Marker-assisted backcross breeding for bacterial blight, blast and tolerance to low soil phosphorus in rice (Oryza sativa L.)

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Manoj Kumar Duppala, Srinivas T, Subba Rao L.V., Suneetha Y, Sundaram R. M., and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6286636/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Modern highly yielding rice varieties are susceptible to various biotic stress including bacterial blight (BB), and blast and abiotic stress like phosphorus (P) starvation tolerance. To address these vulnerabilities, the present study aims to achieve gene pyramiding through marker-assisted backcross breeding (MABB) to develop improved cultivars that harbor the Xa21 (BB), Pi54 (blast resistance) and Pup1 (phosphorus starvation tolerance) genes. The parent lines, AKDRMS 21–54, (carries Xa21 and Pi54 gene, used as donor parent) and YH3 (carries Pup1 gene, used as recipient parent) were used to make a 610 BC 2 F 2 population. Further, this population was screened for blast, BB and low P tolerance with targeted triple gene ( Xa21 + Pi54 + Pup1 ) homozygous plants and total of 16 BC 2 F 2 lines were identified. The identified BC 2 F 2 four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234 and YH3-22-15-311) were highly resistant to blast and six lines (YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61 and YH3-22-15-234) were highly resistant for BB. Moreover, assessment of the enhanced BC 2 F 2 lines for low P tolerance unveiled a consistent decline in various growth parameters under low P conditions as well as normal P conditions. In the BC 2 F 2 population, reductions in most parameters studied, such as grain yield, were relatively small compared to the parents and checks cultivars, indicating their potential for thriving in low P conditions. Among the 16 BC 2 F 2 lines, YH3-22-15-44 possessing maximum (91.2%) RPGR (recurrent parent genome recovery) along with pyramided of target genes ( Xa21 + Pi54 + Pup1 ), which showed the resistant against BB and blast and high grain yield, as compared to parents and checks. This identified potential line can be utilized in multi-location trials to ensure stable performance for rice growers in future. Backcross breeding Bacterial blight Blast Gene pyramiding Rice Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Rice ( Oryza sativa L.) serves as the primary staple food for billions across Asia and Africa, supporting half of the global population (Singh et al., 2024) and serving as the primary income source for disadvantaged rural communities (Satyanarayana et al., 2023). A significant impediment to crop productivity stems from a range of abiotic and biotic stresses, causing annual global yield losses of 30–60% (Duppala et al. , 2023). Bacterial blight (BB), caused by Xanthomonas oryzae pv. oryzae , is a one of the most detrimental and long-standing rice diseases. The yield loss from BB can reach up to 70% when susceptible varieties are grown, and under severe conditions in environments favorable to the disease, it can increase up to 100% (Yagnasree et al., 2024 ). Rice is a major contributor to high-quality nutrition in developing country (Kaur et al., 2023 ), but the presence of BB disease can drastically reduce its grain quality (Sumuni et al., 2024). Rice blast, caused by the fungus Magnaporthe grisea , a member of the Magnaporthaceae family, poses another formidable challenge. Blast fungus infects rice at different growth stages, leading to an annual yield loss of approximately 10–30% in moderate conditions and as high as 80–100% in epidemic conditions (Alam et al., 2024 ). Disease aggressiveness has also been reported to be related to nutrient inadequacies (Bera et al., 2024 ). The severity of pathogens is currently exacerbated by the changing environmental and soil conditions (Sharma et al., 2019 ). Nitrogen and P play crucial roles in crop growth as they constitute the fundamental building blocks of numerous organic molecules, nucleic acids and proteins (Maywald, et al., 2023 ), whereas P leads a pivotal role in local and systemic signaling of disease incidence and triggers the gene regulation to incite systemic disease resistance from plant immune system (Dong et al., 2019 ). Developing cultivars resistant to blast and BB, along with tolerance to low P, a challenge for the rice community, and a recent study combined these three traits through marker-assisted pedigree breeding to improve the elite Indian cultivar MTU1121 (Duppala et al., 2025). P is crucial for plant growth, being absorbed by plants in phosphate (Pi) form and facilitating tillering, root development, early flowering and ripening (Khan et al., 2023 ). It ranks as the second most limiting mineral nutrient in nearly all soils globally, with over 30% of arable land experiencing low P levels (Anjum et al., 2024 ). P deficiency in field conditions leads to stunted growth, reduced tillering, and flowering (Kekulandara et al., 2024 ). Presently, around 50% of agricultural soils in Asia, Africa, and South America exhibit P deficiency, with 51% of Indian soils and 79.7% of state soils falling into the low category for available P (Dey et al., 2017 ). Studies have shown a linear increase in rice yield with higher soil P content (Wang et al. , 2023). Given the increasing cost of fertilizers due to high energy prices and limited natural resources, along with disruptions caused by the Covid-19 pandemic, balanced and sustainable use of P fertilizer is paramount (Shukla et al., 2022 ), with limited fossil fuels and fertilizer reserves, alternative nutrient replenishment strategies are imperative. Addressing BB, blast disease, and low P tolerance in rice necessitates the adoption of low-input agricultural practices for sustain the rice production (Jyoti et al., 2024 ). Conventional breeding is a selective technique used to choose new plant varieties based on their superior performance. However, it is a lengthy process that heavily relies on plant phenotypes and can be influenced by various external factors (Duppala et al., 2025). Consequently, breeders began incorporating different biological disciplines into plant breeding, leading to the development of modern breeding practices (Lamichhane and Thapa, 2022 ). The use of DNA markers in plant breeding has greatly enhanced both its precision and efficiency. Marker-assisted selection (MAS) is now one of the most advanced methods available for introducing one or more desired genes or genomic regions into elite rice varieties in more stable combinations. Developing durable and broad-spectrum resistance in crops through smart breeding for climate-resilient agriculture represents a cost-effective and environmentally friendly method for controlling crop diseases, ultimately supporting sustainable agricultural practices (Bakala et al., 2020 ). Advances in understanding the molecular mechanisms of pathogenesis and plant-pathogen interactions have led to the creation of new breeding strategies that go beyond the limitations of traditional breeding methods (Rai and Rai, 2025). Relying on a single resistance (R) gene often results in resistance breaking down quickly as pathogens evolve to overcome the gene's effects. To achieve longer-lasting and broader resistance, pyramiding multiple R-genes that provide defense against different pathogen races through MAS has proven to be an effective strategy (Jamaloddin et al. , 2021). MAS involves identifying genomic regions linked to desired traits through molecular markers, enhancing selection precision and efficiency while reducing breeding costs (Alemu et al., 2024). Despite this, successful incorporation of beneficial alleles from these resources has been challenging while retaining all the good traits without compromising. Efficient methods such as marker-assisted backcrossing (MABC) and gene pyramiding have been suggested to support these efforts (Sanchez et al., 2023 ). Enriching diversity for quantitative traits presents challenges due to their polygenic nature, yet it remains a key focus of long-term research efforts. Markers tightly linked to resistance genes facilitate effective selection of resistant genotypes (Hasan et al., 2021 ). Backcrossing transfers favorable traits from a donor plant to an elite genotype (recurrent parent), aiming to eliminate donor genes except the target gene. However, donor segments linked to the target allele may persist even after multiple backcrossing generations, while marker assays aid in minimizing this linkage drag (Tourrette et al., 2021 ). MABC enables either control of the target gene (foreground selection) or acceleration of recurrent parent genotype reconstruction (background selection), minimizing linkage drag through recombinant selection (Al-Ashkar et al., 2023 ). MAS-946-1, an aerobic rice variety for dry sowing with scheduled irrigations (Quadri et al., 2023 ), Improved Samba Mahsuri (ISM), is an elite, high-yielding, BB resistant, fine-grained variety with low glycaemic index (Rekha et al. , 2022), Pusa Basmati 1121, that was improved for seedling-stage salinity tolerance (Grover et al. , 2020), Pusa Basmati 1718, a BB resistant version of Pusa Basmati 1121 (Singh et al. , 2023), PB 1509 (Yadav et al., 2020 ) are the successful rice varieties developed with the help of MAS and released at commercial level for rice growers. Sri Dhruthi (MTU 1121), initially improved for low soil P tolerance with the Pup1 gene, and it released as WGL 1487 cultivars for commercial cultivation but later it was high susceptibility to BB and blast diseases (Bisen et al., 2024 ). Hence, there's an urgent need to improve this variety by incorporating resistance genes for BB and blast. Gene pyramiding is key strategy to combined more than two genes in single cultivars through MABC approach. To keep in mind, the present study aims to develop and identify improved plants with low soil P tolerance and resistance to BB and blast through marker-assisted backcross breeding. 2. Materials and methods 2.1. Plant materials and marker-assisted pedigree breeding MTU 1121 (BPT 5204/MTU DP 13) popular as Sri Druthi is a medium duration, medium slender, high yielding variety with tolerance to BPH (brown plant hopper) for dry season cultivation. It was improved for tolerance to low soil P by introgression of Pup1 QTL from Kasalath through marker assisted backcross breeding. In present study, near-isogenic line (NIL) of MTU1121 harbouring Pup1 QTL, which is designated as YH3 (improved MTU1121) was used as female parent. Akshayadhan (BR827-35/SC109-2-2) is a medium duration, long bold, high yielding variety with tolerance to neck blast was improved for BB and blast following double cross of (Akshayadhan/ISM)/(Akshayadhan/Tetep). An advanced breeding line, AKDRMS 21–54 (improved Akshyadhan) carrying Xa21 gene for BB resistance, Pi54 gene for blast resistance and Pup1 QTL for low soil P tolerance was used as male parent for introgression of Xa21 and Pi54 genes into the YH3 for disease resistance (Fig. 1 ). During dry season 2019-20, crossing was initiated between the recurrent parent (RP) YH3 (possessing Pup1 ) and the donor parent (DP), AKDRMS 21–54 (possessing Xa21 , Pi54 and Pup1 ) for development of F 1 ’s. Then F 1 hybrids were verified for their heterozygosity utilizing specific markers targeting resistance genes: pTA248 for Xa21 (Ronald et al., 1992 ), Pi54MAS for Pi54 (Ramkumar et al., 2011 ), and k20-2 for Pup1 (Chin et al., 2010 ) (Table 1 ). Confirmed True F 1 ’s identified were then backcrossed with the RP to generate BC 1 F 1 ’s. They were subjected for foreground selection using gene-specific markers and positive plants ( i.e. , heterozygous BC 1 F 1 plants) were then analysed for recurrent parent genome recovery (RPGR) through background selection using a set of polymorphic SSR markers (122 markers) evenly distributed across the 12 rice chromosomes to identify plants with maximum RPGR (Table 2 ). A singular BC 1 F 1 plant exhibiting positivity for all target traits and maximal RPGR was chosen and backcrossed with RP to produce BC 2 F 1 offspring. Foreground and background selection utilizing marker-assisted techniques were reiterated among BC 2 F 1 plants, and a singular BC 2 F 1 plant harboring the target resistance genes along with maximal RPGR was self-pollinated to generate 610 BC 2 F 2 population (Fig. 1 ). These plants underwent phenotypic screening for BB, blast, and low soil P tolerance and were also evaluated for their agronomic traits and yield components. Table 1 Details of gene specific markers used in the foreground selection Molecular markers Linked gene Primer sequence Chromosome location Amplicon product size (bp) Reference Resistant Susceptible pTA248 Xa21 F: AGACGCGGGAAGGGTGGTTCCCGGA 11 982 725 Ronald et al. ( 1992 ) R: AGACGCGGGTAATCGAAAGATGAAA Pi54 MAS Pi54 F: CAATCTCCAAAGTTTTCAGG 11 216 359 Ramkumar et al. ( 2011 ) R: GCTTCAATCACTGCTAGACC k20-2 Pup1 F: CTGGACTTGACCCCAATGTA 12 402 349 231 582 413 Chin et al. ( 2010 ) R: TCTGATGGAGTGTTCGGAGT Table 2 Details of polymorphic markers used in the background analysis Chromosome number No. of markers used for analysis No. of polymorphic markers Name of the polymorphic primers 1 78 8 RM10092, RM8094, RM493, RM562, RM10936, RM11669, RM431, RM490 2 55 10 RM6367, RM110, RM279, RM555, RM6375, RM1358, RM5791, RM324, RM6, RM208 3 54 6 RM60, RM231, RM15303, RM15331, RM15630, RM520 4 74 11 RM16335, RM8213, RM16868, RM6679, RM3643, RM5979, RM1142, RM3524, RM5511, RM567, RM17705 5 50 13 RM159, RMES5-1, RM169, RM516, RM18362, RM18516, RM430, RM164, RM18704, RM3295, RM3870, RM31, RM334 6 59 12 RM589, RM19346, RM19381, RM19410, RM19660, JGT06-6.81, RM19691, RM287, RM28157, JGT06-18.1, RM20429, RM20577 7 49 9 RM6697, RM21260, RM3583, RM21435, JGT07-17.5, RM533, RM320, RM21649, RM248 8 55 10 RM6925, RM22709, RM38, RM1384, RM331, RM23237, RM23408, RM447, RM23518, RM3761 9 31 10 RMES09-2, RM23736, RM23766, RM23869, RM23877, RM23914, RM23959, RM219, RM566, RM24717 10 45 11 RM222, RM25366, Chr.10-16.3, RM25532, RM6100, RM171, RM1108, RM3808, RM25794, RM228, RM25940 11 39 9 Chr.11 − 2.2, RM26094, Chr.11 − 8.9, Chr.11 − 9.6, RM26567, RM26603, RM209, Chr.11-21.1, RM217 12 77 13 RM235, RM27406, RMES12-2, RM519, RM3448, ESSR12-20.2, RM28465, RM28472, RM28481, RM28484, RM3331, RM23553, RM19 Total 666 122 2.2. Phenotypic screening of improved plants Backcross derived plants (BC 2 F 2 ) together with parents and checks were assessed initially, for blast in Uniform Blast Nursery. Blast screening data was recorded at 30DAS (days after sowing) and highly resistant to moderately resistant plants were identified. The identified plants were transplanted to the main field to further study for blast, agronomic and yield components in normal and low soil P conditions. 