1-pyrroline was synthesized with 1.7% KOH and a modified Zaldal Aroma Test Protocol , which is an efficient and fast method to recognize aromas in rice. It was found particularly for 2-acetyl-1-pyrroline (2AP), which was responsible for the aroma of fragrant rice cultivars. In this method, rice grains were soaked in a 1.7% potassium hydroxide (KOH) solution to aid in the release of volatile aromatic compounds. The breakdown of the cell walls by the KOH facilitated the volatilization of 2AP. After a few minutes, the sample was smelled to determine the aroma. To gauge the strength of the fragrance ranging from low to high, often on a scale of 1 to 10, compare its intensity to that of non-aromatic rice [ 34 , 35 ]. 2.6.3 Leaf Blast Severity Assessment The sum of the length and width of the largest spot was recorded, and the total length of the spot was estimated. The data on the size of the blast spot (mm) were taken at 60 days after planting (DAT). Leaf blast severity indicated the percentage of leaf area affected by blast disease. The data on leaf blast severity were taken at 60 DAT. The development of leaf blotch was observed until maturation. IRRI conducted an assessment of the plants located in the center of each plot; however, the plants at the ends of the row were excluded from the scoring process (Table 4 ). The blast symptoms were graded using the IRRI-established visual scale, which ranges from 0 to 9. A score of 0 indicates that the plant is symptom-free, while a score of 9 suggests that the plant has been entirely damaged and is likely to be stunted or dead. The preceding is contingent upon the severity of the attack. Table 4 The IRRI-established visual scale, ranging from 0 to 9, was employed to grade blast symptoms Scale Description 0 No lesions observed 1 Small brown specks of pin-point size or larger brown specks without sporulating center 2 Small roundish to slightly elongated, necrotic gray spots, about 1–2 mm in diameter, with a distinct brown margin 3 Lesion type is the same as in scale 2, but a significant number of lesions are on the upper leaves 4 Typical susceptible blast lesions 3 mm or longer, infecting less than 4% of the leaf area 5 Typical blast lesions infecting 4–10% of the leaf area 6 Typical blast lesions infection 11–25% of the leaf area 7 Typical blast lesions infection 26–50% of the leaf area 8 Typical blast lesions infection 51–75% of the leaf area and many leaves are dead 9 More than 75% leaf area affected To determine the percent (leaf) blast severity to observe the phenotypic expression of R-gene, the following formula was used: $$\:\text{L}\text{e}\text{a}\text{f}\:\text{b}\text{l}\text{a}\text{s}\text{t}\:\text{s}\text{e}\text{v}\text{e}\text{r}\text{i}\text{t}\text{y}\:\left(\%\right)=\:\frac{\text{S}\text{u}\text{m}\:\text{o}\text{f}\:\text{t}\text{h}\text{e}\:\text{r}\text{a}\text{t}\text{i}\text{n}\text{g}\text{s}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\text{s}\:\text{o}\text{f}\:\text{o}\text{b}\text{s}\text{e}\text{r}\text{v}\text{a}\text{t}\text{i}\text{o}\text{n}\:\times\:\text{M}\text{a}\text{x}\text{i}\text{m}\text{u}\text{m}\:\text{r}\text{a}\text{t}\text{i}\text{n}\text{g}\text{s}}\times\:100\dots\:\dots\:\dots\:\dots\:\dots\:..\left(2\right)$$ 2.6.4 Molecular Analysis Simple sequence repeats (SSR) marker have been widely used in molecular genetics research, including the detection and characterization of blast resistance genes in rice. To identify blast resistance genes using SSR markers across seven parental genotypes and eleven F 1 populations. Genomic DNA extraction was performed on both parental genotypes and F 1 progeny to facilitate marker analysis. Here, in total seven (7) SSR markers were used in 19 (AR08 genotype was observed employing 2 different samples) aromatic rice genotypes (Table 5 ). Table 5 Molecular markers and their associated blast resistance (R) genes employed in the genetic assessment of rice genotypes. Sl. No. Marker Type of the marker R Gene Annealing temperature ( \(\:\varvec{℃}\) ) Expected PCR product size (bp) Sequence* 1 RM247 SSR Pi-ta 60 131 F: TAGTGGGGATCGATGTAACG R: CATATGGTTTGACAAAGCG 2 RM206 SSR Pi-kh 58 147 F: CGTTCCATCGATCCGTATGG R: CCCATGCGTTTAACTATTCT 3 RM72 SSR Pi-33 56.95 166 F: CCGGCGATAAAACAATGAG R: GCATCGGTCCTAACTAAGGG 4 RM208 SSR Pi-b 57.85 173 F: TCTGCAAGCCTTGTCTGATG R: TAAGTCGATCATTGTGTGGACC 5 RM541 SSR Pi-9 55 158 F: TATAACCGACCTCAGTGCCC R: CCTTACTCCCATGCCATGAG 6 RM224 SSR Pi-1 56.4 157 F: ATCGATCGATCTTCACGAGG R: TGCTATAAAAGGCATTCGGG 7 RM527 SSR Piz-5 55 233 F: GGCTCGATCTAGAAAATCCG R: TTGCACAGGTTGCGATAGAG Note: * F = Forwarded primer, R = Reverse primer. 2.7 Statistical analysis The data were analyzed using the Statistix 10 software. PCA biplot and cluster were analyzed by SPSS and OriginPro software. Mean values are separated by Excel to gauge the significance of individual genotypes. Illustration was done using BioRender. 3. Results 3.1 Morphological evaluation 3.1.1 Plant height Significant differences in morphological traits were observed among 18 rice genotypes. The genotypes exhibited significant differences in plant height. At 30 DAT, the AR03 genotype exhibited the highest plant height (107.03 cm), while the Hazardana genotype had the lowest (81.86 cm). The results showed that the AR02 genotype had a higher plant height (155.87 cm) than the other seventeen genotypes at 60 DAT. The AR03 genotype achieved the maximum among the genotypes at 90 DAT, as indicated by the results (Table 6 ). Table 6 Plant height (cm) at 30, 60 and 90 DAT of eighteen rice genotypes Genotype 30 DAT 60 DAT 90 DAT Kalovat sugondhi 93.46 bc 146.80 a-e 181.28 ab BRRI dhan34 87.26 c-e 118.47 h-j 145.45 gh Ranashail 89.06 c-e 148.17 a-c 174.55 a-c Multioverian 89.95 b-e 145.21 a-e 171.48 b-d Sadabadsha 84.26 de 107.74 j 129.43 i Munni 98.96 ab 130.60 d-h 148.79 e-h Hazardana 81.86 e 131.79 b-h 139.54 hi AR01 92.03 b-d 138.83 a-g 146.67 f-h AR02 105.90 a 155.87 a 170.47 b-d AR03 107.03 a 154.20 a 188.40 a AR04 85.90 c-e 126.75 f-i 135.53 hi AR05 88.43 c-e 143.67 a-f 162.07 c-f AR06 93.73 bc 148.47 ab 182.07 ab AR07 91.30 b-d 110.15 ij 135.07 hi AR08 88.60 c-e 131.11 c-h 143.52 g-i AR09 92.50 b-d 147.79 a-d 163.07 c-e AR10 88.63 c-e 125.20 g-i 158.87 d-g AR11 87.60 c-e 129.98 e-h 167.81 b-d LSD 5% 9.13 17.3 15.53 CV (%) 3.26 4.17 3.21 3.1.2 Flag leaf length The flag leaf lengths of the 18 genotypes ranged from 36.67 to 62.58 cm, making them statistically diverse. The AR02 genotype had the largest flag leaf length (62.58 cm), whereas the AR08 genotype had the shortest (36.67 cm), which was statistically similar to the AR10 genotype's (38.25 cm) (Fig. 3 ). 3.2 Phenology (Days to 50% flowering) Significant variations were also observed in terms of the days to 50% flowering, ranging from 42.33 days to 98.33 days. The Munni genotype exhibited the earliest flowering, requiring only 42.33 days to reach 50% flowering. In contrast, the Kalovat Sugondhi genotype demonstrated the maximum days to flowering, taking 98.33 days to achieve the same stage. These differences highlight the diversity in flowering times across the genotypes, as illustrated in Fig. 4 . 3.3 Yield contributing characteristics and yield indices 3.3.1 Number of total tillers hill − 1 At maturity, there were substantial differences in the total number of tillers hill − 1 between the rice genotypes. The AR04 genotype had the highest number of total tillers, reaching 89.60 tillers hill − 1 , which was significantly greater than the other genotypes. In contrast, the Hazardana genotype displayed the lowest number of tillers, with only 14.78 tillers hill − 1 (Table 7 ). 3.3.2 Number of effective tillers hill − 1 The number of effective tillers hill − 1 exhibits the most variations among the different rice genotypes, with values ranging from 8.16 to 60.66 tillers hill − 1 . Notably, the AR04 genotype demonstrated the highest number of effective tillers, reaching 60.66, indicating its superior tillering capacity compared to the other genotypes. In contrast, the Hazardana genotype recorded the lowest number of effective tillers hill − 1 , at just 8.16 (Table 7 ). 3.3.3 Numbers total grains panicle − 1 With (604.0) grains produced, the AR07 genotype yielded the greatest amount, followed of the AR03 genotype with 516.33 grains panicle − 1 . Compared to other genotypes, the grain number of the AR07 and AR03 genotypes were significantly higher. On the other hand, at 191.00 grains, the Kalovat Sugondhi genotype had the lowest grain count (Table 7 ). 3.3.4 Number of filled grains panicle − 1 Significant variations were observed in the number of filled grains panicle − 1 across the rice genotypes. The AR07 genotype exhibited the highest number of filled grains panicle − 1 , with 458.67 grains, closely followed by the AR03 genotype, which had 433.00 grains panicle − 1 . On the other hand, Kalovat Sugondhi showed the lowest number of filled grains panicle − 1 , with 178.67 grains, followed by Ranashail (183.33) grains and Multioverian (184.33). These substantial differences underscore the diverse grain-filling capacities among the genotypes (Table 7 ). 3.3.5 Panicle length A notable level of variation was observed in panicle length among the rice genotypes. The AR05 genotype had the longest panicle length, measuring 32.86 cm, which was statistically similar to the AR02 genotype at 32.60 cm. In contrast, the Sadabadsha genotype exhibited the shortest panicle length at 23.46 cm, which was statistically comparable to the Kalovat Sugondhi genotype's measurement of 23.50 cm. These findings highlight the diversity in panicle length among the genotypes (Table 7 ). 3.3.7 1000-grain weight Significant variations were also observed in the 1000-grain weight among the genotypes, with values ranging from 9.85 g to 29.45 g. The AR06 genotype exhibited the highest 1000-grain weight at 29.45 g, while the Sadabadsha genotype had the lowest weight, measuring only 9.85 g. Other notable weights included Kalovat Sugondhi (28.63 g), Multioverian (27.50 g), and AR11 (26.95 g), all indicating substantial differences in grain weight across the genotypes (Table 7 ). Table 7 Response of genotypes on the total number of tillers hill − 1 , effective tillers hill − 1 , panicle length (cm), total grains panicle − 1 , filled grains panicle − 1 , 1000-grain weight (g). Genotype Total of tillers hill − 1 Effective tillers hill − 1 Panicle length (cm) Total grains panicle − 1 Filled grains panicle − 1 1000-grain weight (g) Kalovat sugondhi 16.94 hi 10.60 hi 23.50 h 191.00 n 178.67 l 28.63 b BRRI dhan34 20.81 g-i 13.28 gh 25.96 g 400.33 e 280.67 f 10.40 o Ranashail 25.86 g 17.39 f 26.00 g 207.67 m 183.33 kl 19.60 i Multioverian 24.48 gh 13.48 f-h 25.16 g 236.00 l 184.33 kl 27.50 c Sadabadsha 22.30 g-i 15.31 fg 23.46 h 298.33 i 193.67 jk 9.85 q Munni 17.53 hi 12.26 gh 29.46 c-e 214.00 m 202.00 ij 23.55 g Hazardana 14.78 i 8.16 i 31.50 ab 487.67 d 397.67 c 25.15 f AR01 55.73 c 41.12 b 30.73 bc 368.33 f 307.00 e 23.40 g AR02 58.64 bc 36.64 c 32.60 a 504.00 c 333.67 d 17.80 k AR03 43.60 ef 31.14 d 30.46 bc 516.33 b 433.00 b 15.25 m AR04 89.60 a 60.66 a 29.66 cd 282.33 j 261.67 h 20.15 h AR05 66.56 b 31.11 d 32.86 a 350.00 g 327.33 d 16.60 l AR06 47.60 de 23.06 e 27.90 f 337.00 h 267 gh 29.45 a AR07 55.41 cd 22.93 e 29.76 cd 604.00 a 458.67 a 10.75 n AR08 60.76 bc 38.63 bc 28.80 d-f 333.33 h 312.00 e 10.20 p AR09 45.94 e 32.40 d 28.10 ef 338.00 h 274.33 fg 18.80 j AR10 36.31 f 21.78 e 26.23 g 260.67 k 199.00 j 26.40 e AR11 47.63 de 30.53 d 24.83 gh 265.67 k 212.33 i 26.95 d LSD 5% 8.05 4.06 1.44 10.08 10.85 0.16 CV (%) 6.31 5.18 1.67 0.96 1.27 0.26 3.4 Sterility percent panicle − 1 Sadabadsha showed the highest percentage of sterility panicle − 1 (35.09%), which was statistically identical to the AR02 genotype (33.80%). The lowest percentage of sterility panicle − 1 was 5.61 percent in Munni, which was compared statistically to (6.40%), (6.48%), and (7.32%) in AR08, AR05, and AR04, respectively (Fig. 5 ) 3.5 Yield indices 3.5.1 Grain yield hill − 1 Regarding grain weight hill − 1 , a substantial difference was observed among the different rice genotypes evaluated. The AR04 genotype demonstrated the highest grain yield hill − 1 , producing an impressive 160.81 g, showcasing its superior performance in comparison to the other genotypes. This high yield indicates the strong potential of AR04 in maximizing grain production under the given conditions. On the other hand, the BRRI dhan34 genotype exhibited the lowest grain yield hill − 1 , with a yield of only 19.91 g. This marked difference highlights the substantial variability in the grain production capacity across the genotypes studied. The findings underscore the importance of selecting high-yielding genotypes such as AR04 for improving rice productivity, while lower-yielding varieties like BRRI dhan34 may require further optimization or specific cultivation strategies to enhance their performance (Table 8 ). 3.5.2 Stover yield hill − 1 Substantial differences were recorded in terms of stover yield hill − 1 across the various rice genotypes. The AR03 genotype stood out with the highest stover weight, producing 226.50 g hill − 1 , indicating its superior biomass accumulation. On the contrary, the Sadabadsha genotype exhibited the lowest stover yield hill − 1 , with a value of 41.49 g. This low value was statistically comparable to the stover yields of the Hazardana (44.56 g), BRRI dhan34 (45.54 g), and Munni (46.65 g) genotypes, all of which displayed relatively lower biomass production (Table 8 ). 3.5.3 Biological Yield hlii − 1 Regarding biological yield hill − 1 , variation was also observed significantly. The AR03 genotype produced the highest biological yield, with a remarkable (355.71 g) hill − 1 , reflecting its exceptional performance in overall biomass production. On the other hand, the Sadabadsha genotype exhibited the lowest biological yield hill − 1 , with a value of 64.23 g. This low yield was statistically similar to that of the BRRI dhan34 genotype, which produced 65.45 g hill − 1 (Table 8 ). 