Exclusion versus Detoxification: Contrasting Molecular Strategies of Aluminium Tolerance in Rice Landraces of Northeast India

Exclusion versus Detoxification: Contrasting Molecular Strategies of Aluminium Tolerance in Rice Landraces of Northeast India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exclusion versus Detoxification: Contrasting Molecular Strategies of Aluminium Tolerance in Rice Landraces of Northeast India Darshana Sharma, Sudipta Sankar Bora, Rahul Kaldate, Ishani Borthakur, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7626626/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in Plant Cell Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Aluminium (Al) toxicity affects rice cultivation in acidic soils, largely by hindering root development and diminishing crop yield. This study investigated 53 rice genotypes from Northeast India under hydroponic conditions to assess their morpho-physiological and molecular responses to aluminium stress. Nine characteristics, including as root length, root tolerance index, relative root elongation, and biomass, were measured to categorise genotypes into tolerant, moderately tolerant, intermediate, moderately susceptible, and susceptible classifications. Root characteristics proved to be the most sensitive predictors of tolerance, with cluster and principal component analyses reliably distinguishing tolerant from susceptible genotypes. SSR marker study (33 polymorphic markers, 103 alleles, mean PIC = 0.48) indicated substantial genetic variety, although did not entirely align with phenotypic grouping. Gene expression profiling revealed divergent molecular strategies: tolerant genotypes upregulated OsSTAR1, OsSTAR2, and OsFRDL4, facilitating aluminium exclusion through cell wall modification and citrate efflux, whereas susceptible genotypes demonstrated increased expression of OsNRAT1 and OsALS1, indicating dependence on internal sequestration. These findings highlight root-based exclusion mechanisms as the principal factor influencing Al tolerance in rice. Aluminium toxicity rice genotypes SSR markers oxidative stress organic acid Al tolerance genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Key Message Rice genotypes differ in aluminium tolerance, with resistant landraces sustaining root growth through STAR1/2-mediated exclusion and organic acid efflux, while sensitive types accumulate Al³⁺ via NRAT1–ALS1, leading to oxidative damage . Introduction Rice ( Oryza sativa L.), a member of the Poaceae family, is one of the world’s three major cereals alongside maize and wheat, serving as the primary staple for more than half of the global population and contributing significantly to food security and socioeconomic development (Landi et al. 2017 ; Abeysekara and Rathnayake 2024 ). Asia accounts for nearly 90% of global rice production, with India ranking second after China, producing 116.42 million tons over 43.79 million hectares (Lin et al. 2022 ; Singh et al. 2021 ; Devi et al. 2025 ; Mishra 2025 ). Rice is the principal food crop in the Northeast India (NEI). It is cultivated in 3.51 million hectares (≈ 8% of the geographical area) and contributes ~ 6.5% to national production (Gogoi et al. 2025 ; Das et al. 2020 ). Northeast India (NEI) is a home to many indigenous rice varieties which are cultivated in diverse topographic and agroclimatic conditions, and normally classified into different types based on the season of cultivation mainly sali , boro and jhum type. These indigenous rice varieties reveal significant genetic variety and are essential to the region's agricultural heritage. Research indicates that these traditional cultivars exhibit greater genetic diversity than agronomically modified types, demonstrating an overall allelic richness of 10.205 per locus and a gene diversity value of 0.776, with the sali type displaying the most diversity among them (Choudhury et al. 2013 ). Characterising and preserving these landraces are essential for sustainable farming practices and the conservation of regional biodiversity. Indigenous rice varieties are crucial to global agriculture, particularly in areas susceptible to abiotic stresses like drought, heat, salinity, and submergence. It is imperative to develop cultivars with improved tolerance to these stresses, as research has revealed numerous physiological, biochemical, and genetic mechanisms that enhance stress resilience, including extensive root systems for drought resistance and specific genetic traits for heat and salinity tolerance (Adzigbe et al. 2025 ).Moreover, these indigenous varieties possess considerable agroeconomic significance as they are suited to local agro-ecosystems, frequently requiring little external inputs and promoting sustainable agricultural practices. These types can be evaluated for abiotic stress tolerance by improved molecular breeding techniques, such as marker-assisted selection, which have enabled the creation of stress-tolerant rice varieties that enhance crop resilience and food security amid climate change(Fernandes et al. 2022 ). Consequently, the characterisation, conservation, and utilisation of these genetic resources are essential for advancing sustainable agriculture, preserving biodiversity, and guaranteeing long-term food and nutritional security. Nearly 80% of soils in Northeast India are acidic, spanning ~ 21.26 million hectares, and pose a major constraint on rice productivity due to metal toxicities, particularly aluminium (Al) (Majumdar et al. 2022 ; Singh et al. 2025 ).In acidic soils (pH < 5.5), Al is solubilized as toxic species such as Al³⁺, Al(H₂O)₆ ³⁺, and Al(OH)₂⁺, which even at micromolar levels disrupt plant growth (Krstic et al. 2017). Root systems are the primary targets, exhibiting inhibited elongation, stubby laterals, and impaired nutrient uptake, ultimately reducing shoot growth, photosynthesis, and biomass (Hayes et al. 2020 ; Hajiboland et al. 2023 ; Kocjan et al. 2024). Prolonged Al exposure induces oxidative stress through excess ROS generation, resulting in cellular damage and eventual cell death (Bera 2017; Jaskowiak et al. 2018 ). Plants counteract this through ROS-scavenging antioxidants, ethylene-mediated growth regulation, and organic acid exudation via ALMT and MATE transporters, processes transcriptionally regulated by STOP1 and ART1, which activate key Al-tolerance genes such as STAR1, STAR2, Nrat1, OsFRDL4, and OsALS1 (Chowra et al. 2017 ; Yang et al. 2019 ; Zhang et al. 2019; Ur Rahman et al. 2024 ; Kocjan et al. 2025 ). Molecular markers, particularly SSRs, have emerged as reliable tools for dissecting genetic variation in Al tolerance owing to their high polymorphism, co-dominance, and reproducibility, surpassing traditional assessments confounded by environmental factors (Verma et al. 2019 ; Yin et al. 2023 ; Zhang et al. 2023 ). Yet, despite the prevalence of acidic, Al-toxic soils in Northeast India, systematic evaluation of traditional rice genotypes for Al tolerance remains limited. To address this gap, the present study integrates morphological, physiological, biochemical, and gene expression analyses to identify reliable markers underlying aluminium tolerance. Materials and Methods Plant materials and experimental conditions The seeds of fifty-three rice genotypes, which represent the genetic and ecological diversity of Northeast India, were procured from the Assam Agricultural University–Assam Rice Research Institute (previously known as the Regional Agricultural Research Station, Titabar), located in Jorhat, Assam (26.575 N, 94.183 E). It included traditional landraces such as Sali (winter), Ahu (autumn), Boro (summer), deepwater Bao types, aromatic Joha, Bora, and Chakua ecotypes, in addition to improved and stress-tolerant cultivars including Bahadur Sub-1, Ranjit, Ranjit Sub-1, Misiri-2, and Mahsuri (Table. 1). Despite all accessions belonging to the species Oryza sativa L. subspecies indica, they exhibited considerable variation in phenological characteristics, ranging from early-maturing varieties such as Disang (100–105 days) to very late-maturing deepwater landraces including Bon Gathu, Joldhubi, Jolkonwari, and Negeri Bao (240–270 days). This representative germplasm panel was subsequently employed for the screening and detection of tolerance to aluminium (Al) toxicity via a hydroponic assay that utilized gradient Al concentrations (0, 50, 100, and 200 µM), adhering to established protocols (Tabassum et al. 2021 ). Table 1 Constituting number of genotypes selected for screening Rice Variety Parentage Classification Country of Origin Subsp. Days to Maturity Ahom Sali Landrace (Assam) Sali rice (winter rice) India Indica 90–135 Aki Sali Landrace (Assam) Sali rice (winter rice) India Indica 150–155 Badshabhog Landrace (Assam) Aromatic rice India Indica 135–140 Bahadur Sub-1 Bahadur/Swarna-Sub1//Bahadur Submergence-tolerant variety India Indica 150–155 Bamki Sali Landrace (Assam) Sali rice (winter rice) India Indica 90–135 Betguti Landrace (Assam) Sali rice (winter rice) India Indica 120–140 Biriabhong Landrace (Assam) Sali rice (winter rice) India Indica 120–140 Boga Manohar Landrace (Assam) Sali rice (winter rice) India Indica 120–140 Bogi Shali Landrace (Assam) Sali rice (winter rice) India Indica 90–135 Boka Chakua Landrace (Assam) Chakua type rice India Indica 120–140 Bokul Bora Landrace (Assam) Bora (waxy) rice India Indica 140–150 Bokul Joha Sabitri/Badshah bhog Sali/Lahi rice (winter rice) India Indica 170–180 Bon gathu Landrace (Assam) Deepwater rice India Indica 240–270 Bor Chakua Landrace (Assam) Chakua type rice India Indica 120–140 Bor Sali Landrace (Assam) Sali rice (winter rice) India Indica 120–140 Bora Chakua Landrace (Assam) Chakua type rice India Indica 120–140 Buli Bao Landrace (Assam) Deep-water rice India Indica 240–270 Cheni lahi Landrace (Assam) Sali rice (winter rice) India Indica 135–140 Dipholu Landrace (Assam) Sali rice India Indica 120–140 Disang (TTB 303-2-23) Breeding line Ahu rice (autumn rice) India Indica 100–105 Dolkosu Landrace (Assam) Sali rice India Indica 120–140 Gajep Sali Landrace (Assam) Sali rice India Indica 120–140 Ghew Bora Landrace (Assam) Bora (waxy) rice India Indica 140–150 Gitesh Akisali/Kushal Bao (Deep-water rice) India Indica 150–160 Gudumoni Landrace (Assam) Sali rice India Indica 120–140 Hati Sali Landrace (Assam) Sali rice India Indica 120–140 Jahinga Landrace (Assam) Sali rice India Indica 120–140 Jamini Dhon Landrace (Assam) Sali rice India Indica 120–140 Joldubi Landrace (Assam) Deepwater rice India Indica 240–270 Jolkonwari Landrace (Assam) Deepwater rice India Indica 240–270 Joymoti Landrace (Assam) Boro (summer rice) India Indica 170–180 Kalamdini Landrace (Assam) Aromatic rice India Indica 120–140 Kanaklata Landrace (Assam) Aromatic rice India Indica 120–140 Keteki Joha Sabitri/Badshah bhog (IET-14390) Sali/Lahi Rice (winter rice) India Indica 140–150 Khamti Lahi Landrace (Assam) Lahi rice (winter rice) India Indica 120–140 Kola Bora Landrace (Assam) Bora (waxy) rice India Indica 140–150 Kola Joha Landrace (Assam) Joha (aromatic) rice India Indica 140–150 Kon Joha Landrace (Assam) Joha (aromatic) rice India Indica 140–150 Mahsuri Traditional variety Rain-fed mega variety India Indica 125–130 Malbhog Landrace (Assam) Aromatic rice India Indica 135–140 Malbhog Bora Landrace (Assam) Bora (waxy) aromatic India Indica 140–150 Misiri-2 Breeding line Improved variety India Indica 120–130 Mulagabhoru Landrace (Assam) Traditional rice India Indica 120–140 Negeri Bao Landrace (Assam) Deep-water rice India Indica 240–270 Numoli Landrace (Assam) Traditional rice India Indica 120–140 Poita Bora Landrace (Assam) Bora (waxy) rice India Indica 140–150 Prashad bhog Landrace (Assam) Aromatic rice India Indica 135–140 Ranjit High-yielding variety Kharif rice India Indica 150–155 Ranjit Sub-1 Ranjit/Swarna-Sub1//Ranjit Submergence-tolerant variety India Indica 150–155 Ronga Joha Landrace (Assam) Joha (aromatic) rice India Indica 140–150 Salpona Landrace (Assam) Traditional rice India Indica 120–140 Sial Kathi Landrace (Assam) Traditional rice India Indica 120–140 Til Bora Landrace (Assam) Bora (waxy) rice India Indica 140–150 Plant growth culture and hydroponic assay Rice seeds from each rice genotype were surface sterilized with 70% ethanol (2 min) followed by 0.1% HgCl₂ (7 min), rinsed thoroughly with sterile water, soaked for 24 h, and germinated on petri plates in the dark at 28–31°C for 72 h. Uniform seedlings were transferred onto Styrofoam floaters placed in plastic containers containing 150 ml MM2 medium (pH 6) and grown for 3 days under a 16/8 h photoperiod (250 µmol m⁻² s⁻¹). The medium was replenished daily to maintain pH stability and nutrient availability. Aluminium stress was imposed by supplementing the medium with AlCl₃ (0, 50, 100, 200 µM) in the presence of 500 µM CaCl₂ (pH 4.5) for 3 days (Jaiswal et al. 2022 ). Growth and biomass measurements After 3 days of Al treatment, root length, shoot length, and root fresh and dry weights were recorded in three seedlings per genotype. Relative root length (RRL), relative root elongation (RRE%), root and shoot tolerance indices (RTI, STI), and relative root reduction (RRR%) were calculated to assess Al tolerance (Laenoi et al. 2014). Relative water content (RWC) was determined as RWC=(FW − DW)/(TW − DW) ×100, with FW = fresh weight, TW = turgid weight after 6 h hydration, and DW = dry weight (Weatherley 1950 ). The germination capacity of 53 rice genotypes was evaluated under toxic Al conditions. Surface-sterilized seeds were germinated in half-strength Modified Magnavaca’s solution supplemented with 200 µM AlCl₃ (pH 4.5) for 4 days in the dark. Seeds were considered germinated upon radical and plumule emergence, and root growth were visually recorded (Kikui et al. 2005 ). SSR genotyping and PCR analysis Genetic diversity among 53 rice accessions was assessed using 33 polymorphic SSR markers, including 10 Al-tolerance–specific markers covering all 12 chromosomes (primers listed in Supplementary Table S1 ). The SSR marker sequences, annealing temperature and chromosomal locations were obtained from the GRAMENE database. Genomic DNA was extracted from young leaves using the CTAB method, quantified (50 ng/µL), and PCR-amplified in 10 µL reactions under standard cycling conditions. PCR products were resolved on 3% agarose gel, stained with ethidium bromide, and visualized using a Gel Documentation System; fragment sizes were estimated relative to a 100 bp ladder (Irsyadi et al. 2024 ; Ravikiran et al. 2018 ). Amplified bands were scored manually as ‘1’ (present) or ‘0’ (absent), and only distinct polymorphic bands were included. Genetic diversity parameters, including number of alleles (Na), effective number of alleles (Ne), Shannon’s information index (I), and Nei’s genetic diversity index (He), were calculated using POPGENE v1.32, and polymorphism information content (PIC) was determined for each marker (Botstein et al. 1980 ). A neighbor-joining (NJ) tree was constructed based on Jaccard’s coefficient to visualize genetic relationships. Physiological and biochemical responses of rice genotypes under aluminium stress Photosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids) were quantified in 100 mg leaf tissue homogenized in 80% acetone, with absorbance recorded at 480, 644, and 663 nm (Vimala and Poonghuzhali 2014). Hydrogen peroxide (H₂O₂) content in roots was measured following Loreto and Velikova (2001), with absorbance recorded at 390 nm and concentrations determined from a standard curve (Martins et al. 2013 ). Lipid peroxidation was estimated by measuring malondialdehyde (MDA) using the thiobarbituric acid reactive substances (TBARS) method (Heath and Packer 1968 ). Proline content was determined in 100 mg root tissue homogenized in 500 µL 3% sulfosalicylic acid, centrifuged at 13,000 rpm for 5 min, and reacted with acid ninhydrin and glacial acetic acid. The mixture was incubated in a hot-water bath, cooled, and the pink upper phase absorbance measured at 520 nm against toluene; proline concentration was expressed as µmol/g fresh weight (Bates et al. 1973 ). For antioxidant enzyme assays, fresh root tissue (500 mg) was macerated in liquid nitrogen, homogenized in 5 mL extraction buffer (50 mM sodium phosphate buffer, pH 7.8, 0.1 mM EDTA, 1% polyvinylpyrrolidone, 0.5% Triton X-100), and centrifuged at 15,000 rpm for 30 min at 4°C. The supernatant was used to assay superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) activities. SOD activity was determined by its inhibition of nitro blue tetrazolium (NBT) photoreduction at 560 nm, with one unit defined as the enzyme amount causing 50% inhibition (Giannopolitis and Reis 1997). CAT activity was measured as µM H₂O₂ decomposed min⁻¹ mg⁻¹ protein at 240 nm (Aebi. 1984). POD activity was assayed at 470 nm, with one unit defined as the enzyme amount producing a 1.0 absorbance increase per g FW per min (Zhang and Kirkham 1996 ). APX activity was measured at 290 nm, with one unit defined as the enzyme amount causing a 1.0 absorbance decrease per g FW per min (Nakano and Asada 1981 ). For organic acid analysis, frozen root tissue (1.7 g) was ground in liquid nitrogen and homogenized in 6.5 mL ice-cold 4% (v/v) HClO₄. The suspension was thawed on ice, incubated for 30 min, and centrifuged at 20,000 × g for 10 min. Five mL of the supernatant was neutralized with 5 M K₂CO₃ at 4°C, and precipitated potassium chlorate removed by centrifugation. Activated charcoal (50 mg) was added, incubated for 15 min at 4°C, and removed by centrifugation. The resulting supernatant was used for metabolite measurements. Malate was determined in a 1 mL reaction containing 50 mM 3-amino-1-propanol-HCl (pH 10), 30 mM glutamate-NaOH (pH 10), 2.7 mM NAD, 1 U glutamate-oxaloacetate transaminase, and 10 U malate dehydrogenase (MDH). Oxaloacetate (OAA) was measured in a 1 mL reaction containing 150 mM Tris-HCl (pH 7.6), 10 mM EDTA-NaOH (pH 7.0), 0.15 mM NADH, and 2 U MDH. Citrate was quantified in a 1 mL mixture of 100 mM Tris (pH 7.6), 0.2 mM NADH, 7 U LDH, 14 U MDH, and 0.5 U citrate lyase (Chen et al. 2002 ). The nutrient content in roots was estimated according to Hayes et al. ( 2020 ). Root samples were oven-dried at 80°C for 72 h, and 0.1 g of dried tissue was acid-digested with 10 mL HNO₃ and 5 mL H₂O₂ at 120°C. The digested solution was diluted with 20 mL deionized water, filtered, and the final volume adjusted to 40 mL. Aluminium and micronutrient contents were quantified using an Atomic Absorption Spectrophotometer (Model 5000, Perkin-Elmer, USA) with standards calibrated at 309.5 nm, and concentrations were expressed as mg/kg dry weight (Hayes et al. 2020 ). Quantitative Real-Time PCR (qRT-PCR) analysis Total RNA was isolated from root tissues using a modified TRIzol method (Mainkar et al. 2023 ). RNA quality and quantity were assessed with a NanoDrop-1000 Spectrophotometer, and integrity was confirmed on 3% agarose gel. First-strand cDNA was synthesized from 1 µg total RNA using Superscript™ reverse transcriptase (Takara, Dalian, China). For qRT-PCR, 1 µL of cDNA (100 ng·µL⁻¹) was amplified in a 10 µL reaction with SYBR Premix Ex Taq (Takara, Dalian, China), using Actin1 as an internal reference. Reactions were performed on a QuantStudio 3 Real-Time PCR System under the following cycling conditions: 95°C for 5 min, followed by 40 cycles of 95°C for 15 s, 52°C for 30 s, and 72°C for 30 s. Amplicon specificity was confirmed by melting curve analysis (70–95°C) and agarose gel electrophoresis. Three biological and two technical replicates were used per gene. Relative expression levels were calculated using the average Rq of biological replicates. Primer sequences are listed as follows: Actin1, forward 5′-GACTCTGGTGATGGTGTCAGC-3′, reverse 5′-GGCTGGAAGAGGACCTCAGG-3′; OsSTAR1, forward 5′-TCGCATTGGCTCGCACCCT-3′, reverse 5′-TCGTCTTCTTCAGCCGCACGAT-3′; OsNrat1, forward 5′-GAGGCCGTCTGCAGGAGAGG-3′, reverse 5′-GGAAGTATCTGCAAGCAGCTCTGATGC-3′; OsALS1, forward 5′-GGTCGTCAGTCTCTGCCTTGTC-3′, reverse 5′-CCTCCCCATCATTTTCATTTGT-3′; FRDL4, forward 5′-CGTCATCAGCACCATCCACAG-3′, reverse 5′-TCATTTGCGAAGAAACTTCCACG-3′. Statistical analysis Experiments were repeated three times, and results are expressed as mean ± SE. One-way ANOVA and Tukey’s HSD (p < 0.05) were performed in SPSS v23. Pearson correlations were calculated to evaluate trait relationships under 0 and 200 µM AlCl₃, and correlation heatmaps were generated using the “corrplot” package in R 4.4.2. Hierarchical clustering (Ward’s linkage) and PCA were conducted in RStudio v2024.12.1 using ggplot2. The binary SSR data matrix was used to calculate a distance matrix, which was then employed to construct a neighbor-joining tree based on Jaccard’s coefficient (DARwin v5.0.158). Results The impact of aluminium (Al) toxicity on rice root growth and biomass was evaluated by assessing key physiological parameters, including root length, fresh weight, and dry weight at different Al concentrations. Brief exposure to aluminium resulted in rice genotypes displaying distinct and noticeable indication of aluminium toxicity, primarily characterised by inhibited root development. These root-related traits were selected to assess the extent of aluminium-induced damage, as root growth is among the primary target of aluminium toxicity, with the severity of its effects assessed through the inhibition of root growth. The extent of stress, the kind of affected tissue, and the duration of exposure all affect the plant's response to AlCl 3+ stress. Growth and biomass responses to aluminium stress Rice seedlings exposed to Al stress exhibited clear morphological changes, primarily reflected in root growth inhibition (Fig. 1 a). Growth and biomass parameters of 53 genotypes—including root length, relative root length (RRL), shoot length, fresh weight, and dry weight—were evaluated under differential Al concentrations. RRL consistently declined with increasing Al concentration, with the strongest reduction observed at 200 μM AlCl₃ after 72 h across most genotypes. Notably, Badshabhog maintained relatively higher RRL (154% increase), whereas Ranjit Sub-1 showed minimal improvement (4.5%) relative to control ( Supplementary Table S2 ). Aluminium stress more severely affected roots than shoots. At 200 μM AlCl₃, root fresh and dry weight were significantly reduced in all genotypes, though the magnitude varied. Highly susceptible genotypes such as Ranjit Sub-1, Jolkonwari, and Mahsuri exhibited marked reductions in fresh (92.64–98.61%) and dry weight (88.89–94.30%) relative to controls, whereas resilient genotypes including Ahom Sali, Disang, Kola Bora, and Badshabhog showed minimal reductions in root fresh (8.03–17.14%) and dry weight (5–11.76%) (Figs. 1 c, 1 d). Relative root water content (RWC) declined under Al stress, reflecting impaired water retention. Genotypes such as Ahom Sali, Kanaklata, Gitesh, Kola Joha, Joymoti, and Dolkosu maintained RWC comparable to controls, indicating effective maintenance of root turgor. In contrast, Ranjit Sub-1, Jolkonwari, Prashad Bhog, and Kon Joha showed substantial RWC reduction, correlating with decreased root elongation. Ahom Sali exhibited the smallest reduction in RWC (8.03%), denoting high Al tolerance, while Joldhubi and Ranjit Sub-1 displayed moderate (27.24%) and severe (63.51%) reductions, respectively (Fig. 1 e, Supplementary Table S3 ). A germination assay was conducted for all 53 rice genotypes under 200 μM Al treatment for 72 h to evaluate sensitivity or tolerance at the germination stage. Root growth of tolerant genotypes, including Badshabhog, Ahom Sali, Joymoti, and Disang, was largely unaffected by Al stress. In contrast, genotypes such as Malbhog Bora, Joldhubi, and Bon Gathu exhibited noticeable root growth reduction, with the most severe inhibition observed in the highly susceptible genotypes Ranjit Sub-1, Jolkonwari, and Prashad Bhog ( Fig. 1b ). Effect of aluminium toxicity on root tolerance index The capacity of genotypes to sustain root growth under Al stress was further quantified using the Root Tolerance Index (RTI), which ranged from 0 to 1.5, with higher values reflecting greater tolerance. Based on RTI at 200 µM Al after 72 h, genotypes were classified as highly tolerant (≥ 1), moderately tolerant (0.5–1), or susceptible (≤ 0.49). Ten genotypes were highly tolerant, 23 moderately tolerant, and 20 sensitive (Fig. 2 ; Supplementary Table S4 ). Genotypes such as Ahom Sali, Disang, Joymoti, and Badshabhog were identified as tolerant, whereas Ranjit Sub-1, Jolkonwari, Prashad Bhog, and Cheni Lahi were highly susceptible. Correlation analysis of root traits under aluminium stress To identify the physiological traits most predictive of seedling aluminium tolerance, correlations among key root and shoot attributes under Al stress were examined (Fig. 3 ). Root length (R.L.) and root tolerance index (R.T.I.) exhibited a strong positive correlation (r = 0.99), indicating that longer roots are associated with greater stress tolerance. Relative root reduction (R.R.R%) was strongly negatively correlated with R.L. (r = − 0.86) and R.T.I. (r = − 0.96), reflecting the impact of stress-induced root growth inhibition on overall tolerance. Conversely, relative root elongation (R.R.E%) correlated positively with R.L. (r = 0.86) and R.T.I. (r = 0.96), highlighting its contribution to stress adaptation. Shoot tolerance index (S.T.I.) showed moderate positive correlations with R.L. (r = 0.60), R.T.I. (r = 0.58), and R.R.E% (r = 0.58), indicating that shoot performance is influenced by root adaptability. Root fresh weight (R.F.W.) and dry weight (R.D.W.) were strongly correlated (r = 0.94) and positively associated with root length and tolerance, underscoring the importance of biomass accumulation. Relative water content (R.W.C.) exhibited a moderate positive correlation with R.F.W. (r = 0.78), reflecting its role in maintaining root turgor under aluminium stress. Genotypic Classification and Multivariate Analysis under Aluminium Stress Hierarchical clustering analysis (HCA) of nine morpho-physiological traits across 53 rice genotypes, using Ward’s linkage and Euclidean distance, grouped the genotypes into five principal clusters, reflecting diverse responses to aluminium stress (Fig. 4 a). Cluster I (genotypes 1–10) comprised highly tolerant genotypes, characterized by superior root traits (root length, RTI, relative root elongation) and enhanced biomass retention, indicative of robust mechanisms mitigating Al-induced growth inhibition. Cluster II (genotypes 11–20) included moderately tolerant genotypes, exhibiting intermediate root and shoot growth with maintained relative water content, suggesting partial tolerance. Cluster III (genotypes 21–33) represented intermediate performers, showing balanced traits between tolerance and susceptibility. Cluster IV (genotypes 34–42, 53) encompassed moderately susceptible genotypes, displaying reduced root biomass and tolerance indices but some compensatory shoot growth. Cluster V (genotypes 20, 22, 25–29, 31, 34, 43) contained sensitive genotypes, distinguished by substantial root loss, reduced RWC, and pronounced Al susceptibility. Principal component analysis (PCA) was performed to further dissect the traits contributing to phenotypic variability under Al stress. The first two components accounted for 83.9% of total variation, with PC1 explaining 62.9% through root-related traits and PC2 explaining 21% via shoot characteristics (Fig. 4 b). On PC1, root dry weight (RDW), root fresh weight (RFW), relative water content (RWC), and relative root elongation (RRE) clustered together, showing strong positive associations and an inverse relationship with relative root reduction (RRR). On PC2, shoot length (SL) and shoot tolerance index (STI) were strongly correlated, reflecting the independent contribution of shoot vigour to stress adaptation. Genotypes 1–14 exhibited enhanced root development and water retention, indicating higher Al tolerance, whereas genotypes 46–53 aligned with RRR, reflecting increased susceptibility. Genotypes in the lower quadrant (20–28) were associated with SL and STI, suggesting moderate tolerance through shoot adaptation. These PCA results corroborated the hierarchical clustering outcomes, emphasizing that root traits are primary determinants of aluminium tolerance, while shoot vigour provides secondary support. Genetic Diversity and Population Structure of Rice Genotypes Based on SSR Markers Of the 56 SSR markers screened, 33 were polymorphic, revealing high allelic diversity among the 53 rice genotypes, with 103 alleles detected and an average of 3.15 alleles per locus. Polymorphism information content (PIC) values ranged from 0.14 to 0.79, with a mean of 0.46, while expected heterozygosity (He) varied from 0.15 to 0.81, averaging 0.46, indicating substantial genetic variability (Table 2 , Fig. 5 ). Shannon’s information index (I) ranged from 0.29 to 1.71, further supporting the observed diversity. Distance-based cluster analysis using a neighbor-joining (N-J) tree, constructed from the 33 polymorphic SSR markers, grouped the 53 rice genotypes into three major clusters with high bootstrap support (> 50%) (Fig. 6 ). The N-J tree captured the genetic relationships among genotypes and reflected their population structure in relation to aluminium tolerance. Clusters were color-coded based on Al-toxicity response: green for highly