2.2.1. Screening for BB disease The chosen breeding plants derived BC 2 F 2 along with along with parents (YH3 and AKDRMS 21–54), resistant (Improved Samba Mahsuri, referred as ISM), and susceptible (BPT 5204 and TN1) checks underwent evaluation for their resistance to BB during the dry season of 2021-22. For screening, IX0-20 , a virulent isolate of Xanthomonas oryzae pv. oryzae obtained from ICAR-IIRR (Fig. 2 ). Symptoms were recorded 15 days after inoculation on the plant's upper three leaves using the Standard Evaluation Scale (IRRI, 2013), designed to assess diseased leaf area and calculate the mean percentage of diseased leaf area (% DLA). The scale for diseased leaf area is as follows, scale 1: 1–5% DLA as resistant (R); scale 3: 6–12% DLA as moderately resistant (MR); scale 5: 13–25% DLA as moderately susceptible (MS); scale 7: 26–50% DLA as susceptible (S), and scale 9: 51–100% DLA as highly susceptible (HS). 2.2.2. Screening for blast disease BC 2 F 2 populations were screened for blast resistance in Uniform Blast Nursery (UBN) beds during the dry season of 2021-22, (Local IIRR isolate-SPI-40) of the blast pathogen Magnaporthe oryzae collected from Rajendranagar, Hyderabad, India. The seed of selected homozygous positive plants of BC 2 F 2, along with RP, DP, Tetep (resistant check) and Samba Mahsuri and HR12 (susceptible checks) were planted (Fig. 3 ). The symptoms and disease scoring were undertaken after 15 days of inoculation and complete drying of HR12, of the susceptible check. Test results were evaluated using IRRI's Standard Evaluation Scale (2013), The blast disease scoring system is as follows, score 0: no visible lesions; score 1: small brown specks, either pin-point size or larger, without a sporulating center; score 2: small, round to slightly elongated necrotic grey spots (1–2 mm in diameter) with a distinct brown margin; score 3: similar lesion characteristics as score 2, but with a significant number of lesions present on the upper leaves; score 4: typical susceptible blast lesions (≥ 3 mm in length) affecting less than 4% of the leaf area; score 5: blast lesions covering 4–10% of the leaf area; score 6: blast lesions spreading across 11–25% of the leaf area; score 7: blast lesions affecting 26–50% of the leaf area; score 8: blast lesions covering 51–75% of the leaf area, with many leaves exhibiting severe damage or dying; score 9: >75% of the leaf area is affected. The scale is based on the percentage of the leaf area affected by the disease and is categorized into five distinct levels, highly resistant (HR)-score 0–3; moderately resistant (MR)-score 4–5; moderately susceptible (MS)-score 6–7; highly susceptible (HS)-score 8–9. 2.2.3. Screening for low soil P tolerance and under controlled conditions The improved breeding lines, along with their parents and checks Swarna (resistant) and Samba Mahsuri and ISM (susceptible) were evaluated under low P conditions (0% P 2 O 5 /ha) and under controlled conditions (100% P 2 O 5 /ha) to assess the effectiveness of Pup1 QTL. The evaluation was conducted at ICAR-IIR, Hyderabad during the dry season ( Rabi 2021-22). This comparative study aimed to determine the advantages of improved breeding lines carrying Pup1 QTL, which enhances P uptake efficiency. The experimental plot was arranged using Augmented Block Design consisting of four blocks, with checks and parents replicated within each block. Phenotypic data was collected on various agro-morphological and yield-related traits, including days to 50% flowering (DFF in days), plant height (PH in cm), number of productive tillers (NPT in number), flag leaf length (FL in cm), flag leaf width (FW in cm), panicle length (PL in cm), number of grains per panicle (NGP in number), thousand kernel weight (TKW in g), root length (RL in cm), root volume (RV in ml), root dry weight (RDW in g), shoot length (SL in cm), shoot dry weight (SDW in g) and single plant yield (SPY in g). 2.3. Statistical analysis The recorded data under low P screening and controlled plot were subjugated to different statistical tests like critical differences (CD), ccoefficient of variation (CV), standard error (SE), analysis of variance (ANOVA), correlation analysis and principal component analysis (PCA), were calculated from both conditions using statistical analysis tools, running R programme 4.3.3.1 software (R Core team, 2013). 3. Results 3.1. Marker-assisted pyramiding of BB and blast disease resistance into YH3 A MABC strategy was employed to introgress the genes Xa21 (BB resistance) and Pi54 (blast resistance) from AKDRMS 21–54 (DP) into YH3 (RP), aiming to develop homozygous lines that maintain the genetic background of YH3 while incorporating the desired traits (Table 3 , Fig. 4 ). Hybridization was performed during dry season of 2019-20 between the RP and the DP, resulting in the generation of 120 F 1 plants. Out of these, 46 F 1 plants were found to be heterozygous for both Xa21 and Pi54 resistance genes, as confirmed by molecular markers. These heterozygous plants were selected for backcrossing to YH3 to develop the BC 1 F 1 generation. During dry season 2020-21, 128 BC 1 F 1 plants were grown from the selected F 1 plants, and these were subjected to foreground selection using molecular markers for Xa21 and Pi54 . Of these, 18 plants were confirmed to be true BC 1 F 1 hybrids, carrying both resistance genes ( Xa21 + Pi54 ) in the heterozygous state. The RPGR confirmed of 18 BC 1 F 1 hybrids were assessed using 122 polymorphic SSR markers (Table 2 ), and the plant, YH3-22 was identified with 76.7% RPGR. This plant was selected for further backcrossing to YH3, resulting in the development of 102 BC 2 F 1 hybrids. Foreground selection was carried out for both Xa21 and Pi54 resistance genes, and 10 plants were found to be heterozygous for both genes ( Xa21 + Pi54 ). RPGR analysis showed that YH3-22-15 had the highest RPGR of 87.5% and this plant was self-pollinated to produce the BC 2 F 2 generation. In the dry season of 2021-22, 610 BC 2 F 2 population were grown and subjected to foreground selection for homozygosity of Xa21 and Pi54 genes, and 16 plants were homozygous for both these genes. RPGR analysis revealed that YH3-22-15-44 exhibited the highest RPGR of 91.2%. Table 3 Plants analyzed for target genes in different backcross generations S. No Generation Total no. of plants analyzed Total no. positive plants to all three target genes ( Xa21 + Pi54 + Pup1 ) Per cent of genome recovery at each generation 1 F 1 120 46 - 2 BC 1 F 1 128 18 76.7% (YH3-22) 3 BC 2 F 1 102 10 87.5% (YH3-22-15) 4/j BC 2 F 2 610 16 91.2% (YH3-22-15-44) 3.2. Assessment of improved breeding lines for BB, blast and low soil P tolerance 3.2.1. BB and blast disease resistance The BB disease severity among the 16 BC 2 F 2 plants varied from 3.90–44.26%, corresponding to scores ranging from 1 (R) to 3 (MR). The plants YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234 were all observed to be resistant, scoring 1, indicating a low level of disease severity. The susceptible checks, TN1 and Samba Mahsuri, exhibited susceptible reactions, with a score of 7. The positive control, ISM, showed a highly resistant reaction with a score of 1 (Table 4 , Figs. 5 and 6 ). The RP demonstrated a MS reaction, with a disease severity score of 5 (13–25% DLA), while the DP showed a MR reaction with a score of 3 (6–12% DLA). These results indicate that the developed BC 2 F 2 plants demonstrated improved BB resistance compared to the RP (YH3). Blast severity scores ranged from 1 to 5, where nine plants exhibited a HR reaction with scores of 1 or 2. Notably, four plants (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311) recorded a blast score of 1, indicating exceptional resistance. The resistant check, Tetep, exhibited a HR reaction with a score of 1. Conversely, the susceptible checks, Samba Mahsuri and HR12, showed MS (score 7) and HS (score 9), respectively. The RP exhibited a MS reaction with a score of 6, while the DP demonstrated a HR reaction with a score of 2. These results suggest that the developed BC 2 F 2 plants have commendable resistance to both BB and blast diseases compared to the female parent, YH3. Table 4 Screening of improved BC 2 F 2 plants for BB and blast S. No. Entry BB resistance against IXO-20 isolate Blast resistance against SPI-40 isolate Diseased leaf area (%) BB Score Reaction Allelic status ( Xa21 ) Blast Score Reaction Allelic status ( Pi54 ) Improved BC 2 F 2 plants 1 YH3-22-15-3 5.87 1 R RR 2.0 HR RR 2 YH3-22-15-8 7.90 3 MR RR 4.0 MR RR 3 YH3-22-15-15 10.50 3 MR RR 5.0 MR RR 4 YH3-22-15-19 4.53 1 R RR 1.0 HR RR 5 YH3-22-15-31 6.85 3 MR RR 4.0 MR RR 6 YH3-22-15-34 7.34 3 MR RR 5.0 MR RR 7 YH3-22-15-36 5.09 1 R RR 3.0 HR RR 8 YH3-22-15-44 4.02 1 R RR 1.0 HR RR 9 YH3-22-15-61 4.31 1 R RR 3.0 HR RR 10 YH3-22-15-84 9.50 3 MR RR 4.0 MR RR 11 YH3-22-15-95 6.87 3 MR RR 3.0 HR RR 12 YH3-22-15-141 12.50 3 MR RR 5.0 MR RR 13 YH3-22-15-182 7.34 3 MR RR 4.0 MR RR 14 YH3-22-15-234 3.90 1 R RR 1.0 HR RR 15 YH3-22-15-250 11.80 3 MR RR 2.0 HR RR 16 YH3-22-15-311 6.76 3 MR RR 1.0 HR RR Parents 1 YH3 (Female Parent) 20.78 5 MS rr 6.0 MS rr 2 AKDRMS 21–54 (Male parent) 6.47 3 MR RR 2.0 HR RR Checks 1 BPT 5204 (Susceptible check) 32.17 7 S rr 7.0 MS rr 2 Taichung Native-1 (Susceptible check) 44.26 7 S rr 9.0 HS rr 3 Improved Samba Mahsuri (Resistance check) 4.90 1 R RR 1.0 HR RR Disease reaction: S- Susceptible; MS- Moderately Susceptible; HS- Highly Susceptible; R- Resistance; MR- Moderately Resistance; HR- Highly Resistance 3.2.2. Low soil P tolerance A general reduction in morphological trait values was observed in the low P plot compared to the normal P plot, highlighting the adverse effects of P deficiency (Table 5 ). Despite this, several BC 2 F 2 plants outperformed both the RP (YH3) and the tolerant check, Swarna, demonstrating superior adaptation to low P stress. Among the evaluated traits, YH3-22-15-250 exhibited a significantly greater FL than both YH3 and Swarna, suggesting improved leaf development under nutrient-limited conditions. Similarly, YH3-22-15-15, YH3-22-15-44, YH3-22-15-84, and YH3-22-15-182 showed enhanced FW, indicating a potential advantage in photosynthetic efficiency. For SL, eight BC 2 F 2 plants, including YH3-22-15-8, YH3-22-15-15, YH3-22-15-19, and YH3-22-15-44, surpassed both parents, suggesting better overall vegetative growth. In terms of biomass accumulation, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, and YH3-22-15-182 exhibited the highest SDW, surpassing both parental lines. Root traits also varied significantly, with RL ranging from 15.00 cm (YH3-22-15-182) to 22.80 cm (YH3-22-15-234), the exceeding Swarna (22.30 cm). Notably, YH3-22-15-234 also recorded superior RDW, indicative of improved root biomass under low P conditions. Additionally, RV was significantly higher in YH3-22-15-34, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311 compared to YH3. Flowering time varied among the BC 2 F 2 plants, with genotypes like YH3-22-15-19 and YH3-22-15-34 exhibiting delayed flowering, whereas YH3-22-15-8 and YH3-22-15-31 flowered earlier than both parents. Furthermore, YH3-22-15-19, YH3-22-15-44, and other promising lines displayed improved PL, NGP, and TKW, exceeding the performance of both YH3 and Swarna. Notably, YH3-22-15-19 and YH3-22-15-44 achieved significantly higher SPY under low P stress, underscoring their potential as high-yielding candidates for nutrient-deficient environments. Table 5 Mean performance of BC 2 F 2 plants under low P conditions Genotype Days to 50 per cent flowering Shoot length (cm) Shoot dry weight (g) Root length (cm) Root dry weight (g) Root volume (ml) Plant height (cm) Number of productive tillers Flag leaf length (cm) Flag leaf width (cm) Panicle length (cm) Number of grains per panicle (g) Thousand kernel weight (g) Single plant yield (g) BC 2 F 2 Plants YH3-22-15-3 102.00 59.80 3.20 20.00 1.22 9.00 89.55 11.00 29.20 1.10 24.70 182.00 22.56 25.86 YH3-22-15-8 99.00 60.40 3.45 18.90 0.89 10.50 93.27 9.00 30.70 1.20 23.30 188.00 23.10 16.78 YH3-22-15-15 100.00 56.80 3.17 19.30 0.92 11.30 105.00 8.00 31.80 1.10 24.70 130.00 25.13 10.72 YH3-22-15-19 105.00 55.80 2.87 22.50 1.32 9.40 92.00 13.00 32.80 1.00 23.10 230.00 31.65 36.56 YH3-22-15-31 98.00 58.90 3.42 21.40 1.20 12.40 106.50 7.00 30.20 1.30 23.20 150.00 24.30 15.62 YH3-22-15-34 106.00 60.00 3.98 25.50 1.30 13.20 100.00 8.00 28.60 1.40 24.20 133.00 23.60 16.78 YH3-22-15-36 101.00 55.00 2.87 26.80 1.32 10.80 85.80 12.00 30.90 1.10 24.80 159.00 23.40 31.67 YH3-22-15-44 108.00 63.00 3.78 31.50 1.50 16.00 91.00 10.00 34.50 1.20 26.40 260.00 26.20 35.67 YH3-22-15-61 106.00 51.50 2.10 29.70 1.45 12.30 86.80 11.00 30.40 1.40 24.30 211.00 24.50 39.67 YH3-22-15-84 100.00 54.80 2.55 28.50 1.35 11.50 92.40 9.00 33.40 1.50 23.80 127.00 24.30 29.76 YH3-22-15-95 111.00 59.80 3.00 25.30 1.24 12.00 95.50 10.00 29.32 1.30 19.20 133.00 23.40 38.40 YH3-22-15-141 107.00 55.80 2.31 23.20 1.20 8.50 100.80 6.00 29.80 1.20 24.40 182.00 25.60 9.85 YH3-22-15-182 116.00 65.30 4.04 23.50 1.35 12.00 106.54 9.00 30.00 1.10 25.10 157.00 23.20 27.20 YH3-22-15-234 98.00 51.20 2.30 27.30 1.42 11.80 111.10 12.00 32.70 1.20 22.10 175.00 24.40 27.80 YH3-22-15-250 100.00 57.80 2.55 24.10 1.23 9.60 94.70 9.00 34.80 1.10 22.30 160.00 27.69 14.42 YH3-22-15-311 106.00 54.60 2.00 20.80 0.92 11.50 86.80 8.00 35.30 1.30 23.20 130.00 34.08 19.42 Minimum 98.00 51.20 2.00 18.90 0.89 8.50 85.80 6.00 28.60 1.00 19.20 127.00 22.56 9.85 Maximum 116.00 65.30 4.04 31.50 1.50 16.00 111.10 13.00 35.30 1.50 26.40 260.00 34.08 39.67 Mean 103.94 57.53 2.97 24.27 1.24 11.36 96.11 9.50 31.53 1.22 23.68 169.19 25.44 24.76 Parents YH3 109.00 58.70 3.32 20.10 1.11 11.00 83.50 9.00 28.50 1.30 25.50 200.00 25.55 33.80 AKDRMS 21–54 120.00 62.00 2.60 22.00 1.00 8.80 93.00 12.00 33.00 1.10 21.80 117.00 20.60 22.20 Mean 114.50 60.35 2.96 21.05 1.06 9.90 88.25 10.50 30.75 1.20 23.65 158.50 23.08 28.00 Checks BPT 5204 111.00 60.50 2.89 23.50 1.05 6.50 94.50 11.00 37.50 1.10 19.40 130.00 15.15 26.65 Improved Samba Mahsuri 110.00 62.50 3.00 22.10 1.11 7.20 87.80 12.00 31.60 1.40 20.40 130.00 16.20 22.45 Swarna 103.00 55.80 3.11 21.56 0.90 10.50 84.76 12.00 35.60 1.40 21.67 148.00 20.12 23.56 Mean 108.00 59.60 3.00 22.40 1.00 8.10 89.00 11.70 34.90 1.30 20.50 136.00 17.16 24.20 Overall Mean 105.50 58.10 3.00 23.70 1.20 10.80 94.30 9.90 31.90 1.20 23.20 163.40 24.03 25.00 3.2.3. Morphological characterization of improved breeding lines in control P conditions Under control conditions, Swarna exhibited the highest FL among all genotypes, while all BC 2 F 2 plants surpassed YH3. Notably, only YH3-22-15-84 displayed a greater FW than both parental lines (Table 6 ). Several BC 2 F 2 plants, including YH3-22-15-3, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, YH3-22-15-95, and YH3-22-15-182, exhibited significantly greater SL than YH3 and Swarna. Additionally, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, and YH3-22-15-182 recorded the highest SDW under both control and low P conditions. RL in the control plot varied from 18.90 cm (YH3-22-15-8) to 31.50 cm (YH3-22-15-44), with Swarna recording 21.56 cm. Several BC 2 F 2 plants, including YH3-22-15-19, YH3-22-15-34, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, YH3-22-15-84, YH3-22-15-95, YH3-22-15-141, YH3-22-15-182, YH3-22-15-234, and YH3-22-15-250, exhibited significantly longer roots than both