Unveiling Stagnant Flooding Tolerance in Lowland NERICAs: Genomic Insights and Breeding Prospects

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Bhadmus, Okanlawon Jolayemi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7510204/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract Rice cultivation in the rainfed lowland ecosystem is prone to encounter substantial flooding challenges in the form of complete submergence or prolonged stagnant flooding. While the Sub1 gene enables rice plants to survive the momentary complete submergence, stagnant flooding, defined by incomplete submergence for extended periods, necessitates moderate stem elongation for survival. In this study, we characterized 60 lowland NERICA varieties under stagnant flooding (SF) conditions, identify tolerant germplasm, and detect genomic regions associated with key traits to aid breeding efforts. Phenotypic evaluations revealed significant genetic variability among the NERICA varieties, with some accessions showing 20–60% yield reduction under SF stress. The derived NERICA-L19/IR64 Sub1 RIL population showed improved grain yield under SF compared to both parents and submergence tolerant checks. A total 27 QTLs were identified associated with plant height, tiller number, panicle number, days to flowering, and grain yield. Stable and major-effect QTLs, such as qPH1.1 , qPH3.1 , and qDTF3.1 , were consistent across environments, explaining up to 48% of the phenotypic variation. Several QTLs co-localized, indicating potential pleiotropy or tight linkage. Candidate genes associated with these regions include regulators of gibberellin signaling, flowering time and other developmental processes. This study highlights the potential of lowland NERICAs as a genetic resource as well as provides molecular resources for improving stagnant flooding tolerance in rice. The integration of phenotypic data, stable QTLs, and functionally relevant candidate genes lays a foundation for marker-assisted breeding of dual-tolerant rice cultivars adapted to climate-induced flooding scenarios in sub-Saharan Africa. Rice NERICA IR64-Sub1 Stagnant Flooding Submergence Figures Figure 1 Figure 2 Introduction Rice is staple food grain for more than half of the world population. Due to highly heterogenetic conditions in rainfed lowland ecosystems, paddy farms are often subjected to various types of flooding stresses (Panda and Barik, 2021 ). Flooding could be complete submergence for shorter periods of time or prolonged stagnant water in farms for longer periods, sometimes for whole rice crop season (Kato et al. 2019 ; Panda and Barik, 2021 ). With the exploitation of genes like Sub1 which contribute to quiescence technique, and help to avoid stem elongation under complete submergence, rice plants can survive up to 2 weeks period (Haque et al. 2023 ; Kato et al. 2019 ). However, under stagnant flooding, partial submergence of plants remains for longer periods, and hence moderately elongated stems are highly desirable for plant survival and sustained growth. Stagnant flooding is a recurring and common problem during wet season in sub-Saharan African countries and many Asian countries (Kovcas et al. 2017; Kuanar et al. 2017 ). Sub-Saharan Africa, in particular, is projected to experience a high and widespread increase in flood frequency in future due to climate change (Hirabayashi et al. 2013 ; Haque et al. 2023 ). Since many countries in Africa are growing rice under rainfed lowland ecologies, stagnant flooding represents a major abiotic stress leading to economic loss to farmers especially during rainy wet season. Currently, there is dearth of stagnant flooding tolerant varieties, as the best locally adopted varieties perform poorly under these conditions. Hence, there is a need and scope for identifying SF tolerant germplasm and breeding more robust SF tolerant rice varieties for Sub-Saharan African countries. Oryza glaberrima (2n = 24, AA) originated and indigenous cultivated rice species of Africa, is known to have tolerance to several abiotic stresses. It is considered to have originated in flood-prone ecology and thus, may be a good source of tolerance to flooding stress (Oka, 1974 , Watarai and Inouye, 1998 , Inouye et al. 1989 , Mochizuki et al. 1997, Futakuchi et al. 2001 , Sarla and Swamy, 2005 , Joho et al. 2008 , Opeyemi and Venuprasad, unpublished results). However, limited research has been carried out to characterize the flooding tolerance of O. glaberrima . Futakuchi et al. ( 2012 ) conducted a physiological study and concluded that O. glaberrima has higher resistance to deepwater stress. Similarly, Sakagami et al. ( 2009 ) studied physiological responses of 27 accessions of O. glaberrima accessions to prolonged submergence and suggested that O. glaberrima could be used in breeding for stagnant flooding (SF) tolerance. Screening efforts involving landraces, improved varieties, Sub1 -introgressed lines, and O. sativa lines such as IRRI 119 and IRRI 154, as well as many O. glaberrima accessions have revealed key adaptive traits associated with SF tolerance (Vergara et al. 2014 ; Agbeleye et al. 2019 ; Mwakyusa et al. 2023 ). Importantly, O. glaberrima accessions were found to possess unique alleles for SF tolerance not present in elite Asian lines (Mwakyusa et al. 2023 ). To harness this genetic variation, researchers have developed mapping and breeding populations for SF tolerance. Key among these breeding populations are recombinant inbred lines (RIL) populations derived from crosses between tolerant and susceptible parents have been utilized by Singh et al. ( 2017 ) and Chattopadhyay et al. ( 2021 ) for QTL mapping to identify loci associated with SF tolerance. O. glaberrima and its interspecific derivatives, are emerging as rich sources of alleles conferring SF tolerance (Futakuchi and Sie, 2009). These varieties combine the high yield potential of Asian rice and the stress tolerance traits of African rice, thereby broadening the genetic base for SF tolerance. The genetic diversity embedded within these interspecific derivatives offers a valuable opportunity to dissect and harness adaptive traits, advancing the development of climate-resilient rice variety relevant to SF tolerance. Among the interspecific derivatives, the lowland NERICA varieties are among the most popular interspecific varieties that farmers prefers to grow in both rainfed and irrigated lowland farms in African subcontinent, currently occupying about 700,000 hectares of land (Somado et al. 2008). The 60 lowland NERICA varieties developed by Africa Rice Center (AfricaRice/WARDA) all have Oryza glaberrima background and are interspecies hybrids of both cultivated rice species O. sativa and O. glaberrima . However, there is dearth of information on performance of these popular varieties under prolonged SF stress which is becoming a serious issue for the farmers in Africa. Hence, characterization of all lowland NERICA varieties to SF stress under field conditions was the first objective of the present study. Additionally, we also aimed to identify tolerant genotypes and exploit the identified SF tolerant germplasm in further breeding efforts with Sub1 background. This would enable the combination of tolerance to two major flooding stresses viz. submergence and stagnant flooding, which would be more useful considering the heterogenic nature and uneven rainfall patterns common in the lowland ecosystems of Sub-Saharan African countries. Finally, the study also aimed to map potential genomic regions/quantitative traits loci (QTLs) associated with traits contributing to higher grain yield under SF stress. Material and methods Experimental site The study was conducted at the Africa Rice Center (AfricaRice) research station located within the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria (Latitude 3 ° 54´32ʹʹ E: Longitude 7 ° 29´15ʹʹN). Experimental plots included both stagnant flooding (SF) and control (normal irrigated) conditions in lowland experimental fields. Plant material A total of sixty (60) lowland NERICAs were sourced from the AfricaRice genebank and screened for tolerance to stagnant flooding. For comparison, Oryza sativa varieties were used as checks. The widely cultivated rainfed lowland variety, FARO 57, was included as a local check across all trials. In addition, known tolerant checks for stagnant flooding, IRRI 119 and IRRI 154 (Vergara et al. 2014 ; Kato et al. 2014), along with Sub1 gene incorporated line IR64- Sub1 , were included as reference checks in the respective experiments. Development of mapping populations A cross was made between a selected lowland NERICA and IR64- Sub1 , where NERICA L-19 was the recurrent parent and IR64- Sub1 as the donor parent. From the F 1 generation, a total of 2500 selfed F 2 plants were generated. Selection at the F 2 stage was based on key agronomic traits including plant height, heading date and other traits associated with SF tolerance. Under irrigated conditions, 600 F 3 families were selected and advanced to the F 4 generation. At the F 3:4 stage, the population was narrowed down to 484 lines, which were subsequently selfed to advance to the F 4:5 generation for further evaluation. Protocol for screening To break seed dormancy, seeds of the rice accessions were subjected to heat treatment at 50°C for three days in an oven. In all experiments, seeds were initially raised in a nursery, and 21-day old seedlings were subsequently transplanted into a well-puddled and leveled field. Each experimental plot consisted of a single row measuring 3 m, with 20 cm spacing between rows and plants. A basal application of NPK fertilizer (15-15-15) was made at 200 kg/ha one day after transplanting. Urea was applied twice: 30 kg/ha at tillering stage and another 30 kg/ha at panicle initiation. Weed control involved the use of herbicides during early growth, followed by manual weeding as the plants matured. In the control experiment, a consistent water depth of 2–5 cm was maintained throughout the growing period until harvest. For the stress experiment, an initial water level of 2–5 cm was sustained for the first 14 days after transplanting. The stress was then imposed by increasing water level to 15 cm on day 15, followed by incremental increases of approximately 5 cm on alternate days until the final water level of 50 cm was reached. This level was maintained for around three months, after which the fields were drained back to 2–5 cm shortly before harvest. The stagnant flooding protocol employed was adapted from the method described by Singh et al. ( 2011 ) at IRRI. Days to flowering (DTF) was recorded when approximately 50% of the plants in a plot had vicible panicle exertion. Plant height (HT) was measured at maturity on three randomly selected plants per plot, from the base to the tip of the tallest panicle, and the average was calculated. The number of tillers (TILL) and panicles (PAN) were counted from five randomly selected plants in each plot and averaged. All panicles from each plot were harvested, threshed, cleaned, and dried to approximately 14% moisture content before weighing to determine grain yield (GY). Genotyping of the RIL Population and QTLs analysis Leaf samples from 484 F5 progenies of the populations and the parents were collected, lyophilized and shipped to the Diversity Arrays Technology (DArT), Australia for genotyping. Data received showed polymorphism between two parents in 1396 SNPs. The genotypic data from 1396 polymorphic SNPs covering all chromosomes was used for QTL mapping in the RIL population. The QTL analysis was performed using the QGene software (Joehanes & Nelson, 2008 ). The analysis utilized genotypic and phenotypic data from the NERICA L-19/IR64- Sub1 RIL population. The genotypic data included a genetic linkage map with SNP marker positions expressed in centimorgans (cM), while the phenotypic data comprised grain yield, plant height, days to 50% flowering, number of tillers, and number of panicles per plant. The input data were formatted as text files, with individuals represented as rows and markers, genotypes, and traits as columns. Markers were assigned to chromosomes and ordered based on their genetic positions. Data consistency was verified using QGene's graphical tools. Composite Interval Mapping (CIM) was employed; this method refined QTL detection by incorporating covariates to account for background genetic effects. Genome-wide scans were conducted, and LOD scores were calculated at regular intervals. QTLs were considered significant if the LOD score exceeded a threshold determined by permutation testing (n = 1,000, p ≤ 0.05). Significant QTLs were annotated with their chromosomal positions, flanking markers, LOD scores, and percentage of phenotypic variance explained (PVE). QTL regions were defined by overlapping QTLs and the presence of a SNP with a significance threshold of -log10 p ≥ 5.0. Identification of Candidate Genes Candidate genes located near major QTLs with PVE greater than 10%, and stable across the environments, were selected for gene annotation. Gene annotation information was retrieved from the Rice Annotation Project database, RAP-db ( https://rapdb.dna.affrc.go.jp/ , accessed on 2 December 2024). For each significant QTL, candidate genes were predicted within a 1.0 Mb genomic window, extending 500kb upstream and downstream of the significant QTLs. Genes annotated as hypothetical, non-protein coding, or associated with transposable elements were excluded from futher analysis. However, priority was on genes associated with traits indicative of stagnant flooding tolerance under both stagnant flooding and control environments. To gain deeper insights into gene function, SNP-associated gene annotations were cross-referenced with the European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL–EBI) database. Results Phenological and morphological parameters in lowland NERICAs Days to flowering (DTF) declined by 5 to 15 days in different lowland NERICAs under stagnant flooding stress (SF) compared to irrigated control (IRR) except for NERICA L-20 and NERICA L-57 were DTF was similar under both SF and IRR (Table 1 ). All NERICAs exhibited moderate stem elongation, leading to 5 to 20% increase in plant height under SF compared to IRR except for NERICA L-20, NERICA L-25 and NERICA L-6 were plant height was more than 30% increased over IRR under SF. Number of tillers per plant declined marginally in all NERICAs and checks used under SF compared to IRR. Among the checks used, DTF was similar in both treatments IRR and SF in IRRI-119 and FARO 57 while declined by 10 days in IRRI-154 and IR 64- Sub1 under SF as compared to IRR. Plant height increased by 5 to 25% under SF in all checks compared to IRR except for IR 64- Sub1 which maintained similar