3.5.4 Harvest index Harvest index notable differences were observed among the rice genotypes. The highest harvest index was recorded in the AR04 genotype, reaching 56.33%, indicating its efficient conversion of biomass into grain yield. In contrast, the AR07 genotype exhibited the lowest harvest index at 23.43%, reflecting a lower efficiency in biomass conversion (Table 8 ). Table 8 Grain yield, stover yield, biological yield, and harvest index (%) of eighteen rice genotypes Genotypes Grain yield (g hill − 1 ) Stover weight (g hill − 1 ) Biological yield (g hlii − 1 ) Harvest index (%) Kalovat sugondhi 35.54 j 74.21 g 109.75 j 32.42 j BRRI dhan34 19.91 k 45.54 h 65.45 l 30.43 j Ranashail 41.04 ij 53.31 h 94.68 k 43.70 ef Multioverian 43.27 i 67.18 g 110.22 j 39.06 h Sadabadsha 22.28 k 41.80 h 64.23 l 34.93 i Munni 44.17 i 46.66 h 90.82 k 48.64 bc Hazardana 47.16 i 44.57 h 91.73 k 51.43 b AR01 140.93 b 187.81 c 331.41 b 43.36 e-g AR02 80.14 h 210.40 b 291.21 cd 27.75 k AR03 129.21 c 226.50 a 355.71 a 36.33 i AR04 160.81 a 130.56 e 298.70 c 56.33 a AR05 108.47 e 156.11 d 270.25 ef 42.24 fg AR06 100.50 f 130.69 e 231.19 g 43.48 e-g AR07 40.97 ij 135.12 e 176.42 i 23.43 l AR08 91.16 g 110.02 f 201.52 h 45.41 de AR09 117.82 d 130.54 e 259.03 f 49.61 bc AR10 90.21 g 128.88 e 220.09 g 41.45 g AR11 114.71 d 150.62 d 279.66 de 46.14 d LSD 5% 6.2 13.43 14.32 2.19 CV (%) 2.55 3.82 2.38 1.75 3.6 Quality trait-Aroma content (2-Acetyl-1-pyrroline) in grain The aromatic level of 2-Acetyl-1-pyrroline (AP) varied significantly among the rice genotypes. Considering an AP content of (9.77), the AR08 genotype had the highest level, confirming its excellent aromatic characteristics. The Ranashail and AR10 genotypes' respective AP levels of (0.83) and (0.85) were statistically similar to the Munni genotype's (0.69) AP content, which was the lowest. The findings demonstrate the genotypes of aromatic traits found in different genotypes (Fig. 6 ). 3.7 Diseases incidence 3.7.1 Number bast spots leaf − 1 Regarding bast spots leaf − 1 in the sadabadsha genotype, the observer possessed the most bast spots leaf − 1 (105.76). The BRRI dhan34 genotype had the spots with the second-highest size (92.77). In AR03, AR04, AR09, and Ranashail, the lowest number of the bast spots leaf − 1 (0.00) was found (Table 9 ). 3.6.2 Maximum size of the blast spots leaf − 1 The most significant variation across rice genotypes and the most important disease characteristic is the maximum size of the blast spots leaf − 1 (mm). In the BRRI dhan34 genotype, the spot leaf − 1 reached the largest size (11.87 mm). According to Table 9 , the blast spot leaf − 1 lowest maximum size (0.00 mm) was observed in AR03, AR04, AR09, and Ranashail. 3.7.3 Percent leaf blast severity The lowest percent leaf blast severity (0.00%) was discovered in the AR03, AR04, AR09, and Ranashail where the highest value was (Table 9 ). The range of percent leaf blast severity was significantly varied from 0.00 to 38.09%. Table 9 Number of spots leaf − 1 , maximum size of spots leaf − 1 (mm), % leaf blast severity of eighteen rice genotypes Genotypes Number of spots leaf − 1 Maximum size of spots leaf − 1 (mm) Leaf blast severity (%) Kalovat sugondhi 6.23 f-h 0.78 d-g 0.56 e BRRI dhan34 92.77 b 11.87 a 38.09 a Ranashail 0.00 i 0.00 h 0.00 e Multioverian 6.61 f-h 1.07 d-f 0.78 e Sadabadsha 105.76 a 10.80 b 35.93 b Munni 4.535h 0.75 e-g 0.47 e Hazardana 6.10 f-h 1.24 de 0.99 e AR01 4.79 h 0.44 gh 0.92 e AR02 57.20 d 4.93 c 18.24 d AR03 0.00 i 0.00 h 0.00 e AR04 0.00 i 0.00 h 0.00 e AR05 9.63 e 0.60 fg 0.62 e AR06 4.47 h 0.56 fg 0.23 e AR07 83.83 c 10.87 b 32.42 c AR08 7.84 e-g 0.89 d-g 0.62 e AR09 0.00 i 0.00 h 0.00 e AR10 5.30 gh 0.84 d-g 0.37 e AR11 8.47 ef 1.33 d 0.63 e LSD 5% 2.94 0.55 1.76 CV (%) 4.28 6.93 7.89 3.7 Multivariate analysis: The clustered heatmap (Fig. 8 ) demonstrated considerable phenotypic heterogeneity across 18 aromatic rice genotypes for 13 agro-morphological and blast-related parameters. Three major genotype clusters were discovered. The first cluster (AR01, AR04, AR08, AR09, AR03) had high z-scores for yield-related traits, including panicle length (PL), filled grains (FG), grain yield (GY), and harvest index (HI), with AR04 having the highest value. However, these genotypes were moderately to highly susceptible to blast, as evidenced by an increased number of spots per leaf (NSL) and blast severity (NBS). The second cluster (AR07, B-34) had poor agronomic performance but had high blast resistance. B-34 had the lowest NBS and NSL scores. The third cluster, which included SB, HD, and others, had overall poor to moderate performance, with SB being the most blast-susceptible. The aromatic characteristic 2-acetyl-1-pyrroline (2-AP) varied considerably between genotypes and had no significant association with yield or disease traits. Principal component analysis (PCA) revealed variations across genotypes based on morphological and yield-related determinants. Dim1 accounted for 45.4% of the total variance and was positively associated with variables such as biological yield hill-1 (BY), panicle length (PL), number of panicle hills − 1 (PN), and filled grains panicle − 1 (FG). Genotypes such as AR01, AR03, AR05, and AR04 made significant contributions to these traits, clustering in the positive Dim1 area. Dim2 accounted for 26.3% of the variation, influenced by negative correlations with characteristics such as leaf blast severity (LBS) and number of blast spots leaf − 1 (NSL), with genotypes such as B-34 and SB occupying this region. Genotypes such as KS, MO, and RS in the negative Dim1 and Dim2 quadrants exhibited low yield and blast susceptibility, whereas AR07 possessed unique clustering due to high leaf blast severity. The investigation highlighted the differential performance of genotypes, allowing the selection of desirable features for the study (Fig. 9 ). The dendrogram (Fig. 10 ), constructed by hierarchical cluster analysis, divided 18 aromatic rice genotypes into four separate clusters based on 13 agro-morphological, yield, and blast-resistance variables. Cluster I (red) included genotypes with superior agronomic performance, particularly higher panicle length (PL), filled grains per panicle (FG), grain yield (GY), 1000-grain weight (1000-GW), and harvest index (HI), along with strong resistance to blast, as indicated by low scores for number of spots per leaf (NSL) and blast severity (NBS), with the exception of AR02 due to its blast susceptibility. Cluster II (green), which includes B-34 and AR07, was distinguished by their significant susceptibility to blast, having its relatively low yield attributes. Cluster III (blue) consisted solely of SB, which distinguished out due to its great the susceptibility to blast, as evidenced by higher NSL and NBS values. Cluster IV (magenta), which included HD, KS, MO, AR06, AR10, AR11, RS, and MN, showed moderate performance in both yield-contributing and blast-resistance characteristics. 3.8 Detection of resistant genes employing molecular study The presence of major blast-resistant (R) genes in diverse rice genotypes was determined molecular study using seven SSR (simple sequence repeat) markers: RM527, RM224, RM541, RM208, RM247, RM72, and RM206. The PCR amplification products were observed on a 2% agarose gel (Fig. 12), revealing different gene-specific bands across several genotypes. The Pi-1 gene (157 bp) was found in genotypes corresponding to lanes 2, 4–6, 9, and 11–19, and the Pi-ta gene (131 bp) was found in lanes 1, 3, 4, 6–8, 10–13, and 15–19, demonstrating the presence of these genes in a large number of cultivars. The Pi-33 gene (166 bp) was amplified in lanes 1, 3, and 8, however the Pi-b gene (173 bp) was not present in any of the genotypes examined. The Piz-5 gene (233 bp) was found in lanes 3, 4, 7, 8, 10, 12, and 17, however no amplification of the Pi-9 gene (158 bp) was discovered, indicating that it was not present in the examined genotypes. The Pi-kh gene (147 base pairs) was successfully amplified in lanes 1, 2, 5, 7, 9, 10, 12, 14, 16, 17, and 19. These findings confirm the presence of multiple blast-resistance genes in the tested rice genotypes and demonstrate the efficacy of SSR markers for precise molecular characterization of resistance traits, making them an important tool for marker-assisted selection in rice breeding programs. The marker-assisted selection of rice blast resistance genes helped to identify the blast-resistant rice genotypes. In the present study, the presence of 7 major rice blast resistance genes ( Pi-9, Pi-1, Pi-5, Piz-b, Pi-ta, Pi-33 , and Pi-kh ) was identified in 19 aromatic rice genotypes, including F 1 hybrids. The available molecular markers that are linked to the major blast R genes were useful for identifying specific genes. Nine genotypes containing at least 2 expected bands of the 7 rice blast resistance markers. Eight genotypes of rice had three blast resistance genes, while two genotypes, AR-05 and AR-09, showed the highest number of blast resistance genes. Table 10 List of eighteen aromatic rice genotypes and their genotypic screening for blast resistance gene with SSR markers Genotypes Pi-9 (RM541) Pi-1 (RM224) Pi-kh (RM206) Piz-5 (RM527) Pi-b (RM208) Pi-33 (RM72) Pi-ta (RM247) Kalovat sugondhi 0 0 1 0 0 1 1 BRRI dhan34 0 1 1 0 0 0 0 Rana shail 0 0 0 1 0 1 1 Multioverian 0 1 0 1 0 0 1 Sadabadsha 0 1 1 0 0 0 0 Munni 0 1 0 0 0 0 1 Hazardana 0 0 1 1 0 0 1 AR01 0 0 0 1 0 1 1 AR02 0 1 1 0 0 0 0 AR03 0 0 1 1 0 0 1 AR04 0 1 0 0 0 0 1 AR05 0 1 1 1 0 0 1 AR06 0 1 0 0 0 0 1 AR07 0 1 1 0 0 0 0 AR08 (S1) 0 1 0 0 0 0 1 AR08 (S2) 0 1 1 0 0 0 1 AR09 0 1 1 1 0 0 1 AR10 0 1 0 0 0 0 1 AR11 0 1 1 0 0 0 1 Here, 0 = Absent, 1 = Present, S1 = Sample 1, S2 = Sample 2 4. Discussion The success of any crop improvement initiative hinges on the magnitude of genetic variability and the heritability of target traits. A comprehensive assessment of genetic diversity is fundamental for understanding relationships among cultivars and facilitating effective selection in breeding programs [ 36 , 37 ]. In this study, both phenotypic and genotypic associations were analyzed for key quality parameters, such as grain aroma, grain dimensions, 1000-grain weight, and yield, suggesting the possible presence of linkage or pleiotropy within specific genomic regions governing these traits. Notably, certain genomic loci associated with grain quality traits appear to have co-segregated in regionally adapted, farmer-preferred aromatic rice varieties through the course of domestication and selection [ 38 , 39 ]. Among the agronomic traits, panicle length, number of tillers per plant, grain weight, and harvest index were identified as critical determinants of yield potential in the F 1 hybrids [ 40 ]. These traits demonstrated strong and consistent associations with grain yield, indicating that indirect selection based on these parameters may significantly enhance breeding efficiency [ 41 ]. Furthermore, plant height showed a positive correlation with panicle length, while reductions in filled grain number and grain yield were linked to lower panicle numbers. The harvest index, reflecting reproductive efficiency, and 1000-grain weight, indicative of seed size, were found to have a direct and substantial impact on grain yield [ 42 ]. These findings collectively underscore the potential of classical breeding approaches in combining desirable quality and yield traits in aromatic rice genotypes. Flag leaf length is a critical morpho-physiological trait in rice, as it serves as a major photosynthetic organ contributing assimilates during the grain-filling period. It has been widely recognized for its positive association with yield-contributing traits, particularly panicle development and grain filling [ 43 , 4 ]. In the present investigation, substantial genotypic variation in flag leaf length was recorded among the aromatic rice genotypes. Such variability is of considerable breeding interest, as prior studies have established a significant positive correlation between flag leaf length and panicle length, thereby underscoring its relevance as a potential selection criterion for yield improvement [ 11 ]. The total number of tillers per hill varied significantly among the genotypes, with AR04 producing the highest and Hazardana the lowest. A similar trend was observed for effective tiller numbers, where AR04 again ranked highest, while Hazardana and Kalovat Sugondhi showed the lowest and statistically comparable values. Regarding grain production, AR07 demonstrated superior performance in total grains panicle − 1 , closely followed by AR03, whereas Kalovat Sugondhi produced the least. For filled grains panicle − 1 , AR07 maintained the highest count, with AR03 slightly lower, and Kalovat Sugondhi again exhibited the lowest. The large or heavy panicle type hybrid exhibited a poorer rate of grain filling (low filled grain number and weight), even under the favorable environment under, resulting in a lower extent of grain filling [ 45 ]. Panicle length showed considerable variation, with AR02 having the longest panicle and Sadabadsha the shortest. In our findings, maximum grain yield hill − 1 and harvest index were observed in AR04, whereas the maximum stover and biological yield were recorded in AR03. Recently numorus study [ 46 – 48 ] observed that grain yields different crops increasing over time have generally been associated with an increase in stover yield, whereas the harvest index has remained relatively stable, and in addition, the contrary situation has been observed in tropical germplasm, where grain yield production has been accompanied by an increasing harvest index while biomass yields were relatively stable. A highly heritable trait, rice aroma is principally impacted by the essential component 2-AP and is regulated by certain genes. However, the genetic make-up of different aromatic rice types differs [ 49 – 51 ]. According to the findings of the present investigation, the AR08 genotype had the highest level of 2-AP content, whereas lowest the Munni genotype showed the lowest level of 2-AP (0.69). Previous study has indicated that no one chemical or combination of compounds distinctly distinguished a particular aromatic rice variety in the volatile analysis of mature grains from both aromatic and non-aromatic rice. According to findings [ 52 , 53 ], the concentration of these compounds was what characterized the cultivars instead. Additionally, principal component analysis (PCA) and hierarchical cluster analysis revealed that the high-yielding genotypes AR01, AR03, AR04, and AR05 clustered together due to their superior yield components, with highly to moderate resistant to blast, as demonstrated by elevated NSL and NBS scales. In contrast, AR07 and B-34 showed great blast resistance but poor yield performance, implying a trade-off between disease resistance and yield-related characteristics. The combination of heatmap clustering, PCA, and hierarchical cluster analysis was beneficial for illustrating genetic diversity and determining behavioral interactions across rice genotypes [ 54 ]. These multivariate approaches supported the identification of resistance patterns and genotype grouping based on blast disease response [ 55 , 56 ], facilitating the selection of genetically diverse and resistant lines in rice breeding programs [ 57 , 58 ]. Molecular screening utilizing seven SSR markers revealed the presence of major blast-resistance genes, Pi-1, Pi-ta, Pi-kh , and Piz-5 , across multiple genotypes, with AR05 and AR09 containing the most resistance genes. The complete absence of Pi-b and Pi-9 in all genotypes reveals a crucial gap in resistance coverage, emphasizing the need of introducing these broad-spectrum R genes through marker-assisted selection (MAS) to enhance resistance persistence [ 59 , 60 ]. Notably, AR03 and AR04 emerged as agronomically superior genotypes with strong blast resistance, demonstrating the value of combining molecular screening and phenotypic selection. These findings support the use of molecular techniques to accelerate the development of high-yielding, blast-resistant aromatic rice varieties [ 61 , 62 ]. Based on comprehensive evaluations of morphological characteristics, yield components, grain quality, and molecular screening, F 1 hybrids AR03 and AR04 emerged as the most promising candidates for the development of high-yielding aromatic rice. Hybrids AR01, AR05, and AR09 also demonstrated significant potential as sources of blast resistance, containing critical resistance genes. The integration of classical breeding with marker-assisted selection (MAS) is crucial for advancing these F 1 hybrids toward the dual goals of high yield and durable blast resistance, in line with modern strategies for aromatic rice improvement [ 62 – 64 ]. 