parents. Except for YH3-22-15-8, all BC 2 F 2 plants recorded a higher RDW than Swarna, with YH3-22-15-234 showing the most significant increase. However, none of the BC 2 F 2 plants exceeded YH3 in RDW. RV was also enhanced in most BC 2 F 2 plants, except for YH3-22-15-3, YH3-22-15-8, YH3-22-15-19, YH3-22-15-141, and YH3-22-15-250, which recorded values lower than Swarna. Variations in flowering time were observed, with YH3-22-15-95 and YH3-22-15-182 flowering delayed than YH3, whereas YH3-22-15-3 and YH3-22-15-31 flowered earlier. All BC 2 F 2 plants exhibited greater PH than YH3 and Swarna. Productive tillers ranged from 6 (YH3-22-15-141) to 13 (YH3-22-15-19), with only YH3-22-15-19 surpassing both parents. YH3-22-15-44 displayed the longest PL, while the NGP ranged from 127 to 260, with YH3-22-15-44 recording the highest values under both control and low P conditions. Most BC 2 F 2 plants exhibited higher TKW than Swarna across both conditions. SPY analysis revealed that YH3-22-15-19 and YH3-22-15-61 outperformed both YH3 and Swarna in the control plot. Overall, in comparison to the normal P plot, reductions in FL (6.90%), SL (7.40%), SDW (30%), RL (24.05%), RDW (50%), and RV (19.44%) were observed in the low P plot, highlighting the stress induced by P deprivation. The low P stress led to a general decline in flowering time, PH, NPT, PL, NGP, TW and SPY compared to the control plot, with reductions of 8.80%, 9.09%, 6.90%, 15.79%, 12.05%, and 34.00%, respectively. Table 6 Mean performance of BC 2 F 2 plants under low P conditions Genotype Days to 50 per cent flowering Shoot length (cm) Shoot dry weight (g) Root length (cm) Root dry weight (g) Root volume (ml) Plant height (cm) Number of productive tillers Flag leaf length (cm) Flag leaf width (cm) Panicle length (cm) Number of grains per panicle (g) Thousand kernel weight (g) Single plant yield (g) BC 2 F 2 Plants YH3-22-15-3 106.00 50.50 2.26 20.60 0.56 8.00 85.00 10.00 27.00 1.20 21.20 152.00 22.35 18.58 YH3-22-15-8 100.00 56.50 2.50 17.80 0.42 9.10 88.90 9.00 32.70 1.10 21.70 168.00 21.10 16.00 YH3-22-15-15 102.00 60.50 3.10 18.00 0.45 9.50 90.70 10.00 30.50 1.30 22.00 91.00 22.00 15.50 YH3-22-15-19 111.00 58.30 2.51 21.50 0.60 8.50 88.50 11.00 30.60 0.95 24.10 171.00 23.80 20.00 YH3-22-15-31 102.00 51.20 2.09 17.10 0.49 9.70 92.50 7.00 28.70 1.20 18.50 118.00 21.30 13.80 YH3-22-15-34 111.00 57.00 2.20 15.00 0.43 10.70 82.40 9.00 30.00 1.10 22.00 129.00 22.50 16.50 YH3-22-15-36 105.00 55.65 2.00 16.50 0.48 8.60 89.60 10.00 33.70 1.00 23.80 141.00 21.80 17.80 YH3-22-15-44 110.00 56.80 2.70 22.20 0.78 11.50 87.50 12.00 31.60 1.21 23.70 182.00 23.20 22.50 YH3-22-15-61 108.00 50.50 1.92 20.20 0.60 9.50 82.00 10.00 25.40 1.10 22.20 179.00 21.00 21.00 YH3-22-15-84 115.00 50.90 1.82 17.70 0.57 9.30 79.80 7.00 32.80 1.50 24.60 132.00 19.50 14.00 YH3-22-15-95 106.00 55.80 2.12 16.80 0.45 9.80 85.00 8.00 28.20 1.10 20.70 108.00 22.25 17.50 YH3-22-15-141 108.00 55.50 1.76 16.00 0.51 7.50 80.00 9.00 29.50 1.10 20.90 120.00 23.80 15.30 YH3-22-15-182 111.00 57.80 2.58 15.00 0.48 9.00 92.40 10.00 25.90 1.30 22.20 129.00 21.30 18.00 YH3-22-15-234 104.00 50.50 1.58 22.80 0.69 10.80 95.50 11.00 28.70 1.20 24.70 162.00 23.10 21.50 YH3-22-15-250 108.00 54.40 1.65 17.80 0.58 8.50 86.50 7.00 36.80 1.20 18.80 134.00 24.80 14.23 YH3-22-15-311 115.00 52.10 1.50 17.50 0.63 11.00 88.00 11.00 30.80 1.11 20.60 165.00 25.00 19.50 Minimum 100.00 50.50 1.50 15.00 0.42 7.50 79.80 7.00 25.40 0.95 18.50 91.00 19.50 13.80 Maximum 115.00 60.50 3.10 22.80 0.78 11.50 95.50 12.00 36.80 1.50 24.70 182.00 25.00 22.50 Mean 107.63 54.62 2.14 18.28 0.54 9.44 87.14 9.44 30.18 1.17 21.98 142.56 22.43 17.61 Parents YH3 108.00 55.60 2.58 20.50 0.89 10.50 88.80 11.00 27.80 1.20 22.80 168.00 22.42 19.50 AKDRMS 21–54 121.00 51.60 1.45 19.60 0.67 7.50 82.80 8.00 25.70 1.10 21.50 107.00 19.12 14.50 Mean 114.50 53.60 2.02 20.05 0.78 9.00 85.80 9.50 26.75 1.15 22.15 137.50 20.77 17.00 Checks BPT 5204 118.00 45.80 1.32 11.50 0.32 2.80 73.80 4.00 24.50 1.00 17.50 102.00 12.02 6.64 Improved Samba Mahsuri 115.00 48.50 1.43 12.30 0.28 3.40 80.65 5.00 27.80 1.20 19.50 106.00 13.23 7.12 Swarna 105.00 53.60 2.12 22.30 0.75 8.50 86.56 10.00 34.60 1.15 20.50 126.00 18.32 17.50 Mean 112.70 49.30 1.60 15.40 0.50 4.90 80.30 6.30 29.00 1.10 19.20 111.30 14.52 10.40 Overall Mean 109.00 53.80 2.10 18.00 0.60 8.70 86.00 9.00 29.70 1.20 21.60 137.60 21.14 16.50 3.3. Correlation analysis for the yield attributes in control and low soil P conditions Under control conditions, among the fourteen traits studied RL (0.49*), RDW (0.53*) and NPT (0.64**) revealed significant and positive association with SPY (Fig. 7 and Table S1 ). Inter correlation among the important traits such as DFF showed significant positive association with SL (0.58**); similarly, SL with SDW (0.73**); RL with RDW (0.82**) and RV (0.51*); RDW with RV (0.46*) and NGP (0.49*); RV with PL (0.54*) and PL with NGP (0.54*). Under low P, among the fourteen yield and yield attributing traits studied, SL (0.47*), SDW (0.47*), RL (0.79**), RDW (0.70**), RV (0.84**), PH (0.59**), NPT (0.94**), PL (0.67**), NGP (0.74**) and TKW (0.60**) revealed significant and positive association with SPY. Inter correlations under low P conditions, among the important traits such as SL showed significant positive association with SDW (0.80**), RV (0.54*), PH (0.49*), NPT (0.58**) and TKW (0.49*); similarly, SDW with RV (0.49*), PH (0.49*), NPT (0.57**); RL with RDW (0.84**), RV (0.61**), PH (0.49**), NPT (0.74**), PL (0.56**) and NGP (0.61**); RDW with RV (0.59**), NPT (0.67**), PL (0.45**) and NGP (0.57**); RV with PH (0.64*), NPT (0.77**), PL (0.56**), NGP (0.53*) and TKW (0.62**); PH with NPT (0.62**) and TKW (0.45*); NPT with PL (0.68**), NGP (0.68**) and TKW (0.58**); PL with NGP (0.53*) and NGP with TKW (0.47*). 3.4. Principal component analysis (PCA) for the yield attributes in control and low soil P conditions In present study, PCA was performed for 14 quantitative traits of rice under control and P stress conditions. Under control conditions, out of 14 principal components (PCs), only five PCs exhibited more than 1.00 eigen value (Fig. 8 and Table S2 ) viz ., PC1 (3.31), PC2 (2.64), PC3 (2.17), PC4 (1.53) and PC5 (1.19) accounting for 77.41% variability among the traits studied for each genotype. Rotated component matrix revealed that the first PC was more negatively related to seed yield and its contributing traits such as seed yield (-0.38), NGP (-0.38), PL (-0.31), along with RL (-0.42) and RDW (-0.47), however it was positively related to vegetative traits such as DFF (0.13), SL (0.20) and FL (0.12) suggesting that PC1 reveals that the tendency of each genotype to emphasize vegetative, as compared to reproductive growth. The second PC was positively related to seed yield and its contributing traits such as seed yield (0.35), NPT (0.45) along with RL (0.16) and FL (0.33). The predominant PCA scores of genotypes on PC1 are exhibited by YH3-22-15-44 (22.04), YH3-22-15-61 (16.50) and BPT 5204 (12.18), further PCA scores of genotypes on PC2 are exhibited by YH3-22-15-31 (10.11), YH3-22-15-34 (11.92) and YH3-22-15-182 (12.58). Similarly, under low soil P conditions out of 14 PCs, only four PCs exhibited more than 1.00 eigen value viz ., PC1 (6.71), PC2 (1.88), PC3 (1.26) and PC4 (1.01) accounting for 77.56% variability among the traits studied. Varimax rotation revealed first PC was positively related to DTF (0.20) alone, and more negatively related to all other traits especially yield attributes like SPY (-0.36), NGP & TW (-0.27), TW (-0.25) and NPT (-0.36) along with other key attributes like RL (-0.31) and RDW (-0.27), however. The second PC was positively related to seed yield and its contributing traits such as seed yield (0.17), NGP (0.34), PL (0.14) and NPT (0.10) along with RL (0.26), RDW (0.37) and DFF (0.41). The predominant PCA scores of genotypes on PC1 are exhibited by YH3-22-15-44 (10.50), BPT 5204 (36.95) and ISM (22.64) further PCA scores of genotypes on PC2 are exhibited by YH3-22-15-15 (28.15), YH3-22-15-61 (10.65) and AKDRMS 21–54 (11.56). Discussion Rice, a vital staple crop for the global population, faces numerous challenges that threaten its productivity and sustainability. Among these, emerging pathotypes of diseases and various abiotic stress factors such as P deficiency are significant obstacles to achieving higher yields. The constant evolution of these biotic and abiotic stresses necessitates continuous improvement of rice varieties to sustain global food security (Singh et al., 2025). Traditional breeding methods, although useful, are often slow and labor-intensive. As such, marker-assisted breeding has emerged as an effective strategy for rapidly introgressing resistance genes from donor varieties into elite cultivars, allowing for the timely deployment of disease-resistant and stress-tolerant varieties (Manojkumar et al., 2022 ; Duppala et al., 2025). This study focused on enhancing the genetic resistance of the short-duration, medium-slender, high-yielding rice variety YH3 by incorporating resistance genes for major biotic stresses such as BB, blast disease, and abiotic stress tolerance to low P through MABC. In this study, the DP (AKDRMS 21–54) was selected for its known resistance genes, Xa21 for BB resistance, Pi54 for blast disease resistance, and Pup1 for P deficiency tolerance. These genes have been extensively studied for their effectiveness in conferring resistance to specific stresses (Duppala et al., 2025). Xa21 gene is well-documented for providing robust resistance to Xoo , by triggering a defense response that effectively limits 88% of Xoo strains in India (Mishra et al., 2013 ). Similarly, Pi54 gene, situated on chromosome 11, confers stable and enduring resistance against various strains of M. oryzae prevalent across India (Thakur et al. , 2015). Upon pathogen entry into the host, the Pi54 gene initiates the synthesis of callose (β-1,3-glucan), forming a physical barrier that impedes fungal hyphae penetration (Gupta et al., 2012 ). The Pup1 QTL, located on chromosome 12, plays a crucial role in enhancing P uptake and utilization, making it vital for rice cultivation in P-deficient soils (Chin et al., 2010 ). In our study, BC 2 F 2 plants carrying Pi54 exhibited resistance scores ranging from 1 to 5, with four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311) showing the HR (score of 1), which was significantly better than the susceptible RP (score = 6) and the DP (score of 2). Chukwu et al. ( 2019 ) highlighted the rice variety PUTRA-1, containing Pi blast resistance genes, exhibiting high resistance to rice blast, with disease scores ranging from 0 to 1 and a mean score of 0, indicating a significant resistance to blast infection. On the other hand, the other parent, IRBB 60, demonstrated MR to blast infection, with a mean disease score of 3. The BC 2 F 2 plants also exhibited resistance to blast disease, with scores ranging from 0 to 1 and a mean score of 1. Identification of these transgressive segregants with enhanced blast resistance is an important achievement, as it not only improves disease resistance in the target variety but also provides valuable genetic material for future breeding programs. In this study, six BC 2 F 2 lines, including YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234, exhibited strong resistance (score of 1) to BB, while ten plants showed MR (score of 3). This performance was notably superior to that of the RP, which displayed moderate susceptibility (score of 5), and DP which exhibited MR (score of 3). These results align with those of Aleena et al. ( 2022 ), who reported the identification of resistant lines in a BC 2 F 2 population derived from the NLR 34449 genotype. The consistent resistance observed in the BC 2 F 2 lines suggests that the Xa21 gene was successfully introgressed into the RP, providing robust protection against BB. These findings highlight the utility of Xa21 in breeding programs aimed at developing BB resistant rice varieties, especially in regions where this disease is endemic. P is an essential macronutrient that significantly influences rice growth and development. Low P availability in soils limits rice productivity by affecting root growth, tillering, grain filling, and overall yield (Chithrameenal et al., 2018 ). Rice plants adapt to low P conditions by modifying root morphology and enhancing the efficiency of P uptake (Swamy et al., 2020). In this study, BC 2 F 2 population were evaluated for their performance under low P stress, and several lines demonstrated superior performance compared to susceptible checks, including Samba Mahsuri and ISM. For instance, the SPY in the low P plot ranged from 13.80 g (YH3-22-15-31) to 22.50 g (YH3-22-15-44), with the RP and DP recorded yields of 19.50 g and 14.50 g, respectively. The tolerant check Swarna produced 17.50 g, indicating that the selected lines outperformed both parents and checks under P-deficient conditions. Furthermore, early flowering was observed in the BC 2 F 2 plants (107.63 days), which contrasts with the delayed flowering observed in susceptible genotypes. This supports with findings from Dobermann and Fairhurst ( 2000 ) and Atakora et al. ( 2015 ), who noted that P deficiency typically causes delayed flowering in rice. The ability of the improved lines to flower earlier than susceptible checks under low P stress suggests that these lines possess enhanced P-use efficiency, a critical trait for growing rice in low-P soils. Similar findings were reported earlier by Honappa ( 2021 ) and Kavitha et al. ( 2022 ). Correlation analysis revealed that RL, RDW, and NPT had a significant positive association with SPY under both normal and low P conditions. These traits are therefore valuable selection criteria for P-efficient genotypes. Among the BC 2 F 2 plants, four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234) exhibited superior yield performance under low P conditions, surpassing both YH3 and Swarna. Notably, YH3-22-15-44, which carries the resistance genes Xa21 , Pi54 , and Pup1 , demonstrated the highest SPY under both normal and low P conditions. This line exhibited a 5.53% yield advantage over YH3 under normal phosphorus conditions and a 15.3% advantage under low P stress, making it a promising candidate for future cultivation. Conclusion The BC 2 F 2 plant, YH3-22-15-44, harboring the Xa21 + Pi54 + Pup1 , has emerged as a promising candidate for improved rice cultivation with resistance to multiple stresses. This line achieved a remarkable 91.2% RPGR, demonstrating its close resemblance to the elite cultivar YH3. Notably, it exhibited exceptional disease resistance, with a blast score of 1 and a BB score of 1, indicating its robust defense against both M. oryzae and X. oryzae infections. Additionally, YH3-22-15-44 showed a significant improvement in SPY, surpassing both of its parents and the checks under both normal and low P conditions. The yield advantage was particularly evident under stress conditions, with a 15.3% increase in yield under low P, compared to a 