tolerant, black for moderately tolerant, and red for susceptible genotypes. Comparison of genotypic clustering with phenotypic Root Tolerance Index (RTI) data and morphological clustering revealed that most highly tolerant genotypes grouped in Cluster I, while moderately tolerant genotypes primarily occupied Cluster III. Genotypes such as Gudumoni, Bokul Bora, Ghew Bora, Poita Bora, and Bor Chakua were accommodated in Cluster II, exhibiting mixed tolerance profiles. Cluster I included five tolerant, one susceptible, and one moderately tolerant genotype, whereas Cluster III contained susceptible genotypes like Sial Khati and Dipholu alongside moderately tolerant accessions. Jaccard’s similarity matrix indicated the highest genetic similarity (0.91) between Boka Chakua and Jolkonwari, and the greatest dissimilarity (0.38) between Kalamdini and Kanaklata, confirming substantial genetic variation within the panel. Table 2 Diversity statistics using SSR marker attributes of Observed number of alleles (N A ), Expected no of alleles (N E ) polymorphic information content (PIC), Shannon Information Index (I), and Expected heterozygosity (H E ) for 33 polymorphic loci studied in 53 rice genotypes S. No Locus Name na ne I He PIC 1. RM17 3.00 2.10 0.82 0.54 0.42 2. RM26 3.00 2.47 0.97 0.61 0.58 3. RM53 3.00 2.07 0.81 0.53 0.42 4. RM138 2.00 1.45 0.36 0.32 0.26 5. RM524 2.00 1.17 0.49 0.15 0.14 6. RM174 2.00 1.26 0.32 0.21 0.18 7. RM203 4.00 2.12 1.00 0.54 0.49 8. RM209 3.00 1.49 0.62 0.34 0.50 9. RM237 3.00 2.36 0.95 0.59 0.61 10. RM259 3.00 1.51 0.64 0.35 0.41 11. RM261 3.00 1.24 0.33 0.21 0.47↻ 12. RM262 3.00 1.24 0.33 0.21 0.38 13. RM341 5.00 3.48 1.71 0.73 0.71 14. RM430 4.00 1.44 0.63 0.31 0.45 15. RM475 3.00 2.47 0.97 0.61 0.51 16. RM482 5.00 3.95 1.31 0.81 0.79 17. RM526 3.00 1.37 0.52 0.28 0.48 18. RM556 3.00 1.37 0.52 0.28 0.48 19. RM573 3.00 1.37 0.52 0.28 0.48 20. RM3602 3.00 2.73 1.05 0.65 0.56 21. RM3825 4.00 2.22 1.01 0.56 0.50 22. RM6327 4.00 1.38 0.59 0.28 0.36 23. RM496 3.00 1.53 0.62 0.35 0.31 24. RM149 3.00 1.18 0.29 0.18 0.15 25. RM242 3.00 2.70 1.04 0.64 0.56 26. RM5442 3.00 1.27 0.43 0.24 0.35 27. RM180 3.00 2.13 0.82 0.54 0.62 28. RM205 3.00 2.31 0.64 0.64 0.56 29. RM214 3.00 1.77 0.64 0.64 0.65 30. RM247 3.00 2.67 0.48 0.48 0.54 31. RM257 3.00 2.87 0.64 0.64 0.55 32. RM271 3.00 1.23 0.63 0.63 0.67 33. RM490 3.00 2.11 0.63 0.63 0.61 Mean 3.15 1.97 0.72 0.46 0.48 STDEV 0.12 0.12 0.05 0.03 0.03 Physiological and Biochemical Responses of Rice Genotypes under Aluminium Stress Chlorophyll degradation was more pronounced under Al stress, particularly in susceptible genotypes. Tolerant genotypes (Badshabhog, Ahom Sali) exhibited 38–53% reductions in chlorophyll a, 28–55% in chlorophyll b, and 8–36% in carotenoids relative to control, whereas Jolkonwari and Ranjit Sub-1 displayed 78–79% loss of chlorophyll a, 89–94% of chlorophyll b, and 64–78% of carotenoids (Figs. 7 a, 7 b, 7 c). Aluminium accumulation in roots mirrored these trends, with tolerant genotypes accumulating significantly lower Al than moderately tolerant or susceptible genotypes (Fig. 7 d), highlighting the role of Al exclusion or sequestration in stress tolerance.Al stress also induced oxidative stress in all genotypes. H₂O₂ and malondialdehyde (MDA) levels increased markedly in susceptible genotypes (Jolkonwari, Ranjit Sub-1), while tolerant genotypes (Badshabhog, Ahom Sali) exhibited lower ROS accumulation, reflecting enhanced antioxidant defense (Figs. 8 a, 8 b). Proline levels rose across all genotypes, with the highest induction in tolerant cultivars (Badshabhog + 136.35%, Ahom Sali + 129.54%), indicating an adaptive osmoprotective response (Fig. 8 c). Antioxidant enzyme activities were also differentially modulated: SOD, POD, CAT, and APX activities increased significantly in tolerant genotypes, with Badshabhog showing the greatest induction (SOD- 67.6%, POD- 40%, CAT- 23.59%, APX- 88%), whereas susceptible genotypes especially Ranjit Sub-1 displayed lower or highest reduction in enzyme activities (SOD- 90%, POD- 42.7%, APX- 109.45%, CAT- 25.91%) (Figs. 8 d, 8 e, 8 g, 8 f). These results highlight the coordinated role of organic acid exudation, ROS detoxification, proline accumulation, and enzymatic defense in conferring Al tolerance. Organic acid-mediated detoxification was a key tolerance mechanism. Under 200 µM Al, Ahom Sali showed the highest accumulation of citrate (19.06 µmol/g F.W.), malate (6.31 µmol/g F.W.), and oxalate (3.41 µmol/g F.W.), indicating effective chelation of toxic Al³⁺ ions. Joldhubi exhibited moderate increases, while Ranjit Sub-1 showed comparatively low organic acid levels, highlighting the importance of root exudation in Al detoxification (Fig. 7 e). Nutrient analysis revealed significant genotypic variation in the uptake of essential macro- and micronutrients under Al stress. Tolerant genotypes maintained relatively higher levels of key nutrients, whereas susceptible genotypes exhibited reduced accumulation, particularly of phosphorus (P), calcium (Ca), and magnesium (Mg), consistent with Al-induced disruption of root ion transport and membrane integrity (Fig. 7 d). Potassium (K) and micronutrients such as iron (Fe) and manganese (Mn) showed variable responses, with some tolerant genotypes maintaining uptake or displaying increased accumulation, possibly as a compensatory mechanism against oxidative stress. Collectively, these results indicate that root growth, biomass retention, water status, chlorophyll stability, and nutrient homeostasis are closely associated with Al tolerance in rice, with early germination performance providing a predictive indicator of subsequent seedling resilience. Transcriptional Responses of Candidate Genes Associated with Aluminium Tolerance The highly tolerant Al-resistant genotype Ahom Sali, moderately tolerant Joldhubi, and significantly susceptible Al-sensitive genotype Ranjit Sub-1 was further identified for screening the molecular transcriptional responses underlying the differences in Al-resistant and Al-sensitive responses. The change in expression of selected genes in the root system under 200 µM aluminium (Al) stress after a 72-h interval was analysed by quantitative real-time qRT-PCR. To investigate the impact of aluminium stress on various genotypes, we assessed the expression of candidate genes for Al tolerance ( OsNrat1, OsALS1, OsSTAR1, OsSTAR2 , and OsFRDL4 ) to elucidate their physiological and molecular responses under Al stress conditions. Distinct expression patterns were observed for these candidate genes among the three genotypes.In both varieties Al enhanced the expression of Nrat1 . However, upregulation was highest (3-fold increase) in Ranjit Sub-1 compared to Ahom Sali and Joldhubi (Fig. 9 a). As illustrated in Fig. 9 d, Al notably diminished the expression of ALS1 in roots of the tolerant variety Ahom Sali, while its expression increased in the susceptible variety Ranjit Sub-1. The expression of OsSTAR1 and OsSTAR2 was significantly upregulated in the Al-tolerant rice genotype Ahom Sali and moderately tolerant genotype Joldhubi, but downregulated in the Al-sensitive genotype Ranjit Sub-1 under Al stress (Figs. 9 b, 9 c). These findings further confirm that tolerant varieties reduce Al toxicity by modifying cell wall components and reducing Al accumulation through enhanced expression of OsSTAR1 and OsSTAR2 . Correlation Analysis of Morphological, Physiological, and Biochemical Traits Pearson’s correlation analysis revealed significant associations among morphological, physiological, and biochemical traits under aluminium (Al) stress (Fig. 10 ). Al content showed strong negative correlations with root length, fresh weight, dry weight, tolerance indices, chlorophyll pigments, antioxidant enzymes (SOD, CAT, APX), and organic acids (oxalate, malate, citrate). In contrast, root biomass traits, antioxidant enzyme activities, and stress-associated metabolites such as proline and organic acids were positively correlated in tolerant genotypes. Antioxidant activity exhibited positive associations with chlorophyll content and negative associations with oxidative stress indicators (H₂O₂ and MDA). Proline showed positive correlations with antioxidant defense and chlorophyll stability. These results highlight root biomass, antioxidant enzymes, proline, and organic acids as key contributors to Al tolerance, while high Al accumulation, increased MDA and H₂O₂, and reduced root growth were indicators of Al sensitivity. Discussion Soil acidity and aluminium (Al) toxicity are major constraints to rice cultivation in acid soil regions worldwide. In neutral soils, Al exists in non-toxic oxide or silicate forms, but under acidic conditions (pH < 5), it solubilizes into the phytotoxic Al³⁺ ion, rapidly inhibiting root elongation and altering root morphology (Ofoe et al. 2023 ; Shetty & Prakash 2020 ). Root growth suppression is the earliest and most sensitive symptom of Al toxicity and has been widely used as a screening criterion for tolerance (Awasthi et al. 2017 ). In this study, 53 rice genotypes from Northeast India were evaluated under hydroponic conditions to dissect their responses to Al stress. At 200 µM Al, identified as the threshold concentration, root growth was markedly inhibited, consistent with earlier findings (Yang et al. 2024 ; Bhattacharjee et al. 2023 ). Root growth reduction was accompanied by decreases in biomass, root tolerance index (RTI), and relative water content. Susceptible genotypes such as Ranjit Sub-1, Jolkonwari, and Prashad Bhog exhibited severe inhibition, whereas tolerant genotypes such as Ahom Sali, Disang, and Kola Bora maintained high RTI values. Occasionally, tolerant genotypes displayed paradoxical growth stimulation under cytotoxic concentrations, corroborating reports by Rout & Das ( 2002 ). Principal component analysis confirmed that root-related parameters contributed most strongly to Al tolerance, while shoot traits played a secondary, compensatory role (Ma et al. 2014; Wijayanto 2021). Cluster analysis grouped the genotypes into five tolerance classes, ranging from highly tolerant to susceptible, with PCA results congruent to clustering. Highly tolerant genotypes clustered together, characterized by robust root traits and biomass, whereas susceptible groups were defined by pronounced root reduction and poor water retention. Such integrated morpho-physiological characterization provides a robust framework for identifying elite donors for breeding programs. Molecular profiling with 55 SSR markers revealed moderate allelic diversity, with 33 markers polymorphic and 103 alleles detected. The mean PIC value of 0.48 is comparable to previous reports in Indian rice germplasm (Das et al. 2013 ; Pradhan et al. 2016 ). However, molecular clustering poorly aligned with phenotypic grouping, with tolerant and susceptible genotypes often clustered together. This inconsistency likely reflects the use of general rice SSRs rather than stress-specific markers (Verma et al. 2019 ; Saha et al. 2024 ). Similar discrepancies between phenotypic and molecular classification have been reported in other crops (Sun et al. 2015 ). These findings emphasize the need for high-density, Al-linked molecular markers to complement phenotypic screening. At the physiological and biochemical level, Al accumulation disrupted nutrient uptake, decreased biomass, and induced oxidative stress, as also noted by Phukunkamkaew et al. 2021 . At the cellular level, Al binds to negatively charged phospholipid bilayers, disrupting H⁺-ATPase function and nutrient transport (Zhang et al. 2017 ). Sensitive genotypes exhibited reduced chlorophyll and carotenoids, coupled with impaired photosynthetic function, consistent with reports of Al-induced membrane lipid peroxidation and ion imbalance (Ofoe et al. 2023 ). Elevated Al content triggered ROS accumulation, lipid peroxidation, and mitochondrial dysfunction (Ribeiro et al. 2022 ), with MDA accumulation confirming membrane damage. Reduced root hydraulic conductivity and ABA-mediated stomatal closure further impaired water uptake (Gavassi et al. 2020 ). Antioxidant defense systems were more strongly induced in tolerant genotypes, aligning with studies linking enhanced SOD, CAT, and APX activities to stress resilience (Fiza et al. 2025 ; Lum et al. 2014 ). Additionally, proline accumulation was higher in tolerant genotypes, highlighting its role as an osmoprotectant and ROS scavenger. Organic acid exudation, particularly citrate secretion, played a central role in external detoxification, consistent with the function of OsFRDL4 (Yokosho et al. 2016 ). Ahom Sali exhibited high citrate and malate secretion, conferring effective Al exclusion, while susceptible genotypes secreted significantly less. At the molecular level, tolerance was associated with upregulation of OsSTAR1 and OsSTAR2 , which modify root cell walls to reduce Al binding (Huang et al. 2011; Zhu et al. 2019 ; Huang et al. 2009 ). Ahom Sali and Joldhubi upregulated both genes, whereas Ranjit Sub-1 showed downregulation, highlighting their importance in exclusion mechanisms. In contrast, OsNRAT1 , which mediates Al³⁺ influx, was highly expressed in Ranjit Sub-1, consistent with elevated Al accumulation and sensitivity (Li et al. 2013; Roselló et al. 2015 ). Although OsALS1 sequesters Al into vacuoles, its induction in Ranjit Sub-1 was insufficient to counteract high Al uptake. These findings underscore that internal detoxification alone is less effective compared to combined exclusion strategies. Overall, our results highlight a dual mechanism of Al tolerance in rice. Tolerant genotypes primarily employ exclusion strategies, including cell wall modification and organic acid exudation, supported by strong antioxidant responses. Sensitive genotypes rely more on internal detoxification via OsNRAT1– OsALS1 pathways, which appears less efficient under high Al stress. The divergence in molecular and physiological strategies explains the observed variability in tolerance among Northeast Indian landraces. This integrated morpho-physiological, biochemical, and molecular framework provides valuable insights into Al tolerance mechanisms. By combining phenotypic screening with stress-specific molecular markers, it will be possible to identify and exploit elite tolerant donors such as Ahom Sali and Disang for breeding programs. Such approaches will be critical for developing rice cultivars resilient to acid soils and sustaining productivity in Al-affected regions. Conclusion Our study revealed clear genotype-specific differences in aluminium tolerance among 53 rice landraces from Northeast India. Tolerant genotypes such as Ahom Sali , Disang , and Kola Bora sustained root vigor and