5. Conclusion The findings of this study show the expression of hybrid vigor in the F 1 aromatic rice generation, which outperforms and is more resilient than its parental lines. Nine promising F 1 lines with moderate blast disease resistance have been developed through targeted classical breeding. AR01, AR03, AR04, AR05, and AR09 have the highest blast resistance and yield potential, making them promising prospects for future varietal advancement. In addition, lines including AR01, AR02, AR06, AR08, AR10, and AR11 exceeded their parents in yield attributes while preserving moderate blast resistance. he recommended genotypes AR01, AR03, AR04, AR05, and AR09 present valuable genetic resources for breeding programs aimed at enhancing yield, disease resistance, and sustainability, thereby contributing to food security and preserving the cultural heritage of aromatic rice. Declarations Author Contributions: M.M.R.: investigation, methodology, formal analysis and writing; M.M.R.: methodology, conceptualization, writing-review and editing; M.M.R.: methodology, conceptualization, writing-review and editing; M.A: methodology, conceptualization, writing-review and editing; D.A.N.M.: methodology; M.M.H.: investigation and writing; N.C.H.: investigation, formal analysis and writing; W.S.: funding acquisition; A.E.S.: funding acquisition; M.S.I.: methodology, writing-review and editing, conceptualization and funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Institute of Research and Training (IRT), Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, Bangladesh and journal publication funded by the Researchers Supporting Project number (RSP2024R298) at King Saud University, Riyadh, Saudi Arabia. Data Availability Statement: Data are available upon request. Acknowledgements: All the authors are thankful to Jute Research Centre, Bangladesh Jute Research Institute, Nashipur, Dinajpur, Bangladesh; BRAC Agricultural Research and Development centre, Gazipur and also thankful to the Chairman, Department of Agronomy, HSTU, Dinajpur, Bangladesh, for conducting the experiment smoothly. Conflicts of Interest: The authors declare no conflicts of interest. References FAO. World Food Situation & Food and Agriculture Organization of the United Nations. ; Rome, Italy. Accessed 11 Available online: October (2024). https://www.fao.org/worldfoodsituation/csdb/en/ Rahman, M. M. et al. Impacts of climate change on food system security and sustainability in Bangladesh. J. Water Clim. Change . 15 , 2162–2187. https://doi.org/10.2166/wcc.2024.631 (2024). Bandumula, N. Rice production in Asia: Key to global food security. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 88 , 1323–1328. 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Supplementary Files floatimage1.png Graphical Abstract Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviewers agreed at journal 20 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers agreed at journal 04 Jan, 2026 Reviewers agreed at journal 04 Jan, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Editor invited by journal 08 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 05 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8276066","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":559189864,"identity":"bd2b98e9-1aca-4b39-b334-cd1bed803b90","order_by":0,"name":"Md. 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1","display":"","copyAsset":false,"role":"figure","size":88621,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly variations in maximum and minimum temperatures (°C), relative humidity (%), and total rainfall (mm) recorded at the experimental site during the crop-growing periods: June to November 2021 and June to November 2022\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/2d0f58fd034229d588b02078.png"},{"id":98215505,"identity":"19cef635-ff45-41a5-83f0-7b8d125403da","added_by":"auto","created_at":"2025-12-15 10:27:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2718255,"visible":true,"origin":"","legend":"\u003cp\u003eGradual development of F₁ seeds under controlled conditions in a Petri dish.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/5e458756231704ca219fbff1.png"},{"id":98431688,"identity":"dc1a1dee-44ce-4019-a61f-1785cbcba5b5","added_by":"auto","created_at":"2025-12-17 16:48:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55787,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in flag leaf length among the eighteen studied rice genotypes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/bc2fa9d4d0cb7cd99233ef2a.png"},{"id":98433498,"identity":"213d704f-0c4a-4aa7-9d2d-98fdd9b35ef1","added_by":"auto","created_at":"2025-12-17 16:50:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56063,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in days to 50% flowering after transplanting among eighteen aromatic rice genotypes evaluated under field conditions.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/d55381c6a9b8705269f90959.png"},{"id":98215502,"identity":"02a11df6-82af-4afe-9987-3f1784e3f3c4","added_by":"auto","created_at":"2025-12-15 10:27:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52695,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level of the sterility percentage panicle\u003csup\u003e-1\u003c/sup\u003e of eighteen genotypes\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/c866c6a4e7b954ae223ae2a8.png"},{"id":98432483,"identity":"6465c2e9-7388-4c96-a115-565e09b06e87","added_by":"auto","created_at":"2025-12-17 16:49:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53038,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level of aroma content (2-Acetyl-1-pyrroline) in grains of eighteen genotypes\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/377408fc3a7646f75b69af08.png"},{"id":98215529,"identity":"39d27ac6-c351-4b20-aa04-0464cb7296ff","added_by":"auto","created_at":"2025-12-15 10:27:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3404637,"visible":true,"origin":"","legend":"\u003cp\u003eComparative assessment of disease symptoms in progeny versus parent genotypes, demonstrating distinct variation in resistance and susceptibility profiles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/a3c0d9bb564c3ddee9b7b933.png"},{"id":98215527,"identity":"8e13a032-f55b-4fe5-b309-49dddceec82a","added_by":"auto","created_at":"2025-12-15 10:27:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1248599,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map with clustering depicting the variation and correlation among phenotypic traits across genotypes, where color intensity reflects trait values. Genotype codes used are: KS = Kalovat Sugondhi, B-34 = BRRI dhan34, RS = Rasnashil, MO = Multioverian, SB = Sadabadsha, MN = Munni, and HD = Hazardana. Trait abbreviations include: PH = plant height, GY = grain yield hill\u003csup\u003e-1\u003c/sup\u003e (g), BY = biological yield hill\u003csup\u003e-1\u003c/sup\u003e (g), PL = panicle length (cm), PN = panicle number hill\u003csup\u003e-1\u003c/sup\u003e, FG = filled grains panicle\u003csup\u003e-1\u003c/sup\u003e, HI = harvest index (%), NSL = number of blast spots leaf\u003csup\u003e-1\u003c/sup\u003e, and LBS = leaf blast severity (%).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/b1f09a48ebd8ffdafaa0179e.png"},{"id":98433520,"identity":"3d997a8c-9f83-437f-ac42-860cbaced0e3","added_by":"auto","created_at":"2025-12-17 16:50:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":169989,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplot displaying the spatial distribution of genotypes and trait loadings on the first two principal components (PC1 and PC2), which together explain the major portion of variance in the dataset. Genotype codes used are: KS = Kalovat Sugondhi, B-34 = BRRI dhan34, RS = Rasnashil, MO = Multioverian, SB = Sadabadsha, MN = Munni, and HD = Hazardana. Trait abbreviations include: PH = plant height, GY = grain yield hill\u003csup\u003e-1\u003c/sup\u003e (g), BY = biological yield hill\u003csup\u003e-1\u003c/sup\u003e (g), PL = panicle length (cm), PN = panicle number hill\u003csup\u003e-1\u003c/sup\u003e, FG = filled grains panicle\u003csup\u003e-1\u003c/sup\u003e, HI = harvest index (%), NSL = number of blast spots leaf\u003csup\u003e-1\u003c/sup\u003e, and LBS = leaf blast severity (%).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/36330e907168de6caa6ab09e.png"},{"id":98432498,"identity":"5b501d70-c996-4494-95d4-98dedda44cc4","added_by":"auto","created_at":"2025-12-17 16:49:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":73354,"visible":true,"origin":"","legend":"\u003cp\u003eCluster dendrogram grouping genotypes based on phenotypic similarity. Genotype codes used are: KS = Kalovat Sugondhi, B-34 = BRRI dhan34, RS = Rasnashil, MO = Multioverian, SB = Sadabadsha, MN = Munni, and HD = Hazardana. Trait abbreviations include: PH = plant height, GY = grain yield hill\u003csup\u003e-1\u003c/sup\u003e (g), BY = biological yield hill\u003csup\u003e-1\u003c/sup\u003e (g), PL = panicle length (cm), PN = panicle number hill\u003csup\u003e-1\u003c/sup\u003e, FG = filled grains panicle\u003csup\u003e-1\u003c/sup\u003e, HI = harvest index (%), NSL = number of blast spots leaf\u003csup\u003e-1\u003c/sup\u003e, and LBS = leaf blast severity (%).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/9ee10979a0f2e82ef1b76a06.png"},{"id":98215528,"identity":"6db757ff-be91-41d2-a23f-1398654ae264","added_by":"auto","created_at":"2025-12-15 10:27:38","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":6104366,"visible":true,"origin":"","legend":"\u003cp\u003eDNA figure print picture of RM224, RM208, RM247, RM527, and RM72 microsatellite markers obtained from 2% agarose gel electrophoresis using 100 bp DNA ladder, respectively. [\u003cstrong\u003eLegends:\u003c/strong\u003e 1= Kalovat sugondhi, 2= BRRI dhan34, 3= Ranashail, 4= Multioverian, 5 = Sadabadsha, 6= Munni, 7= Hazardana, 8= AR01, 9=AR02, 10= AR03, 11= AR04, 12= AR05, 13= AR06, 14= AR07, 15= AR08 (sample 1), 16= AR08 (sample 2), 17= AR09, 18= AR10, 19= AR11 genotypes]\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/a42f7d9e757a4c9241352c0b.png"},{"id":98445068,"identity":"f4801e37-f10f-42ba-81f8-b6044a6f87f4","added_by":"auto","created_at":"2025-12-17 17:18:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15890193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/e14b03dc-6262-4467-a32c-48ea5a9a2558.pdf"},{"id":98433429,"identity":"28c3048b-5b2e-4b57-af2e-9f1ed059631d","added_by":"auto","created_at":"2025-12-17 16:50:44","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7987452,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8276066/v1/55290a955157a23efcdd7466.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCombining Blast Resistance and High Yield in F\u003csub\u003e1\u003c/sub\u003e Aromatic Rice Through Classical Hybrid Breeding Approaches\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L., 2n\u0026thinsp;=\u0026thinsp;2x\u0026thinsp;=\u0026thinsp;24) is the leading staple food in Asia, leading in both production and productivity. It is essential to the livelihoods of 160\u0026nbsp;million people in Bangladesh, which ranks as the third-largest producer and consumer of rice, with an annual output of 37\u0026nbsp;million metric tons [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As of 2023/2024, global rice production is reached 520.65\u0026nbsp;million metric tons, China leads global production with 144.62\u0026nbsp;million metric tons (28% of the total), followed by India with 137\u0026nbsp;million metric tons (26%). Bangladesh contributes approximately 7% of the world's rice production [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRice production in Bangladesh is significantly affected by various diseases, including blast, sheath rot, sheath blight, bacterial blight, and brown spot. Both biotic and abiotic factors contribute to low yields, with diseases being the most common biotic stress [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These diseases cause 36 fungal, 21 bacterial, and several nematode infections. Currently, 31 rice diseases have been identified in Bangladesh, with ten considered major [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these, rice blast, caused by the fungus \u003cem\u003ePyricularia oryzae\u003c/em\u003e (\u003cem\u003eMagnaporthe oryzae\u003c/em\u003e), is the most destructive, reducing grain yield and quality significantly [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Documented first in China in 1637 and later in Japan, Italy, the USA, and India, the rice blast causes a 30% yield loss in Bangladesh, particularly severe in the northern and central regions considered as the hotspot for blast disease, with up to 40% loss in Dinajpur [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to a relevant research during the Boro season, the incidences of blast disease were as high as 58.2% in the high-yielding rice cultivar BRRI dhan47, 39.8% in BRRI dhan29, and 20.3% in BRRI dhan28, and 59.8% in BRRI dhan34 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrently, 267 races of rice blast have been identified, and the disease pressure is increasing, posing a significant threat globally. Rice blasts can affect all growth stages, from seedling to pre-maturity, with severe conditions causing 70\u0026ndash;90% yield losses. Favorable conditions for epidemic development include high temperatures, high humidity, dew, drought stress, and excessive nitrogenous fertilization [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The disease manifests in forms such as leaf, node, neck, and panicle blast, each with specific symptoms causing severe damage.