5.53% increase under normal P conditions. Given these promising results, YH3-22-15-44 is identified as a potential improved line for developing a high-yielding, disease-resistant variety with enhanced tolerance to low soil P. However, further evaluation is required in subsequent generations (BC 2 F 4 and beyond) across multiple locations and growing seasons to confirm its stability and performance for both target stresses and yield traits before considering commercial exploitation. Additionally, grain quality and resistance to other major pests should be investigated to ensure the overall suitability of this line for large-scale cultivation. Furthermore, two other lines, YH3-22-15-19 and YH3-22-15-234, exhibited exceptional resistance to both blast and BB, coupled with higher SPY than the checks under both normal and low P conditions. These lines also hold promise and warrant further evaluation for their grain quality, yield potential, and stress resistance across different environments and seasons. Overall, this study has identified several promising BC 2 F 2 lines with enhanced resistance to major biotic and abiotic stresses, coupled with superior yield performance, making them suitable candidates for future breeding programs aimed at improving rice productivity and resilience under challenging growing conditions. Declarations Funding Declaration: The financial support provided in the form of INSPIRE JRF and SRF Fellowships by the Department of Science & Technology (DST), Ministry of Science & Technology, New Delhi, Government of India, through order No. DST/INSPIRE Fellowship/[IF180552] Dated: 24 July, 2019 to the first and corresponding author, for pursuing full time Doctoral research (Ph.D.) program of Acharya N G Ranga Agricultural University, Lam, Guntur, at Agricultural College, Bapatla, Regional Agricultural Research Station, Maruteru, Andhra Pradesh, and ICAR – IIRR, Hyderabad, Telangana, India is acknowledged. Data Availability declaration: The datasets generated during the current study are available from the corresponding author on reasonable request. Competing Interest declaration: The authors declare that they have no conflict of interest. Author Contribution declaration: Manoj kumar Duppala *, has carried out entire research work and drafted the manuscript, Srinivas T.,has supervised theresearch programme at Bapatla, Subba Rao L. V.,has supervised theresearch programme at Hyderabad, Suneetha Y.,has supervised thecrossing programme at Bapatla, Sundaram R. M.,has conceptulaised & supervised theresearch programme, Prasanna Kumari V., has supervised thepathotypes screening programme, Satyanaryana P. V., has developed the elite cultivar MTU 1121, Abdul Fiyaz R., has supervised the molecular analysis at ICAR-IIRR Hyderabad, Raveendra Ch., has helped in data collection and manuscript revisions and Gurjeet Singh has helped in data analysis and manuscript revisions. References Alam S, Sundaram KT, Singh UM, Srinivas Prasad M, Laha GS, Sinha P, Singh VK (2024) Superior haplotypes towards the development of blast and bacterial blight-resistant rice. 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PLoS ONE 13(9):1–20 Chukwu SC, Rafii MY, Ramlee SI, Ismail SI, Oladosu Y, Okporie E, Onyishi G, Utobo E, Ekwu L, Swaray S, Jalloh M (2019) Marker-assisted selection and gene pyramiding for resistance to bacterial leaf blight disease of rice ( Oryza sativa L). Biotechnol Biotechnol Equip 33(1):440–455 Dey P, Santhi R, Maragatham S, Sellamuthu KM (2017) Status of phosphorus and potassium in the Indian soils vis-a-vis world soils. Indian journal of fertilisers . 13 (4): 44–59 Dobermann A, Fairhurst T (2000) Rice: nutrient disorders and nutrient management. Handbook series. Potash & Phosphate Institute (PPI), Potash and Phosphate Institute of Canada (PPIC) and International Rice Research Institute . 191 Dong Z, Li W, Li LJ, Pan L, Liu S, Gao S, Liu J, Liu L, Wang X, G.L and, Dai L (2019) The rice phosphate transporter protein OsPT8 regulates disease resistance and plant growth. Sci Rep 9(1):1–10 Grover, N., Kumar, A., Yadav, A. K., Gopala Krishnan, S., Ellur, R. K., Bhowmick,P. K., … Singh, A. K. (2020). Marker assisted development and characterization of herbicide tolerant near isogenic lines of a mega Basmati rice variety,Pusa Basmati 1121. Rice, 13, 1–13. https://doi.org/10.1186/s12284-020-00423-2 Gupta SK, Rai AK, Kanwar SS, Chand D, Singh NK, Sharma TR (2012) The single functional blast resistance gene Pi54 activates a complex defence mechanism in rice. J Exp Bot 63(2):757–772 Hasan N, Choudhary S, Naaz N, Sharma N, Laskar RA (2021) Recent advancements in molecular marker-assisted selection and applications in plant breeding programmes. J Genetic Eng Biotechnol 19(1):128. https://doi.org/10.1186/s43141-021-00231-1 Honappa A (2021) Identification, molecular analysis of low phosphorous tolerance and marker trait association studies in rice. Ph. D. (Ag.) Thesis. University of Agricultural Sciences, Raichur, Karnataka International Rice Research Institute (IRRI) (2013) Standard Evaluation System of Rice.13–17 Jyoti SD, Singh G, Pradhan AK, Tarpley L, Septiningsih EM, Talukder SK (2024) Rice breeding for low input agriculture. Front Plant Sci 15:1408356 Kaur R, Kaur R, Sharma N, Kumari N, Khanna R, Singh G (2023) Protein profiling in a set of wild rice species and rice cultivars: a stepping stone to protein quality improvement. Cereal Res Commun 51(1):163–177 Kavitha G, Sekhar RM, Reddy MD, Reddy VL, Kalyani MB, Sudhakar P, Senguttuvel P (2022) Marker assisted backcrossing to develop the low phosphorus tolerant version of KMR-3R, a popular restorer line of hybrid rice. Pharma Innov J SP–11(6):1983–1991 Kekulandara DS, Suriyagoda LDB, Bandaranayake PCG, Sirisena DN, Thilakarathne NS, Samarasinghe WLG (2024) Development of High Yielding Rice Varieties Tolerant to Phosphorus Deficiency. Trop Agricultural Res 35(2):94–106. https://doi.org/10.4038/tar.v35i2.8578 Khan F, Siddique AB, Shabala S, Zhou M, Zhao C (2023) Phosphorus plays key roles in regulating plants’ physiological responses to abiotic stresses. Plants 12(15):2861. https://doi.org/10.3390/plants12152861 Lamichhane S, Thapa S (2022) Advances from conventional to modern plant breeding methodologies. Plant Breed Biotechnol 10(1):1–14. https://doi.org/10.9787/PBB.2022.10.1.1 Manojkumar D, Srinivas T, Rao LS, Suneetha Y, Sundaram RM, Kumari VP (2022) Genetic Variability and trait association analysis in F 3 population of YH3 x AKDRMS 21–54 cross. Andhra Agricultural J 69(1):46–57 Maywald NJ, Francioli D, Mang M, Ludewig U (2023) Role of mineral nitrogen nutrition in fungal plant diseases of cereal crops. CRC Crit Rev Plant Sci 42(3):93–123 Mishra D, Vishnupriya MR, Anil MG, Konda K, Raj Y, Sonti RY (2013) Pathotype and genetic diversity amongst Indian isolates of Xanthomonas oryzae pv. oryzae . PLoS ONE 8(11):81996 Quadri SS, Naik SN, Reddy UG, Vishwanath RH, Lamani K, Siddaiah AM (2023) Screening of rice (Oryza sativa L.) genotypes for root characters related to drought tolerance and its association with yield under aerobic condition. Journal of Cereal Research 15 (1): 56–64. http:// doi.org/10.25174/2582-2675/2023, 132461 Ramkumar G, Rao SK, Mohan MK, Sudarshan I, Sivaranjani AKP, Krishna GK, Neeraja CN, Balachandran SM, Sundaram RM, Prasad MS, Rani SN, Prasad AMR, Virakmath BC, Madhav MS (2011) Development and validation of functional marker targeting an In Del in the major rice blast disease resistance gene Pi54(Pikh ). Mol Breeding 27:129–135 Rekha, G., Abhilash Kumar, V., Gokulan, C. G., Koushik, M. B. V. N., Laxmi Prasanna,B., Kulkarni, S., … Sundaram, R. M. (2022). DRR Dhan 58, a Seedling stage salinity tolerant NIL of Improved Samba Mahsuri shows superior performance in multi-location trials. 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Founder of the Rice Breeding Programme at the Indian Agricultural Research Institute. Review of Agrarian Studies, 13(2), 129–135. https://doi.org/10.25003/RAS.13.02.0016 Singh, G., Kaur, N., Khanna, R., Kaur, R., Gudi, S., Kaur, R., … Mangat, G. S. (2024).2Gs and plant architecture: breaking grain yield ceiling through breeding approaches for next wave of revolution in rice (Oryza sativa L.). Critical Reviews in Biotechnology, 44(1), 139–162. Singh, G., Pradhan, A. K., Jyoti, S. D., Harper, C. L., Elumalai, P., Sanchez, D.L., … Talukder, S. K. (2025). Deciphering the genomic regions associated with seedling cold tolerance traits in rice (Oryza sativa L.). Plant Stress, 15, 100707. Singh R, Sunder S, Dodan DS, Ram L (2011) Sources of resistance to blast and its management through chemicals. J Mycol Plant Pathol 41(3):422 Sumuni, S. M., Kaur, R., Kaur, R., Khanna, R., Kaur, K., Lore, J. S., … Mangat, G.S. (2024). Multivariate analysis for morpho-physiological and milling traits along with molecular profiling of known bacterial blight resistance genes in advanced breeding lines of rice. Cereal Research Communications, 52(2), 759–775. Swamy, H. M., Anila, M., Kale, R. R., Rekha, G., Bhadana, V. P., Anantha, M. S., …Sundaram, R. M. (2020). Marker assisted improvement of low soil phosphorus tolerance in the bacterial blight resistant, fine-grain type rice variety, Improved Samba Mahsuri.Scientific reports, 10(1), 21143. Thakur, S., Singh, P. K., Das, A., Rathour, R., Variar, M., Prashanthi, S. K., … Sharma,T. R. (2015). Extensive sequence variation in rice blast resistance gene Pi54 makes it broad spectrum in nature. Frontiers in plant science, 6, 345. Tourrette E, Falque M, Martin OC (2021) Enhancing backcross programs through increased recombination. Genet Sel Evol 53:25. https://doi.org/10.1186/s12711-021-00619-0 Wang, Y., Cui, Y., Wang, K., He, X., Dong, Y., Li, S., … Zhang, W. (2023). The agronomic and environmental assessment of soil phosphorus levels for crop production: a meta-analysis.Agronomy for Sustainable Development, 43(2), 35. Yadav AK, Kumar A, Grover N et al (2020) Marker aided introgression of ‘ Saltol’ , a major QTL for seedling stage salinity tolerance into an elite Basmati rice variety ‘Pusa Basmati 1509’. Sci Rep 10:13877. https://doi.org/10.1038/s41598-020-70664-0 Yagnasree S, Jyosthna MK, Arunasri P, Kumari PL (2024) Scenario of Bacterial Leaf Blight of Rice Caused by Xanthomonas oryzae pv. oryzae in Major Rice Growing Areas of Andhra Pradesh, India. J Experimental Agric Int 46(8):891–899. https://doi.org/10.9734/jeai/2024/v46i82775 Supplementary Files TableS1.docx TableS2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 25 Mar, 2025 First submitted to journal 25 Mar, 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. 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08:38:07","extension":"docx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":25467,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6286636/v1/8542be3a79acc9df453c8967.docx"},{"id":79820898,"identity":"1b8201dc-e384-41b2-b41b-7d60fb5fabc9","added_by":"auto","created_at":"2025-04-03 08:46:07","extension":"docx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":21104,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6286636/v1/968154b4a2ef15b87f3f3049.docx"}],"financialInterests":"","formattedTitle":"Marker-assisted backcross breeding for bacterial blight, blast and tolerance to low soil phosphorus in rice (Oryza sativa L.)","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) serves as the primary staple food for billions across Asia and Africa, supporting half of the global population (Singh et al., 2024) and serving as the primary income source for disadvantaged rural communities (Satyanarayana et al., 2023). A significant impediment to crop productivity stems from a range of abiotic and biotic stresses, causing annual global yield losses of 30\u0026ndash;60% (Duppala \u003cem\u003eet al.\u003c/em\u003e, 2023). Bacterial blight (BB), caused by \u003cem\u003eXanthomonas oryzae\u003c/em\u003e pv. \u003cem\u003eoryzae\u003c/em\u003e, is a one of the most detrimental and long-standing rice diseases. The yield loss from BB can reach up to 70% when susceptible varieties are grown, and under severe conditions in environments favorable to the disease, it can increase up to 100% (Yagnasree et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Rice is a major contributor to high-quality nutrition in developing country (Kaur et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but the presence of BB disease can drastically reduce its grain quality (Sumuni et al., 2024). Rice blast, caused by the fungus \u003cem\u003eMagnaporthe grisea\u003c/em\u003e, a member of the Magnaporthaceae family, poses another formidable challenge. Blast fungus infects rice at different growth stages, leading to an annual yield loss of approximately 10\u0026ndash;30% in moderate conditions and as high as 80\u0026ndash;100% in epidemic conditions (Alam et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Disease aggressiveness has also been reported to be related to nutrient inadequacies (Bera et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The severity of pathogens is currently exacerbated by the changing environmental and soil conditions (Sharma et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nitrogen and P play crucial roles in crop growth as they constitute the fundamental building blocks of numerous organic molecules, nucleic acids and proteins (Maywald, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas P leads a pivotal role in local and systemic signaling of disease incidence and triggers the gene regulation to incite systemic disease resistance from plant immune system (Dong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Developing cultivars resistant to blast and BB, along with tolerance to low P, a challenge for the rice community, and a recent study combined these three traits through marker-assisted pedigree breeding to improve the elite Indian cultivar MTU1121 (Duppala et al., 2025).\u003c/p\u003e \u003cp\u003eP is crucial for plant growth, being absorbed by plants in phosphate (Pi) form and facilitating tillering, root development, early flowering and ripening (Khan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It ranks as the second most limiting mineral nutrient in nearly all soils globally, with over 30% of arable land experiencing low P levels (Anjum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). P deficiency in field conditions leads to stunted growth, reduced tillering, and flowering (Kekulandara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Presently, around 50% of agricultural soils in Asia, Africa, and South America exhibit P deficiency, with 51% of Indian soils and 79.7% of state soils falling into the low category for available P (Dey et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Studies have shown a linear increase in rice yield with higher soil P content (Wang \u003cem\u003eet al.