physiological stability under 200 µM Al³⁺, while sensitive genotypes like Ranjit Sub-1 , Jolkonwari , and Cheni Lahi showed severe inhibition and oxidative damage. Tolerance mechanisms in these indigenous varieties was associated with enhanced expression of exclusion-related genes ( OsSTAR1/2, OsFRDL4 ) and suppression of OsNRAT1 , while sensitive genotypes relied on less effective internal detoxification via OsNRAT1 and OsALS1. The findings indicate that indigenous cultivars not only have strong mechanisms to mitigate Al toxicity but may also have supplementary genes that improve stress resilience. Moreover, morphological clustering aligned with tolerance classes, but SSR markers showed weak correlation, highlighting the genetic complexity of Al tolerance. Overall, the findings underscore exclusion mechanisms as central to Al resistance and identify Ahom Sali and Disang as promising donor lines for breeding rice suited to acid soils. Therefore, conservation and detailed genetic exploration of these landraces are essential for breeding stress-tolerant rice varieties and ensuring sustainable agriculture in acid soil regions. The exhibited tolerance mechanisms and the potential for unrecognised genetic resources render the conservation and systematic characterisation of Northeast India’s indigenous rice varieties. These landraces constitute a vital gene pool for abiotic stress resistance and promote sustainable and resilient agricultural methods in the face of shifting climatic conditions. Declarations Conflict of interest The authors certify that they have no competing interests to declare which are relevant to the content of this article. Funding This study received funding from student and research facility of Assam Agricultural University, Jorhat, Assam. Author Contributions MB, DS, SSB: Conceptualization; DS: Investigation, Data collection, Visualization and Manuscript writing; MB: Funding acquisition and Supervision; SSB: Data curation and Methodology; RK: SSR marker analysis; PCD: Plant material acquisition; IS: Formal statistical analysis; MB: Writing-review, editing. Acknowledgement The authors would like to express their gratitude to Dr. Bidyut Kumar Sarmah, Director, DBT-NECAB and HoD, Department of Agricultural University, Assam Agricultural University, Jorhat, Assam. Data availability Data is provided within the manuscript and supplementary information files. References Abeysekara I, Rathnayake I (2024) Global Trends in Rice Production, Consumption and Trade. SSRN J. https://doi.org/10.2139/ssrn.4948477 Aebi H (1984) Catalase in vitro. Methods Enzymol 105:121–126. https://doi.org/10.1016/S0076-6879(84)05016-3 Adzigbe J, Frimpong F, Danquah A, Danquah EY, Asante IK, Abebrese SO, Dormatey R, Afriyie-Debrah C, Ribeiro PF, Owusu Danquah E, Agyeman K, Bam RK, Asante MD (2025) The responses and adaptations of rice ( Oryza sativa L.) to drought stress: A review. Clim Smart Agric 2(4):100080. https://doi.org/10.1016/j.csag.2025.100080 Awasthi JP, Saha B, Regon P, Sahoo S, Chowra U, Pradhan A, Roy A, Panda SK (2017) Morpho-physiological analysis of tolerance to aluminum toxicity in rice varieties of North East India. PLoS ONE 12(4):e0176357. https://doi.org/10.1371/journal.pone.0176357 Bates LS, Waldren RP, Teare ID (1973) Rapid determination of free proline for water-stress studies. Plant Soil 39(1):205–207. https://doi.org/10.1007/BF00018060 Bera S, De AK, Adak MK (2019) Modulation of Glycine Betaine Accumulation with Oxidative Stress Induced by Aluminium Toxicity in Rice. Proc Natl Acad Sci India Sect B Biol Sci 89(1):291–301. https://doi.org/10.1007/s40011-017-0948-7 Bhattacharjee B, Ali A, Tuteja N, Gill S, Pattanayak A (2023) Identification and expression pattern of aluminium-responsive genes in roots of rice genotype with reference to Al-sensitivity. Sci Rep 13(1):12184. https://doi.org/10.1038/s41598-023-39238-8 Bojórquez-Quintal E, Escalante-Magaña C, Echevarría-Machado I, Martínez-Estévez M (2017) Aluminum, a Friend or Foe of Higher Plants in Acid Soils. Front Plant Sci 8:1767. https://doi.org/10.3389/fpls.2017.01767 Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314–331 Chen L, Lin Q, Nose A (2002) A comparative study on diurnal changes in metabolite levels in the leaves of three crassulacean acid metabolism (CAM) species, Ananas comosus, Kalanchoë daigremontiana and K. pinnata. J Exp Bot 53(367):341–350. https://doi.org/10.1093/jexbot/53.367.341 Choudhury B, Khan ML, Dayanandan S (2013) Genetic structure and diversity of indigenous rice ( Oryza sativa) varieties in the Eastern Himalayan region of Northeast India. SpringerPlus 2(1):228. https://doi.org/10.1186/2193-1801-2-228 Chowra U, Yanase E, Koyama H, Panda SK (2017) Aluminium-induced excessive ROS causes cellular damage and metabolic shifts in black gram Vigna mungo (L.) Hepper. Protoplasma 254(1):293–302. https://doi.org/10.1007/s00709-016-0943-5 Das B, Sengupta S, Parida SK, Roy B, Ghosh M, Prasad M, Ghose TK (2013) Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genet 14(1):71. https://doi.org/10.1186/1471-2156-14-71 Das J, Rangappa K, Kalita MC, Thakuria D (2020) Endophyte Bacteria Alter Physiological Traits and Promote Growth of Rice ( Oryza sativa L.) in Aluminium Toxic and Phosphorus Deficient Acid Inceptisols. J Pure Appl Microbiol 14(1):627–639. https://doi.org/10.22207/JPAM.14.1.65 Devi NT, Devi AS, Singh KR (2025) Impact of chemical fertilizer and composts application on growth and yield of rice in Northeast India. Sci Rep 15(1):8575. https://doi.org/10.1038/s41598-025-93684-0 Fernandes T, Melo F, Vieira MB, Lourenço TF, Pucciariello C, Saibo NJM, Abreu IA, Oliveira MM (2022) Screening for Abiotic Stress Response in Rice. In: Duque P, Szakonyi D (eds) Environmental Responses in Plants. Springer US, New York, NY, pp 161–194. https://doi.org/10.1007/978-1-0716-2297-1_12 Fiza A, Chugh V, Mishra AC, Mishra V, Purwar S, Dwivedi SV, Mishra A, Nagar S, Kharor N, Kumar V (2025) Physiological and biochemical adaptations for salt stress tolerance in eggplant ( Solanum melongena ). Plant Physiol Rep 30(2):352–368. https://doi.org/10.1007/s40502-025-00863-2 Gavassi MA, Dodd IC, Puértolas J, Silva GS, Carvalho RF, Habermann G (2020) Aluminum-induced stomatal closure is related to low root hydraulic conductance and high ABA accumulation. Environ Exp Bot 179:104233. https://doi.org/10.1016/j.envexpbot.2020.104233 Giannopolitis CN, Ries SK (1977) Superoxide Dismutases: II. Purification and Quantitative Relationship with Water-soluble Protein in Seedlings. Plant Physiol 59(2):315–318. https://doi.org/10.1104/pp.59.2.315 Gogoi N, Pathak B, Rehman R, Upadhyaya S, Mahanta P, Borah A, Baishya KJ, Bhuyan K (2025) Rice yield responses to climate variability in Northeast India using machine learning approach. Theor Appl Climatol 156(4):233. https://doi.org/10.1007/s00704-025-05452-9 Hajiboland R, Panda CK, Lastochkina O, Gavassi MA, Habermann G, Pereira JF (2023) Aluminum Toxicity in Plants: Present and Future. J Plant Growth Regul 42(7):3967–3999. https://doi.org/10.1007/s00344-022-10866-0 Hayes KL, Mui J, Song B, Sani ES, Eisenman SW, Sheffield JB, Kim B (2020) Effects, uptake, and translocation of aluminum oxide nanoparticles in lettuce: A comparison study to phytotoxic aluminum ions. Sci Total Environ 719:137393. https://doi.org/10.1016/j.scitotenv.2020.137393 Heath RL, Packer L (1968) Photoperoxidation in isolated chloroplasts. Arch Biochem Biophys 125(1):189–198. https://doi.org/10.1016/0003-9861(68)90654-1 Huang C, Yamaji N, Chen Z, Ma JF (2012) A tonoplast-localized half‐size ABC transporter is required for internal detoxification of aluminum in rice. Plant J 69(5):857–867. https://doi.org/10.1111/j.1365-313X.2011.04837.x Huang CF, Yamaji N, Mitani N, Yano M, Nagamura Y, Ma JF (2009) A Bacterial-Type ABC Transporter Is Involved in Aluminum Tolerance in Rice. Plant Cell 21(2):655–667. https://doi.org/10.1105/tpc.108.064543 Irsyadi MB, Sari SK, Oktiastuti E, Rineksane IA (2024) Rapid genomic DNA extraction for Soybean (Glycine max L. Merr) using modified CTAB protocol to obtain high-quality DNA. https://doi.org/10.56042/ijbb.v61i2.7603 . IJBB Jaiswal S, Kumar A, Choudhury BU, Thangam R, Lal MK, Shettigar N, Kumar R, Verma H, Bhattacharjee B, Mishra VK (2022) Development of ammonium nitrate free nutrient media for aluminium toxicity tolerance screening of rice genotypes from North-Eastern India. J Plant Nutr 46(8):1766–1776. https://doi.org/10.1080/01904167.2022.2099892 Jaskowiak J, Tkaczyk O, Slota M, Kwasniewska J, Szarejko I (2018) Analysis of aluminum toxicity in Hordeum vulgare roots with an emphasis on DNA integrity and cell cycle. PLoS ONE 13(2):e0193156. https://doi.org/10.1371/journal.pone.0193156 Kikui S, Sasaki T, Maekawa M, Miyao A, Hirochika H, Matsumoto H, Yamamoto Y (2005) Physiological and genetic analyses of aluminium tolerance in rice, focusing on root growth during germination. J Inorg Biochem 99(9):1837–1844. https://doi.org/10.1016/j.jinorgbio.2005.06.031 Kocjan A, Kwasniewska J, Szurman-Zubrzycka M (2025) Understanding plant tolerance to aluminum: exploring mechanisms and perspectives. Plant Soil 507(1–2):195–219. https://doi.org/10.1007/s11104-024-06745-0 Krstic D, Djalovic I, Nikezic D, Bjelic D (2012) Aluminium in Acid Soils: Chemistry, Toxicity and Impact on Maize Plants. In: Aladjadjiyan A (ed) Food Production - Approaches, Challenges and Tasks. InTech, pp 231–242 Laenoi S, Phattarakul N, Jamjod S, Yimyam N, Dell B, Rerkasem B (2015) Genotypic variation in adaptation to soil acidity in local upland rice varieties. Plant Genet Resour 13(3):206–212. https://doi.org/10.1017/S1479262114000896 Landi S, Hausman J-F, Guerriero G, Esposito S (2017) Poaceae vs. Abiotic Stress: Focus on Drought and Salt Stress, Recent Insights and Perspectives. Front Plant Sci 8:1214. https://doi.org/10.3389/fpls.2017.01214 Li J-Y, Liu J, Dong D, Jia X, McCouch SR, Kochian LV (2014) Natural variation underlies alterations in Nramp aluminum transporter ( NRAT1 ) expression and function that play a key role in rice aluminum tolerance. Proc Natl Acad Sci USA 111(17):6503–6508. https://doi.org/10.1073/pnas.1318975111 Lin H-I, Yu Y-Y, Wen F-I, Liu P-T (2022) Status of Food Security in East and Southeast Asia and Challenges of Climate Change. Climate 10(3):40. https://doi.org/10.3390/cli10030040 Lum M, Hanafi M, Rafii Y, Akmar A (2014) Effect of drought stress on growth, proline and antioxidant enzyme activities of upland rice. JAPS: J Anim Plant Sci 24(5) Ma JF, Chen ZC, Shen RF (2014b) Molecular mechanisms of Al tolerance in gramineous plants. Plant Soil 381(1):1–12. https://doi.org/10.1007/s11104-014-2073-1 Mainkar P, Jayaswal D, Kumar D, Jayaswall K, Jaiswal S, Singh AN, Kumar S, Kansal R (2023) Development of modified CTAB and Trizol protocols to isolate high molecular weight (HMW) RNA from polyphenol and polysaccharides rich pigeonpea ( Cajanus cajan (L.) Millsp. PLoS ONE 18(12):e0291949. https://doi.org/10.1371/journal.pone.0291949 Majumdar S, Wanniang SK, Behera U (2022) Acid soil management in north eastern hill region of India Authors. Indian Farming 72(3) Martins N, Gonçalves S, Romano A (2013) Aluminum inhibits root growth and induces hydrogen peroxide accumulation in Plantago algarbiensis and P. almogravensis seedlings. Protoplasma 250(6):1295–1302. https://doi.org/10.1007/s00709-013-0511-1 Mishra P (2025) Agri Business India climbs the Great Wall to emerge as world’s top rice produce r . Business Line. https://www.thehindubusinessline.com/economy/agri-business/india-climbs-the-great-wall-to-emerge-as-worlds-top-rice-producer/article69589877.ece# . Accessed 30th May 2025 Nakano Y, Asada K (1981) Hydrogen Peroxide is Scavenged by Ascorbate-specific Peroxidase in Spinach Chloroplasts. Plant Cell Physiol 22(5):867–880. https://doi.org/10.1093/oxfordjournals.pcp.a076232 Ofoe R, Thomas RH, Asiedu SK, Wang-Pruski G, Fofana B, Abbey L (2023) Aluminum in plant: Benefits, toxicity and tolerance mechanisms. Front Plant Sci 13:1085998. https://doi.org/10.3389/fpls.2022.1085998 Phukunkamkaew S, Tisarum R, Pipatsitee P, Samphumphuang T, Maksup S, Cha-um S (2021) Morpho-physiological responses of indica rice ( Oryza sativa sub. indica) to aluminum toxicity at seedling stage. Environ Sci Pollut Res 28(23):29321–29331. https://doi.org/10.1007/s11356-021-12804-1 Pradhan SK, Barik SR, Sahoo A, Mohapatra S, Nayak DK, Mahender A, Meher J, Anandan A, Pandit E (2016) Population Structure, Genetic Diversity and Molecular Marker-Trait Association Analysis for High Temperature Stress Tolerance in Rice. PLoS ONE 11(8):e0160027. https://doi.org/10.1371/journal.pone.0160027 Ramesh P, Mallikarjuna G, Sameena S, Kumar A, Gurulakshmi K, Reddy BV, Reddy PCO, Sekhar AC (2020) Advancements in molecular marker technologies and their applications in diversity studies. J Biosci 45(1):123. https://doi.org/10.1007/s12038-020-00089-4 Ravikiran KT, Krishnamurthy SL, Warraich AS, Sharma PC (2018) Diversity and haplotypes of rice genotypes for seedling stage salinity tolerance analyzed through morpho-physiological and SSR markers. Field Crops Res 220:10–18. https://doi.org/10.1016/j.fcr.2017.04.006 Ribeiro C, De Marcos Lapaz A, De Freitas-Silva L, Ribeiro KVG, Yoshida CHP, Dal-Bianco M, Cambraia J (2022) Aluminum promotes changes in rice root structure and ascorbate and glutathione metabolism. Physiol Mol Biol Plants 28(11–12):2085–2098. https://doi.org/10.1007/s12298-022-01262-9 Roselló M, Poschenrieder C, Gunsé B, Barceló J, Llugany M (2015) Differential activation of genes related to aluminium tolerance in two contrasting rice cultivars. J Inorg Biochem 152:160–166. https://doi.org/10.1016/j.jinorgbio.2015.08.021 Rout GR, Das P (2002) Rapid hydroponic screening for molybdenum tolerance in rice through morphological and biochemical analysis. Plant Soil Environ 48(11):505–512. https://doi.org/10.17221/4404-PSE Saha D, Mishra UN, Sahoo C, Tripathy S, Behera UK, Das S, Sahu C, Datt S, Rout MK, Mohanty TL, Mohanty SP, Barik SR, Mohanty IC, Pradhan SK (2024) Donor Identification, Genetic Diversity, Population Structure and Marker–Trait Association Analyses for Iron Toxicity Tolerance Using Rice Landraces. Diversity 17(1):33. https://doi.org/10.3390/d17010033 Shetty R, Prakash NB (2020) Effect of different biochars on acid soil and growth parameters of rice plants under aluminium toxicity. Sci Rep 10(1):12249. https://doi.org/10.1038/s41598-020-69262-x Singh KM, Ahmad N, Pandey VV, Kumari T, Ritambhara S (2021) Growth Performance and Profitability of Rice Production in India: An Assertive Analysis. Economic Affairs 66(3):481–486. https://doi.org/10.46852/0424-2513.3.2021.18 Singh NU, Roy A, Sharma PM, Yumnam A, Paul P, Singh B (2025) Addressing Soil Acidity for Agricultural