\u003c/p\u003e\u003cp\u003eSSR markers in the selection process for blast resistance have revolutionized rice efforts to enhance. Their specificity, effectiveness, and simplicity in Marker-Assisted Selection (MAS) make it an essential tool in developing blast-resistant aromatic rice genotypes [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As molecular marker technology continues to progress, rice breeding is gradually more accurate and effective in treating the challenges resulting from rice blast disease [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDeveloping blast-resistant aromatic rice is critical for enhancing Bangladesh\u0026rsquo;s agricultural productivity and ensuring long-term food security [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Aromatic varieties such as BRRI dhan34 and Sadabahar are widely prized for their fragrance and superior grain quality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], yet their susceptibility to blast results in significant yield losses in major growing regions. Improving disease resistance in these cultivars will promote production stability, support farmers in blast-affected areas, and enhance the economic value of aromatic rice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, resilient aromatic varieties hold strong potential for boosting export opportunities and strengthening Bangladesh\u0026rsquo;s position in the global rice market. Therefore, integrating blast resistance into aromatic rice is essential for maintaining crop resilience, sustaining food supply, and promoting national economic growth.\u003c/p\u003e\u003cp\u003eDespite notable advancements in blast epidemiology and the development of resistant rice varieties, a substantial research gap persists in the improvement of aromatic rice in Bangladesh. Most previous study has focused on non-aromatic cultivars with very little effort to combine blast resistance with high yield potential in this premium rice group. Although molecular markers such as SSRs have aided in more accurate resistance breeding, their integration with traditional hybridization methods of developing new aromatic genotypes adapted to blast-prone environments remains limited. Consequently, comprehensive evaluations of parental genotypes and their F\u003csub\u003e1\u003c/sub\u003e hybrids for blast resistance, yield performance, and agronomic traits under local field conditions are still lacking. Addressing this gap, the present study develops and assesses F\u003csub\u003e1\u003c/sub\u003e aromatic rice hybrids that combine blast resistance with improved yield through classical hybrid breeding, providing valuable insights for breeding resilient, high-yielding aromatic cultivars suited to the evolving challenges of blast disease in Bangladesh.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Experimental location and duration\u003c/h2\u003e\u003cp\u003eThe study was carried out in the BJRI Research Field in Nashipur, Dinajpur, and consisted of two phases: a preparatory phase and an experimental phase. The preparatory phase (July 2021\u0026ndash;December 2022) involved the development of F\u003csub\u003e1\u003c/sub\u003e hybrid seeds. The experimental phase (July 2022-December 2022) included a field evaluation of these F\u003csub\u003e1\u003c/sub\u003e hybrids to determine agronomic traits, yield performance, and blast resistance. The location is in the Old Himalayan Piedmont Plain (AEZ-1) at 24.000\u0026deg; N latitude and 90.250\u0026deg; E longitude, 34 m above sea level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Soil conditions\u003c/h2\u003e\u003cp\u003eSoil samples were collected from a depth of 0 to 15 cm at the experimental field. After being dried, ground, and passed through a 20-mesh sieve, the samples were analyzed at the Soil Resources Development Institute (SRDI), Nashipur, Dinajpur. The experimental pot soil was sandy loam, with 65% sand, 30% silt, 5% clay, had an acidic pH of 4.70 and low water retention capacity. Initial soil analysis showed 0.514% organic carbon, 0.04% total nitrogen, 55.85 ppm available phosphorus, 0.26 meq/100 g soil exchangeable potassium, and 9.93 ppm available sulfur (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\u003ePhysicochemical characteristics of the surface soil (0\u0026ndash;15 cm depth) at the experimental site\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCritical level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExtraction methods\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSandy loam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH (Soil: water\u0026thinsp;=\u0026thinsp;1: 25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlass-electrode pH meter [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic carbon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWet oxidation method [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic matter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCalculated by Van Bemmelen factor [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMicro-Kjeldahl method [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable P (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMolybdate blue ascorbic acid method [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExchangeable K (meq)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetermined by a Flame photometer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable S (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTurbidity method using BaCl\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable B (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMono-calcium bi-phosphate method, determined by Spectro-photometer following Azomethine H method [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable Zn (ppm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAAS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Critical nutrient levels were adapted from the Bangladesh Agricultural Research Council (BARC) Fertilizer Recommendation Guide 2018, which provides scientifically established threshold values for assessing soil nutrient sufficiency in Bangladesh. These values were used to classify soil nutrient status as low (L), medium (M), or high (H) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Weather conditions of the experimental site\u003c/h2\u003e\u003cp\u003eThe area experiences a subtropical climate with heavy rainfall from May to September and lighter precipitation from October to April. Throughout the experimental period, temperature, rainfall, and relative humidity (RH) data were collected from the BWMRI Meteorological Station in Dinajpur. The weather data of the experimental site is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Experimental treatments and design\u003c/h2\u003e\u003cp\u003eFor the development of F\u003csub\u003e1\u003c/sub\u003e genotypes, seven aromatic rice genotypes were collected from different locations during July and December 2021: Kalovat Sugondhi, BRRI dhan34, Ranashail, Munni, Hazardana, Sadabadsha, and Multioverian (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Different levels of pathogen susceptibility and resistance have been detected by these genotypes. To determine parental lines with desired agronomic attributes and specific resistance to blast disease, the germplasm, which includes the entire genetic makeup of the crop and its associated species, was examined. Genotypes with positive attributes, such as high yield and blast tolerance, were used to select seeds.\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\u003eName and source of experimental parent 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBlast reaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKalovat Sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTanor, Rajshahi, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBRRI Dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSadar, Dinajpur, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTanor, Rajshahi, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiovarian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSyedpur, Nilphamari, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTanor, Rajshahi, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTanor, Rajshahi, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTanor, Rajshahi, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eHere, R\u0026thinsp;=\u0026thinsp;Resistance, S\u0026thinsp;=\u0026thinsp;Susceptible\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe preparatory phase of the research was to develop F\u003csub\u003e1\u003c/sub\u003e hybrids of aromatic rice that are resistant to blast. Emasculation, or the removal of anthers from bisexual flowers prior to pollination, was the first step in the controlled hybridization process. One day before the anthesis, emasculation was carried out between 4 and 6 p.m. utilizing techniques such as vacuum suction. To avoid accidental pollination, panicles containing treated spikelets were immediately covered with paper bags after emasculation. On the following morning, pollen from designated male parents was transferred to emasculated flowers in order to perform cross-pollination. To keep everything under control, the treated flowers were re-bagged after pollination. The resulting hybrid seeds were ready for harvesting after maturing in 28\u0026ndash;34 days. Eleven F\u003csub\u003e1\u003c/sub\u003e hybrids were created as a result of cross-polination between all progenitor genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). F\u003csub\u003e1\u003c/sub\u003e rice seeds were carefully ditched from the spikes using a needle and then stored in a regulated dry atmosphere with relative humidity values below 25% for preservation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of cross-pollination events among selected aromatic rice parental genotypes and the corresponding F₁ hybrids developed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale parents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMale parents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003csub\u003e1\u003c/sub\u003e hybrids\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAR11\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\u003eThe 2nd phase of the research was from June to December 2022 and featured 18 genotypes in total, including 7 parental lines and 11 F\u003csub\u003e1\u003c/sub\u003e hybrids were selected for field trial, selection of suitable genotypes, and to make an overall comparison.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eField trials were carried out to assess the genotypic performance of both parental lines and the associated F\u003csub\u003e1\u003c/sub\u003e hybrids. For direct comparison, the parental genotypes were planted in plots adjacent to the central plots containing the F\u003csub\u003e1\u003c/sub\u003e hybrids. To avoid accidental cross-pollination, the F\u003csub\u003e1\u003c/sub\u003e hybrid plots were covered with nets. The randomized complete block design (RCBD) was used, with three replications of each genotype. Each plot had three raised planting rows, ensuring a thorough evaluation across replicates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Experimentation\u003c/h2\u003e\u003cp\u003eIn late June 2022, the experimental plot was prepared by ploughing and laddering before transplanting aromatic rice seedlings aged 28\u0026ndash;30 days. The parental seeds were pre-treated by soaking in water and sprouting, then placed on the seedbed while, the F\u003csub\u003e1\u003c/sub\u003e was allowed to grow in a petri dish under close supervision (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Fertilization followed the Bangladesh Agricultural Research Council's (BARC) recommendations, with cow dung, triple superphosphate (TSP), muriate of potash (MOP), gypsum, and zinc sulfate applied during field preparation. Urea was used in three divided doses at 15, 30, and 50 days following transplantation. Gap filling, weeding, watering, pest management, and harvesting were all standard intercultural operations during the cropping season. Gaps were filled rapidly after transplanting with extra seedlings, and weeds were manually controlled, supplemented by urea top-dressing. Due to low rainfall, timely irrigation was essential for optimal crop growth. Pest management practices were undertaken to reduce infestations. For artificial pathogen inoculum, \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e isolates were initially isolated from naturally infected plant parts and purified on potato dextrose agar (PDA) at 25\u0026deg;C for 7 days to promote sporulation. Conidial spores were extracted by pouring each PDA plate with sterile distilled water containing 0.02% Tween 20 and adjusting the suspension to 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e conidia mL⁻\u0026sup1;. During the rice tillering stage, plants were uniformly sprayed with this spore suspension till runoff. Harvesting targeted mature seeds for future study and yield assessment. Meteorological data for the research period were collected from the Bangladesh Jute Research Institute (BJRI) at Nashipur, Dinajpur, Bangladesh.