\u003c/em\u003e, 2023). Given the increasing cost of fertilizers due to high energy prices and limited natural resources, along with disruptions caused by the Covid-19 pandemic, balanced and sustainable use of P fertilizer is paramount (Shukla et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with limited fossil fuels and fertilizer reserves, alternative nutrient replenishment strategies are imperative. Addressing BB, blast disease, and low P tolerance in rice necessitates the adoption of low-input agricultural practices for sustain the rice production (Jyoti et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConventional breeding is a selective technique used to choose new plant varieties based on their superior performance. However, it is a lengthy process that heavily relies on plant phenotypes and can be influenced by various external factors (Duppala et al., 2025). Consequently, breeders began incorporating different biological disciplines into plant breeding, leading to the development of modern breeding practices (Lamichhane and Thapa, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The use of DNA markers in plant breeding has greatly enhanced both its precision and efficiency. Marker-assisted selection (MAS) is now one of the most advanced methods available for introducing one or more desired genes or genomic regions into elite rice varieties in more stable combinations. Developing durable and broad-spectrum resistance in crops through smart breeding for climate-resilient agriculture represents a cost-effective and environmentally friendly method for controlling crop diseases, ultimately supporting sustainable agricultural practices (Bakala et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Advances in understanding the molecular mechanisms of pathogenesis and plant-pathogen interactions have led to the creation of new breeding strategies that go beyond the limitations of traditional breeding methods (Rai and Rai, 2025). Relying on a single resistance (R) gene often results in resistance breaking down quickly as pathogens evolve to overcome the gene's effects. To achieve longer-lasting and broader resistance, pyramiding multiple R-genes that provide defense against different pathogen races through MAS has proven to be an effective strategy (Jamaloddin \u003cem\u003eet al.\u003c/em\u003e, 2021). MAS involves identifying genomic regions linked to desired traits through molecular markers, enhancing selection precision and efficiency while reducing breeding costs (Alemu et al., 2024).\u003c/p\u003e \u003cp\u003eDespite this, successful incorporation of beneficial alleles from these resources has been challenging while retaining all the good traits without compromising. Efficient methods such as marker-assisted backcrossing (MABC) and gene pyramiding have been suggested to support these efforts (Sanchez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Enriching diversity for quantitative traits presents challenges due to their polygenic nature, yet it remains a key focus of long-term research efforts. Markers tightly linked to resistance genes facilitate effective selection of resistant genotypes (Hasan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Backcrossing transfers favorable traits from a donor plant to an elite genotype (recurrent parent), aiming to eliminate donor genes except the target gene. However, donor segments linked to the target allele may persist even after multiple backcrossing generations, while marker assays aid in minimizing this linkage drag (Tourrette et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). MABC enables either control of the target gene (foreground selection) or acceleration of recurrent parent genotype reconstruction (background selection), minimizing linkage drag through recombinant selection (Al-Ashkar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). MAS-946-1, an aerobic rice variety for dry sowing with scheduled irrigations (Quadri et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Improved Samba Mahsuri (ISM), is an elite, high-yielding, BB resistant, fine-grained variety with low glycaemic index (Rekha \u003cem\u003eet al.\u003c/em\u003e, 2022), Pusa Basmati 1121, that was improved for seedling-stage salinity tolerance (Grover \u003cem\u003eet al.\u003c/em\u003e, 2020), Pusa Basmati 1718, a BB resistant version of Pusa Basmati 1121 (Singh \u003cem\u003eet al.\u003c/em\u003e, 2023), PB 1509 (Yadav et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) are the successful rice varieties developed with the help of MAS and released at commercial level for rice growers. Sri Dhruthi (MTU 1121), initially improved for low soil P tolerance with the \u003cem\u003ePup1\u003c/em\u003e gene, and it released as WGL 1487 cultivars for commercial cultivation but later it was high susceptibility to BB and blast diseases (Bisen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, there's an urgent need to improve this variety by incorporating resistance genes for BB and blast. Gene pyramiding is key strategy to combined more than two genes in single cultivars through MABC approach. To keep in mind, the present study aims to develop and identify improved plants with low soil P tolerance and resistance to BB and blast through marker-assisted backcross breeding.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant materials and marker-assisted pedigree breeding\u003c/h2\u003e \u003cp\u003eMTU 1121 (BPT 5204/MTU DP 13) popular as Sri Druthi is a medium duration, medium slender, high yielding variety with tolerance to BPH (brown plant hopper) for dry season cultivation. It was improved for tolerance to low soil P by introgression of \u003cem\u003ePup1\u003c/em\u003e QTL from Kasalath through marker assisted backcross breeding. In present study, near-isogenic line (NIL) of MTU1121 harbouring \u003cem\u003ePup1\u003c/em\u003e QTL, which is designated as YH3 (improved MTU1121) was used as female parent. Akshayadhan (BR827-35/SC109-2-2) is a medium duration, long bold, high yielding variety with tolerance to neck blast was improved for BB and blast following double cross of (Akshayadhan/ISM)/(Akshayadhan/Tetep). An advanced breeding line, AKDRMS 21\u0026ndash;54 (improved Akshyadhan) carrying \u003cem\u003eXa21\u003c/em\u003e gene for BB resistance, \u003cem\u003ePi54\u003c/em\u003e gene for blast resistance and \u003cem\u003ePup1\u003c/em\u003e QTL for low soil P tolerance was used as male parent for introgression of \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e genes into the YH3 for disease resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During \u003cem\u003edry season\u003c/em\u003e 2019-20, crossing was initiated between the recurrent parent (RP) YH3 (possessing \u003cem\u003ePup1\u003c/em\u003e) and the donor parent (DP), AKDRMS 21\u0026ndash;54 (possessing \u003cem\u003eXa21\u003c/em\u003e, \u003cem\u003ePi54\u003c/em\u003e and \u003cem\u003ePup1\u003c/em\u003e) for development of F\u003csub\u003e1\u003c/sub\u003e\u0026rsquo;s. Then F\u003csub\u003e1\u003c/sub\u003e hybrids were verified for their heterozygosity utilizing specific markers targeting resistance genes: pTA248 for \u003cem\u003eXa21\u003c/em\u003e (Ronald et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), Pi54MAS for \u003cem\u003ePi54\u003c/em\u003e (Ramkumar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and k20-2 for \u003cem\u003ePup1\u003c/em\u003e (Chin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Confirmed True F\u003csub\u003e1\u003c/sub\u003e\u0026rsquo;s identified were then backcrossed with the RP to generate BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e\u0026rsquo;s. They were subjected for foreground selection using gene-specific markers and positive plants (\u003cem\u003ei.e.\u003c/em\u003e, heterozygous BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plants) were then analysed for recurrent parent genome recovery (RPGR) through background selection using a set of polymorphic SSR markers (122 markers) evenly distributed across the 12 rice chromosomes to identify plants with maximum RPGR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A singular BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plant exhibiting positivity for all target traits and maximal RPGR was chosen and backcrossed with RP to produce BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e offspring. Foreground and background selection utilizing marker-assisted techniques were reiterated among BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plants, and a singular BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plant harboring the target resistance genes along with maximal RPGR was self-pollinated to generate 610 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These plants underwent phenotypic screening for BB, blast, and low soil P tolerance and were also evaluated for their agronomic traits and yield components.\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\u003eDetails of gene specific markers used in the foreground selection\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMolecular markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLinked gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChromosome location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAmplicon product size (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epTA248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eXa21\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: AGACGCGGGAAGGGTGGTTCCCGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRonald et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1992\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: AGACGCGGGTAATCGAAAGATGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePi54 MAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePi54\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: CAATCTCCAAAGTTTTCAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRamkumar et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: GCTTCAATCACTGCTAGACC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ek20-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePup1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF: CTGGACTTGACCCCAATGTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e402\u003c/p\u003e \u003cp\u003e349\u003c/p\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e582\u003c/p\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChin et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR: TCTGATGGAGTGTTCGGAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eDetails of polymorphic markers used in the background analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of markers used for analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of polymorphic markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eName of the polymorphic primers\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM10092, RM8094, RM493, RM562, RM10936, RM11669, RM431, RM490\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM6367, RM110, RM279, RM555, RM6375, RM1358, RM5791, RM324, RM6, RM208\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM60, RM231, RM15303, RM15331, RM15630, RM520\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM16335, RM8213, RM16868, RM6679, RM3643, RM5979, RM1142, RM3524, RM5511, RM567, RM17705\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM159, RMES5-1, RM169, RM516, RM18362, RM18516, RM430, RM164, RM18704, RM3295, RM3870, RM31, RM334\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM589, RM19346, RM19381, RM19410, RM19660, JGT06-6.81, RM19691, RM287, RM28157, JGT06-18.1, RM20429, RM20577\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM6697, RM21260, RM3583, RM21435, JGT07-17.5, RM533, RM320, RM21649, RM248\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM6925, RM22709, RM38, RM1384, RM331, RM23237, RM23408, RM447, RM23518, RM3761\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMES09-2, RM23736, RM23766, RM23869, RM23877, RM23914, RM23959, RM219, RM566, RM24717\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM222, RM25366, Chr.10-16.3, RM25532, RM6100, RM171, RM1108, RM3808, RM25794, RM228, RM25940\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr.11\u0026thinsp;\u0026minus;\u0026thinsp;2.2, RM26094, Chr.11\u0026thinsp;\u0026minus;\u0026thinsp;8.9, Chr.11\u0026thinsp;\u0026minus;\u0026thinsp;9.6, RM26567, RM26603, RM209, Chr.11-21.1, RM217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRM235, RM27406, RMES12-2, RM519, RM3448, ESSR12-20.2, RM28465, RM28472, RM28481, RM28484, RM3331, RM23553, RM19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e666\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e122\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Phenotypic screening of improved plants\u003c/h2\u003e \u003cp\u003eBackcross derived plants (BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e) together with parents and checks were assessed initially, for blast in Uniform Blast Nursery. Blast screening data was recorded at 30DAS (days after sowing) and highly resistant to moderately resistant plants were identified. The identified plants were transplanted to the main field to further study for blast, agronomic and yield components in normal and low soil P conditions.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Screening for BB disease\u003c/h2\u003e \u003cp\u003eThe chosen breeding plants derived BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e along with along with parents (YH3 and AKDRMS 21\u0026ndash;54), resistant (Improved Samba Mahsuri, referred as ISM), and susceptible (BPT 5204 and TN1) checks underwent evaluation for their resistance to BB during the \u003cem\u003edry\u003c/em\u003e season of 2021-22. For screening, \u003cem\u003eIX0-20\u003c/em\u003e, a virulent isolate of \u003cem\u003eXanthomonas oryzae\u003c/em\u003e pv. \u003cem\u003eoryzae\u003c/em\u003e obtained from ICAR-IIRR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Symptoms were recorded 15 days after inoculation on the plant's upper three leaves using the Standard Evaluation Scale (IRRI, 2013), designed to assess diseased leaf area and calculate the mean percentage of diseased leaf area (% DLA). The scale for diseased leaf area is as follows, scale 1: 1\u0026ndash;5% DLA as resistant (R); scale 3: 6\u0026ndash;12% DLA as moderately resistant (MR); scale 5: 13\u0026ndash;25% DLA as moderately susceptible (MS); scale 7: 26\u0026ndash;50% DLA as susceptible (S), and scale 9: 51\u0026ndash;100% DLA as highly susceptible (HS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Screening for blast disease\u003c/h2\u003e \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e populations were screened for blast resistance in Uniform Blast Nursery (UBN) beds during the \u003cem\u003edry\u003c/em\u003e season of 2021-22, (Local IIRR isolate-SPI-40) of the blast pathogen \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e collected from Rajendranagar, Hyderabad, India. The seed of selected homozygous positive plants of BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2,\u003c/sub\u003e along with RP, DP, Tetep (resistant check) and Samba Mahsuri and HR12 (susceptible checks) were planted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The symptoms and disease scoring were undertaken after 15 days of inoculation and complete drying of HR12, of the susceptible check. Test results were evaluated using IRRI's Standard Evaluation Scale (2013), The blast disease scoring system is as follows, score 0: no visible lesions; score 1: small brown specks, either pin-point size or larger, without a sporulating center; score 2: small, round to slightly elongated necrotic grey spots (1\u0026ndash;2 mm in diameter) with a distinct brown margin; score 3: similar lesion characteristics as score 2, but with a significant number of lesions present on the upper leaves; score 4: typical susceptible blast lesions (\u0026ge;\u0026thinsp;3 mm in length) affecting less than 4% of the leaf area; score 5: blast lesions covering 4\u0026ndash;10% of the leaf area; score 6: blast lesions spreading across 11\u0026ndash;25% of the leaf area; score 7: blast lesions affecting 26\u0026ndash;50% of the leaf area; score 8: blast lesions covering 51\u0026ndash;75% of the leaf area, with many leaves exhibiting severe damage or dying; score 9: \u0026gt;75% of the leaf area is affected. The scale is based on the percentage of the leaf area affected by the disease and is categorized into five distinct levels, highly resistant (HR)-score 0\u0026ndash;3; moderately resistant (MR)-score 4\u0026ndash;5; moderately susceptible (MS)-score 6\u0026ndash;7; highly susceptible (HS)-score 8\u0026ndash;9.