plant height under both IRR and SF treatments (Table 1 ). Table 1 Days to flowering and plant height of lowland NERICAs and checks under stagnant flooding stress and control conditions during WS 2014 and WS 2015 at Ibadan, Nigeria. Nigeria. Values are average of 2 trials for both control and stagnant flooding stress. Genotypes Days to flowering Plant height (cm) Tiller number (per hill) Panicle number (per hill) Control Stress Control Stress Control Stress Control Stress NERICAs Minimum 98 89 86 116 10 7 9 7 Maximum 112 110 143 146 12 11 11 11 Mean 105 99 107 115 11 9 10 8 Selections NERICA-L-19 106 103 142 145 10 8 9 7 NERICA-L-41 106 99 111 131 10 8 9 7 NERICA-L-27 101 94 107 130 11 10 10 10 NERICA-L-40 105 99 103 132 12 9 10 8 Tolerant and local checks IRRI 119 106 102 130 138 11 8 10 7 IRRI 154 102 96 111 128 10 11 10 10 FARO57 106 106 136 155 10 9 10 10 IR 64-Sub1 104 101 121 123 11 9 9 9 Trial Mean 105 99 108 126 11 9 10 8 LSD (0.05) 2.1 2.3 4.1 4 2.2 1.5 1.5 1.5 Heritability 0.86 0.90 0.87 0.81 0.5 0.53 0.42 0.67 WS – wet season; DS – dry season Yield and yield components in lowland NERICAs Number of panicle per hill (PN) declined marginally in all NERICAs under SF stress compared to IRR, except for NERICA L-6, NERICA L-18, NERICA L-21 and NERICA L-60 which maintained similar PN under both IRR and SF treatments (Table 2 ). Among the checks IRRI-154 and IR 64-Sub1 had similar PN under IRR and SF treatment, while IRRI-119 and FARO 57 exhibited marginal decline in PN under SF as compared to IRR. Grain yield (GY) decline under SF in all NERICAs and checks compared to IRR. Wide variation was observed in grain yield reduction under SF in NERICAs ranged from 20 to 60% decline in grain yield under SF. Some high yielding NERICAs out yielded the tolerant checks and local check under SF (Table 2 ). Table 2 Grain yield (kg/h) of Lowland NERICAs and checks under stagnant flooding stress and control conditions during WS 2014 and WS 2015 at Ibadan, Nigeria. WS 2014 WS 2015 Name Control Stress Control Stress NERICAs Minimum 212 686 3061 627 Maximum 6252 5074 5770 3397 Mean 3694 3219 4771 2319 Selections NERICA-L-19 5102 4194 5204 2156 NERICA-L-41 6048 5033 5246 3052 NERICA-L-27 4520 4147 4447 3397 NERICA-L-40 3803 2722 5074 3942 Tolerant and local checks IRRI 119 5050 3601 6080 2417 IRRI 154 5246 3812 4172 1745 FARO 57 7248 3707 5580 1722 IR64-Sub1 - - 5488 3083 Trial Mean 3745 3246 4806 2314 LSD (0.05) 840 563.7 624.5 711.7 Heritability 0.70 0.72 0.73 0.8 WS – wet season; DS – dry season Phenological parameters and yield components in NERICA L-19/IR64-Sub1 population Since NERICA L-19 was one of the best genotypes performing well under SF stress (Table 1 , 2 , 3 ), a RIL mapping population was developed under Sub1 background using NERICA l-19 and IR64-Sub1. F3 and F4 population was evaluated under both IRR and SF conditions in WS 2015 and DS 2016. The RIL population exhibited wide genetic variation, normal distribution and transgressive segregation for phenomorphological traits and yield related traits like plant height, DTF, number of tillers, PN and GY (Table 4 ). Some of progenies yielded higher than both parents and tolerant/local checks both under SF and IRR conditions. Normal distribution and transgressive segregation in yield and other traits indicate that the RIL population having some promising high yielding SF tolerant lines and the population was suitable for QTL mapping. Table 3 Grain yield (kg/h) of NERICA L-19/IR64-Sub1 F6 population, parents and checks under stagnant flooding stress and control conditions during WS 2016, DS2017, WS 2017 and WS 2018 at Ibadan, Nigeria. Name WS2016-SF DS2017-SF WS2017-SF DS2017-IRR WS2018-IRR Population Minimum 568 641 752 648 720 Maximum 4605 4680 4680 8281 7949 Mean 1984 1883 1887 4575 4327 Selections ART217-75-1 3453 5860 3106 4259 4572 ART217-11-1 4065 2451 3233 5173 5429 ART217-194-1 3693 2793 3010 4702 5225 ART217-174-1 3518 2605 2878 5903 5782 Parents NERICA L-19 3431 1919 1736 4581 4479 IR64-Sub1 1566 2075 1904 3683 4210 Checks IRRI119 3113 2247 1987 5958 5411 Swarna-Sub1 1686 2664 2287 4913 4719 Trail mean 1995 1889 1890 4584 4237 LSD (0.05) 918 397 293 369 402 Heritability 0.7 0.96 0.87 0.97 0.82 WS – wet season; DS – dry season Table 4 Days to flowering and plant height of NERICA L-19/IR64-Sub1 F6 population, parents and checks under stagnant flooding stress and control conditions during WS 2016, DS2017, WS 2017 and WS 2018 at Ibadan, Nigeria. Values for stress are average of 3 trials for stagnant flooding and 2 trials for control treatment. Genotypes Days to flowering Plant height (cm) Tiller number (per hill) Panicle number (per hill) Control Stress Control Stress Control Stress Control Stress Population Minimum 57 52 87 77 7.6 3.3 7.3 2.7 Maximum 93 96 159 165 14 9.2 13 7.7 Mean 81 84 120 130 10 6.1 9.8 5.6 Selections ART217-75-1 73 82 106 120 9.3 6.3 9.3 5.8 ART217-11-1 80 84 139 150 9 8.5 8.6 7.6 ART217-194-1 85 87 114 126 9.7 6.9 9 5.3 ART217-174-1 92 92 105 129 12.6 7 12 6 Parents NERICA L-19 91 95 139 157 9.7 5.8 9.3 4.7 IR 64-Sub1 80 89 120 140 10.7 6.4 10 5.5 Checks IRRI 119 85 90 120 140 10.3 6.4 10 5.5 Swarna-Sub1 92 95 104 117 9.8 6.3 9.4 5.3 Trial Mean 80 85 120 130 10.2 6.3 9.8 5.5 LSD (0.05) 3.1 3.5 6.2 5.6 1.8 1.3 1.8 1.4 Heritability 0.93 0.91 0.97 0.95 0.29 0.72 0.2 0.6 WS – wet season; DS – dry season Phenotyping of the F5 RIL Population The F5 RIL population along with the parents and SF tolerant and popular local checks were evaluated both under IRR and SF condition in three seasons WS 2016, DS 2017 and WS 2017. Population exhibited wide variation in phenomorphological traits like DTF and HT. The pattern of segregation in the population was normal and transgressive for yield and phenomorphological traits (Table 3 , 4 ). Plant height increased significantly in majority of the progenies, parents and checks under SF stress as compared to IRR (Table 4 ). An obvious decline in number of tillers per plant, PN was observed under SF stress in the progenies, parents and checks under SF compared to IRR. However, many progenies produced higher number of tillers, and panicles than both the parents and SF tolerant checks under prolonged SF stress condition leading to higher grain yield than checks and parents under SF stress (Table 3 , 4 ). Some of the consistent progenies across trails exhibited less decline in GY under SF compared to IRR than both parents and checks, indicating potentially higher tolerance to SF than parents and checks (Table 4 ). Under stagnant flooding conditions, the traits recorded in the F5 RIL Population exhibited a continuous phenotypic distribution, ranging from highly tolerant to highly susceptible. Grain yield showed moderate and statistically significant positive correlations with most other measured traits. Notably, days to 50% flowering was moderately correlation with plant height (r = 0.35, p < 0.001) (Fig. 1 ), while the strongest correlation was observed between number of tillers and number of panicles (r = 0.90, p < 0.001). Across the environments, the broad sense heritability of the grain yield ranged from 70–96%. For the other traits, it varied from 60% for number of panicles per hill to 95% for plant height and 20% for number of panicles to 97% for plant height under SF and control environment, respectively (Tables 2 and 3 ). QTL detection in F5 RIL population A total of 1396 high-quality SNPs, evenly distributed across the 12 rice chromosomes, were used to construct a genetic linkage map spanning 1456.2 cM, with an average distance between every pair of markers of 1.11 cM (Table 5 ). The number of SNP markers on each chromosome ranged from 66 (chromosome 9) to 186 (chromosome 1), with a linkage distance ranging from 89.5 to 172.4 cM. QTL mapping conducted in the NERICA L-19/IR64-Sub1 RIL population identified 27 QTLs associated with five agronomic traits important for stagnant flooding (SF) tolerance, distributed across 12 chromosomes. Among the 27 QTLs, 13 QTLs were detected under the irrigated (IRR) conditions, while 19 were detected under SF, and 5 were consistently identified across the five environments (Table 6 ). The phenotypic variance explained (PVE) by these QTLs ranged from 2–48%, with LOD scores between 2 and 68. Both parents contributed favorable alleles, with 16 QTLs from IR64- Sub1 and 11 QTLs from NERICA-L19 (Table 6 ). Table 5 Summary of the genetic linkage map constructed from F5 RIL population derived from a cross between NERICA L-19 and IR64- Sub1 using 1,396 markers. Chromosome Number of markers Total Distance (cM) Average Genetic Distance between Markers (cM) 1 186 172.4 0.92 2 145 143.4 0.89 3 87 145.4 1.68 4 167 130.2 0.76 5 115 118.4 1.00 6 69 123.3 1.81 7 116 115.7 1.00 8 133 109.6 0.83 9 79 89.5 1.12 10 98 90.4 0.84 11 135 115.6 0.86 12 66 102.3 1.57 Total 1396 1456.2 - Table 6 QTLs for agronomic and GY traits under irrigated and SF stress in NL19/IR64Sub1 population. Trait QTL Chr. Posit. Flanking Markers Source DS 2017 IRR WS 2018 IRR WS 2016 SF DS 2017 SF WS 2017 SF cm. LOD R 2 Add. LOD R 2 Add. LOD R 2 Add. LOD R 2 Add. LOD R 2 Add. PH qPH1.1 1 148.2 S1_37014282–37103278 IR 53 40 -8.8 48 39 -7.9 42 33 -10.3 35 28 -8.8 34 28 -7.2 qPH1.2 1 116 S1_28624315–30476133 IR 3.7 3 -2.8 3.4 3 -3.1 5.3 5 -3.1 qPH3.1 3 4.6 S3_1042202–1177403 IR 10.4 10 -4.9 11.2 13 -5.1 8.4 10 -4.2 10.7 10 -5 5.5 5 -3.1 qPH5.1 5 67.1 S5_16329311–18134381 IR 2.2 2 -2.1 3.1 2.9 -2.6 qPH8.1 8 20.5 S8_4716302–5157478 N19 2.4 3 0.3 2.1 2 0.2 3.2 3 0.3 DTF qDTF3.1 3 4.4 S3_1042196–1177403 IR 68 48 -1.8 62 44 -2.7 42 32 -2.6 27 23 -1.8 47 37 -0.89 qDTF8.1 8 20.5 S8_4716302–5157478 IR 4.9 5 -1.2 5.2 7.2 -1.3 5.3 6 -1.1 5.5 5.2 -0.9 3.4 4 -0.9 qDTF12.1 12 96.2 S12_23980869–24530396 N19 3.1 3 0.77 4.2 4 1 TILL qTILL3.1 3 0.8 S3_219976–249595 IR 3.2 3.1 0.1 4.5 5.3 0.7 qTILL8.1 8 70.5 S8_13825109–17748434 N19 2.2 2 -0.19 3.3 3.1 -0.16 qTILL9.1 9 27.9 S9_6879619–7006897 N19 2.6 3 -0.2 3.7 4 -0.2 3.2 3.1 -0.15 qTILL12.1 12 14.2 S12_1575820–4409640 N19 2.1 2 -0.22 3.5 3 -0.19 2.9 3 -0.2 PAN qPAN1.1 1 154.2 S1_38497502–38659941 IR 2.2 2 0.16 2.3 2 0.08 qPAN2.1 2 39.3 S2_9730359–9925379 IR 2.9 3 0.1 3.6 4 0.2 qPAN3.1 3 0.8 S3_219976–249595 IR 2.7 2.5 0.08 2.8 2.5 0.1 qPAN9.1 9 27.9 S9_6879619–7006897 N19 2.6 3 -0.16 3.2 3 -0.13 qPAN9.2 9 81.9 S9_20369578–20695138 N19 2.4 2.5 -0.12 qPAN11.1 11 53.1 S11_12837746–13446500 N19 2.1 2 -0.1 2 2 -0.09 2 2 -0.09 qPAN12.1 12 14.2 S12_1575820–4409640 N19 2 2 -0.2 3.1 3 -0.16 GY qGY2.1 2 65.6 S2_16355796–16464015 N19 4.4 4.1 209 5.3 5 201 2.1 2 100 2.4 2.3 150 5.4 6 206 qGY2.2 2 93.6 S2_22984666–23650298 N19 4.3 4 187 5.2 5.8 159 qGY3.1 3 4.8 S3_1042196–1177403 N19 8.1 7.9 262 7.9 7.2 268 5.2 5 60 qGY5.1 5 25.1 S5_5838683–7854237 IR 3.1 2.9 -165 4.1 4 -78 qGY5.2 5 75.1 S5_18628440–18960064 N19 4.7 4.2 163 3.5 3.3 155 2.2 2.1 55 qGY7.1 7 60.8 S7_13405336–15244874 IR 4.5 4.2 -41 4.3 4.2 -38 qGY8.1 8 20.5 S8_4716302–5157478 N19 3.6 3.4 -107 2.1 2 145 2.1 2.2 140 qGY9.1 9 29.9 S9_7142916–15272672 IR 5.3 4.9 -149 2.3 2.2 -139 *The one underlined are common between IR, SF. Bold are stagnant flooding specific, and italics are control specific. *PH-Plant height, DTF-Days to flowering, TILL-Number of tillers, PAN-Number of panicles, GY-Grain yield kg/h, N19-NERICA L-19, IR-IR64- Sub1 . Five QTLs for plant height were detected on chromosomes 1, 3, 5, and 8. Among these, a major QTL qPH1.1 (PVE; 28 and 40%) and QTL qPH3.1 were stable across environments, while the remaining QTLs, qPH1.2 and qPH8.1 were identified under the stagnant flooding environment while qPH5.1 was detected under the irrigated condition. The additive main effect of decreased plant height was attributed to IR64-Sub1, while increased plant height by NERICA L-19. For days to 50% flowering, three QTLs were detected on chromosomes 3, 8, and 12. The major QTL qDTF3.1 showed the highest PVE of 48% and stable across the environments. Four minor QTLs for number of tillers were detected on chromosomes 3,8,9 and 12, with PVE ranging from 2 to 5.3%, and LOD scores of 2.1 to 4.5. Seven QTLs associated with number of panicles were identified on chromosomes 1, 2. 3, 9, 11, and 12, each with moderate PVE of ~ 2.5% and with allele contributions from both parents. Eight QTLs associated with grain yield were detected across six chromosomes, with QTL qGY2.1 stable across environments, explaining PVE of 2 to 6% (Table 6 ). Seven QTL-rich regions were identified on chromosomes 1, 2, 3, 8 and 9, with overlapping QTLs suggesting pleiotropy or tight linkage (Table 6 ; Fig. 2 ). For example, on chromosome 1, Region 1 contained five QTLs for plant height on position 148.2 cM. Similarly, on chromosome 2, Region 2 comprised of five QTLs for grain yield positioned at 65.6 cM. On chromosome 3, two regions were identified, Region 3 containing 13 QTLs mapped between 4.0 and 5.0 cM, while Region 4 had four QTLs mapped on ∼1 cM. The fifth QTL region, located on chromosome 8, like the region on chromosome 3, contained 11 QTLs mapped on 20.5 cM. The sixth region on chromosome 9 comprises seven QTLs positioned between ∼28 cM and ∼30 cM. Interestingly, some of the QTLs identified for different traits were in the same region of a chromosome. For example, QTL qPH8.1 , qDTF8.1 , and qGY8.1 co-localized on chromosome 8 at a peak position of 20.5 cM. Similar overlaps were observed for QTL qPH3.1, qGY3.1 and qDTF3.1 on chromosome 3. Several QTLs for number of tillers and panicles ( qTILL3.1/qPAN3.1, qTILL9.1 / qPAN9.1, and qTILL12.1/ qPAN12.1 ) were co-localized at the same position 14.2 cM. The genomic regions of the significant QTLs with PVE greater than 10%, and stable across the environments were explored to identify the protein-coding genes within 1.0 Mb at 500kb interval downstream and upstream of the significant QTLs on the Rice Annotation Project database, RAP-db ( https://rapdb.dna.affrc.go.jp/ ). The gene annotation led to the identification of over 60 candidate genes associated with the five measured traits. Of the 60 candidate genes, 18 important genes were found to have functions related to the traits involved in stagnant flooding tolerance mechanism (Table 7 ). Table 7 Putative candidate genes associated with the major and stable identified QTLs under stagnant flooding stress environments. Trait QTL Chromosome position Gene ID Gene description Plant height qPH1.1 chr01:37097377..37099130 Os01g0858350 CYP94C3-Cytochrome P450 94C3 chr01:37174575..37176344 Os01g0859300 OsABF1-Abscisic acid insensitive 5 chr01:37181505..37188288 Os01g0859500 OsLG2- bZIP transcriptional factor chr01:37396215..37397433 Os01g0864000 OsOFP8-OVATE family protein 8 chr01:37519585..37522414 Os01g0866400 OscFBP1-Cytosolic fructose-1,6-bisphosphatase chr01:36998338..37004512 Os01g0856500 auxin transporter 1 chr01:36936986..36939375 Os01g0855400 R2R3-MYB