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Collection of data\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1 Morphology, phenology, growth, yield characteristics and yield\u003c/h2\u003e\u003cp\u003ePlant height was measured with a measuring scale from the plant's base to the tip of the tallest leaf and recorded in centimetres (cm). Flag leaf lengths were also measured in centimetres (cm) by a measuring scale. Effective tillers were those having at least one-grain panicle. The total number of panicle-bearing tillers on each sample hill was counted, and an average was determined. Five randomly selected hills plot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, each with three replications, were counted for fully grown tillers. By measuring the days from transplanting till 50% of the spikes became apparent as determined visually, the number of days to 50% flowering was calculated. Ten randomly selected panicles were utilized to calculate averages of the panicle length, which was measured from the rachis's basal node to the panicle apex. The total grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e includes all completely mature grains, fertilized and unfertilized. These were calculated from five plants selected at random from each plot. Filled grains (fertilized and completely grown) and unfilled grains (sterile or partially formed) were counted individually in the panicles of five randomly chosen plants plot\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A thousand dry, clean grains were randomly selected from the harvested seed harvest and weighed with a digital scale. Grain yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was calculated by sun-drying and weighing the grains. After the plant stover had completely dried, the weigh hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was measured. The harvest index (HI) was determined as the ratio of grain yield to total shoot dry matter, which served as an indicator of reproductive efficiency. The harvest index was measured by following the formula:\u003c/div\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2 Synthesis of 2-Acetyl-1-pyrroline\u003c/h2\u003e\u003cp\u003e2-Acetyl-1-pyrroline was synthesized with 1.7% KOH and a modified \u003cem\u003eZaldal Aroma Test Protocol\u003c/em\u003e, which is an efficient and fast method to recognize aromas in rice. It was found particularly for 2-acetyl-1-pyrroline (2AP), which was responsible for the aroma of fragrant rice cultivars. In this method, rice grains were soaked in a 1.7% potassium hydroxide (KOH) solution to aid in the release of volatile aromatic compounds. The breakdown of the cell walls by the KOH facilitated the volatilization of 2AP. After a few minutes, the sample was smelled to determine the aroma. To gauge the strength of the fragrance ranging from low to high, often on a scale of 1 to 10, compare its intensity to that of non-aromatic rice [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.6.3 Leaf Blast Severity Assessment\u003c/h2\u003e\u003cp\u003eThe sum of the length and width of the largest spot was recorded, and the total length of the spot was estimated. The data on the size of the blast spot (mm) were taken at 60 days after planting (DAT). Leaf blast severity indicated the percentage of leaf area affected by blast disease. The data on leaf blast severity were taken at 60 DAT. The development of leaf blotch was observed until maturation. IRRI conducted an assessment of the plants located in the center of each plot; however, the plants at the ends of the row were excluded from the scoring process (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The blast symptoms were graded using the IRRI-established visual scale, which ranges from 0 to 9. A score of 0 indicates that the plant is symptom-free, while a score of 9 suggests that the plant has been entirely damaged and is likely to be stunted or dead. The preceding is contingent upon the severity of the attack.\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\u003eThe IRRI-established visual scale, ranging from 0 to 9, was employed to grade blast symptoms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo lesions observed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall brown specks of pin-point size or larger brown specks without sporulating center\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmall roundish to slightly elongated, necrotic gray spots, about 1\u0026ndash;2 mm in diameter, with a distinct brown margin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLesion type is the same as in scale 2, but a significant number of lesions are on the upper leaves\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical susceptible blast lesions 3 mm or longer, infecting less than 4% of the leaf area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical blast lesions infecting 4\u0026ndash;10% of the leaf area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical blast lesions infection 11\u0026ndash;25% of the leaf area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical blast lesions infection 26\u0026ndash;50% of the leaf area\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypical blast lesions infection 51\u0026ndash;75% of the leaf area and many leaves are dead\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMore than 75% leaf area affected\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\u003eTo determine the percent (leaf) blast severity to observe the phenotypic expression of R-gene, the following formula was used:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{L}\\text{e}\\text{a}\\text{f}\\:\\text{b}\\text{l}\\text{a}\\text{s}\\text{t}\\:\\text{s}\\text{e}\\text{v}\\text{e}\\text{r}\\text{i}\\text{t}\\text{y}\\:\\left(\\%\\right)=\\:\\frac{\\text{S}\\text{u}\\text{m}\\:\\text{o}\\text{f}\\:\\text{t}\\text{h}\\text{e}\\:\\text{r}\\text{a}\\text{t}\\text{i}\\text{n}\\text{g}\\text{s}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\text{s}\\:\\text{o}\\text{f}\\:\\text{o}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\times\\:\\text{M}\\text{a}\\text{x}\\text{i}\\text{m}\\text{u}\\text{m}\\:\\text{r}\\text{a}\\text{t}\\text{i}\\text{n}\\text{g}\\text{s}}\\times\\:100\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:..\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.6.4 Molecular Analysis\u003c/h2\u003e\u003cp\u003eSimple sequence repeats (SSR) marker have been widely used in molecular genetics research, including the detection and characterization of blast resistance genes in rice. To identify blast resistance genes using SSR markers across seven parental genotypes and eleven F\u003csub\u003e1\u003c/sub\u003e populations. Genomic DNA extraction was performed on both parental genotypes and F\u003csub\u003e1\u003c/sub\u003e progeny to facilitate marker analysis. Here, in total seven (7) SSR markers were used in 19 (AR08 genotype was observed employing 2 different samples) aromatic rice genotypes (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\u003eMolecular markers and their associated blast resistance (R) genes employed in the genetic assessment of rice genotypes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSl. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eType of the marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR Gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnealing temperature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{℃}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExpected PCR product size (bp)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSequence*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-ta\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: TAGTGGGGATCGATGTAACG\u003c/p\u003e\u003cp\u003eR: CATATGGTTTGACAAAGCG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-kh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: CGTTCCATCGATCCGTATGG\u003c/p\u003e\u003cp\u003eR: CCCATGCGTTTAACTATTCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-33\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: CCGGCGATAAAACAATGAG\u003c/p\u003e\u003cp\u003eR: GCATCGGTCCTAACTAAGGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: TCTGCAAGCCTTGTCTGATG\u003c/p\u003e\u003cp\u003eR: TAAGTCGATCATTGTGTGGACC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: TATAACCGACCTCAGTGCCC\u003c/p\u003e\u003cp\u003eR: CCTTACTCCCATGCCATGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: ATCGATCGATCTTCACGAGG\u003c/p\u003e\u003cp\u003eR: TGCTATAAAAGGCATTCGGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePiz-5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF: GGCTCGATCTAGAAAATCCG\u003c/p\u003e\u003cp\u003eR: TTGCACAGGTTGCGATAGAG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: * F\u0026thinsp;=\u0026thinsp;Forwarded primer, R\u0026thinsp;=\u0026thinsp;Reverse primer.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe data were analyzed using the Statistix 10 software. PCA biplot and cluster were analyzed by SPSS and OriginPro software. Mean values are separated by Excel to gauge the significance of individual genotypes. Illustration was done using BioRender.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Morphological evaluation\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Plant height\u003c/h2\u003e\u003cp\u003eSignificant differences in morphological traits were observed among 18 rice genotypes. The genotypes exhibited significant differences in plant height. At 30 DAT, the AR03 genotype exhibited the highest plant height (107.03 cm), while the Hazardana genotype had the lowest (81.86 cm). The results showed that the AR02 genotype had a higher plant height (155.87 cm) than the other seventeen genotypes at 60 DAT. The AR03 genotype achieved the maximum among the genotypes at 90 DAT, as indicated by the results (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePlant height (cm) at 30, 60 and 90 DAT of eighteen 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 DAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 DAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 DAT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.46 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146.80 a-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181.28 ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.26 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.47 h-j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145.45 gh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.06 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148.17 a-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174.55 a-c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.95 b-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145.21 a-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171.48 b-d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.26 de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107.74 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.43 i\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.96 ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.60 d-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148.79 e-h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.86 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.79 b-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.54 hi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.03 b-d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.83 a-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e146.67 f-h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105.90 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155.87 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170.47 b-d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.03 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e154.20 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e188.40 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.90 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126.75 f-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135.53 hi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.43 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143.67 a-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162.07 c-f\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.73 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148.47 ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182.07 ab\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.30 b-d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.15 ij\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135.07 hi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.60 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.11 c-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143.52 g-i\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.50 b-d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147.79 a-d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e163.07 c-e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.63 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.20 g-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158.87 d-g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.60 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.98 e-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e167.81 b-d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD\u003csub\u003e5%\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Flag leaf length\u003c/h2\u003e\u003cp\u003eThe flag leaf lengths of the 18 genotypes ranged from 36.67 to 62.58 cm, making them statistically diverse. The AR02 genotype had the largest flag leaf length (62.58 cm), whereas the AR08 genotype had the shortest (36.67 cm), which was statistically similar to the AR10 genotype's (38.25 cm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Phenology (Days to 50% flowering)\u003c/h2\u003e\u003cp\u003eSignificant variations were also observed in terms of the days to 50% flowering, ranging from 42.33 days to 98.33 days. The Munni genotype exhibited the earliest flowering, requiring only 42.33 days to reach 50% flowering. In contrast, the Kalovat Sugondhi genotype demonstrated the maximum days to flowering, taking 98.33 days to achieve the same stage. These differences highlight the diversity in flowering times across the genotypes, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Yield contributing characteristics and yield indices\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Number of total tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eAt maturity, there were substantial differences in the total number of tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e between the rice genotypes. The AR04 genotype had the highest number of total tillers, reaching 89.60 tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which was significantly greater than the other genotypes. In contrast, the Hazardana genotype displayed the lowest number of tillers, with only 14.78 tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Number of effective tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eThe number of effective tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e exhibits the most variations among the different rice genotypes, with values ranging from 8.16 to 60.66 tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Notably, the AR04 genotype demonstrated the highest number of effective tillers, reaching 60.66, indicating its superior tillering capacity compared to the other genotypes. In contrast, the Hazardana genotype recorded the lowest number of effective tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, at just 8.16 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Numbers total grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eWith (604.0) grains produced, the AR07 genotype yielded the greatest amount, followed of the AR03 genotype with 516.33 grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Compared to other genotypes, the grain number of the AR07 and AR03 genotypes were significantly higher. On the other hand, at 191.00 grains, the Kalovat Sugondhi genotype had the lowest grain count (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 Number of filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eSignificant variations were observed in the number of filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across the rice genotypes. The AR07 genotype exhibited the highest number of filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with 458.67 grains, closely followed by the AR03 genotype, which had 433.00 grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. On the other hand, Kalovat Sugondhi showed the lowest number of filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with 178.67 grains, followed by Ranashail (183.33) grains and Multioverian (184.33). These substantial differences underscore the diverse grain-filling capacities among the genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e3.3.5 Panicle length\u003c/h2\u003e\u003cp\u003eA notable level of variation was observed in panicle length among the rice genotypes. The AR05 genotype had the longest panicle length, measuring 32.86 cm, which was statistically similar to the AR02 genotype at 32.60 cm. In contrast, the Sadabadsha genotype exhibited the shortest panicle length at 23.46 cm, which was statistically comparable to the Kalovat Sugondhi genotype's measurement of 23.50 cm. These findings highlight the diversity in panicle length among the genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e3.3.7 1000-grain weight\u003c/h2\u003e\u003cp\u003eSignificant variations were also observed in the 1000-grain weight among the genotypes, with values ranging from 9.85 g to 29.45 g. The AR06 genotype exhibited the highest 1000-grain weight at 29.45 g, while the Sadabadsha genotype had the lowest weight, measuring only 9.85 g. Other notable weights included Kalovat Sugondhi (28.63 g), Multioverian (27.50 g), and AR11 (26.95 g), all indicating substantial differences in grain weight across the genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResponse of genotypes on the total number of tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, effective tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, panicle length (cm), total grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1000-grain weight (g).