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Screening for low soil P tolerance and under controlled conditions\u003c/h2\u003e \u003cp\u003eThe improved breeding lines, along with their parents and checks Swarna (resistant) and Samba Mahsuri and ISM (susceptible) were evaluated under low P conditions (0% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e/ha) and under controlled conditions (100% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e/ha) to assess the effectiveness of \u003cem\u003ePup1\u003c/em\u003e QTL. The evaluation was conducted at ICAR-IIR, Hyderabad during the dry season (\u003cem\u003eRabi\u003c/em\u003e 2021-22). This comparative study aimed to determine the advantages of improved breeding lines carrying \u003cem\u003ePup1\u003c/em\u003e QTL, which enhances P uptake efficiency. The experimental plot was arranged using Augmented Block Design consisting of four blocks, with checks and parents replicated within each block. Phenotypic data was collected on various agro-morphological and yield-related traits, including days to 50% flowering (DFF in days), plant height (PH in cm), number of productive tillers (NPT in number), flag leaf length (FL in cm), flag leaf width (FW in cm), panicle length (PL in cm), number of grains per panicle (NGP in number), thousand kernel weight (TKW in g), root length (RL in cm), root volume (RV in ml), root dry weight (RDW in g), shoot length (SL in cm), shoot dry weight (SDW in g) and single plant yield (SPY in g).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe recorded data under low P screening and controlled plot were subjugated to different statistical tests like critical differences (CD), ccoefficient of variation (CV), standard error (SE), analysis of variance (ANOVA), correlation analysis and principal component analysis (PCA), were calculated from both conditions using statistical analysis tools, running R programme 4.3.3.1 software (R Core team, 2013).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Marker-assisted pyramiding of BB and blast disease resistance into YH3\u003c/h2\u003e \u003cp\u003eA MABC strategy was employed to introgress the genes \u003cem\u003eXa21\u003c/em\u003e (BB resistance) and \u003cem\u003ePi54\u003c/em\u003e (blast resistance) from AKDRMS 21\u0026ndash;54 (DP) into YH3 (RP), aiming to develop homozygous lines that maintain the genetic background of YH3 while incorporating the desired traits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hybridization was performed during dry season of 2019-20 between the RP and the DP, resulting in the generation of 120 F\u003csub\u003e1\u003c/sub\u003e plants. Out of these, 46 F\u003csub\u003e1\u003c/sub\u003e plants were found to be heterozygous for both \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e resistance genes, as confirmed by molecular markers. These heterozygous plants were selected for backcrossing to YH3 to develop the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e generation. During dry season 2020-21, 128 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plants were grown from the selected F\u003csub\u003e1\u003c/sub\u003e plants, and these were subjected to foreground selection using molecular markers for \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e. Of these, 18 plants were confirmed to be true BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e hybrids, carrying both resistance genes (\u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e) in the heterozygous state. The RPGR confirmed of 18 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e hybrids were assessed using 122 polymorphic SSR markers (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the plant, YH3-22 was identified with 76.7% RPGR. This plant was selected for further backcrossing to YH3, resulting in the development of 102 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e hybrids. Foreground selection was carried out for both \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e resistance genes, and 10 plants were found to be heterozygous for both genes (\u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e). RPGR analysis showed that YH3-22-15 had the highest RPGR of 87.5% and this plant was self-pollinated to produce the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e generation. In the dry season of 2021-22, 610 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population were grown and subjected to foreground selection for homozygosity of \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e genes, and 16 plants were homozygous for both these genes. RPGR analysis revealed that YH3-22-15-44 exhibited the highest RPGR of 91.2%.\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\u003ePlants analyzed for target genes in different backcross generations\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal no. of \u0026nbsp; plants analyzed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal no. positive plants\u003c/p\u003e \u003cp\u003eto all three target genes\u003c/p\u003e \u003cp\u003e(\u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePup1\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePer cent of genome recovery at each generation\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\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003cp\u003e(YH3-22)\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\u003eBC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.5%\u003c/p\u003e \u003cp\u003e(YH3-22-15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4/j\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.2%\u003c/p\u003e \u003cp\u003e(YH3-22-15-44)\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Assessment of improved breeding lines for BB, blast and low soil P tolerance\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. BB and blast disease resistance\u003c/h2\u003e \u003cp\u003eThe BB disease severity among the 16 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants varied from 3.90\u0026ndash;44.26%, corresponding to scores ranging from 1 (R) to 3 (MR). The plants YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234 were all observed to be resistant, scoring 1, indicating a low level of disease severity. The susceptible checks, TN1 and Samba Mahsuri, exhibited susceptible reactions, with a score of 7. The positive control, ISM, showed a highly resistant reaction with a score of 1 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The RP demonstrated a MS reaction, with a disease severity score of 5 (13\u0026ndash;25% DLA), while the DP showed a MR reaction with a score of 3 (6\u0026ndash;12% DLA). These results indicate that the developed BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants demonstrated improved BB resistance compared to the RP (YH3). Blast severity scores ranged from 1 to 5, where nine plants exhibited a HR reaction with scores of 1 or 2. Notably, four plants (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311) recorded a blast score of 1, indicating exceptional resistance. The resistant check, Tetep, exhibited a HR reaction with a score of 1. Conversely, the susceptible checks, Samba Mahsuri and HR12, showed MS (score 7) and HS (score 9), respectively. The RP exhibited a MS reaction with a score of 6, while the DP demonstrated a HR reaction with a score of 2. These results suggest that the developed BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants have commendable resistance to both BB and blast diseases compared to the female parent, YH3.\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\u003eScreening of improved BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants for BB and blast\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEntry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eBB resistance against\u003c/p\u003e \u003cp\u003e\u003cem\u003eIXO-20\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eBlast resistance against\u003c/p\u003e \u003cp\u003e\u003cem\u003eSPI-40\u003c/em\u003e isolate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiseased\u003c/p\u003e \u003cp\u003eleaf area (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBB Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAllelic status\u003c/p\u003e \u003cp\u003e(\u003cem\u003eXa21\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBlast Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAllelic status\u003c/p\u003e \u003cp\u003e(\u003cem\u003ePi54\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eImproved BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \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\u003eYH3-22-15-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\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\u003eYH3-22-15-95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYH3-22-15-141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYH3-22-15-182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYH3-22-15-234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYH3-22-15-250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYH3-22-15-311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003eYH3 (Female Parent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003err\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003err\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\u003eAKDRMS 21\u0026ndash;54\u003c/p\u003e \u003cp\u003e(Male parent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChecks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \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\u003eBPT 5204\u003c/p\u003e \u003cp\u003e(Susceptible check)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003err\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003err\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\u003eTaichung Native-1\u003c/p\u003e \u003cp\u003e(Susceptible check)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003err\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003err\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\u003eImproved Samba Mahsuri\u003c/p\u003e \u003cp\u003e(Resistance check)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eDisease reaction: S- Susceptible; MS- Moderately Susceptible; HS- Highly Susceptible; R- Resistance; MR- Moderately Resistance; HR- Highly Resistance\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=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Low soil P tolerance\u003c/h2\u003e \u003cp\u003eA general reduction in morphological trait values was observed in the low P plot compared to the normal P plot, highlighting the adverse effects of P deficiency (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Despite this, several BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants outperformed both the RP (YH3) and the tolerant check, Swarna, demonstrating superior adaptation to low P stress. Among the evaluated traits, YH3-22-15-250 exhibited a significantly greater FL than both YH3 and Swarna, suggesting improved leaf development under nutrient-limited conditions. Similarly, YH3-22-15-15, YH3-22-15-44, YH3-22-15-84, and YH3-22-15-182 showed enhanced FW, indicating a potential advantage in photosynthetic efficiency. For SL, eight BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, including YH3-22-15-8, YH3-22-15-15, YH3-22-15-19, and YH3-22-15-44, surpassed both parents, suggesting better overall vegetative growth. In terms of biomass accumulation, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, and YH3-22-15-182 exhibited the highest SDW, surpassing both parental lines. Root traits also varied significantly, with RL ranging from 15.00 cm (YH3-22-15-182) to 22.80 cm (YH3-22-15-234), the exceeding Swarna (22.30 cm). Notably, YH3-22-15-234 also recorded superior RDW, indicative of improved root biomass under low P conditions. Additionally, RV was significantly higher in YH3-22-15-34, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311 compared to YH3. Flowering time varied among the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, with genotypes like YH3-22-15-19 and YH3-22-15-34 exhibiting delayed flowering, whereas YH3-22-15-8 and YH3-22-15-31 flowered earlier than both parents. Furthermore, YH3-22-15-19, YH3-22-15-44, and other promising lines displayed improved PL, NGP, and TKW, exceeding the performance of both YH3 and Swarna. Notably, YH3-22-15-19 and YH3-22-15-44 achieved significantly higher SPY under low P stress, underscoring their potential as high-yielding candidates for nutrient-deficient environments.\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\u003eMean performance of BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants under low P conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \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\u003eDays to 50 per cent flowering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShoot length\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShoot dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRoot length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRoot dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRoot volume (ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNumber of productive tillers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFlag leaf length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFlag leaf width (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePanicle length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNumber of grains per panicle (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eThousand kernel weight\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSingle plant yield (g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBC\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eF\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ePlants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e182.