Growth in Northeast India. Vigyan Varta 6(2):204–206 Sun X, Xie Y, Bi Y, Liu J, Amombo E, Hu T, Fu J (2015) Comparative study of diversity based on heat tolerant-related morpho-physiological traits and molecular markers in tall fescue accessions. Sci Rep 5(1):18213. https://doi.org/10.1038/srep18213 Tabassum R, Tahjib-Ul-Arif Md H, Md, Sohag AAM, Islam MS, Shafi SMSH, Islam MM, Hassan L (2021) Screening salt-tolerant rice at the seedling and reproductive stages: An effective and reliable approach. Environ Exp Bot 192:104629. https://doi.org/10.1016/j.envexpbot.2021.104629 Ur Rahman S, Han J-C, Ahmad M, Ashraf MN, Khaliq MA, Yousaf M, Wang Y, Yasin G, Nawaz MF, Khan KA, Du Z (2024) Aluminum phytotoxicity in acidic environments: A comprehensive review of plant tolerance and adaptation strategies. Ecotoxicol Environ Saf 269:115791. https://doi.org/10.1016/j.ecoenv.2023.115791 Velikova V, Loreto F (2005) On the relationship between isoprene emission and thermotolerance in Phragmites australis leaves exposed to high temperatures and during the recovery from a heat stress. Plant Cell Environ 28(3):318–327. https://doi.org/10.1111/j.1365-3040.2004.01314.x Verma H, Borah JL, Sarma RN (2019) Variability Assessment for Root and Drought Tolerance Traits and Genetic Diversity Analysis of Rice Germplasm using SSR Markers. Sci Rep 9(1):16513. https://doi.org/10.1038/s41598-019-52884-1 Vimala T, Poonghuzhali T (2013) Estimation of pigments from seaweeds by using acetone and DMSO. Int J Sci Res 4(10):1850–1854. https://doi.org/10.13140/RG.2.2.35655.47524 Weatherley P (1950) Studies in the water relations of the cotton plant. I. The field measurement of water deficits in leaves. New Phytologist:81–97 Wijayanto T, Susanti Y, Rahni NM, Tufaila M, Rembon FS (2021) Seedling-stage screening method for tolerance of upland rice genotypes to low pH stress. IOP Conf Ser: Earth Environ Sci 782(3):032030. https://doi.org/10.1088/1755-1315/782/3/032030 Yang J, Fan W, Zheng S (2019) Mechanisms and regulation of aluminum-induced secretion of organic acid anions from plant roots. J Zhejiang Univ Sci B 20(6):513–527. https://doi.org/10.1631/jzus.B1900188 Yang L-T, Hu N-J, Fu Q-X, Chen X-Y, Ren Y-M, Ye X, Lai N-W, Chen L-S (2024) Effects of aluminum (Al) stress on nitrogen (N) metabolism of leaves and roots in two Citrus species with different Al tolerance. Sci Hort 334:113331. https://doi.org/10.1016/j.scienta.2024.113331 Yin J, Zhao H, Wu X, Ma Y, Zhang J, Li Y, Shao G, Chen H, Han R, Xu Z (2023) SSR marker-based analysis for identification and of genetic diversity of non-heading Chinese cabbage varieties. Front Plant Sci 14:1112748. https://doi.org/10.3389/fpls.2023.1112748 Yokosho K, Yamaji N, Mitani-Ueno N, Shen RF, Ma JF (2016) An Aluminum-Inducible IREG Gene is Required for Internal Detoxification of Aluminum in Buckwheat. Plant Cell Physiol 57(6):1169–1178. https://doi.org/10.1093/pcp/pcw065 Zhang J, Kirkham MB (1996) Antioxidant responses to drought in sunflower and sorghum seedlings. New Phytol 132(3):361–373. https://doi.org/10.1111/j.1469-8137.1996.tb01856.x Zhang J, Wei J, Li D, Kong X, Rengel Z, Chen L, Yang Y, Cui X, Chen Q (2017) The Role of the Plasma Membrane H+-ATPase in Plant Responses to Aluminum Toxicity. Front Plant Sci 8:1757. https://doi.org/10.3389/fpls.2017.01757 Zhang J, Yang J, Lv Y, Zhang X, Xia C, Zhao H, Wen C (2023) Genetic diversity analysis and variety identification using SSR and SNP markers in melon. BMC Plant Biol 23(1):39. https://doi.org/10.1186/s12870-023-04056-7 Zhu CQ, Cao XC, Zhu LF, Hu WJ, Hu AY, Abliz B, Bai ZG, Huang J, Liang QD, Sajid H, Li YF, Wang LP, Jin QY, Zhang JH (2019) Boron reduces cell wall aluminum content in rice ( Oryza sativa ) roots by decreasing H2O2 accumulation. Plant Physiol Biochem 138:80–90. https://doi.org/10.1016/j.plaphy.2019.02.022 Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2026 Read the published version in Plant Cell Reports → Version 1 posted Editorial decision: Minor revisions 03 Nov, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 21 Sep, 2025 Editor assigned by journal 18 Sep, 2025 First submitted to journal 15 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7626626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518393964,"identity":"6ed0d299-261b-4930-817f-d69f7086e0cc","order_by":0,"name":"Darshana Sharma","email":"","orcid":"","institution":"Assam Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Darshana","middleName":"","lastName":"Sharma","suffix":""},{"id":518393965,"identity":"f80501f7-e64c-404f-826d-e5ab9126cb5f","order_by":1,"name":"Sudipta Sankar Bora","email":"","orcid":"","institution":"Assam Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Sudipta","middleName":"Sankar","lastName":"Bora","suffix":""},{"id":518393966,"identity":"bcce2a89-dc1c-40e7-9ed8-07bb2bf3d83d","order_by":2,"name":"Rahul Kaldate","email":"","orcid":"","institution":"Assam Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Kaldate","suffix":""},{"id":518393967,"identity":"7ddcfafe-faf2-4240-a4d3-36df3474870e","order_by":3,"name":"Ishani Borthakur","email":"","orcid":"","institution":"Assam Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ishani","middleName":"","lastName":"Borthakur","suffix":""},{"id":518393968,"identity":"73dc94d7-73b2-4836-85d3-4c2f9237849e","order_by":4,"name":"Pradip Chandra Dey","email":"","orcid":"","institution":"Assam Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pradip","middleName":"Chandra","lastName":"Dey","suffix":""},{"id":518393969,"identity":"81cb0e00-e145-4a76-bfb7-ccdc00198f2e","order_by":5,"name":"Madhumita Barooah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYHACMyCWAGIeCNeAvQEqwYxfiwRCC88BorQwIGmRSMDvKt32w9se8+ZY1PE38B78XFFhI28u+fjhZx4GO3kGdt4D2LSYnUkrN+bdJiEhcYAvWfLMmTTDnbPTjKV5GJING5j5sNpndiDHTBqkheEAj4FkY9thxg23EwyAWpgTGJh5DLBqOf8GokX+AI/xT6AW+w03j3/+zcNQj1vLDagtBgd4zEC2JG64wWMGtOUwHi3PyiTnbpOQ3HiYL82y4Uxa8oYzOWWWcwyOG7bhdFjyNom32+r45Y73Hr7ZUGFju+H48c033lRUy/Pzn8GqBQFQIw6omA2/+lEwCkbBKBgFeAAA0yhV353pwzsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3768-5336","institution":"Assam Agricultural University Faculty of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Madhumita","middleName":"","lastName":"Barooah","suffix":""}],"badges":[],"createdAt":"2025-09-16 06:32:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7626626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7626626/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00299-026-03791-z","type":"published","date":"2026-04-02T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92750526,"identity":"a9947bce-d2d3-432b-99d3-3aa3d783b621","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8983,"visible":true,"origin":"","legend":"","description":"","filename":"pcrePCRED2501073.xml","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/56a3e73021b33137c25eaa0d.xml"},{"id":92750503,"identity":"e70e4c33-508b-4115-ba97-4373332470a7","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1013,"visible":true,"origin":"","legend":"","description":"","filename":"PCRED250107319595.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/22d6716ae71f74537f2c360f.xml"},{"id":92750616,"identity":"c1c7bdda-23b7-4a03-9f5b-7c992ab6efc9","added_by":"auto","created_at":"2025-10-03 21:35:23","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":817,"visible":true,"origin":"","legend":"","description":"","filename":"PCRED2501073Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/a9e1b94770dcad25f9c189a3.xml"},{"id":92750531,"identity":"9c39bfd6-99dd-4e8c-8d53-44dd279c7f78","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":238021,"visible":true,"origin":"","legend":"","description":"","filename":"PCRED25010730enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/364fd652076f4700715f5196.xml"},{"id":92750530,"identity":"412e457f-b397-4d09-b7a3-1956b44cb94c","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":846970,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/69ff139deb40e77319953b47.jpeg"},{"id":92750498,"identity":"9c17d501-ac19-44ce-8722-5886d2a03838","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6238,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/ee08abc0df992c6230151d5d.jpeg"},{"id":92750614,"identity":"1ff64bd2-046f-4ba5-b903-3ed94bbe8911","added_by":"auto","created_at":"2025-10-03 21:35:23","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":192522,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/8a34573901bd4384ced24ef7.png"},{"id":92750512,"identity":"78587e25-637d-4ce5-b2d8-b62be03b74a9","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/4f1ce44ae20b8c8a889a77ac.jpeg"},{"id":92750541,"identity":"43f16388-ae95-4cb5-aa5e-6cce7fa45ead","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150196,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/3118c79133989b326bdf7b99.jpeg"},{"id":92750504,"identity":"f3d3897e-89d2-4f29-a38d-80d809aaab1a","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80708,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/9d0400c1b026c475c6904296.jpeg"},{"id":92750489,"identity":"cfd06d8f-e6d1-43aa-8bf5-bef66713cad8","added_by":"auto","created_at":"2025-10-03 21:27:22","extension":"jpeg","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":231084,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/a4fdc5f9457d10be208976ce.jpeg"},{"id":92750619,"identity":"119b7bdb-1011-41da-b165-e85470327ffe","added_by":"auto","created_at":"2025-10-03 21:35:24","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/6a6c529a38a543d2f13fd65e.jpeg"},{"id":92750491,"identity":"7f6e2b82-95f0-49d8-ad06-905136aa6e66","added_by":"auto","created_at":"2025-10-03 21:27:22","extension":"jpeg","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256008,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/38cdaf7d9afabfaa8425cc98.jpeg"},{"id":92750544,"identity":"24edcadf-678d-40c8-804d-b4a65987c75c","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/77dab86c9bbaad49d27f638d.jpeg"},{"id":92750507,"identity":"201da725-26a2-4efd-b14b-218ad92fd7e2","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1074,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/9159c86d64cb7009d652784e.jpeg"},{"id":92750534,"identity":"19fe7541-7af8-48a4-8dae-d27a58449d2d","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50589,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/bf51eb000e603f88337302c7.jpeg"},{"id":92750537,"identity":"b2102484-60b1-4669-8587-cbe75205262a","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31491,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/bceecc62a5f1adee1b40bd3d.jpeg"},{"id":92750488,"identity":"80c95e55-08e8-4c15-84b5-2330b19cc057","added_by":"auto","created_at":"2025-10-03 21:27:22","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84376,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/458ccb4dbcc3dc8281d3c3b6.jpeg"},{"id":92750550,"identity":"e9a614dc-d96e-4324-ad9f-3b783cd0994f","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"jpeg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32578,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/b95ffc8c34b22d991ed58686.jpeg"},{"id":92750518,"identity":"d72daea9-ceb9-44e1-834a-cf47a359df68","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35442,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/82780721e0f57a5a41cc6fb4.jpeg"},{"id":92750623,"identity":"ac779f32-ce96-4ea0-b260-eefb0764ace9","added_by":"auto","created_at":"2025-10-03 21:35:25","extension":"jpeg","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19364,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/5c645c3832eb3a54dcd92093.jpeg"},{"id":92750551,"identity":"e4ab3b0e-f9fd-4379-a8ae-7cfa993aeaad","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"jpeg","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39703,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/b45b1dab6453383f080dbc3a.jpeg"},{"id":92750509,"identity":"848b3ace-d509-4e76-b6bf-ff282f3f48e2","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41311,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/75e1c8a4f3cd65022c93f7b6.jpeg"},{"id":92750539,"identity":"778ff982-8d44-4420-8cf8-39d1e0748103","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"jpeg","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24280,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/c286e80d94ee523a3948e0d0.jpeg"},{"id":92750542,"identity":"03271f9d-f569-4aef-8373-853940380a8f","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"jpeg","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22029,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/5e9c85bffab6205796db26ba.jpeg"},{"id":92750522,"identity":"f637750a-347a-4520-b363-43364e9dacff","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21033,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/096b5e021a07937529b5b6a3.jpeg"},{"id":92750523,"identity":"6d602216-53a1-40e1-822a-d630fcf965dc","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19733,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/c921aad113dc123bc9f6a23e.jpeg"},{"id":92750497,"identity":"49164b61-9354-4779-9c7a-44b0cab15a82","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpeg","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21168,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/32e467335b55d1eb081e623c.jpeg"},{"id":92750525,"identity":"9106dd05-4c49-4e0e-ac54-cc2dd6818bd2","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpeg","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86695,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/fef7a97b765439b10a952161.jpeg"},{"id":92750532,"identity":"38d1d2b8-575f-4c4c-b2c3-d7c98affab77","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":220727,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/5c240a38dbc19abe0bf25c5d.png"},{"id":92750548,"identity":"68994546-b616-4703-9586-95c128306f93","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1267,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/fac338871c6a2f3cccafac9e.png"},{"id":92750554,"identity":"f66cd147-cef7-4c49-8558-7aa170ea04df","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99634,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/63a683b388c25738284a570a.png"},{"id":92750621,"identity":"7059d821-9588-491b-8d9c-cd475707d890","added_by":"auto","created_at":"2025-10-03 21:35:24","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/44c94e2e43ed4849c0ea76c1.png"},{"id":92750622,"identity":"5fb739ca-1696-4836-95bf-bc352c1c7c0e","added_by":"auto","created_at":"2025-10-03 