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\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\u003eTotal of tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEffective tillers hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePanicle length (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFilled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1000-grain weight (g)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.94 hi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.60 hi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.50 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e191.00 n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178.67 l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.63 b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.81 g-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.28 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.96 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e400.33 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e280.67 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.40 o\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.86 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.39 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.00 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207.67 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e183.33 kl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.60 i\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.48 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.48 f-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.16 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e236.00 l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e184.33 kl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.50 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.30 g-i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.31 fg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.46 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e298.33 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e193.67 jk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.85 q\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.53 hi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.26 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.46 c-e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e214.00 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e202.00 ij\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.55 g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.78 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.16 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.50 ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e487.67 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e397.67 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.15 f\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.73 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.12 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.73 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e368.33 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e307.00 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.40 g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.64 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.64 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.60 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e504.00 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e333.67 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.80 k\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.60 ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.14 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.46 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e516.33 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e433.00 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.25 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.60 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.66 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.66 cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e282.33 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e261.67 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.15 h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.56 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.11 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.86 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e350.00 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e327.33 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.60 l\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.60 de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.06 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.90 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e267 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.45 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.41 cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.93 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.76 cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e604.00 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e458.67 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.75 n\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.76 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.63 bc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.80 d-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e333.33 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e312.00 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.20 p\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.94 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.40 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.10 ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e338.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e274.33 fg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.80 j\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.31 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.78 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.23 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260.67 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e199.00 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.40 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.63 de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.53 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.83 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e265.67 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e212.33 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.95 d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD\u003csub\u003e5%\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Sterility percent panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eSadabadsha showed the highest percentage of sterility panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (35.09%), which was statistically identical to the AR02 genotype (33.80%). The lowest percentage of sterility panicle\u003csub\u003e\u0026minus;\u0026thinsp;1\u003c/sub\u003e was 5.61 percent in Munni, which was compared statistically to (6.40%), (6.48%), and (7.32%) in AR08, AR05, and AR04, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Yield indices\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e3.5.1 Grain yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eRegarding grain weight hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, a substantial difference was observed among the different rice genotypes evaluated. The AR04 genotype demonstrated the highest grain yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, producing an impressive 160.81 g, showcasing its superior performance in comparison to the other genotypes. This high yield indicates the strong potential of AR04 in maximizing grain production under the given conditions. On the other hand, the BRRI dhan34 genotype exhibited the lowest grain yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a yield of only 19.91 g. This marked difference highlights the substantial variability in the grain production capacity across the genotypes studied. The findings underscore the importance of selecting high-yielding genotypes such as AR04 for improving rice productivity, while lower-yielding varieties like BRRI dhan34 may require further optimization or specific cultivation strategies to enhance their performance (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e3.5.2 Stover yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eSubstantial differences were recorded in terms of stover yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across the various rice genotypes. The AR03 genotype stood out with the highest stover weight, producing 226.50 g hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, indicating its superior biomass accumulation. On the contrary, the Sadabadsha genotype exhibited the lowest stover yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a value of 41.49 g. This low value was statistically comparable to the stover yields of the Hazardana (44.56 g), BRRI dhan34 (45.54 g), and Munni (46.65 g) genotypes, all of which displayed relatively lower biomass production (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e3.5.3 Biological Yield hlii\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eRegarding biological yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, variation was also observed significantly. The AR03 genotype produced the highest biological yield, with a remarkable (355.71 g) hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, reflecting its exceptional performance in overall biomass production. On the other hand, the Sadabadsha genotype exhibited the lowest biological yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a value of 64.23 g. This low yield was statistically similar to that of the BRRI dhan34 genotype, which produced 65.45 g hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\u003ch2\u003e3.5.4 Harvest index\u003c/h2\u003e\u003cp\u003eHarvest index notable differences were observed among the rice genotypes. The highest harvest index was recorded in the AR04 genotype, reaching 56.33%, indicating its efficient conversion of biomass into grain yield. In contrast, the AR07 genotype exhibited the lowest harvest index at 23.43%, reflecting a lower efficiency in biomass conversion (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGrain yield, stover yield, biological yield, and harvest index (%) of eighteen rice genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrain yield\u003c/p\u003e\u003cp\u003e(g hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStover weight\u003c/p\u003e\u003cp\u003e(g hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBiological yield (g hlii\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHarvest index (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.54 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.21 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.75 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.42 j\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.91 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.54 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.45 l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.43 j\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.04 ij\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.31 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.68 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.70 ef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.27 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.18 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.22 j\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.06 h\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.28 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.80 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.23 l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.93 i\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.17 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.66 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.82 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.64 bc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.16 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.57 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.73 k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.43 b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.93 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187.81 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e331.41 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.36 e-g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.14 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210.40 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e291.21 cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.75 k\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129.21 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e226.50 