Transcription Factor 17 chr01:36813736..36815023 Os01g0854500 WUSCHEL-type homeobox protein chr01:36687788..36691363 Os01g0852200 Phosphate transporter 4;3 chr01:36691678..36692022 Os01g0852300 SUMO family protein Plant height, Days to 50% flowering, Grain yield qPH3.1, qDTF3.1, qGY3.1 chr03:1089453..1093410 Os03g0119966 ONAC54-NAC domain-containing protein 054 chr03:1195075..1204839 Os03g0121800 OsDCL1-Dicer-like protein 1 chr03:1270230..1271217 Os03g0122500 Long noncoding RNA (lncRNA) chr03:1270328..1300273 Os03g0122600 MADS-box transcription factor 50 chr03:1318268..1321639 Os03g0123100 SUMO conjugating enzyme chr03:1327450..1331022 Os03g0123300 TILLERING AND DWARF 1 chr03:717447..720837 Os03g0112700 CCCH-type zinc finger protein chr03:935129..939195 Os03g0116500 COP9 signaling complex Discussion In lowland rice ecosystem, occurrences of stagnant flooding is a serious threat despite the water-tolerant nature of the rice plant (Sarkar et al. 2014 ). At different growth stages, the complete inundation of water reduces the optimal performance and productivity of the rice plant (Ray et al 2016). In this study, we aimed to identify NERICA varieties that are tolerant to stagnant flooding, that farmers in the flood prone regions can adopt to grow in both rainfed and irrigated lowland farms in sub-Saharan African. In addition, in an effort to development a tolerant variety, the identification of genomic regions linked to stagnant flooding tolerance traits in rice is critical to speed up the development of stagnant flooding tolerance germplasm using marker assisted selection (Akhtar et al. 2010 ). Marker-assisted selection is a valuable tool in precision plant breeding, enabling the indirect selection of desired traits by using genomic regions that are genetically linked to traits of interest (Haque et al. 2023 ). In the present study, in our effort to characterize all lowland NERICA varieties under the stagnant flooding field condition, significant phenotypic variation was observed among the NERICA varieties for all traits. This suggests there is considerable genetic variability resource to facilitate the development of stagnant flooding tolerant variety. Similar contrastive responses were reported of rice genotypes under stagnant flooding by other authors (Kuanar et al. 2017 ; Singh et al. 2017 ; Zhu et al. 2023 ). In addition, an increase of 5 to 20% was observed in shoot elongation, which translates to increased plant height of the NERICAs. This finding is in agreement with other studies reported by Vergara et al. 2014 (7 to 34%), Kaunar et al. 2017 (9 to 22%), Singh et al. 2017 (~ 3.4%), and Kato et al. 2019 (~ 13%). This further confirms the importance to concentrate breeding efforts into developing high-yielding and moderately tall rice varieties tolerant to stagnant flooding, as excessive stem elongation has been reported to reduce yield and increase the risk of lodging, complicating harvest operations (Sarkar et al. 2021). In contrast to the increase in height, a decline in the number of tillers and panicles per hill was recorded for most NERICAs and checks used under stagnant flooding compared to the control. This finding is similar to reports by Singh et al. ( 2017 ), Kato et al. ( 2019 ), and Zhu et al. ( 2023 ) which indicate that stagnant flooding imposes stress on the rice plant by limiting oxygen and nutrient availability, thereby reducing tillers and panicle production and, ultimately, affecting the overall grain yield. In our study, we observed a 20 to 60% reduction in grain yield under stagnant flooding, consistent with previous findings by Singh et al. ( 2017 ), Kaunar et al. (2017), Kato et al. (2014; 2019 ) and Chattopadhyay et al. ( 2021 ) who reported grain yield reductions of 52.1%, 43%, 47%, 48% and 46% respectively, under stagnant flooding stress conditions. These results suggest that breeding efforts should prioritize selection of traits that sustain tiller and panicle production under stagnant flooding stress to prevent grain yield losses. The heritability estimates provides valuable insights into the genetic control of traits and their potential improvement through cycles of selection. The high broad-sense heritability observed for grain yield under stagnant flooding in this study indicates strong genetic influence with minimal environmental interference, even under stress conditions. The heritability estimates obtained in our study exceed those reported by Singh et al. ( 2017 ) for grain yield and other traits, suggesting a greater scope for genetic gain. Given the polygenic nature of stagnant flooding tolerance, indirect selection through strongly correlated traits offers a practical breeding strategy (Collard et al. 2013). In our study, grain yield under stagnant flooding showed moderate correlations with plant height, days to 50% flowering, number of tillers and panicles under stress. This implies that targeting these traits can facilitate incremental improvement of grain yield under stagnant flooding. Interspecific lines such as Lowland NERICAs derived from O. glaberrima and O. sativa crosses are known to combine he high yielding potential of Asian rice with the stress tolerance of African rice (Kehinde et al. 2024 ). In this study, the RIL population derived from the cross NERICA L-19/IR64- Sub1 exhibited significantly higher grain yield than both the parent lines and the submergence-tolerant checks, IRRI119 and Swarna- Sub1 , under stagnant flooding conditions, with no significant yield difference under control conditions. This suggests that the RIL populations possess the Sub1 gene and harbor alleles conferring tolerance to stagnant flooding. Having both submergence and stagnant flooding tolerance in genotypes is an advantageous combination for rainfed lowland rice ecosystems, increasingly affected by unpredictable flooding due to climate change (Sarkar et al. 2021). These findings align with the study by Kato et al. ( 2019 ), supporting the possibility of developing dual-tolerant genotypes and contrast earlier reports by Singh et al. ( 2011 ), who questioned the effectiveness of combining submergence and stagnant flooding tolerance. A linkage map was constructed using 1396 markers, spanning 1456.2 cM, with an average marker interval of 1.11 cM. This reflects good genome coverage and high recombination, enhancing the resolution and precision of QTL detection. The map length was shorter than those reported by Sripongpangkul, et al. (2000) and Chattopadhyay et al. ( 2021 ) but longer than that reported by Singh et al. ( 2017 ). The linkage map enabled the identification of 27 QTLs with both major and minor effects across the environments. Among these, stable QTLs such as qPH1 .1, qPH3 .1, qDTF3.1 and qGY2.1 stand out as reliable targets for marker-assisted selection due to their consistent expression and meaningful contributions to the traitts under stagnant flooding stress. The identification of novel QTLs, particularly for plant height and grain yield, points to untapped genetic variation in the NERICA/IR64-Sub1 RIL population. Additive main effects observed further clarify the contributions of IR 64-Sub1 and NERICA L-19. The QTLs identified for number of tillers, number of panicles and grain yield exhibited low phenotypic variation, indicating they are small contributors to these traits. However, their collective effects may still be valuable in breeding programs targeting complex traits like grain yield. Although individually small, such QTLs can contribute cumulatively to trait improvement when pyramided through marker-assisted selection. These results highlight the importance of integrating both major and minor QTLs into breeding strategies to enhance resilience and productivity in flood-prone rice growing environments. Seven major QTL regions were identified on chromosomes 1, 2, 3, 8, 9 and 12, with overlapping QTLs on chromosome 3, 8 and 9. The QTL qPH8.1 detected for plant height was co-localized with qDTF8.1 and qGY8.1 , which were identified for days to 50% flowering and grain yield, respectively, at a peak position of 20.5 cM, a genomic hotspot for improving these traits. Similarly, QTL qPH3.1 having a peak position in close proximity to qGY3.1 and qDTF3.1 , suggests a shared genomic region influencing multiple traits such that selection for early flowering ( qDTF3.1 ) could simultaneously reduce plant height and influence grain yield. Addtionally, co-localized QTLs for number of tillers and number of panicles were observed at 0.8 cM, 27.9 cM and 14.2 cM on chromosomes 3, 9, 12, respectively. This finding further confirm the positive correlation observed between the two traits. These overlapping QTL regions imply potential genetic linkage or pleiotropic effects in these chromosomal regions, which may play a significant role in the coordinated expression of these traits. Increasing plant height has been identified as an important adaptive strategy employed by the rice plant to escape stagnant flooding stress (Kuanar et al. 2017 ; Sarkar et al. 2021). The major QTL qPH1.1 identified in this study was linked to several candidate genes involved in hormone regulation, internode elongation, and plant architecture. Notably, LOC_Os01g0858350, encoding a cytochrome P450 enzymes, is associated with increase internode elongation through modulation of gibberellin levels (Luo et al. 2006 ; Kurotani et al. 2015 ; Hazman et al. 2019 ; Dang et al. 2024 ). The presence of this gene within the qPH1.1 region suggests its potential role in promoting elongation under waterlogged conditions. Another notable gene LOC_Os01g0859300, encodes a basic leucine zipper (bZIP) transcription factor involved in growth regulation and abiotic stress responses (Zou et al. 2008 ; Liu et al. 2014 ; Das et al. 2019 ). LOC_Os01g0859500 (OsLG1) contributes to leaf erectness, which can potentially lead to internode elongation and increased plant height (Wang et al. 2021 ), while LOC_Os01g0864000 (OsOFP8) is a regulator of brassinisteriod signaling pathways involved in plant growth and development, including plant height (Yang et alet al 2016; Sun et al. 2024 ). Carbohydrate metabolism also appears relevant, as LOC_Os01g0866400, encoding cytosolic fructose-1,6-bisphosphatase, plays a role in sucrose biosynthesis, photosynthetic efficiency, and tiller development (Lee et al. 2008 ; Koumoto et al. 2013 ). In addition, LOC_Os01g0856500 and LOC_Os01g0855400 encodes an auxin transporters and an R2R3-MYB transcription factor, respectively. They are linked to hormone-mediated elongation and plant height regulation (Yu et al. 2015 ; Kang et al. 2022 ; Zhang et al. 2023 ). Regulatory proteins such as WOX (LOC_Os01g0854500) and SUMO-conjugating enzymes (LOC_Os01g0852300) also occur within this region and may influence plant development, by modulating hormone signaling pathways such as gibberellins and cytokinnins (Kamiya et al. 2003 ; Wang et al. 2014 ; Teramura et al. 2021 ; Li et al. 2024 ). Similarly, the genomic region between 4.4 cM and 4.8 cM on chromosome 3 for significant QTLs qPH3.1 , qDTF3.1 and qGY3.1 associated with plant height, days to 50% flowering and grain yield, respectively, revealed putative candidate gene involved in the growth and development of rice. LOC_Os03g0119966 encoding a NAC transcription factor, regulates developmental processes such as internode elongation, flowering and grain filling (Mathew et al. 2020 ; Li et al. 2024 ). Genes such as LOC_Os03g0121800, encoding dicer-like proteins and LOC_Os03g0122500 ((lncRNA) were implicated in RNA interference and gibberellin pathways that regulate stem elongation and hormone signaling pathways critical for flowering time (Kapoor et al. 2008 ; Li et al. 2021 ; Bhat et al. 2024 ). Additional regulatory elements within this region include LOC_Os03g0122600, a MIKC-type MADS-box genes such as SOC1, have been reported to influence internode elongation in maize and activate early flowering in rice (Lee et al. 2004 ; Song et al. 2021 ), and LOC_Os03g0123100, encoding SUMO-conjugating enzyme (OsSCE1a), which regulates both plant height, and grain yield (Joo et al. 2019 ). Genes like TAD1 (LOC_Os03g0123300) and Ehd4 (LOC_Os03g0112700) regulate tillering and flowering, respectively, while (Lin et al. 2012 ; Gao et al. 2013 ) LOC_Os03g0126500, part of the COP9 signalosome, regulates gibberellin signaling pathways critical for rice growth and development (Han et al. 2023 ). This study highlights the significant genetic potential of lowland NERICA varieties and their derived RIL population for improving rice adaptation to stagnant flooding stress conditions. The observed phenotypic variability among lowland NERICA and the identification of key QTLs linked to grain yield, plant height, flowering time, number of tillers and panicles highlight valuable genetic resources for breeding. Notably, the RIL population derived from NERICA L-19/IR64-Sub1, combined submergence and stagnant flooding tolerance, offering a dual-tolerant platform for future variety development. Stable and novel QTLs, such as qPH1 .1, qPH3.1 , and qGY2.1 , as well as co-localized genomic regions, provide strong candidates for marker-assisted selection. Candidate genes involved in hormone signaling, growth regulation, and developmental timing were lined to these QTLs, reinforcing their relevance. This study provides a comprehensive genetic framework for stagnant flooding tolerance in rice and the development of high-yielding, flood-resilient rice varieties that can sustain productivity amidst climate variability. Future efforts should focus on fine mapping of identified QTLs, functional validation of candidate genes, and field-based evaluations to translate these insights into practical breeding solutions. Declarations Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Funding This research was financially supported by Bill and Melinda Gates Foundation through the project “Rapid Mobilization of Alleles for Rice Cultivar Improvement in Sub-Saharan Africa” (OPP1080832). Author contribution statement Vimal Kumar Semwal and Venuprasad Ramaiah contributed to the conceptualization and design of the study. Vimal Kumar Semwal and Shittu Azeez carried out data collection. Vimal Kumar Semwal and Olatunde Azeez Bhadmus contributed to data analysis and manuscript revision. Vimal Kumar Semwal, Shittu Azeez, Olatunde Azeez Bhadmus, Okanlawon Jolayemi and Venuprasad Ramaiah were involved in the investigation and methodology of the study. 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Africa Rice Center (WARDA), Cotonou, Benin; FAO, Rome, Italy; Sasakawa Africa Association, Tokyo, Japan. 