21:35:25","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14069,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/42d15141645b5bb82e5424a2.png"},{"id":92750521,"identity":"5f2c8c26-30e8-4c33-94a5-33f3a3f58940","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9712,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/eb1d691440556b07611caf45.png"},{"id":92750516,"identity":"bf5eff39-720c-4e19-a36e-5944ba04bd45","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32883,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/bdee2c1f7030f3dc0a7bc7f5.png"},{"id":92750618,"identity":"3561dc96-015b-4954-9809-851b18c659ed","added_by":"auto","created_at":"2025-10-03 21:35:24","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/09fa91b2480ae05815cfa4bd.png"},{"id":92750492,"identity":"77fea547-87a2-4525-9a7a-8f9d78dd819a","added_by":"auto","created_at":"2025-10-03 21:27:22","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47506,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/e7dd0683951183d574353822.png"},{"id":92750528,"identity":"c8ea29c7-dae3-4db9-911b-d85f32636f18","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"png","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/6984f2885767fa5b8c14f26c.png"},{"id":92750549,"identity":"ded1d232-60ea-4c94-bf44-c03f970c5526","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"png","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":935,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/2125cf1a08353445ba08faa1.png"},{"id":92750547,"identity":"9fa6a1b5-a0e2-4bb1-8bab-a506cdffd571","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"png","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21165,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/94783460bb444cbb3425b40f.png"},{"id":92750553,"identity":"eae33364-a201-45ac-b04a-04c78115ef19","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"png","order_by":44,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13537,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/1017e3da166035331423d6ca.png"},{"id":92750493,"identity":"4be4b6dc-756a-4d8c-abe8-47e020199d3b","added_by":"auto","created_at":"2025-10-03 21:27:22","extension":"png","order_by":45,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33786,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/694866a3d918bcde8d1b2a7b.png"},{"id":92750494,"identity":"6e6af48a-ced2-41b9-b583-b7ce9e4e93ca","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"png","order_by":46,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11624,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/01d527175b51f873d8b26fcc.png"},{"id":92750506,"identity":"85dc7d59-6748-4731-a139-f9645d7764ad","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"png","order_by":47,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12026,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/afdf4e23532315470623a53a.png"},{"id":92750524,"identity":"81dd61ec-4bd1-4b93-a883-380fd30dd385","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"png","order_by":48,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6579,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/50561415b5d8e19b9868d8e2.png"},{"id":92750625,"identity":"7ea7ccd7-da5c-4c8b-8068-059b17ab7e67","added_by":"auto","created_at":"2025-10-03 21:35:26","extension":"png","order_by":49,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19326,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/80c1b7f1b5fb998b8e232d5e.png"},{"id":92750626,"identity":"94d6cf15-9857-4522-b20f-ed12f1dfadfe","added_by":"auto","created_at":"2025-10-03 21:35:26","extension":"png","order_by":50,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14509,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/fd65a617e79fe56473da32c1.png"},{"id":92750538,"identity":"3d0cd466-42b5-4845-92d9-8ccf83952bf2","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"png","order_by":51,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50413,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/3d471f8ed1edad1327b43808.png"},{"id":92750620,"identity":"c6cc557b-8a0f-4152-be0d-a6614d1f55a5","added_by":"auto","created_at":"2025-10-03 21:35:24","extension":"png","order_by":52,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":47622,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/8f42ca9db8bb92e32015a313.png"},{"id":92750540,"identity":"ad8f3ae7-bcfd-4274-bad7-0de17b5a9e38","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"png","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42306,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/55ccc2d5aad776028a04379f.png"},{"id":92750535,"identity":"039686a0-a4a9-4c70-82f8-0f4e4a765c48","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"png","order_by":54,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39620,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/ad2cfd8954e0affc0cda504e.png"},{"id":92750511,"identity":"7df7905d-b6c4-40f0-a164-ba56a081e029","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"png","order_by":55,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37386,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/fa6eb9614de87146a0c65010.png"},{"id":92750517,"identity":"662b306a-562f-4399-bc54-f7be7f18f6ba","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"png","order_by":56,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32464,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/baf94317bd222f1a1b809f0b.png"},{"id":92750536,"identity":"b0362d76-41c0-4329-83af-8c09bcc4bb43","added_by":"auto","created_at":"2025-10-03 21:27:25","extension":"xml","order_by":57,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235475,"visible":true,"origin":"","legend":"","description":"","filename":"PCRED25010730structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/dac3881210d6638d23a81242.xml"},{"id":92750546,"identity":"f8dab592-f3de-40d3-b946-ed7624711e8b","added_by":"auto","created_at":"2025-10-03 21:27:26","extension":"html","order_by":58,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":249965,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/e59d8e6b1656a5332303b25d.html"},{"id":92750612,"identity":"56a12377-51c5-46c4-b91d-c932f5d894ef","added_by":"auto","created_at":"2025-10-03 21:35:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":450469,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of aluminium toxicity (200 μM) on plant growth, biomass, and germination ability of 53 rice genotypes after 72hrs under hydroponic assay.\u003cstrong\u003e a \u003c/strong\u003eRepresentative image of a hydroponics assay under aluminium toxicity (200 μM); \u003cstrong\u003eb \u003c/strong\u003eEffect of aluminium toxicity on germination capability of the rice varieties which were categorized into (i) Tolerant (ii) Moderately tolerant and (iii) Susceptible;\u003cstrong\u003e c \u003c/strong\u003eResponse of aluminium toxicity on root dry weight; \u003cstrong\u003ed\u003c/strong\u003eEffect of aluminium toxicity on root fresh weight;\u003cstrong\u003e e \u003c/strong\u003eEffect on relative water content (RWC)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/8282ee3dabe4430493852879.png"},{"id":92750615,"identity":"efb553d7-1e79-43ae-a8db-59356ed02f18","added_by":"auto","created_at":"2025-10-03 21:35:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45033,"visible":true,"origin":"","legend":"\u003cp\u003eA box plot representing a visual clustering of the 53 rice genotypes into Al-tolerant, moderately tolerant, and susceptible based on their RTI performance under toxic Al exposure\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/fff799b92176f74d2c7e2db7.jpg"},{"id":92750510,"identity":"45afce4f-8281-4805-ba75-9fe5db76d153","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84160,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing Pearson coefficient correlations for the different traits of the 53 genotypes under aluminium stress. The abbreviations used are as follows: root length (RL), root elongation rate (RRE), root dry weight (RDW), root fresh weight (RFW), root tolerance index (RTI), relative root reduction (RRR), shoot tolerance index (STI) and relative water content (RWC)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/7ba7a7cdd45cfccfd88c25d6.jpg"},{"id":92750505,"identity":"9b1725ca-6b3d-4bbf-8a86-1e96e4aa0b7a","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358447,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering and principal component analysis of 53 rice genotypes based on 9 quantitative characters recorded under aluminium stress (200μM).\u003cstrong\u003e a \u003c/strong\u003ePhenogram showing 5 different clusters generated using Ward method of hierarchical clustering. Numbering in x-axis indicates the genotypes serial number in \u003cstrong\u003eTable 1\u003c/strong\u003e; \u003cstrong\u003eb \u003c/strong\u003ePrincipal Component Analysis (PCA) biplot showing grouping of 53 rice genotypes under Al stress conditions for total variation among 9 morphological markers\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/83f920d3367f1e5896f14b47.jpg"},{"id":92750500,"identity":"3ec85654-c559-49ce-ac13-c2bc63915bde","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117843,"visible":true,"origin":"","legend":"\u003cp\u003eSSR banding patterns obtained from 53 rice genotypes using polymorphic primers.\u003cstrong\u003e a \u003c/strong\u003eAmplified bands of RM482;\u003cstrong\u003e b \u003c/strong\u003eAmplified bands of RM341;\u003cstrong\u003e c \u003c/strong\u003eAmplified bands of RM430;\u003cstrong\u003e d \u003c/strong\u003eAmplified bands of RM174;\u003cstrong\u003e e \u003c/strong\u003eAmplified bands of RM524.\u003cstrong\u003e \u003c/strong\u003eL is a 100 bp DNA marker, and numbers 1 to 53 are rice genotype\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/439506991b570a74aa8d0d11.jpg"},{"id":92750508,"identity":"f92eba94-101b-41ab-8c23-9e1f0139dfe9","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":54301,"visible":true,"origin":"","legend":"\u003cp\u003eRadial neighbor-joining tree based from 33 SSR loci among 53 genotypes. Bootstrap values ≥50% are shown\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/efd2a8ebebb95778f6deb938.jpg"},{"id":92750520,"identity":"4bd28c64-02b0-4538-9dd5-56dea85dd4f5","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":382354,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Al toxicity on physiology and biochemistry of rice genotypes.\u003cstrong\u003e a \u003c/strong\u003eEffect on chlorophyll a;\u003cstrong\u003e b \u003c/strong\u003eEffect on chlorophyll b.\u003cstrong\u003ec \u003c/strong\u003eEffect on carotenoid content;\u003cstrong\u003e d \u003c/strong\u003eDistinct patterns of macronutrient and micronutrient uptake in roots of different genotypes;\u003cstrong\u003e e \u003c/strong\u003eA heatmap showing the quantitative variation in organic acid content (malate, citrate, and oxalate) in root of three rice genotypes Ahom Sali, Joldhubi, and Ranjit Sub-1 hydroponically cultivated under control and 200 µM aluminium stress (Treatment) conditions.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/35662db68136234391586892.jpg"},{"id":92750495,"identity":"7a11536b-586c-44b5-bd3b-4712ff798d8a","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":456197,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Al toxicity on stress markers and antioxidant capacity of seven rice genotypes.\u003cstrong\u003e a \u003c/strong\u003eEffect of Al toxicity on accumulation of ROS in terms of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content;\u003cstrong\u003e b \u003c/strong\u003eEffect of Al toxicity on lipid peroxidation in terms of MDA content;\u003cstrong\u003e c \u003c/strong\u003eEffect of Al toxicity on proline content in stressed and non-stressed rice seedlings under control and Al stress condition;\u003cstrong\u003e d \u003c/strong\u003eEffect of aluminium toxicity on\u0026nbsp; SOD activity;\u003cstrong\u003e e \u003c/strong\u003eEffect of aluminium toxicity on POD activity;\u003cstrong\u003e f \u003c/strong\u003eEffect of aluminium toxicity on APX\u0026nbsp; activity;\u003cstrong\u003e g \u003c/strong\u003eEffect of aluminium toxicity on\u0026nbsp; CAT activity in rice genotypes;\u003cstrong\u003e \u003c/strong\u003eEach bar graph includes values for control (green) and treatment (blue) with mean and standard error bars. Data presented as mean ± SE (\u003cem\u003en\u003c/em\u003e\u0026nbsp;= 3). Different letters along bars represent significant difference at\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;≤ 0.05 according to Tukey’s HSD test.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/468b07f0cf3761458ed4c874.jpg"},{"id":92750513,"identity":"031d2725-fcb8-4071-bd87-153a78c4587d","added_by":"auto","created_at":"2025-10-03 21:27:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":102668,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of candidate aluminium (Al) tolerance genes in roots of rice genotypes under Al stress (200 µM AlCl₃, 72 h).\u003cstrong\u003e a \u003c/strong\u003eExpression pattern of \u003cem\u003eOsNrat1;\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eb \u003c/strong\u003eExpression pattern of \u003cem\u003eOsSTAR1\u003c/em\u003e;\u003cstrong\u003e c \u003c/strong\u003eExpression pattern of \u003cem\u003eOsSTAR2;\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e d \u003c/strong\u003e\u003c/em\u003eExpression pattern of \u003cem\u003eOsALS1\u003c/em\u003e and \u003cstrong\u003ee\u003c/strong\u003e Expression pattern of \u003cem\u003eOsFRDL4\u003c/em\u003e.