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e355.71 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.33 i\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e160.81 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.56 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e298.70 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.33 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108.47 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e156.11 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e270.25 ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.24 fg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.50 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.69 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e231.19 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.48 e-g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.97 ij\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.12 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176.42 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.43 l\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.16 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.02 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201.52 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.41 de\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.82 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.54 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e259.03 f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.61 bc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.21 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.88 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e220.09 g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.45 g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.71 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150.62 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e279.66 de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.14 d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD\u003csub\u003e5%\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Quality trait-Aroma content (2-Acetyl-1-pyrroline) in grain\u003c/h2\u003e\u003cp\u003eThe aromatic level of 2-Acetyl-1-pyrroline (AP) varied significantly among the rice genotypes. Considering an AP content of (9.77), the AR08 genotype had the highest level, confirming its excellent aromatic characteristics. The Ranashail and AR10 genotypes' respective AP levels of (0.83) and (0.85) were statistically similar to the Munni genotype's (0.69) AP content, which was the lowest. The findings demonstrate the genotypes of aromatic traits found in different genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Diseases incidence\u003c/h2\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003e3.7.1 Number bast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eRegarding bast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the sadabadsha genotype, the observer possessed the most bast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (105.76). The BRRI dhan34 genotype had the spots with the second-highest size (92.77). In AR03, AR04, AR09, and Ranashail, the lowest number of the bast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (0.00) was found (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2 Maximum size of the blast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/h2\u003e\u003cp\u003eThe most significant variation across rice genotypes and the most important disease characteristic is the maximum size of the blast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mm). In the BRRI dhan34 genotype, the spot leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e reached the largest size (11.87 mm). According to Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the blast spot leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e lowest maximum size (0.00 mm) was observed in AR03, AR04, AR09, and Ranashail.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec36\" class=\"Section3\"\u003e\u003ch2\u003e3.7.3 Percent leaf blast severity\u003c/h2\u003e\u003cp\u003eThe lowest percent leaf blast severity (0.00%) was discovered in the AR03, AR04, AR09, and Ranashail where the highest value was (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The range of percent leaf blast severity was significantly varied from 0.00 to 38.09%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, maximum size of spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mm), % leaf blast severity of eighteen 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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of spots\u003c/p\u003e\u003cp\u003eleaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaximum size of spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeaf blast severity\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.23 f-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78 d-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.77 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.87 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.09 a\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanashail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.61 f-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07 d-f\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105.76 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.80 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.93 b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.535h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75 e-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.10 f-h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.24 de\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.79 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.20 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.93 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.24 d\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.63 e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60 fg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.47 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56 fg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.83 c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.87 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.42 c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.84 e-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89 d-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00 i\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.30 gh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84 d-g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.47 ef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33 d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD\u003csub\u003e5%\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Multivariate analysis:\u003c/h2\u003e\u003cp\u003eThe clustered heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) demonstrated considerable phenotypic heterogeneity across 18 aromatic rice genotypes for 13 agro-morphological and blast-related parameters. Three major genotype clusters were discovered. The first cluster (AR01, AR04, AR08, AR09, AR03) had high z-scores for yield-related traits, including panicle length (PL), filled grains (FG), grain yield (GY), and harvest index (HI), with AR04 having the highest value. However, these genotypes were moderately to highly susceptible to blast, as evidenced by an increased number of spots per leaf (NSL) and blast severity (NBS). The second cluster (AR07, B-34) had poor agronomic performance but had high blast resistance. B-34 had the lowest NBS and NSL scores. The third cluster, which included SB, HD, and others, had overall poor to moderate performance, with SB being the most blast-susceptible. The aromatic characteristic 2-acetyl-1-pyrroline (2-AP) varied considerably between genotypes and had no significant association with yield or disease traits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) revealed variations across genotypes based on morphological and yield-related determinants. Dim1 accounted for 45.4% of the total variance and was positively associated with variables such as biological yield hill-1 (BY), panicle length (PL), number of panicle hills\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (PN), and filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (FG). Genotypes such as AR01, AR03, AR05, and AR04 made significant contributions to these traits, clustering in the positive Dim1 area. Dim2 accounted for 26.3% of the variation, influenced by negative correlations with characteristics such as leaf blast severity (LBS) and number of blast spots leaf\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (NSL), with genotypes such as B-34 and SB occupying this region. Genotypes such as KS, MO, and RS in the negative Dim1 and Dim2 quadrants exhibited low yield and blast susceptibility, whereas AR07 possessed unique clustering due to high leaf blast severity. The investigation highlighted the differential performance of genotypes, allowing the selection of desirable features for the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), constructed by hierarchical cluster analysis, divided 18 aromatic rice genotypes into four separate clusters based on 13 agro-morphological, yield, and blast-resistance variables. Cluster I (red) included genotypes with superior agronomic performance, particularly higher panicle length (PL), filled grains per panicle (FG), grain yield (GY), 1000-grain weight (1000-GW), and harvest index (HI), along with strong resistance to blast, as indicated by low scores for number of spots per leaf (NSL) and blast severity (NBS), with the exception of AR02 due to its blast susceptibility. Cluster II (green), which includes B-34 and AR07, was distinguished by their significant susceptibility to blast, having its relatively low yield attributes. Cluster III (blue) consisted solely of SB, which distinguished out due to its great the susceptibility to blast, as evidenced by higher NSL and NBS values. Cluster IV (magenta), which included HD, KS, MO, AR06, AR10, AR11, RS, and MN, showed moderate performance in both yield-contributing and blast-resistance characteristics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Detection of resistant genes employing molecular study\u003c/h2\u003e\u003cp\u003eThe presence of major blast-resistant (R) genes in diverse rice genotypes was determined molecular study using seven SSR (simple sequence repeat) markers: RM527, RM224, RM541, RM208, RM247, RM72, and RM206. The PCR amplification products were observed on a 2% agarose gel (Fig.\u0026nbsp;12), revealing different gene-specific bands across several genotypes. The Pi-1 gene (157 bp) was found in genotypes corresponding to lanes 2, 4\u0026ndash;6, 9, and 11\u0026ndash;19, and the Pi-ta gene (131 bp) was found in lanes 1, 3, 4, 6\u0026ndash;8, 10\u0026ndash;13, and 15\u0026ndash;19, demonstrating the presence of these genes in a large number of cultivars. The Pi-33 gene (166 bp) was amplified in lanes 1, 3, and 8, however the Pi-b gene (173 bp) was not present in any of the genotypes examined. The Piz-5 gene (233 bp) was found in lanes 3, 4, 7, 8, 10, 12, and 17, however no amplification of the Pi-9 gene (158 bp) was discovered, indicating that it was not present in the examined genotypes. The Pi-kh gene (147 base pairs) was successfully amplified in lanes 1, 2, 5, 7, 9, 10, 12, 14, 16, 17, and 19. These findings confirm the presence of multiple blast-resistance genes in the tested rice genotypes and demonstrate the efficacy of SSR markers for precise molecular characterization of resistance traits, making them an important tool for marker-assisted selection in rice breeding programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe marker-assisted selection of rice blast resistance genes helped to identify the blast-resistant rice genotypes. In the present study, the presence of 7 major rice blast resistance genes (\u003cem\u003ePi-9, Pi-1, Pi-5, Piz-b, Pi-ta, Pi-33\u003c/em\u003e, and \u003cem\u003ePi-kh\u003c/em\u003e) was identified in 19 aromatic rice genotypes, including F\u003csub\u003e1\u003c/sub\u003e hybrids. The available molecular markers that are linked to the major blast R genes were useful for identifying specific genes. Nine genotypes containing at least 2 expected bands of the 7 rice blast resistance markers. Eight genotypes of rice had three blast resistance genes, while two genotypes, AR-05 and AR-09, showed the highest number of blast resistance genes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of eighteen aromatic rice genotypes and their genotypic screening for blast resistance gene with SSR markers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\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\u003e\u003cem\u003ePi-9\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM541)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePi-1\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM224)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePi-kh\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM206)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePiz-5\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM527)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ePi-b\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM208)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ePi-33\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM72)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ePi-ta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e(RM247)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalovat sugondhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRRI dhan34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRana shail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultioverian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSadabadsha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHazardana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08 (S1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR08 (S2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eHere, 0\u0026thinsp;=\u0026thinsp;Absent, 1\u0026thinsp;=\u0026thinsp;Present, S1\u0026thinsp;=\u0026thinsp;Sample 1, S2\u0026thinsp;=\u0026thinsp;Sample 2\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe success of any crop improvement initiative hinges on the magnitude of genetic variability and the heritability of target traits. A comprehensive assessment of genetic diversity is fundamental for understanding relationships among cultivars and facilitating effective selection in breeding programs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, both phenotypic and genotypic associations were analyzed for key quality parameters, such as grain aroma, grain dimensions, 1000-grain weight, and yield, suggesting the possible presence of linkage or pleiotropy within specific genomic regions governing these traits. Notably, certain genomic loci associated with grain quality traits appear to have co-segregated in regionally adapted, farmer-preferred aromatic rice varieties through the course of domestication and selection [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Among the agronomic traits, panicle length, number of tillers per plant, grain weight, and harvest index were identified as critical determinants of yield potential in the F\u003csub\u003e1\u003c/sub\u003e hybrids [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These traits demonstrated strong and consistent associations with grain yield, indicating that indirect selection based on these parameters may significantly enhance breeding efficiency [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, plant height showed a positive correlation with panicle length, while reductions in filled grain number and grain yield were linked to lower panicle numbers. The harvest index, reflecting reproductive efficiency, and 1000-grain weight, indicative of seed size, were found to have a direct and substantial impact on grain yield [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These findings collectively underscore the potential of classical breeding approaches in combining desirable quality and yield traits in aromatic rice genotypes.