210 pp. Song GQ, Han X, Ryner JT, Thompson A, Wang K (2021) Utilizing MIKC-type MADS-box protein SOC1 for yield potential enhancement in maize. Plant Cell Reports 40:1679-1693. https://doi.org/10.1007/s00299-021-02722-4 Sun X, Xie Y, Xu K, Li J (2024) Regulatory networks of the F-box protein FBX206 and OVATE family proteins modulate brassinosteroid biosynthesis to regulate grain size and yield in rice. J Exp Bot 75:789-801. https://doi.org/10.1093/jxb/erad397 Teramura H, Yamada K, Ito K, Kasahara K, Kikuchi T, Kioka N, Fukuda M, Kusano H, Tanaka K, Shimada H (2021) Characterization of novel SUMO family genes in the rice genome. Genes & Genetic Systems. 96:25-32 https://doi.org/10.1266/ggs.20-00034 Thangasamy S, Guo CL, Chuang MH, Lai MH, Chen J, Jauh GY (2011) Rice SIZ1, a SUMO E3 ligase, controls spikelet fertility through regulation of anther dehiscence. New Phytol 189:869-882. https://doi.org/10.1111/j.1469-8137.2010.03538.x Vergara GV, Nugraha Y, Esguerra MQ, Mackill DJ, Ismail AM (2014) Variation in tolerance of rice to long-term stagnant flooding that submerges most of the shoot will aid in breeding tolerant cultivars. AoB Plants 6:plu055. Vergara GV, Nugraha Y, Esguerra MQ, Mackill DJ, Ismail AM (2014). Variation in tolerance of rice to long-term stagnant flooding that submerges most of the shoot will aid in breeding tolerant cultivars. AoB Plants 6:plu055. https://doi.org/10.1093/aobpla/plu055 Wang R, Liu C, Chen Z, Sun S, Wang X (2021) Oryza sativa LIGULELESS 2s determine lamina joint positioning and differentiation by inhibiting auxin signaling. The New Phytologist 229:1832-1839. Wang T, Jin Y, Deng L, Li F, Wang Z, Zhu Y, Wu Y, Qu H, Zhang S, Liu Y, Mei H (2024) The transcription factor MYB110 regulates plant height, lodging resistance, and grain yield in rice. 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The Plant Journal 83:818-830. https://doi.org/10.1111/tpj.12929 Zhang Y, Han S, Lin Y, Qiao J, Han N, Li Y, Feng Y, Li D, Qi Y (2023) Auxin transporter OsPIN1b, a novel regulator of leaf inclination in rice ( Oryza sativa L.). Plants 12:409. https://doi.org/10.3390/plants12020409 Zhu G, Wu H, Chen Y, Mondal S, Ismail AM (2023). Growth characteristics and yield of contrasting rice genotypes under long-term stagnant flooding. Field Crops Res 301:109020. https://doi.org/10.1016/j.fcr.2023.109020 Zou M, Guan Y, Ren H, Zhang F, Chen F (2008) A bZIP transcription factor, OsABI5, is involved in rice fertility and stress tolerance. Plant Mol Biol 66:675-683. https://doi.org/10.1007/s11103-008-9298-4 Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 25 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 02 Sep, 2025 First submitted to journal 01 Sep, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7510204","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515713377,"identity":"a6aa42f2-ddab-4cca-85fa-f03634b8fbc3","order_by":0,"name":"Vimal Kumar Semwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBADGX4gcYAkLTySDSRrMSBavcEB9oePbrbZ8RgfP37xwM89DHLm/QsIaeExNs5tS+YxO5NTcLDnGYOxzI0H+LVINvCwSeduY+Yxu8GTcIDnAEPiDAkCTpRsYH/+O3dbPY/xDJ6Eg3+I0QIMWzPm3G2HeQwk2A8cBtvC30BACzOPsXTuv+M8EmdyGA7LHJAwlpDAr4OBjb394eecM9Vy/O3HH398c8BGToKfgMMYmOEsHgMgAbRCIoGAFgRgfwB1KyFbRsEoGAWjYKQBAEAYQOtbf2uaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0903-9492","institution":"AfricaRice: Africa Rice Center","correspondingAuthor":true,"prefix":"","firstName":"Vimal","middleName":"Kumar","lastName":"Semwal","suffix":""},{"id":515713378,"identity":"cbd88c34-d7d1-445b-8f4c-97173f74e335","order_by":1,"name":"Shittu Afeez","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shittu","middleName":"","lastName":"Afeez","suffix":""},{"id":515713379,"identity":"8c6f7117-e114-4433-91fa-d436cf0c0113","order_by":2,"name":"Olatunde A. Bhadmus","email":"","orcid":"","institution":"AfricaRice: Africa Rice Center","correspondingAuthor":false,"prefix":"","firstName":"Olatunde","middleName":"A.","lastName":"Bhadmus","suffix":""},{"id":515713380,"identity":"d5336130-86d7-4772-ae6d-026cbbe036c6","order_by":3,"name":"Okanlawon Jolayemi","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences Alnarp Campus: Sveriges lantbruksuniversitet - Campus Alnarp","correspondingAuthor":false,"prefix":"","firstName":"Okanlawon","middleName":"","lastName":"Jolayemi","suffix":""},{"id":515713381,"identity":"e6a59c85-0b3b-4d0f-b2f5-26279b99da0e","order_by":4,"name":"Venuprasad Ramaiah","email":"","orcid":"","institution":"International Rice Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Venuprasad","middleName":"","lastName":"Ramaiah","suffix":""}],"badges":[],"createdAt":"2025-09-01 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1","display":"","copyAsset":false,"role":"figure","size":366662,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between grain yield and other traits in the F5 RIL mapping population derived from NERICA L-19 × IR64 sub1 under stagnant flooding condition. The axis displayed the range of values obtained for each trait; the black shaded region represent the most predominant values for each trait among the genotypes, while the red line represents the direction of the relationship between two traits, * = 0.05, ** = 0.01, *** = 0.001 and it indicates highly significant.\u003c/p\u003e","description":"","filename":"Figure1.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7510204/v1/54562ca53404bfc0c3cb63f3.jpg"},{"id":92081304,"identity":"572d53ca-0e9a-4d06-9236-f82b7911034f","added_by":"auto","created_at":"2025-09-24 11:55:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176696,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of QTLs mapped in the F5 RIL NERICA-L-19 × IR64-Sub1 population. Percentage of phenotypic variance explained (PVE) by each QTL is reported in brackets (gray color if PVE \u0026lt; 7.5%). Chromosome regions showing high density of overlapping QTLs of more than 3 (≥3) related to HIA stress tolerance are indicated in red. DTF: days to 50% flowering; PH: plant height; GY: grain yield; TILL: number of tillers per hill; PAN: number of panicles per hill.\u003c/p\u003e","description":"","filename":"Figure2.tiff.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7510204/v1/7b64b67b5e30ec2fd3dcc9ac.jpg"},{"id":100069521,"identity":"55024b54-53b1-408b-99ed-fb6633d4d58a","added_by":"auto","created_at":"2026-01-12 16:14:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2254696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510204/v1/4ec80e90-5f24-4995-9f04-0ee1d1150b88.pdf"}],"financialInterests":"","formattedTitle":"Unveiling Stagnant Flooding Tolerance in Lowland NERICAs: Genomic Insights and Breeding Prospects","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice is staple food grain for more than half of the world population. Due to highly heterogenetic conditions in rainfed lowland ecosystems, paddy farms are often subjected to various types of flooding stresses (Panda and Barik, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Flooding could be complete submergence for shorter periods of time or prolonged stagnant water in farms for longer periods, sometimes for whole rice crop season (Kato et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Panda and Barik, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the exploitation of genes like \u003cem\u003eSub1\u003c/em\u003e which contribute to quiescence technique, and help to avoid stem elongation under complete submergence, rice plants can survive up to 2 weeks period (Haque et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kato et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, under stagnant flooding, partial submergence of plants remains for longer periods, and hence moderately elongated stems are highly desirable for plant survival and sustained growth. Stagnant flooding is a recurring and common problem during wet season in sub-Saharan African countries and many Asian countries (Kovcas et al. 2017; Kuanar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Sub-Saharan Africa, in particular, is projected to experience a high and widespread increase in flood frequency in future due to climate change (Hirabayashi et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Haque et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since many countries in Africa are growing rice under rainfed lowland ecologies, stagnant flooding represents a major abiotic stress leading to economic loss to farmers especially during rainy wet season. Currently, there is dearth of stagnant flooding tolerant varieties, as the best locally adopted varieties perform poorly under these conditions. Hence, there is a need and scope for identifying SF tolerant germplasm and breeding more robust SF tolerant rice varieties for Sub-Saharan African countries.\u003c/p\u003e\u003cp\u003e\u003cem\u003eOryza glaberrima\u003c/em\u003e (2n\u0026thinsp;=\u0026thinsp;24, AA) originated and indigenous cultivated rice species of Africa, is known to have tolerance to several abiotic stresses. It is considered to have originated in flood-prone ecology and thus, may be a good source of tolerance to flooding stress (Oka, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1974\u003c/span\u003e, Watarai and Inouye, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Inouye et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e, Mochizuki et al. 1997, Futakuchi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, Sarla and Swamy, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Joho et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Opeyemi and Venuprasad, unpublished results). However, limited research has been carried out to characterize the flooding tolerance of \u003cem\u003eO. glaberrima\u003c/em\u003e. Futakuchi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) conducted a physiological study and concluded that \u003cem\u003eO. glaberrima\u003c/em\u003e has higher resistance to deepwater stress. Similarly, Sakagami et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) studied physiological responses of 27 accessions of \u003cem\u003eO. glaberrima\u003c/em\u003e accessions to prolonged submergence and suggested that \u003cem\u003eO. glaberrima\u003c/em\u003e could be used in breeding for stagnant flooding (SF) tolerance.\u003c/p\u003e\u003cp\u003eScreening efforts involving landraces, improved varieties, \u003cem\u003eSub1\u003c/em\u003e-introgressed lines, and \u003cem\u003eO. sativa\u003c/em\u003e lines such as IRRI 119 and IRRI 154, as well as many \u003cem\u003eO. glaberrima\u003c/em\u003e accessions have revealed key adaptive traits associated with SF tolerance (Vergara et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Agbeleye et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mwakyusa et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, \u003cem\u003eO. glaberrima\u003c/em\u003e accessions were found to possess unique alleles for SF tolerance not present in elite Asian lines (Mwakyusa et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To harness this genetic variation, researchers have developed mapping and breeding populations for SF tolerance. Key among these breeding populations are recombinant inbred lines (RIL) populations derived from crosses between tolerant and susceptible parents have been utilized by Singh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Chattopadhyay et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for QTL mapping to identify loci associated with SF tolerance. \u003cem\u003eO. glaberrima\u003c/em\u003e and its interspecific derivatives, are emerging as rich sources of alleles conferring SF tolerance (Futakuchi and Sie, 2009). These varieties combine the high yield potential of Asian rice and the stress tolerance traits of African rice, thereby broadening the genetic base for SF tolerance. The genetic diversity embedded within these interspecific derivatives offers a valuable opportunity to dissect and harness adaptive traits, advancing the development of climate-resilient rice variety relevant to SF tolerance.\u003c/p\u003e\u003cp\u003eAmong the interspecific derivatives, the lowland NERICA varieties are among the most popular interspecific varieties that farmers prefers to grow in both rainfed and irrigated lowland farms in African subcontinent, currently occupying about 700,000 hectares of land (Somado et al. 2008). The 60 lowland NERICA varieties developed by Africa Rice Center (AfricaRice/WARDA) all have \u003cem\u003eOryza glaberrima\u003c/em\u003e background and are interspecies hybrids of both cultivated rice species \u003cem\u003eO. sativa\u003c/em\u003e and \u003cem\u003eO. glaberrima\u003c/em\u003e. However, there is dearth of information on performance of these popular varieties under prolonged SF stress which is becoming a serious issue for the farmers in Africa. Hence, characterization of all lowland NERICA varieties to SF stress under field conditions was the first objective of the present study. Additionally, we also aimed to identify tolerant genotypes and exploit the identified SF tolerant germplasm in further breeding efforts with \u003cem\u003eSub1\u003c/em\u003e background. This would enable the combination of tolerance to two major flooding stresses viz. submergence and stagnant flooding, which would be more useful considering the heterogenic nature and uneven rainfall patterns common in the lowland ecosystems of Sub-Saharan African countries. Finally, the study also aimed to map potential genomic regions/quantitative traits loci (QTLs) associated with traits contributing to higher grain yield under SF stress.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExperimental site\u003c/h2\u003e\u003cp\u003eThe study was conducted at the Africa Rice Center (AfricaRice) research station located within the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria (Latitude 3\u003csup\u003e\u0026deg;\u003c/sup\u003e54\u0026acute;32ʹʹ E: Longitude 7\u003csup\u003e\u0026deg;\u003c/sup\u003e29\u0026acute;15ʹʹN). Experimental plots included both stagnant flooding (SF) and control (normal irrigated) conditions in lowland experimental fields.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePlant material\u003c/h3\u003e\n\u003cp\u003eA total of sixty (60) lowland NERICAs were sourced from the AfricaRice genebank and screened for tolerance to stagnant flooding. For comparison, \u003cem\u003eOryza sativa\u003c/em\u003e varieties were used as checks. The widely cultivated rainfed lowland variety, FARO 57, was included as a local check across all trials. In addition, known tolerant checks for stagnant flooding, IRRI 119 and IRRI 154 (Vergara et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kato \u003cem\u003eet al.\u003c/em\u003e 2014), along with \u003cem\u003eSub1\u003c/em\u003e gene incorporated line IR64-\u003cem\u003eSub1\u003c/em\u003e, were included as reference checks in the respective experiments.