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e25.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e188.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e23.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e130.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e25.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e230.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e31.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e36.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e106.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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\u003cp\u003e200.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e25.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e33.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKDRMS 21\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e117.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e114.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e60.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.96\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e21.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e88.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e10.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e30.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e23.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e158.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e23.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e28.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChecks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPT 5204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e130.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e15.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e26.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved Samba Mahsuri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e31.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e130.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwarna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e148.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e23.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e108.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e59.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e22.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e89.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e11.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e34.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e20.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e136.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e17.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e24.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e105.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e58.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e23.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e10.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e94.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e31.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e23.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e163.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e24.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e25.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Morphological characterization of improved breeding lines in control P conditions\u003c/h2\u003e \u003cp\u003eUnder control conditions, Swarna exhibited the highest FL among all genotypes, while all BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants surpassed YH3. Notably, only YH3-22-15-84 displayed a greater FW than both parental lines (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Several BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, including YH3-22-15-3, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, YH3-22-15-95, and YH3-22-15-182, exhibited significantly greater SL than YH3 and Swarna. Additionally, YH3-22-15-8, YH3-22-15-31, YH3-22-15-34, YH3-22-15-44, and YH3-22-15-182 recorded the highest SDW under both control and low P conditions. RL in the control plot varied from 18.90 cm (YH3-22-15-8) to 31.50 cm (YH3-22-15-44), with Swarna recording 21.56 cm. Several BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, including YH3-22-15-19, YH3-22-15-34, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, YH3-22-15-84, YH3-22-15-95, YH3-22-15-141, YH3-22-15-182, YH3-22-15-234, and YH3-22-15-250, exhibited significantly longer roots than both parents. Except for YH3-22-15-8, all BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants recorded a higher RDW than Swarna, with YH3-22-15-234 showing the most significant increase. However, none of the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants exceeded YH3 in RDW. RV was also enhanced in most BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, except for YH3-22-15-3, YH3-22-15-8, YH3-22-15-19, YH3-22-15-141, and YH3-22-15-250, which recorded values lower than Swarna. Variations in flowering time were observed, with YH3-22-15-95 and YH3-22-15-182 flowering delayed than YH3, whereas YH3-22-15-3 and YH3-22-15-31 flowered earlier. All BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants exhibited greater PH than YH3 and Swarna. Productive tillers ranged from 6 (YH3-22-15-141) to 13 (YH3-22-15-19), with only YH3-22-15-19 surpassing both parents. YH3-22-15-44 displayed the longest PL, while the NGP ranged from 127 to 260, with YH3-22-15-44 recording the highest values under both control and low P conditions. Most BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants exhibited higher TKW than Swarna across both conditions. SPY analysis revealed that YH3-22-15-19 and YH3-22-15-61 outperformed both YH3 and Swarna in the control plot. Overall, in comparison to the normal P plot, reductions in FL (6.90%), SL (7.40%), SDW (30%), RL (24.05%), RDW (50%), and RV (19.44%) were observed in the low P plot, highlighting the stress induced by P deprivation. The low P stress led to a general decline in flowering time, PH, NPT, PL, NGP, TW and SPY compared to the control plot, with reductions of 8.80%, 9.09%, 6.90%, 15.79%, 12.05%, and 34.00%, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean performance of BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants under low P conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \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\u003eDays to 50 per cent flowering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShoot length\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShoot dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRoot length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRoot dry weight (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRoot volume (ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNumber of productive tillers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFlag leaf length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFlag leaf width (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePanicle length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNumber of grains per panicle (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eThousand kernel weight\u003c/p\u003e \u003cp\u003e(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eSingle plant yield (g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBC\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eF\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ePlants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e152.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e18.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e168.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e91.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e15.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e171.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e118.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c15\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYH3-22-15-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e22.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e137.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e20.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e17.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChecks\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPT 5204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e17.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImproved Samba Mahsuri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e106.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwarna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e126.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e18.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e112.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e49.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e15.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e80.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e6.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e29.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e19.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e111.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e14.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e10.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e109.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e53.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e18.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e86.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e29.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e21.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e137.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e21.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e16.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation analysis for the yield attributes in control and low soil P conditions\u003c/h2\u003e \u003cp\u003eUnder control conditions, among the fourteen traits studied RL (0.49*), RDW (0.53*) and NPT (0.64**) revealed significant and positive association with SPY (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Inter correlation among the important traits such as DFF showed significant positive association with SL (0.58**); similarly, SL with SDW (0.73**); RL with RDW (0.82**) and RV (0.51*); RDW with RV (0.46*) and NGP (0.49*); RV with PL (0.54*) and PL with NGP (0.54*). Under low P, among the fourteen yield and yield attributing traits studied, SL (0.47*), SDW (0.47*), RL (0.79**), RDW (0.70**), RV (0.84**), PH (0.59**), NPT (0.94**), PL (0.67**), NGP (0.74**) and TKW (0.60**) revealed significant and positive association with SPY. Inter correlations under low P conditions, among the important traits such as SL showed significant positive association with SDW (0.80**), RV (0.54*), PH (0.49*), NPT (0.58**) and TKW (0.49*); similarly, SDW with RV (0.49*), PH (0.49*), NPT (0.57**); RL with RDW (0.84**), RV (0.61**), PH (0.49**), NPT (0.74**), PL (0.56**) and NGP (0.61**); RDW with RV (0.59**), NPT (0.67**), PL (0.45**) and NGP (0.57**); RV with PH (0.64*), NPT (0.77**), PL (0.56**), NGP (0.53*) and TKW (0.62**); PH with NPT (0.62**) and TKW (0.45*); NPT with PL (0.68**), NGP (0.68**) and TKW (0.58**); PL with NGP (0.53*) and NGP with TKW (0.47*).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Principal component analysis (PCA) for the yield attributes in control and low soil P conditions\u003c/h2\u003e \u003cp\u003eIn present study, PCA was performed for 14 quantitative traits of rice under control and P stress conditions. Under control conditions, out of 14 principal components (PCs), only five PCs exhibited more than 1.00 eigen value (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) \u003cem\u003eviz\u003c/em\u003e., PC1 (3.31), PC2 (2.64), PC3 (2.17), PC4 (1.53) and PC5 (1.19) accounting for 77.41% variability among the traits studied for each genotype. Rotated component matrix revealed that the first PC was more negatively related to seed yield and its contributing traits such as seed yield (-0.38), NGP (-0.38), PL (-0.31), along with RL (-0.42) and RDW (-0.47), however it was positively related to vegetative traits such as DFF (0.13), SL (0.20) and FL (0.12) suggesting that PC1 reveals that the tendency of each genotype to emphasize vegetative, as compared to reproductive growth. The second PC was positively related to seed yield and its contributing traits such as seed yield (0.35), NPT (0.45) along with RL (0.16) and FL (0.33). The predominant PCA scores of genotypes on PC1 are exhibited by YH3-22-15-44 (22.04), YH3-22-15-61 (16.50) and BPT 5204 (12.18), further PCA scores of genotypes on PC2 are exhibited by YH3-22-15-31 (10.11), YH3-22-15-34 (11.92) and YH3-22-15-182 (12.58). Similarly, under low soil P conditions out of 14 PCs, only four PCs exhibited more than 1.00 eigen value \u003cem\u003eviz\u003c/em\u003e., PC1 (6.71), PC2 (1.88), PC3 (1.26) and PC4 (1.01) accounting for 77.56% variability among the traits studied. Varimax rotation revealed first PC was positively related to DTF (0.20) alone, and more negatively related to all other traits especially yield attributes like SPY (-0.36), NGP \u0026amp; TW (-0.27), TW (-0.25) and NPT (-0.36) along with other key attributes like RL (-0.31) and RDW (-0.27), however. The second PC was positively related to seed yield and its contributing traits such as seed yield (0.17), NGP (0.34), PL (0.14) and NPT (0.10) along with RL (0.26), RDW (0.37) and DFF (0.41). The predominant PCA scores of genotypes on PC1 are exhibited by YH3-22-15-44 (10.50), BPT 5204 (36.95) and ISM (22.64) further PCA scores of genotypes on PC2 are exhibited by YH3-22-15-15 (28.15), YH3-22-15-61 (10.65) and AKDRMS 21\u0026ndash;54 (11.56).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRice, a vital staple crop for the global population, faces numerous challenges that threaten its productivity and sustainability. Among these, emerging pathotypes of diseases and various abiotic stress factors such as P deficiency are significant obstacles to achieving higher yields. The constant evolution of these biotic and abiotic stresses necessitates continuous improvement of rice varieties to sustain global food security (Singh et al., 2025). Traditional breeding methods, although useful, are often slow and labor-intensive. As such, marker-assisted breeding has emerged as an effective strategy for rapidly introgressing resistance genes from donor varieties into elite cultivars, allowing for the timely deployment of disease-resistant and stress-tolerant varieties (Manojkumar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Duppala et al., 2025). This study focused on enhancing the genetic resistance of the short-duration, medium-slender, high-yielding rice variety YH3 by incorporating resistance genes for major biotic stresses such as BB, blast disease, and abiotic stress tolerance to low P through MABC.