\u003c/p\u003e\u003cp\u003eFlag leaf length is a critical morpho-physiological trait in rice, as it serves as a major photosynthetic organ contributing assimilates during the grain-filling period. It has been widely recognized for its positive association with yield-contributing traits, particularly panicle development and grain filling [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the present investigation, substantial genotypic variation in flag leaf length was recorded among the aromatic rice genotypes. Such variability is of considerable breeding interest, as prior studies have established a significant positive correlation between flag leaf length and panicle length, thereby underscoring its relevance as a potential selection criterion for yield improvement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe total number of tillers per hill varied significantly among the genotypes, with AR04 producing the highest and Hazardana the lowest. A similar trend was observed for effective tiller numbers, where AR04 again ranked highest, while Hazardana and Kalovat Sugondhi showed the lowest and statistically comparable values. Regarding grain production, AR07 demonstrated superior performance in total grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, closely followed by AR03, whereas Kalovat Sugondhi produced the least. For filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, AR07 maintained the highest count, with AR03 slightly lower, and Kalovat Sugondhi again exhibited the lowest. The large or heavy panicle type hybrid exhibited a poorer rate of grain filling (low filled grain number and weight), even under the favorable environment under, resulting in a lower extent of grain filling [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Panicle length showed considerable variation, with AR02 having the longest panicle and Sadabadsha the shortest. In our findings, maximum grain yield hill\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and harvest index were observed in AR04, whereas the maximum stover and biological yield were recorded in AR03. Recently numorus study [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] observed that grain yields different crops increasing over time have generally been associated with an increase in stover yield, whereas the harvest index has remained relatively stable, and in addition, the contrary situation has been observed in tropical germplasm, where grain yield production has been accompanied by an increasing harvest index while biomass yields were relatively stable.\u003c/p\u003e\u003cp\u003eA highly heritable trait, rice aroma is principally impacted by the essential component 2-AP and is regulated by certain genes. However, the genetic make-up of different aromatic rice types differs [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. According to the findings of the present investigation, the AR08 genotype had the highest level of 2-AP content, whereas lowest the Munni genotype showed the lowest level of 2-AP (0.69). Previous study has indicated that no one chemical or combination of compounds distinctly distinguished a particular aromatic rice variety in the volatile analysis of mature grains from both aromatic and non-aromatic rice. According to findings [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], the concentration of these compounds was what characterized the cultivars instead.\u003c/p\u003e\u003cp\u003eAdditionally, principal component analysis (PCA) and hierarchical cluster analysis revealed that the high-yielding genotypes AR01, AR03, AR04, and AR05 clustered together due to their superior yield components, with highly to moderate resistant to blast, as demonstrated by elevated NSL and NBS scales. In contrast, AR07 and B-34 showed great blast resistance but poor yield performance, implying a trade-off between disease resistance and yield-related characteristics. The combination of heatmap clustering, PCA, and hierarchical cluster analysis was beneficial for illustrating genetic diversity and determining behavioral interactions across rice genotypes [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These multivariate approaches supported the identification of resistance patterns and genotype grouping based on blast disease response [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], facilitating the selection of genetically diverse and resistant lines in rice breeding programs [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMolecular screening utilizing seven SSR markers revealed the presence of major blast-resistance genes, \u003cem\u003ePi-1, Pi-ta, Pi-kh\u003c/em\u003e, and \u003cem\u003ePiz-5\u003c/em\u003e, across multiple genotypes, with AR05 and AR09 containing the most resistance genes. The complete absence of \u003cem\u003ePi-b\u003c/em\u003e and \u003cem\u003ePi-9\u003c/em\u003e in all genotypes reveals a crucial gap in resistance coverage, emphasizing the need of introducing these broad-spectrum R genes through marker-assisted selection (MAS) to enhance resistance persistence [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Notably, AR03 and AR04 emerged as agronomically superior genotypes with strong blast resistance, demonstrating the value of combining molecular screening and phenotypic selection. These findings support the use of molecular techniques to accelerate the development of high-yielding, blast-resistant aromatic rice varieties [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on comprehensive evaluations of morphological characteristics, yield components, grain quality, and molecular screening, F\u003csub\u003e1\u003c/sub\u003e hybrids AR03 and AR04 emerged as the most promising candidates for the development of high-yielding aromatic rice. Hybrids AR01, AR05, and AR09 also demonstrated significant potential as sources of blast resistance, containing critical resistance genes. The integration of classical breeding with marker-assisted selection (MAS) is crucial for advancing these F\u003csub\u003e1\u003c/sub\u003e hybrids toward the dual goals of high yield and durable blast resistance, in line with modern strategies for aromatic rice improvement [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe findings of this study show the expression of hybrid vigor in the F\u003csub\u003e1\u003c/sub\u003e aromatic rice generation, which outperforms and is more resilient than its parental lines. Nine promising F\u003csub\u003e1\u003c/sub\u003e lines with moderate blast disease resistance have been developed through targeted classical breeding. AR01, AR03, AR04, AR05, and AR09 have the highest blast resistance and yield potential, making them promising prospects for future varietal advancement. In addition, lines including AR01, AR02, AR06, AR08, AR10, and AR11 exceeded their parents in yield attributes while preserving moderate blast resistance. he recommended genotypes AR01, AR03, AR04, AR05, and AR09 present valuable genetic resources for breeding programs aimed at enhancing yield, disease resistance, and sustainability, thereby contributing to food security and preserving the cultural heritage of aromatic rice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e M.M.R.: investigation, methodology, formal analysis and writing; M.M.R.: methodology, conceptualization, writing-review and editing; M.M.R.: methodology, conceptualization, writing-review and editing; M.A: methodology, conceptualization, writing-review and editing; D.A.N.M.: methodology; M.M.H.: investigation and writing; N.C.H.: investigation, formal analysis and writing; W.S.: funding acquisition; A.E.S.: funding acquisition; M.S.I.: methodology, writing-review and editing, conceptualization and funding acquisition. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the Institute of Research and Training (IRT), Hajee Mohammad Danesh Science and Technology University, Dinajpur-5200, Bangladesh and journal publication funded by the Researchers Supporting Project number (RSP2024R298) at King Saud University, Riyadh, Saudi Arabia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Data are available upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eAll the authors are thankful to Jute Research Centre, Bangladesh Jute Research Institute, Nashipur, Dinajpur, Bangladesh; BRAC Agricultural Research and Development centre, Gazipur and also thankful to the Chairman, Department of Agronomy, HSTU, Dinajpur, Bangladesh, for conducting the experiment smoothly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO. 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Genet.\u003c/em\u003e \u003cb\u003e138\u003c/b\u003e, 174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-025-04962-4\u003c/span\u003e\u003cspan address=\"10.1007/s00122-025-04962-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Classical breeding, Rice blast, Molecular markers, Oryza sativa, Rice","lastPublishedDoi":"10.21203/rs.3.rs-8276066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8276066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rice blast caused by the outbreak rapidly in Bangladesh brings about an imminent threat to the production of rice causing an average 40% yield loss in boro rice with infection rates ranging from 20 to 80%. Fewer high-yielding aromatic rice are available due to limiting genetic potential for high yields. To address these problems a study was conducted from July 2021 to December 2022 at the Jute Research Institute, Nashipur, Dinajpur, to develop high-yielding, blast-resistant F aromatic rice employing conventional breeding methods. The site at 24.000\u0026deg; N, 90.250\u0026deg; E, and 34 meters above sea level, is part of the Old Himalayan Piedmont Plain in Agro-Ecological Zone-1 (AEZ-1). The study shows that the F generation often exhibits hybrid vigor, characterised by greater resilience and performance compared to parent lines. This is promising for agriculture, especially in developing high-yielding, blast-resistant rice varieties that support sustainable farming and food security. The F genotype AR08 exhibited the maximum 2-acetyl-1-pyrroline (2AP) content, while the Munni genotype showed the minimum. Nine promising F aromatic rice lines with moderate to high blast resistance were developed through targeted parental crosses. Among them, AR03, AR04, and AR09 showed the best resistance to blast and yield potential, while other F genotypes like AR02, AR06, AR08, AR10, and AR11 outperformed their parents in yield traits with moderate blast resistance. AR01, AR03, AR04, AR05 and AR09 are recommended for developing blast-resistant, high-yielding rice varieties, offering significant potential for sustainable farming and preserving the cultural heritage of aromatic rice.","manuscriptTitle":"Combining Blast Resistance and High Yield in F1 Aromatic Rice Through Classical Hybrid Breeding Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 10:27:28","doi":"10.21203/rs.3.rs-8276066/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T13:19:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T06:19:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T06:10:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T13:25:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294496486045076970036260791146109123469","date":"2026-01-21T12:44:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T21:32:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75244455996696057245645387636451854130","date":"2026-01-20T13:45:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227063302987376426082381267121210461792","date":"2026-01-18T13:55:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62740815589088212248719626509611394245","date":"2026-01-18T08:21:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249428334292453264092999936594532612985","date":"2026-01-18T07:19:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327865414566456804956151038446805802469","date":"2026-01-04T16:37:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241273386028821799356923262836810338607","date":"2026-01-04T09:01:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T08:15:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133740185543967332358875267654754671160","date":"2025-12-23T05:42:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T05:39:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213628718400931563933434604122580543951","date":"2025-12-12T05:56:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310008459689464445355685142567608546582","date":"2025-12-10T14:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T12:02:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T12:01:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-08T09:27:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T11:34:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-05T11:23:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e61fad8-9098-4fe7-a241-ddfc31074d79","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59655746,"name":"Biological sciences/Biotechnology"},{"id":59655747,"name":"Biological sciences/Genetics"},{"id":59655748,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-27T03:25:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 10:27:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8276066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8276066","identity":"rs-8276066","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}