\u003c/p\u003e\n\u003ch3\u003eDevelopment of mapping populations\u003c/h3\u003e\n\u003cp\u003eA cross was made between a selected lowland NERICA and IR64-\u003cem\u003eSub1\u003c/em\u003e, where NERICA L-19 was the recurrent parent and IR64-\u003cem\u003eSub1\u003c/em\u003e as the donor parent. From the F\u003csub\u003e1\u003c/sub\u003e generation, a total of 2500 selfed F\u003csub\u003e2\u003c/sub\u003e plants were generated. Selection at the F\u003csub\u003e2\u003c/sub\u003e stage was based on key agronomic traits including plant height, heading date and other traits associated with SF tolerance. Under irrigated conditions, 600 F\u003csub\u003e3\u003c/sub\u003e families were selected and advanced to the F\u003csub\u003e4\u003c/sub\u003e generation. At the F\u003csub\u003e3:4\u003c/sub\u003e stage, the population was narrowed down to 484 lines, which were subsequently selfed to advance to the F\u003csub\u003e4:5\u003c/sub\u003e generation for further evaluation.\u003c/p\u003e\n\u003ch3\u003eProtocol for screening\u003c/h3\u003e\n\u003cp\u003eTo break seed dormancy, seeds of the rice accessions were subjected to heat treatment at 50\u0026deg;C for three days in an oven. In all experiments, seeds were initially raised in a nursery, and 21-day old seedlings were subsequently transplanted into a well-puddled and leveled field. Each experimental plot consisted of a single row measuring 3 m, with 20 cm spacing between rows and plants. A basal application of NPK fertilizer (15-15-15) was made at 200 kg/ha one day after transplanting. Urea was applied twice: 30 kg/ha at tillering stage and another 30 kg/ha at panicle initiation. Weed control involved the use of herbicides during early growth, followed by manual weeding as the plants matured.\u003c/p\u003e\u003cp\u003eIn the control experiment, a consistent water depth of 2\u0026ndash;5 cm was maintained throughout the growing period until harvest. For the stress experiment, an initial water level of 2\u0026ndash;5 cm was sustained for the first 14 days after transplanting. The stress was then imposed by increasing water level to 15 cm on day 15, followed by incremental increases of approximately 5 cm on alternate days until the final water level of 50 cm was reached. This level was maintained for around three months, after which the fields were drained back to 2\u0026ndash;5 cm shortly before harvest. The stagnant flooding protocol employed was adapted from the method described by Singh et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) at IRRI.\u003c/p\u003e\u003cp\u003eDays to flowering (DTF) was recorded when approximately 50% of the plants in a plot had vicible panicle exertion. Plant height (HT) was measured at maturity on three randomly selected plants per plot, from the base to the tip of the tallest panicle, and the average was calculated. The number of tillers (TILL) and panicles (PAN) were counted from five randomly selected plants in each plot and averaged. All panicles from each plot were harvested, threshed, cleaned, and dried to approximately 14% moisture content before weighing to determine grain yield (GY).\u003c/p\u003e\n\u003ch3\u003eGenotyping of the RIL Population and QTLs analysis\u003c/h3\u003e\n\u003cp\u003eLeaf samples from 484 F5 progenies of the populations and the parents were collected, lyophilized and shipped to the Diversity Arrays Technology (DArT), Australia for genotyping. Data received showed polymorphism between two parents in 1396 SNPs. The genotypic data from 1396 polymorphic SNPs covering all chromosomes was used for QTL mapping in the RIL population. The QTL analysis was performed using the QGene software (Joehanes \u0026amp; Nelson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The analysis utilized genotypic and phenotypic data from the NERICA L-19/IR64-\u003cem\u003eSub1\u003c/em\u003e RIL population. The genotypic data included a genetic linkage map with SNP marker positions expressed in centimorgans (cM), while the phenotypic data comprised grain yield, plant height, days to 50% flowering, number of tillers, and number of panicles per plant. The input data were formatted as text files, with individuals represented as rows and markers, genotypes, and traits as columns. Markers were assigned to chromosomes and ordered based on their genetic positions. Data consistency was verified using QGene's graphical tools. Composite Interval Mapping (CIM) was employed; this method refined QTL detection by incorporating covariates to account for background genetic effects. Genome-wide scans were conducted, and LOD scores were calculated at regular intervals. QTLs were considered significant if the LOD score exceeded a threshold determined by permutation testing (n\u0026thinsp;=\u0026thinsp;1,000, p\u0026thinsp;\u0026le;\u0026thinsp;0.05). Significant QTLs were annotated with their chromosomal positions, flanking markers, LOD scores, and percentage of phenotypic variance explained (PVE). QTL regions were defined by overlapping QTLs and the presence of a SNP with a significance threshold of -log10 p\u0026thinsp;\u0026ge;\u0026thinsp;5.0.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of Candidate Genes\u003c/h2\u003e\u003cp\u003eCandidate genes located near major QTLs with PVE greater than 10%, and stable across the environments, were selected for gene annotation. Gene annotation information was retrieved from the Rice Annotation Project database, RAP-db (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rapdb.dna.affrc.go.jp/\u003c/span\u003e\u003cspan address=\"https://rapdb.dna.affrc.go.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 2 December 2024). For each significant QTL, candidate genes were predicted within a 1.0 Mb genomic window, extending 500kb upstream and downstream of the significant QTLs. Genes annotated as hypothetical, non-protein coding, or associated with transposable elements were excluded from futher analysis. However, priority was on genes associated with traits indicative of stagnant flooding tolerance under both stagnant flooding and control environments. To gain deeper insights into gene function, SNP-associated gene annotations were cross-referenced with the European Molecular Biology Laboratory\u0026ndash;European Bioinformatics Institute (EMBL\u0026ndash;EBI) database.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePhenological and morphological parameters in lowland NERICAs\u003c/h2\u003e\u003cp\u003eDays to flowering (DTF) declined by 5 to 15 days in different lowland NERICAs under stagnant flooding stress (SF) compared to irrigated control (IRR) except for NERICA L-20 and NERICA L-57 were DTF was similar under both SF and IRR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All NERICAs exhibited moderate stem elongation, leading to 5 to 20% increase in plant height under SF compared to IRR except for NERICA L-20, NERICA L-25 and NERICA L-6 were plant height was more than 30% increased over IRR under SF. Number of tillers per plant declined marginally in all NERICAs and checks used under SF compared to IRR. Among the checks used, DTF was similar in both treatments IRR and SF in IRRI-119 and FARO 57 while declined by 10 days in IRRI-154 and IR 64-\u003cem\u003eSub1\u003c/em\u003e under SF as compared to IRR. Plant height increased by 5 to 25% under SF in all checks compared to IRR except for IR 64-\u003cem\u003eSub1\u003c/em\u003ewhich maintained similar plant height under both IRR and SF treatments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDays to flowering and plant height of lowland NERICAs and checks under stagnant flooding stress and control conditions during WS 2014 and WS 2015 at Ibadan, Nigeria. Nigeria. Values are average of 2 trials for both control and stagnant flooding stress.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDays to flowering\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePlant height (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eTiller number\u003c/p\u003e\u003cp\u003e(per hill)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003ePanicle number\u003c/p\u003e\u003cp\u003e(per hill)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNERICAs\u003c/em\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelections\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003eTolerant and local checks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI 119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI 154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR 64-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrial Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eWS \u0026ndash; wet season; DS \u0026ndash; dry season\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eYield and yield components in lowland NERICAs\u003c/h2\u003e\u003cp\u003eNumber of panicle per hill (PN) declined marginally in all NERICAs under SF stress compared to IRR, except for NERICA L-6, NERICA L-18, NERICA L-21 and NERICA L-60 which maintained similar PN under both IRR and SF treatments (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the checks IRRI-154 and IR 64-Sub1 had similar PN under IRR and SF treatment, while IRRI-119 and FARO 57 exhibited marginal decline in PN under SF as compared to IRR. Grain yield (GY) decline under SF in all NERICAs and checks compared to IRR. Wide variation was observed in grain yield reduction under SF in NERICAs ranged from 20 to 60% decline in grain yield under SF. Some high yielding NERICAs out yielded the tolerant checks and local check under SF (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGrain yield (kg/h) of Lowland NERICAs and checks under stagnant flooding stress and control conditions during WS 2014 and WS 2015 at Ibadan, Nigeria.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eWS 2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eWS 2015\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNERICAs\u003c/em\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelections\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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA-L-40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eTolerant and local checks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI 119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI 154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFARO 57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR64-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrial Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e563.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e624.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e711.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eWS \u0026ndash; wet season; DS \u0026ndash; dry season\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhenological parameters and yield components in NERICA L-19/IR64-Sub1 population\u003c/h2\u003e\u003cp\u003eSince NERICA L-19 was one of the best genotypes performing well under SF stress (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), a RIL mapping population was developed under Sub1 background using NERICA l-19 and IR64-Sub1. F3 and F4 population was evaluated under both IRR and SF conditions in WS 2015 and DS 2016. The RIL population exhibited wide genetic variation, normal distribution and transgressive segregation for phenomorphological traits and yield related traits like plant height, DTF, number of tillers, PN and GY (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Some of progenies yielded higher than both parents and tolerant/local checks both under SF and IRR conditions. Normal distribution and transgressive segregation in yield and other traits indicate that the RIL population having some promising high yielding SF tolerant lines and the population was suitable for QTL mapping.\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\u003eGrain yield (kg/h) of NERICA L-19/IR64-Sub1 F6 population, parents and checks under stagnant flooding stress and control conditions during WS 2016, DS2017, WS 2017 and WS 2018 at Ibadan, Nigeria.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWS2016-SF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDS2017-SF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWS2017-SF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDS2017-IRR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWS2018-IRR\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\u003ePopulation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e720\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelections\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-75-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-11-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-194-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-174-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParents\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA L-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR64-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4210\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwarna-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrail mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eWS \u0026ndash; wet season; DS \u0026ndash; dry season\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDays to flowering and plant height of NERICA L-19/IR64-Sub1 F6 population, parents and checks under stagnant flooding stress and control conditions during WS 2016, DS2017, WS 2017 and WS 2018 at Ibadan, Nigeria. Values for stress are average of 3 trials for stagnant flooding and 2 trials for control treatment.