\u003c/p\u003e \u003cp\u003eIn this study, the DP (AKDRMS 21\u0026ndash;54) was selected for its known resistance genes, \u003cem\u003eXa21\u003c/em\u003e for BB resistance, \u003cem\u003ePi54\u003c/em\u003e for blast disease resistance, and \u003cem\u003ePup1\u003c/em\u003e for P deficiency tolerance. These genes have been extensively studied for their effectiveness in conferring resistance to specific stresses (Duppala et al., 2025). \u003cem\u003eXa21\u003c/em\u003e gene is well-documented for providing robust resistance to \u003cem\u003eXoo\u003c/em\u003e, by triggering a defense response that effectively limits 88% of \u003cem\u003eXoo\u003c/em\u003e strains in India (Mishra et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, \u003cem\u003ePi54\u003c/em\u003e gene, situated on chromosome 11, confers stable and enduring resistance against various strains of \u003cem\u003eM. oryzae\u003c/em\u003e prevalent across India (Thakur \u003cem\u003eet al.\u003c/em\u003e, 2015). Upon pathogen entry into the host, the \u003cem\u003ePi54\u003c/em\u003e gene initiates the synthesis of callose (β-1,3-glucan), forming a physical barrier that impedes fungal hyphae penetration (Gupta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The \u003cem\u003ePup1\u003c/em\u003e QTL, located on chromosome 12, plays a crucial role in enhancing P uptake and utilization, making it vital for rice cultivation in P-deficient soils (Chin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In our study, BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants carrying \u003cem\u003ePi54\u003c/em\u003e exhibited resistance scores ranging from 1 to 5, with four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234, and YH3-22-15-311) showing the HR (score of 1), which was significantly better than the susceptible RP (score\u0026thinsp;=\u0026thinsp;6) and the DP (score of 2). Chukwu et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlighted the rice variety PUTRA-1, containing \u003cem\u003ePi\u003c/em\u003e blast resistance genes, exhibiting high resistance to rice blast, with disease scores ranging from 0 to 1 and a mean score of 0, indicating a significant resistance to blast infection. On the other hand, the other parent, IRBB 60, demonstrated MR to blast infection, with a mean disease score of 3. The BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants also exhibited resistance to blast disease, with scores ranging from 0 to 1 and a mean score of 1. Identification of these transgressive segregants with enhanced blast resistance is an important achievement, as it not only improves disease resistance in the target variety but also provides valuable genetic material for future breeding programs.\u003c/p\u003e \u003cp\u003eIn this study, six BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines, including YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234, exhibited strong resistance (score of 1) to BB, while ten plants showed MR (score of 3). This performance was notably superior to that of the RP, which displayed moderate susceptibility (score of 5), and DP which exhibited MR (score of 3). These results align with those of Aleena et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who reported the identification of resistant lines in a BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population derived from the NLR 34449 genotype. The consistent resistance observed in the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines suggests that the \u003cem\u003eXa21\u003c/em\u003e gene was successfully introgressed into the RP, providing robust protection against BB. These findings highlight the utility of \u003cem\u003eXa21\u003c/em\u003e in breeding programs aimed at developing BB resistant rice varieties, especially in regions where this disease is endemic. P is an essential macronutrient that significantly influences rice growth and development. Low P availability in soils limits rice productivity by affecting root growth, tillering, grain filling, and overall yield (Chithrameenal et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Rice plants adapt to low P conditions by modifying root morphology and enhancing the efficiency of P uptake (Swamy et al., 2020). In this study, BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population were evaluated for their performance under low P stress, and several lines demonstrated superior performance compared to susceptible checks, including Samba Mahsuri and ISM. For instance, the SPY in the low P plot ranged from 13.80 g (YH3-22-15-31) to 22.50 g (YH3-22-15-44), with the RP and DP recorded yields of 19.50 g and 14.50 g, respectively. The tolerant check Swarna produced 17.50 g, indicating that the selected lines outperformed both parents and checks under P-deficient conditions.\u003c/p\u003e \u003cp\u003eFurthermore, early flowering was observed in the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants (107.63 days), which contrasts with the delayed flowering observed in susceptible genotypes. This supports with findings from Dobermann and Fairhurst (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Atakora et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who noted that P deficiency typically causes delayed flowering in rice. The ability of the improved lines to flower earlier than susceptible checks under low P stress suggests that these lines possess enhanced P-use efficiency, a critical trait for growing rice in low-P soils. Similar findings were reported earlier by Honappa (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Kavitha et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Correlation analysis revealed that RL, RDW, and NPT had a significant positive association with SPY under both normal and low P conditions. These traits are therefore valuable selection criteria for P-efficient genotypes. Among the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plants, four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-61, and YH3-22-15-234) exhibited superior yield performance under low P conditions, surpassing both YH3 and Swarna. Notably, YH3-22-15-44, which carries the resistance genes \u003cem\u003eXa21\u003c/em\u003e, \u003cem\u003ePi54\u003c/em\u003e, and \u003cem\u003ePup1\u003c/em\u003e, demonstrated the highest SPY under both normal and low P conditions. This line exhibited a 5.53% yield advantage over YH3 under normal phosphorus conditions and a 15.3% advantage under low P stress, making it a promising candidate for future cultivation.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e plant, YH3-22-15-44, harboring the \u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePup1\u003c/em\u003e, has emerged as a promising candidate for improved rice cultivation with resistance to multiple stresses. This line achieved a remarkable 91.2% RPGR, demonstrating its close resemblance to the elite cultivar YH3. Notably, it exhibited exceptional disease resistance, with a blast score of 1 and a BB score of 1, indicating its robust defense against both \u003cem\u003eM. oryzae\u003c/em\u003e and \u003cem\u003eX. oryzae\u003c/em\u003e infections. Additionally, YH3-22-15-44 showed a significant improvement in SPY, surpassing both of its parents and the checks under both normal and low P conditions. The yield advantage was particularly evident under stress conditions, with a 15.3% increase in yield under low P, compared to a 5.53% increase under normal P conditions. Given these promising results, YH3-22-15-44 is identified as a potential improved line for developing a high-yielding, disease-resistant variety with enhanced tolerance to low soil P. However, further evaluation is required in subsequent generations (BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e4\u003c/sub\u003e and beyond) across multiple locations and growing seasons to confirm its stability and performance for both target stresses and yield traits before considering commercial exploitation. Additionally, grain quality and resistance to other major pests should be investigated to ensure the overall suitability of this line for large-scale cultivation. Furthermore, two other lines, YH3-22-15-19 and YH3-22-15-234, exhibited exceptional resistance to both blast and BB, coupled with higher SPY than the checks under both normal and low P conditions. These lines also hold promise and warrant further evaluation for their grain quality, yield potential, and stress resistance across different environments and seasons. Overall, this study has identified several promising BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines with enhanced resistance to major biotic and abiotic stresses, coupled with superior yield performance, making them suitable candidates for future breeding programs aimed at improving rice productivity and resilience under challenging growing conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial support provided in the form of INSPIRE JRF and SRF Fellowships by the Department of Science \u0026amp; Technology (DST), Ministry of Science \u0026amp; Technology, New Delhi, Government of India, through order No. DST/INSPIRE Fellowship/[IF180552] Dated: 24 July, 2019 to the first and corresponding author, for pursuing full time Doctoral research (Ph.D.) program of Acharya N G Ranga Agricultural University, Lam, Guntur, at Agricultural College, Bapatla, Regional Agricultural Research Station, Maruteru, Andhra Pradesh, and ICAR – IIRR, Hyderabad, Telangana, India is acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing Interest declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Author Contribution declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManoj kumar Duppala\u003csup\u003e*,\u0026nbsp;\u003c/sup\u003e has carried out entire research work and drafted the manuscript, Srinivas T.,has supervised theresearch programme at Bapatla, Subba Rao L. V.,has supervised theresearch programme at Hyderabad, Suneetha Y.,has supervised thecrossing programme at Bapatla, Sundaram R. M.,has conceptulaised \u0026amp; supervised theresearch programme, Prasanna Kumari V., has supervised thepathotypes screening programme, Satyanaryana P. V., has developed the elite cultivar MTU 1121, Abdul Fiyaz R., has supervised the molecular analysis at ICAR-IIRR Hyderabad, Raveendra Ch., has helped in data collection and manuscript revisions and Gurjeet Singh has helped in data analysis and manuscript revisions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlam S, Sundaram KT, Singh UM, Srinivas Prasad M, Laha GS, Sinha P, Singh VK (2024) Superior haplotypes towards the development of blast and bacterial blight-resistant rice. 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J Experimental Agric Int 46(8):891\u0026ndash;899. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/jeai/2024/v46i82775\u003c/span\u003e\u003cspan address=\"10.9734/jeai/2024/v46i82775\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Backcross breeding, Bacterial blight, Blast, Gene pyramiding, Rice","lastPublishedDoi":"10.21203/rs.3.rs-6286636/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6286636/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eModern highly yielding rice varieties are susceptible to various biotic stress including bacterial blight (BB), and blast and abiotic stress like phosphorus (P) starvation tolerance. To address these vulnerabilities, the present study aims to achieve gene pyramiding through marker-assisted backcross breeding (MABB) to develop improved cultivars that harbor the \u003cem\u003eXa21\u003c/em\u003e (BB), \u003cem\u003ePi54\u003c/em\u003e (blast resistance) and \u003cem\u003ePup1\u003c/em\u003e (phosphorus starvation tolerance) genes. The parent lines, AKDRMS 21\u0026ndash;54, (carries \u003cem\u003eXa21\u003c/em\u003e and \u003cem\u003ePi54\u003c/em\u003e gene, used as donor parent) and YH3 (carries \u003cem\u003ePup1\u003c/em\u003e gene, used as recipient parent) were used to make a 610 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population. Further, this population was screened for blast, BB and low P tolerance with targeted triple gene (\u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePup1\u003c/em\u003e) homozygous plants and total of 16 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines were identified. The identified BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e four lines (YH3-22-15-19, YH3-22-15-44, YH3-22-15-234 and YH3-22-15-311) were highly resistant to blast and six lines (YH3-22-15-3, YH3-22-15-19, YH3-22-15-36, YH3-22-15-44, YH3-22-15-61 and YH3-22-15-234) were highly resistant for BB. Moreover, assessment of the enhanced BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines for low P tolerance unveiled a consistent decline in various growth parameters under low P conditions as well as normal P conditions. In the BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e population, reductions in most parameters studied, such as grain yield, were relatively small compared to the parents and checks cultivars, indicating their potential for thriving in low P conditions. Among the 16 BC\u003csub\u003e2\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e lines, YH3-22-15-44 possessing maximum (91.2%) RPGR (recurrent parent genome recovery) along with pyramided of target genes (\u003cem\u003eXa21\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePi54\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003ePup1\u003c/em\u003e), which showed the resistant against BB and blast and high grain yield, as compared to parents and checks. This identified potential line can be utilized in multi-location trials to ensure stable performance for rice growers in future.\u003c/p\u003e","manuscriptTitle":"Marker-assisted backcross breeding for bacterial blight, blast and tolerance to low soil phosphorus in rice (Oryza sativa L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 08:38:01","doi":"10.21203/rs.3.rs-6286636/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-03-27T11:07:37+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T08:48:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-25T22:31:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Breeding","date":"2025-03-25T13:26:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-breeding","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"molb","sideBox":"Learn more about [Molecular Breeding](https://www.springer.com/journal/11032)","snPcode":"11032","submissionUrl":"https://submission.nature.com/new-submission/11032/3","title":"Molecular Breeding","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e4a73b53-d182-4e28-a396-f8464f05a263","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-25T14:09:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-03 08:38:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6286636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6286636","identity":"rs-6286636","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}