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDays to flowering\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003ePlant height (cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eTiller number\u003c/p\u003e\u003cp\u003e(per hill)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003ePanicle number\u003c/p\u003e\u003cp\u003e(per hill)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eStress\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePopulation\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelections\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-75-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-11-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-194-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eART217-174-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParents\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNERICA L-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR 64-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.5\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIRRI 119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSwarna-Sub1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrial Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD (0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeritability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eWS \u0026ndash; wet season; DS \u0026ndash; dry season\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePhenotyping of the F5 RIL Population\u003c/h2\u003e\u003cp\u003eThe F5 RIL population along with the parents and SF tolerant and popular local checks were evaluated both under IRR and SF condition in three seasons WS 2016, DS 2017 and WS 2017. Population exhibited wide variation in phenomorphological traits like DTF and HT. The pattern of segregation in the population was normal and transgressive for yield and phenomorphological traits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Plant height increased significantly in majority of the progenies, parents and checks under SF stress as compared to IRR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). An obvious decline in number of tillers per plant, PN was observed under SF stress in the progenies, parents and checks under SF compared to IRR. However, many progenies produced higher number of tillers, and panicles than both the parents and SF tolerant checks under prolonged SF stress condition leading to higher grain yield than checks and parents under SF stress (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Some of the consistent progenies across trails exhibited less decline in GY under SF compared to IRR than both parents and checks, indicating potentially higher tolerance to SF than parents and checks (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder stagnant flooding conditions, the traits recorded in the F5 RIL Population exhibited a continuous phenotypic distribution, ranging from highly tolerant to highly susceptible. Grain yield showed moderate and statistically significant positive correlations with most other measured traits. Notably, days to 50% flowering was moderately correlation with plant height (r\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while the strongest correlation was observed between number of tillers and number of panicles (r\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Across the environments, the broad sense heritability of the grain yield ranged from 70\u0026ndash;96%. For the other traits, it varied from 60% for number of panicles per hill to 95% for plant height and 20% for number of panicles to 97% for plant height under SF and control environment, respectively (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eQTL detection in F5 RIL population\u003c/h2\u003e\u003cp\u003eA total of 1396 high-quality SNPs, evenly distributed across the 12 rice chromosomes, were used to construct a genetic linkage map spanning 1456.2 cM, with an average distance between every pair of markers of 1.11 cM (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The number of SNP markers on each chromosome ranged from 66 (chromosome 9) to 186 (chromosome 1), with a linkage distance ranging from 89.5 to 172.4 cM. QTL mapping conducted in the NERICA L-19/IR64-Sub1 RIL population identified 27 QTLs associated with five agronomic traits important for stagnant flooding (SF) tolerance, distributed across 12 chromosomes. Among the 27 QTLs, 13 QTLs were detected under the irrigated (IRR) conditions, while 19 were detected under SF, and 5 were consistently identified across the five environments (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The phenotypic variance explained (PVE) by these QTLs ranged from 2\u0026ndash;48%, with LOD scores between 2 and 68. Both parents contributed favorable alleles, with 16 QTLs from IR64-\u003cem\u003eSub1\u003c/em\u003e and 11 QTLs from NERICA-L19 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the genetic linkage map constructed from F5 RIL population derived from a cross between NERICA L-19 and IR64-\u003cem\u003eSub1\u003c/em\u003e using 1,396 markers.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChromosome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Distance (cM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage Genetic Distance between Markers (cM)\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\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\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\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\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\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.68\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\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\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\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81\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\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\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\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\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\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\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\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\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\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\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\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1456.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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\u003eQTLs for agronomic and GY traits under irrigated and SF stress in NL19/IR64Sub1 population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"21\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChr.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePosit.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFlanking Markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eDS 2017 IRR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e\u003cp\u003eWS 2018 IRR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e\u003cp\u003eWS 2016 SF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e\u003cp\u003eDS 2017 SF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e\u003cp\u003eWS 2017 SF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecm.\u003c/p\u003e\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\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAdd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eAdd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003eAdd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003eAdd.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003eAdd.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH1.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS1_37014282\u0026ndash;37103278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e53\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e40\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-8.8\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e48\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e39\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-7.9\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e42\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e33\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-10.3\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e35\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e28\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-8.8\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e34\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e28\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-7.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH1.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS1_28624315\u0026ndash;30476133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\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\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\u003cp\u003e\u003cb\u003e3.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e-2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e3.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e-3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e5.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e-3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS3_1042202\u0026ndash;1177403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-4.9\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e11.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e13\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-5.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e8.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-4.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10.7\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-3.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH5.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH8.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS8_4716302\u0026ndash;5157478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\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\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\u003cp\u003e\u003cb\u003e2.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e3.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDTF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqDTF3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS3_1042196\u0026ndash;1177403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e68\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e48\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-1.8\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e62\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e44\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-2.7\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e42\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e32\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-2.6\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e27\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e23\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-1.8\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e47\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e37\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-0.89\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqDTF8.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS8_4716302\u0026ndash;5157478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.9\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-1.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e7.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-1.3\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.3\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-1.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-0.9\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e3.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e-0.9\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqDTF12.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS12_23980869\u0026ndash;24530396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e-0.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPAN9.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS9_20369578\u0026ndash;20695138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003eN19\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\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\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e-0.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e-0.16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY2.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS2_16355796\u0026ndash;16464015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e209\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.3\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e201\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.1\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e100\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.3\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e150\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5.4\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e206\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY2.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS2_22984666\u0026ndash;23650298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e4.3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e187\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e5.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e5.8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003e159\u003c/em\u003e\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY3.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS3_1042196\u0026ndash;1177403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\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\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\u003cp\u003e\u003cb\u003e8.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e7.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e262\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e7.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e7.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e268\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e5.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY5.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS5_5838683\u0026ndash;7854237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\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\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\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e2.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e-165\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e4.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e-78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY5.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS5_18628440\u0026ndash;18960064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\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\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\u003cp\u003e\u003cb\u003e4.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e4.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e163\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e3.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e3.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e155\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e2.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY7.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS7_13405336\u0026ndash;15244874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e4.5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e4.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e-41\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e4.3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003e4.2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003e-38\u003c/em\u003e\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY8.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS8_4716302\u0026ndash;5157478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN19\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\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\u003cp\u003e\u003cb\u003e3.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e3.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e-107\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e145\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003e\u003cb\u003e2.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003e\u003cb\u003e2.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003e\u003cb\u003e140\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqGY9.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS9_7142916\u0026ndash;15272672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR\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\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\u003cp\u003e\u003cb\u003e5.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e4.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e-149\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e2.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e\u003cb\u003e2.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003e\u003cb\u003e-139\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"21\"\u003e*The one underlined are common between IR, SF. Bold are stagnant flooding specific, and italics are control specific.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"21\"\u003e*PH-Plant height, DTF-Days to flowering, TILL-Number of tillers, PAN-Number of panicles, GY-Grain yield kg/h, N19-NERICA L-19, IR-IR64-\u003cem\u003eSub1\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFive QTLs for plant height were detected on chromosomes 1, 3, 5, and 8. Among these, a major QTL \u003cem\u003eqPH1.1\u003c/em\u003e (PVE; 28 and 40%) and QTL \u003cem\u003eqPH3.1\u003c/em\u003e were stable across environments, while the remaining QTLs, \u003cem\u003eqPH1.2 and qPH8.1\u003c/em\u003e were identified under the stagnant flooding environment while \u003cem\u003eqPH5.1\u003c/em\u003e was detected under the irrigated condition. The additive main effect of decreased plant height was attributed to IR64-Sub1, while increased plant height by NERICA L-19. For days to 50% flowering, three QTLs were detected on chromosomes 3, 8, and 12. The major QTL \u003cem\u003eqDTF3.1\u003c/em\u003e showed the highest PVE of 48% and stable across the environments. Four minor QTLs for number of tillers were detected on chromosomes 3,8,9 and 12, with PVE ranging from 2 to 5.3%, and LOD scores of 2.1 to 4.5. Seven QTLs associated with number of panicles were identified on chromosomes 1, 2. 3, 9, 11, and 12, each with moderate PVE of ~\u0026thinsp;2.5% and with allele contributions from both parents. Eight QTLs associated with grain yield were detected across six chromosomes, with QTL \u003cem\u003eqGY2.1\u003c/em\u003e stable across environments, explaining PVE of 2 to 6% (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Seven QTL-rich regions were identified on chromosomes 1, 2, 3, 8 and 9, with overlapping QTLs suggesting pleiotropy or tight linkage (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, on chromosome 1, Region 1 contained five QTLs for plant height on position 148.2 cM. Similarly, on chromosome 2, Region 2 comprised of five QTLs for grain yield positioned at 65.6 cM. On chromosome 3, two regions were identified, Region 3 containing 13 QTLs mapped between 4.0 and 5.0 cM, while Region 4 had four QTLs mapped on \u0026sim;1 cM. The fifth QTL region, located on chromosome 8, like the region on chromosome 3, contained 11 QTLs mapped on 20.5 cM. The sixth region on chromosome 9 comprises seven QTLs positioned between \u0026sim;28 cM and \u0026sim;30 cM. Interestingly, some of the QTLs identified for different traits were in the same region of a chromosome. For example, QTL \u003cem\u003eqPH8.1\u003c/em\u003e, \u003cem\u003eqDTF8.1\u003c/em\u003e, and \u003cem\u003eqGY8.1\u003c/em\u003e co-localized on chromosome 8 at a peak position of 20.5 cM. Similar overlaps were observed for QTL \u003cem\u003eqPH3.1, qGY3.1 and qDTF3.1\u003c/em\u003e on chromosome 3. Several QTLs for number of tillers and panicles (\u003cem\u003eqTILL3.1/qPAN3.1, qTILL9.1\u003c/em\u003e/\u003cem\u003eqPAN9.1, and\u003c/em\u003e qTILL12.1/\u003cem\u003eqPAN12.1\u003c/em\u003e) were co-localized at the same position 14.2 cM. The genomic regions of the significant QTLs with PVE greater than 10%, and stable across the environments were explored to identify the protein-coding genes within 1.0 Mb at 500kb interval downstream and upstream of the significant QTLs on the Rice Annotation Project database, RAP-db (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rapdb.dna.affrc.go.jp/\u003c/span\u003e\u003cspan address=\"https://rapdb.dna.affrc.go.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gene annotation led to the identification of over 60 candidate genes associated with the five measured traits. Of the 60 candidate genes, 18 important genes were found to have functions related to the traits involved in stagnant flooding tolerance mechanism (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePutative candidate genes associated with the major and stable identified QTLs under stagnant flooding stress environments.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQTL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChromosome position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGene description\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eqPH1.1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:37097377..37099130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0858350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCYP94C3-Cytochrome P450 94C3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:37174575..37176344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0859300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsABF1-Abscisic acid insensitive 5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:37181505..37188288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0859500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsLG2- bZIP transcriptional factor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:37396215..37397433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0864000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsOFP8-OVATE family protein 8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:37519585..37522414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0866400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOscFBP1-Cytosolic fructose-1,6-bisphosphatase\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:36998338..37004512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0856500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eauxin transporter 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:36936986..36939375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0855400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR2R3-MYB Transcription Factor 17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:36813736..36815023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0854500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUSCHEL-type homeobox protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:36687788..36691363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0852200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhosphate transporter 4;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr01:36691678..36692022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs01g0852300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSUMO family protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlant height,\u003c/p\u003e\u003cp\u003eDays to 50% flowering,\u003c/p\u003e\u003cp\u003eGrain yield\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eqPH3.1, qDTF3.1, qGY3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1089453..1093410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0119966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eONAC54-NAC domain-containing protein 054\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1195075..1204839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0121800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsDCL1-Dicer-like protein 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1270230..1271217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0122500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLong noncoding RNA (lncRNA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1270328..1300273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0122600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMADS-box transcription factor 50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1318268..1321639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0123100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSUMO conjugating enzyme\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:1327450..1331022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0123300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTILLERING AND DWARF 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:717447..720837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0112700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCCCH-type zinc finger protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr03:935129..939195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOs03g0116500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCOP9 signaling complex\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"},{"header":"Discussion","content":"\u003cp\u003eIn lowland rice ecosystem, occurrences of stagnant flooding is a serious threat despite the water-tolerant nature of the rice plant (Sarkar et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). At different growth stages, the complete inundation of water reduces the optimal performance and productivity of the rice plant (Ray \u003cem\u003eet al\u003c/em\u003e 2016). In this study, we aimed to identify NERICA varieties that are tolerant to stagnant flooding, that farmers in the flood prone regions can adopt to grow in both rainfed and irrigated lowland farms in sub-Saharan African. In addition, in an effort to development a tolerant variety, the identification of genomic regions linked to stagnant flooding tolerance traits in rice is critical to speed up the development of stagnant flooding tolerance germplasm using marker assisted selection (Akhtar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Marker-assisted selection is a valuable tool in precision plant breeding, enabling the indirect selection of desired traits by using genomic regions that are genetically linked to traits of interest (Haque et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, in our effort to characterize all lowland NERICA varieties under the stagnant flooding field condition, significant phenotypic variation was observed among the NERICA varieties for all traits. This suggests there is considerable genetic variability resource to facilitate the development of stagnant flooding tolerant variety. Similar contrastive responses were reported of rice genotypes under stagnant flooding by other authors (Kuanar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, an increase of 5 to 20% was observed in shoot elongation, which translates to increased plant height of the NERICAs. This finding is in agreement with other studies reported by Vergara et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2014\u003c/span\u003e (7 to 34%), Kaunar et al. 2017 (9 to 22%), Singh et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e (~\u0026thinsp;3.4%), and Kato et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e (~\u0026thinsp;13%). This further confirms the importance to concentrate breeding efforts into developing high-yielding and moderately tall rice varieties tolerant to stagnant flooding, as excessive stem elongation has been reported to reduce yield and increase the risk of lodging, complicating harvest operations (Sarkar et al. 2021). In contrast to the increase in height, a decline in the number of tillers and panicles per hill was recorded for most NERICAs and checks used under stagnant flooding compared to the control. This finding is similar to reports by Singh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Kato et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Zhu et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which indicate that stagnant flooding imposes stress on the rice plant by limiting oxygen and nutrient availability, thereby reducing tillers and panicle production and, ultimately, affecting the overall grain yield. In our study, we observed a 20 to 60% reduction in grain yield under stagnant flooding, consistent with previous findings by Singh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Kaunar et al. (2017), Kato et al. (2014; \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Chattopadhyay et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) who reported grain yield reductions of 52.1%, 43%, 47%, 48% and 46% respectively, under stagnant flooding stress conditions. These results suggest that breeding efforts should prioritize selection of traits that sustain tiller and panicle production under stagnant flooding stress to prevent grain yield losses.\u003c/p\u003e\u003cp\u003eThe heritability estimates provides valuable insights into the genetic control of traits and their potential improvement through cycles of selection. The high broad-sense heritability observed for grain yield under stagnant flooding in this study indicates strong genetic influence with minimal environmental interference, even under stress conditions. The heritability estimates obtained in our study exceed those reported by Singh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for grain yield and other traits, suggesting a greater scope for genetic gain. Given the polygenic nature of stagnant flooding tolerance, indirect selection through strongly correlated traits offers a practical breeding strategy (Collard et al. 2013). In our study, grain yield under stagnant flooding showed moderate correlations with plant height, days to 50% flowering, number of tillers and panicles under stress. This implies that targeting these traits can facilitate incremental improvement of grain yield under stagnant flooding.\u003c/p\u003e\u003cp\u003eInterspecific lines such as Lowland NERICAs derived from O. glaberrima and O. sativa crosses are known to combine he high yielding potential of Asian rice with the stress tolerance of African rice (Kehinde et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, the RIL population derived from the cross NERICA L-19/IR64-\u003cem\u003eSub1\u003c/em\u003e exhibited significantly higher grain yield than both the parent lines and the submergence-tolerant checks, IRRI119 and Swarna-\u003cem\u003eSub1\u003c/em\u003e, under stagnant flooding conditions, with no significant yield difference under control conditions. This suggests that the RIL populations possess the \u003cem\u003eSub1\u003c/em\u003e gene and harbor alleles conferring tolerance to stagnant flooding. Having both submergence and stagnant flooding tolerance in genotypes is an advantageous combination for rainfed lowland rice ecosystems, increasingly affected by unpredictable flooding due to climate change (Sarkar et al. 2021). These findings align with the study by Kato et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), supporting the possibility of developing dual-tolerant genotypes and contrast earlier reports by Singh et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who questioned the effectiveness of combining submergence and stagnant flooding tolerance. A linkage map was constructed using 1396 markers, spanning 1456.2 cM, with an average marker interval of 1.11 cM. This reflects good genome coverage and high recombination, enhancing the resolution and precision of QTL detection. The map length was shorter than those reported by Sripongpangkul, et al. (2000) and Chattopadhyay et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but longer than that reported by Singh et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The linkage map enabled the identification of 27 QTLs with both major and minor effects across the environments. Among these, stable QTLs such as \u003cem\u003eqPH1\u003c/em\u003e.1, \u003cem\u003eqPH3\u003c/em\u003e.1, \u003cem\u003eqDTF3.1 and qGY2.1\u003c/em\u003e stand out as reliable targets for marker-assisted selection due to their consistent expression and meaningful contributions to the traitts under stagnant flooding stress. The identification of novel QTLs, particularly for plant height and grain yield, points to untapped genetic variation in the NERICA/IR64-Sub1 RIL population. Additive main effects observed further clarify the contributions of IR 64-Sub1 and NERICA L-19.\u003c/p\u003e\u003cp\u003eThe QTLs identified for number of tillers, number of panicles and grain yield exhibited low phenotypic variation, indicating they are small contributors to these traits. However, their collective effects may still be valuable in breeding programs targeting complex traits like grain yield. Although individually small, such QTLs can contribute cumulatively to trait improvement when pyramided through marker-assisted selection. These results highlight the importance of integrating both major and minor QTLs into breeding strategies to enhance resilience and productivity in flood-prone rice growing environments.\u003c/p\u003e\u003cp\u003eSeven major QTL regions were identified on chromosomes 1, 2, 3, 8, 9 and 12, with overlapping QTLs on chromosome 3, 8 and 9. The QTL \u003cem\u003eqPH8.1\u003c/em\u003e detected for plant height was co-localized with \u003cem\u003eqDTF8.1\u003c/em\u003e and \u003cem\u003eqGY8.1\u003c/em\u003e, which were identified for days to 50% flowering and grain yield, respectively, at a peak position of 20.5 cM, a genomic hotspot for improving these traits. Similarly, QTL \u003cem\u003eqPH3.1\u003c/em\u003e having a peak position in close proximity to \u003cem\u003eqGY3.1\u003c/em\u003e and \u003cem\u003eqDTF3.1\u003c/em\u003e, suggests a shared genomic region influencing multiple traits such that selection for early flowering (\u003cem\u003eqDTF3.1\u003c/em\u003e) could simultaneously reduce plant height and influence grain yield. Addtionally, co-localized QTLs for number of tillers and number of panicles were observed at 0.8 cM, 27.9 cM and 14.2 cM on chromosomes 3, 9, 12, respectively. This finding further confirm the positive correlation observed between the two traits. These overlapping QTL regions imply potential genetic linkage or pleiotropic effects in these chromosomal regions, which may play a significant role in the coordinated expression of these traits.\u003c/p\u003e\u003cp\u003eIncreasing plant height has been identified as an important adaptive strategy employed by the rice plant to escape stagnant flooding stress (Kuanar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sarkar et al. 2021). The major QTL \u003cem\u003eqPH1.1\u003c/em\u003e identified in this study was linked to several candidate genes involved in hormone regulation, internode elongation, and plant architecture. Notably, LOC_Os01g0858350, encoding a cytochrome P450 enzymes, is associated with increase internode elongation through modulation of gibberellin levels (Luo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kurotani et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hazman et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The presence of this gene within the \u003cem\u003eqPH1.1\u003c/em\u003e region suggests its potential role in promoting elongation under waterlogged conditions. Another notable gene LOC_Os01g0859300, encodes a basic leucine zipper (bZIP) transcription factor involved in growth regulation and abiotic stress responses (Zou et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). LOC_Os01g0859500 (OsLG1) contributes to leaf erectness, which can potentially lead to internode elongation and increased plant height (Wang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while LOC_Os01g0864000 (OsOFP8) is a regulator of brassinisteriod signaling pathways involved in plant growth and development, including plant height (Yang \u003cem\u003eet alet al\u003c/em\u003e2016; Sun et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Carbohydrate metabolism also appears relevant, as LOC_Os01g0866400, encoding cytosolic fructose-1,6-bisphosphatase, plays a role in sucrose biosynthesis, photosynthetic efficiency, and tiller development (Lee et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Koumoto et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, LOC_Os01g0856500 and LOC_Os01g0855400 encodes an auxin transporters and an R2R3-MYB transcription factor, respectively. They are linked to hormone-mediated elongation and plant height regulation (Yu et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Regulatory proteins such as WOX (LOC_Os01g0854500) and SUMO-conjugating enzymes (LOC_Os01g0852300) also occur within this region and may influence plant development, by modulating hormone signaling pathways such as gibberellins and cytokinnins (Kamiya et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Teramura et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, the genomic region between 4.4 cM and 4.8 cM on chromosome 3 for significant QTLs \u003cem\u003eqPH3.1\u003c/em\u003e, \u003cem\u003eqDTF3.1\u003c/em\u003e and \u003cem\u003eqGY3.1\u003c/em\u003e associated with plant height, days to 50% flowering and grain yield, respectively, revealed putative candidate gene involved in the growth and development of rice. LOC_Os03g0119966 encoding a NAC transcription factor, regulates developmental processes such as internode elongation, flowering and grain filling (Mathew et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Genes such as LOC_Os03g0121800, encoding dicer-like proteins and LOC_Os03g0122500 ((lncRNA) were implicated in RNA interference and gibberellin pathways that regulate stem elongation and hormone signaling pathways critical for flowering time (Kapoor et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bhat et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditional regulatory elements within this region include LOC_Os03g0122600, a MIKC-type MADS-box genes such as SOC1, have been reported to influence internode elongation in maize and activate early flowering in rice (Lee et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and LOC_Os03g0123100, encoding SUMO-conjugating enzyme (OsSCE1a), which regulates both plant height, and grain yield (Joo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genes like TAD1 (LOC_Os03g0123300) and Ehd4 (LOC_Os03g0112700) regulate tillering and flowering, respectively, while (Lin et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) LOC_Os03g0126500, part of the COP9 signalosome, regulates gibberellin signaling pathways critical for rice growth and development (Han et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study highlights the significant genetic potential of lowland NERICA varieties and their derived RIL population for improving rice adaptation to stagnant flooding stress conditions. The observed phenotypic variability among lowland NERICA and the identification of key QTLs linked to grain yield, plant height, flowering time, number of tillers and panicles highlight valuable genetic resources for breeding. Notably, the RIL population derived from NERICA L-19/IR64-Sub1, combined submergence and stagnant flooding tolerance, offering a dual-tolerant platform for future variety development. Stable and novel QTLs, such as \u003cem\u003eqPH1\u003c/em\u003e.1, \u003cem\u003eqPH3.1\u003c/em\u003e, and \u003cem\u003eqGY2.1\u003c/em\u003e, as well as co-localized genomic regions, provide strong candidates for marker-assisted selection. Candidate genes involved in hormone signaling, growth regulation, and developmental timing were lined to these QTLs, reinforcing their relevance. This study provides a comprehensive genetic framework for stagnant flooding tolerance in rice and the development of high-yielding, flood-resilient rice varieties that can sustain productivity amidst climate variability. Future efforts should focus on fine mapping of identified QTLs, functional validation of candidate genes, and field-based evaluations to translate these insights into practical breeding solutions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was financially supported by Bill and Melinda Gates Foundation through the project \u0026ldquo;Rapid Mobilization of Alleles for Rice Cultivar Improvement in Sub-Saharan Africa\u0026rdquo; (OPP1080832).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAuthor contribution statement\u003c/b\u003e Vimal Kumar Semwal and Venuprasad Ramaiah contributed to the conceptualization and design of the study. Vimal Kumar Semwal and Shittu Azeez carried out data collection. Vimal Kumar Semwal and Olatunde Azeez Bhadmus contributed to data analysis and manuscript revision. Vimal Kumar Semwal, Shittu Azeez, Olatunde Azeez Bhadmus, Okanlawon Jolayemi and Venuprasad Ramaiah were involved in the investigation and methodology of the study. Vimal Kumar Semwal prepared the first draft of the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgbeleye OA, Olubiyi MR, Ehirim BO, Shittu AO, Jolayemi OL, Adetimirin VO, Ariyo OJ, Sanni KA, Venuprasad R (2019) Screening African rice (O. glaberrima Steud.) for tolerance to abiotic stress. III. Flooding. SABRAO Journal of Breeding and Genetics 51:128-150\u003c/li\u003e\n\u003cli\u003eAkhtar S, Bhat MA, Wani SA, Bhat KA, Chalkoo S, Mir MR, Wani SA (2010) Marker assisted selection in rice. 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Plant Mol Biol 66:675-683. https://doi.org/10.1007/s11103-008-9298-4 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rice, NERICA, IR64-Sub1, Stagnant Flooding, Submergence","lastPublishedDoi":"10.21203/rs.3.rs-7510204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7510204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRice cultivation in the rainfed lowland ecosystem is prone to encounter substantial flooding challenges in the form of complete submergence or prolonged stagnant flooding. While the \u003cem\u003eSub1\u003c/em\u003e gene enables rice plants to survive the momentary complete submergence, stagnant flooding, defined by incomplete submergence for extended periods, necessitates moderate stem elongation for survival. In this study, we characterized 60 lowland NERICA varieties under stagnant flooding (SF) conditions, identify tolerant germplasm, and detect genomic regions associated with key traits to aid breeding efforts. Phenotypic evaluations revealed significant genetic variability among the NERICA varieties, with some accessions showing 20\u0026ndash;60% yield reduction under SF stress. The derived NERICA-L19/IR64 \u003cem\u003eSub1\u003c/em\u003e RIL population showed improved grain yield under SF compared to both parents and submergence tolerant checks. A total 27 QTLs were identified associated with plant height, tiller number, panicle number, days to flowering, and grain yield. Stable and major-effect QTLs, such as \u003cem\u003eqPH1.1\u003c/em\u003e, \u003cem\u003eqPH3.1\u003c/em\u003e, and \u003cem\u003eqDTF3.1\u003c/em\u003e, were consistent across environments, explaining up to 48% of the phenotypic variation. Several QTLs co-localized, indicating potential pleiotropy or tight linkage. Candidate genes associated with these regions include regulators of gibberellin signaling, flowering time and other developmental processes. This study highlights the potential of lowland NERICAs as a genetic resource as well as provides molecular resources for improving stagnant flooding tolerance in rice. The integration of phenotypic data, stable QTLs, and functionally relevant candidate genes lays a foundation for marker-assisted breeding of dual-tolerant rice cultivars adapted to climate-induced flooding scenarios in sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Unveiling Stagnant Flooding Tolerance in Lowland NERICAs: Genomic Insights and Breeding Prospects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 11:54:03","doi":"10.21203/rs.3.rs-7510204/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-10-25T09:23:13+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-17T05:06:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T06:26:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T08:23:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2025-09-01T11:57:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a95f6daa-5207-4506-8841-ff1bc6e241cf","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-7510204","link":"https://doi.org/10.1007/s00122-025-05129-x","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2026-01-06 15:58:05","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-09-24 11:54:03","video":"","vorDoi":"10.1007/s00122-025-05129-x","vorDoiUrl":"https://doi.org/10.1007/s00122-025-05129-x","workflowStages":[]},"version":"v1","identity":"rs-7510204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7510204","identity":"rs-7510204","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}