\u003cstrong\u003e \u003c/strong\u003eData are presented as mean ± SE of three biological replicates. Statistical significance (p \u0026lt; 0.05) is denoted by *, **, ***\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/8cc62439aabce402fd2f0173.jpg"},{"id":92750624,"identity":"2b8c9e8c-f9de-44bc-a942-79d5f0659bb6","added_by":"auto","created_at":"2025-10-03 21:35:25","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":255365,"visible":true,"origin":"","legend":"\u003cp\u003eA correlation matrix plot (specifically a pie-style correlogram) that visualizes the Pearson correlation coefficients between a wide range of morphological, physiological, and biochemical parameters in rice genotypes subjected to 200 µM aluminium (Al) toxicity stress cultivated hydroponically for 72 hrs\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/cdb364d7270fa96856d7d823.jpg"},{"id":106108884,"identity":"4022c8d8-43b1-4636-8819-019c5cc7f7fe","added_by":"auto","created_at":"2026-04-03 14:32:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4324404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/dac9b3cb-bb59-49d6-a9cd-7787d29f5662.pdf"},{"id":92750502,"identity":"9a5387be-5835-4854-960b-401cd9118265","added_by":"auto","created_at":"2025-10-03 21:27:23","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":36579,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7626626/v1/b4bdd48c72b3276aacbaacf2.docx"}],"financialInterests":"","formattedTitle":"Exclusion versus Detoxification: Contrasting Molecular Strategies of Aluminium Tolerance in Rice Landraces of Northeast India","fulltext":[{"header":"Key Message","content":"\u003cp\u003eRice genotypes differ in aluminium tolerance, with resistant landraces sustaining root growth through STAR1/2-mediated exclusion and organic acid efflux, while sensitive types accumulate Al\u0026sup3;⁺ via NRAT1\u0026ndash;ALS1, leading to oxidative damage\u003cem\u003e. \u003c/em\u003e\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), a member of the Poaceae family, is one of the world\u0026rsquo;s three major cereals alongside maize and wheat, serving as the primary staple for more than half of the global population and contributing significantly to food security and socioeconomic development (Landi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abeysekara and Rathnayake \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Asia accounts for nearly 90% of global rice production, with India ranking second after China, producing 116.42\u0026nbsp;million tons over 43.79\u0026nbsp;million hectares (Lin et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Devi et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mishra \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Rice is the principal food crop in the Northeast India (NEI). It is cultivated in 3.51\u0026nbsp;million hectares (\u0026asymp;\u0026thinsp;8% of the geographical area) and contributes\u0026thinsp;~\u0026thinsp;6.5% to national production (Gogoi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Northeast India (NEI) is a home to many indigenous rice varieties which are cultivated in diverse topographic and agroclimatic conditions, and normally classified into different types based on the season of cultivation mainly \u003cem\u003esali\u003c/em\u003e, \u003cem\u003eboro\u003c/em\u003e and \u003cem\u003ejhum\u003c/em\u003e type. These indigenous rice varieties reveal significant genetic variety and are essential to the region's agricultural heritage. Research indicates that these traditional cultivars exhibit greater genetic diversity than agronomically modified types, demonstrating an overall allelic richness of 10.205 per locus and a gene diversity value of 0.776, with the sali type displaying the most diversity among them (Choudhury et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Characterising and preserving these landraces are essential for sustainable farming practices and the conservation of regional biodiversity. Indigenous rice varieties are crucial to global agriculture, particularly in areas susceptible to abiotic stresses like drought, heat, salinity, and submergence. It is imperative to develop cultivars with improved tolerance to these stresses, as research has revealed numerous physiological, biochemical, and genetic mechanisms that enhance stress resilience, including extensive root systems for drought resistance and specific genetic traits for heat and salinity tolerance (Adzigbe et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Moreover, these indigenous varieties possess considerable agroeconomic significance as they are suited to local agro-ecosystems, frequently requiring little external inputs and promoting sustainable agricultural practices. These types can be evaluated for abiotic stress tolerance by improved molecular breeding techniques, such as marker-assisted selection, which have enabled the creation of stress-tolerant rice varieties that enhance crop resilience and food security amid climate change(Fernandes et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the characterisation, conservation, and utilisation of these genetic resources are essential for advancing sustainable agriculture, preserving biodiversity, and guaranteeing long-term food and nutritional security.\u003c/p\u003e\u003cp\u003eNearly 80% of soils in Northeast India are acidic, spanning\u0026thinsp;~\u0026thinsp;21.26\u0026nbsp;million hectares, and pose a major constraint on rice productivity due to metal toxicities, particularly aluminium (Al) (Majumdar et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).In acidic soils (pH\u0026thinsp;\u0026lt;\u0026thinsp;5.5), Al is solubilized as toxic species such as Al\u0026sup3;⁺, Al(H₂O)₆ \u0026sup3;⁺, and Al(OH)₂⁺, which even at micromolar levels disrupt plant growth (Krstic et al. 2017). Root systems are the primary targets, exhibiting inhibited elongation, stubby laterals, and impaired nutrient uptake, ultimately reducing shoot growth, photosynthesis, and biomass (Hayes et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hajiboland et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kocjan et al. 2024). Prolonged Al exposure induces oxidative stress through excess ROS generation, resulting in cellular damage and eventual cell death (Bera 2017; Jaskowiak et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Plants counteract this through ROS-scavenging antioxidants, ethylene-mediated growth regulation, and organic acid exudation via ALMT and MATE transporters, processes transcriptionally regulated by STOP1 and ART1, which activate key Al-tolerance genes such as STAR1, STAR2, Nrat1, OsFRDL4, and OsALS1 (Chowra et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. 2019; Ur Rahman et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kocjan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Molecular markers, particularly SSRs, have emerged as reliable tools for dissecting genetic variation in Al tolerance owing to their high polymorphism, co-dominance, and reproducibility, surpassing traditional assessments confounded by environmental factors (Verma et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yin et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yet, despite the prevalence of acidic, Al-toxic soils in Northeast India, systematic evaluation of traditional rice genotypes for Al tolerance remains limited. To address this gap, the present study integrates morphological, physiological, biochemical, and gene expression analyses to identify reliable markers underlying aluminium tolerance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePlant materials and experimental conditions\u003c/h2\u003e\u003cp\u003eThe seeds of fifty-three rice genotypes, which represent the genetic and ecological diversity of Northeast India, were procured from the Assam Agricultural University\u0026ndash;Assam Rice Research Institute (previously known as the Regional Agricultural Research Station, Titabar), located in Jorhat, Assam (26.575 N, 94.183 E). It included traditional landraces such as Sali (winter), Ahu (autumn), Boro (summer), deepwater Bao types, aromatic Joha, Bora, and Chakua ecotypes, in addition to improved and stress-tolerant cultivars including Bahadur Sub-1, Ranjit, Ranjit Sub-1, Misiri-2, and Mahsuri (Table. 1). Despite all accessions belonging to the species \u003cem\u003eOryza sativa\u003c/em\u003e L. subspecies indica, they exhibited considerable variation in phenological characteristics, ranging from early-maturing varieties such as Disang (100\u0026ndash;105 days) to very late-maturing deepwater landraces including Bon Gathu, Joldhubi, Jolkonwari, and Negeri Bao (240\u0026ndash;270 days). This representative germplasm panel was subsequently employed for the screening and detection of tolerance to aluminium (Al) toxicity via a hydroponic assay that utilized gradient Al concentrations (0, 50, 100, and 200 \u0026micro;M), adhering to established protocols (Tabassum et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\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\u003eConstituting number of genotypes selected for screening\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\u003eRice Variety\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCountry of Origin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSubsp.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDays to Maturity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAhom Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90\u0026ndash;135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAki Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u0026ndash;155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBadshabhog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAromatic rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBahadur Sub-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBahadur/Swarna-Sub1//Bahadur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubmergence-tolerant variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u0026ndash;155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBamki Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90\u0026ndash;135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetguti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBiriabhong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoga Manohar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBogi Shali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e90\u0026ndash;135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoka Chakua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChakua type rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBokul Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBokul Joha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSabitri/Badshah bhog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali/Lahi rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170\u0026ndash;180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBon gathu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeepwater rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240\u0026ndash;270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBor Chakua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChakua type rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBor Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBora Chakua\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChakua type rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuli Bao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeep-water rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240\u0026ndash;270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCheni lahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDipholu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisang (TTB 303-2-23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreeding line\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAhu rice (autumn rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100\u0026ndash;105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDolkosu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGajep Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGhew Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGitesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAkisali/Kushal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBao (Deep-water rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u0026ndash;160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGudumoni\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHati Sali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJahinga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJamini Dhon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoldubi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeepwater rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240\u0026ndash;270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJolkonwari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeepwater rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240\u0026ndash;270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJoymoti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBoro (summer rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170\u0026ndash;180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKalamdini\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAromatic rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKanaklata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAromatic rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKeteki Joha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSabitri/Badshah bhog (IET-14390)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSali/Lahi Rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhamti Lahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLahi rice (winter rice)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKola Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKola Joha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoha (aromatic) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKon Joha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoha (aromatic) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMahsuri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraditional variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRain-fed mega variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e125\u0026ndash;130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalbhog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAromatic rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalbhog Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) aromatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMisiri-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreeding line\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImproved variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulagabhoru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraditional rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegeri Bao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeep-water rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e240\u0026ndash;270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumoli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraditional rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoita Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrashad bhog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAromatic rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e135\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanjit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-yielding variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKharif rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u0026ndash;155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRanjit Sub-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanjit/Swarna-Sub1//Ranjit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSubmergence-tolerant variety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e150\u0026ndash;155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRonga Joha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJoha (aromatic) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSalpona\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraditional rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSial Kathi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraditional rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTil Bora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLandrace (Assam)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBora (waxy) rice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndica\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e140\u0026ndash;150\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\n\u003ch3\u003ePlant growth culture and hydroponic assay\u003c/h3\u003e\n\u003cp\u003eRice seeds from each rice genotype were surface sterilized with 70% ethanol (2 min) followed by 0.1% HgCl₂ (7 min), rinsed thoroughly with sterile water, soaked for 24 h, and germinated on petri plates in the dark at 28\u0026ndash;31\u0026deg;C for 72 h. Uniform seedlings were transferred onto Styrofoam floaters placed in plastic containers containing 150 ml MM2 medium (pH 6) and grown for 3 days under a 16/8 h photoperiod (250 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;). The medium was replenished daily to maintain pH stability and nutrient availability. Aluminium stress was imposed by supplementing the medium with AlCl₃ (0, 50, 100, 200 \u0026micro;M) in the presence of 500 \u0026micro;M CaCl₂ (pH 4.5) for 3 days (Jaiswal et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGrowth and biomass measurements\u003c/h3\u003e\n\u003cp\u003eAfter 3 days of Al treatment, root length, shoot length, and root fresh and dry weights were recorded in three seedlings per genotype. Relative root length (RRL), relative root elongation (RRE%), root and shoot tolerance indices (RTI, STI), and relative root reduction (RRR%) were calculated to assess Al tolerance (Laenoi et al. 2014). Relative water content (RWC) was determined as RWC=(FW\u0026thinsp;\u0026minus;\u0026thinsp;DW)/(TW\u0026thinsp;\u0026minus;\u0026thinsp;DW) \u0026times;100, with FW\u0026thinsp;=\u0026thinsp;fresh weight, TW\u0026thinsp;=\u0026thinsp;turgid weight after 6 h hydration, and DW\u0026thinsp;=\u0026thinsp;dry weight (Weatherley \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1950\u003c/span\u003e). The germination capacity of 53 rice genotypes was evaluated under toxic Al conditions. Surface-sterilized seeds were germinated in half-strength Modified Magnavaca\u0026rsquo;s solution supplemented with 200 \u0026micro;M AlCl₃ (pH 4.5) for 4 days in the dark. Seeds were considered germinated upon radical and plumule emergence, and root growth were visually recorded (Kikui et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSSR genotyping and PCR analysis\u003c/h3\u003e\n\u003cp\u003eGenetic diversity among 53 rice accessions was assessed using 33 polymorphic SSR markers, including 10 Al-tolerance\u0026ndash;specific markers covering all 12 chromosomes (primers listed in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The SSR marker sequences, annealing temperature and chromosomal locations were obtained from the GRAMENE database. Genomic DNA was extracted from young leaves using the CTAB method, quantified (50 ng/\u0026micro;L), and PCR-amplified in 10 \u0026micro;L reactions under standard cycling conditions. PCR products were resolved on 3% agarose gel, stained with ethidium bromide, and visualized using a Gel Documentation System; fragment sizes were estimated relative to a 100 bp ladder (Irsyadi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ravikiran et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Amplified bands were scored manually as \u0026lsquo;1\u0026rsquo; (present) or \u0026lsquo;0\u0026rsquo; (absent), and only distinct polymorphic bands were included. Genetic diversity parameters, including number of alleles (Na), effective number of alleles (Ne), Shannon\u0026rsquo;s information index (I), and Nei\u0026rsquo;s genetic diversity index (He), were calculated using POPGENE v1.32, and polymorphism information content (PIC) was determined for each marker (Botstein et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). A neighbor-joining (NJ) tree was constructed based on Jaccard\u0026rsquo;s coefficient to visualize genetic relationships.\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003ePhysiological and biochemical responses of rice genotypes under aluminium stress\u003c/b\u003e\u003c/div\u003e\u003cp\u003ePhotosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids) were quantified in 100 mg leaf tissue homogenized in 80% acetone, with absorbance recorded at 480, 644, and 663 nm (Vimala and Poonghuzhali 2014).\u003c/p\u003e\u003cp\u003eHydrogen peroxide (H₂O₂) content in roots was measured following Loreto and Velikova (2001), with absorbance recorded at 390 nm and concentrations determined from a standard curve (Martins et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Lipid peroxidation was estimated by measuring malondialdehyde (MDA) using the thiobarbituric acid reactive substances (TBARS) method (Heath and Packer \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1968\u003c/span\u003e). Proline content was determined in 100 mg root tissue homogenized in 500 \u0026micro;L 3% sulfosalicylic acid, centrifuged at 13,000 rpm for 5 min, and reacted with acid ninhydrin and glacial acetic acid. The mixture was incubated in a hot-water bath, cooled, and the pink upper phase absorbance measured at 520 nm against toluene; proline concentration was expressed as \u0026micro;mol/g fresh weight (Bates et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1973\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor antioxidant enzyme assays, fresh root tissue (500 mg) was macerated in liquid nitrogen, homogenized in 5 mL extraction buffer (50 mM sodium phosphate buffer, pH 7.8, 0.1 mM EDTA, 1% polyvinylpyrrolidone, 0.5% Triton X-100), and centrifuged at 15,000 rpm for 30 min at 4\u0026deg;C. The supernatant was used to assay superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) activities. SOD activity was determined by its inhibition of nitro blue tetrazolium (NBT) photoreduction at 560 nm, with one unit defined as the enzyme amount causing 50% inhibition (Giannopolitis and Reis 1997). CAT activity was measured as \u0026micro;M H₂O₂ decomposed min⁻\u0026sup1; mg⁻\u0026sup1; protein at 240 nm (Aebi. 1984). POD activity was assayed at 470 nm, with one unit defined as the enzyme amount producing a 1.0 absorbance increase per g FW per min (Zhang and Kirkham \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). APX activity was measured at 290 nm, with one unit defined as the enzyme amount causing a 1.0 absorbance decrease per g FW per min (Nakano and Asada \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor organic acid analysis, frozen root tissue (1.7 g) was ground in liquid nitrogen and homogenized in 6.5 mL ice-cold 4% (v/v) HClO₄. The suspension was thawed on ice, incubated for 30 min, and centrifuged at 20,000 \u0026times; g for 10 min. Five mL of the supernatant was neutralized with 5 M K₂CO₃ at 4\u0026deg;C, and precipitated potassium chlorate removed by centrifugation. Activated charcoal (50 mg) was added, incubated for 15 min at 4\u0026deg;C, and removed by centrifugation. The resulting supernatant was used for metabolite measurements. Malate was determined in a 1 mL reaction containing 50 mM 3-amino-1-propanol-HCl (pH 10), 30 mM glutamate-NaOH (pH 10), 2.7 mM NAD, 1 U glutamate-oxaloacetate transaminase, and 10 U malate dehydrogenase (MDH). Oxaloacetate (OAA) was measured in a 1 mL reaction containing 150 mM Tris-HCl (pH 7.6), 10 mM EDTA-NaOH (pH 7.0), 0.15 mM NADH, and 2 U MDH. Citrate was quantified in a 1 mL mixture of 100 mM Tris (pH 7.6), 0.2 mM NADH, 7 U LDH, 14 U MDH, and 0.5 U citrate lyase (Chen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe nutrient content in roots was estimated according to Hayes et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Root samples were oven-dried at 80\u0026deg;C for 72 h, and 0.1 g of dried tissue was acid-digested with 10 mL HNO₃ and 5 mL H₂O₂ at 120\u0026deg;C. The digested solution was diluted with 20 mL deionized water, filtered, and the final volume adjusted to 40 mL. Aluminium and micronutrient contents were quantified using an Atomic Absorption Spectrophotometer (Model 5000, Perkin-Elmer, USA) with standards calibrated at 309.5 nm, and concentrations were expressed as mg/kg dry weight (Hayes et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative Real-Time PCR (qRT-PCR) analysis\u003c/h2\u003e\u003cp\u003eTotal RNA was isolated from root tissues using a modified TRIzol method (Mainkar et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). RNA quality and quantity were assessed with a NanoDrop-1000 Spectrophotometer, and integrity was confirmed on 3% agarose gel. First-strand cDNA was synthesized from 1 \u0026micro;g total RNA using Superscript\u0026trade; reverse transcriptase (Takara, Dalian, China). For qRT-PCR, 1 \u0026micro;L of cDNA (100 ng\u0026middot;\u0026micro;L⁻\u0026sup1;) was amplified in a 10 \u0026micro;L reaction with SYBR Premix Ex Taq (Takara, Dalian, China), using Actin1 as an internal reference. Reactions were performed on a QuantStudio 3 Real-Time PCR System under the following cycling conditions: 95\u0026deg;C for 5 min, followed by 40 cycles of 95\u0026deg;C for 15 s, 52\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s. Amplicon specificity was confirmed by melting curve analysis (70\u0026ndash;95\u0026deg;C) and agarose gel electrophoresis. Three biological and two technical replicates were used per gene. Relative expression levels were calculated using the average Rq of biological replicates. Primer sequences are listed as follows: Actin1, forward 5\u0026prime;-GACTCTGGTGATGGTGTCAGC-3\u0026prime;, reverse 5\u0026prime;-GGCTGGAAGAGGACCTCAGG-3\u0026prime;; OsSTAR1, forward 5\u0026prime;-TCGCATTGGCTCGCACCCT-3\u0026prime;, reverse 5\u0026prime;-TCGTCTTCTTCAGCCGCACGAT-3\u0026prime;; OsNrat1, forward 5\u0026prime;-GAGGCCGTCTGCAGGAGAGG-3\u0026prime;, reverse 5\u0026prime;-GGAAGTATCTGCAAGCAGCTCTGATGC-3\u0026prime;; OsALS1, forward 5\u0026prime;-GGTCGTCAGTCTCTGCCTTGTC-3\u0026prime;, reverse 5\u0026prime;-CCTCCCCATCATTTTCATTTGT-3\u0026prime;; FRDL4, forward 5\u0026prime;-CGTCATCAGCACCATCCACAG-3\u0026prime;, reverse 5\u0026prime;-TCATTTGCGAAGAAACTTCCACG-3\u0026prime;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eExperiments were repeated three times, and results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE. One-way ANOVA and Tukey\u0026rsquo;s HSD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were performed in SPSS v23. Pearson correlations were calculated to evaluate trait relationships under 0 and 200 \u0026micro;M AlCl₃, and correlation heatmaps were generated using the \u0026ldquo;corrplot\u0026rdquo; package in R 4.4.2. Hierarchical clustering (Ward\u0026rsquo;s linkage) and PCA were conducted in RStudio v2024.12.1 using ggplot2. The binary SSR data matrix was used to calculate a distance matrix, which was then employed to construct a neighbor-joining tree based on Jaccard\u0026rsquo;s coefficient (DARwin v5.0.158).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe impact of aluminium (Al) toxicity on rice root growth and biomass was evaluated by assessing key physiological parameters, including root length, fresh weight, and dry weight at different Al concentrations. Brief exposure to aluminium resulted in rice genotypes displaying distinct and noticeable indication of aluminium toxicity, primarily characterised by inhibited root development. These root-related traits were selected to assess the extent of aluminium-induced damage, as root growth is among the primary target of aluminium toxicity, with the severity of its effects assessed through the inhibition of root growth. The extent of stress, the kind of affected tissue, and the duration of exposure all affect the plant's response to AlCl\u003csup\u003e3+\u003c/sup\u003e stress.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eGrowth and biomass responses to aluminium stress\u003c/h2\u003e\u003cp\u003eRice seedlings exposed to Al stress exhibited clear morphological changes, primarily reflected in root growth inhibition (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Growth and biomass parameters of 53 genotypes\u0026mdash;including root length, relative root length (RRL), shoot length, fresh weight, and dry weight\u0026mdash;were evaluated under differential Al concentrations. RRL consistently declined with increasing Al concentration, with the strongest reduction observed at 200 μM AlCl₃ after 72 h across most genotypes. Notably, Badshabhog maintained relatively higher RRL (154% increase), whereas Ranjit Sub-1 showed minimal improvement (4.5%) relative to control (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). Aluminium stress more severely affected roots than shoots. At 200 μM AlCl₃, root fresh and dry weight were significantly reduced in all genotypes, though the magnitude varied. Highly susceptible genotypes such as Ranjit Sub-1, Jolkonwari, and Mahsuri exhibited marked reductions in fresh (92.64\u0026ndash;98.61%) and dry weight (88.89\u0026ndash;94.30%) relative to controls, whereas resilient genotypes including Ahom Sali, Disang, Kola Bora, and Badshabhog showed minimal reductions in root fresh (8.03\u0026ndash;17.14%) and dry weight (5\u0026ndash;11.76%) (Figs. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Relative root water content (RWC) declined under Al stress, reflecting impaired water retention. Genotypes such as Ahom Sali, Kanaklata, Gitesh, Kola Joha, Joymoti, and Dolkosu maintained RWC comparable to controls, indicating effective maintenance of root turgor. In contrast, Ranjit Sub-1, Jolkonwari, Prashad Bhog, and Kon Joha showed substantial RWC reduction, correlating with decreased root elongation. Ahom Sali exhibited the smallest reduction in RWC (8.03%), denoting high Al tolerance, while Joldhubi and Ranjit Sub-1 displayed moderate (27.24%) and severe (63.51%) reductions, respectively (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, \u003cb\u003eSupplementary Table S3\u003c/b\u003e). A germination assay was conducted for all 53 rice genotypes under 200 μM Al treatment for 72 h to evaluate sensitivity or tolerance at the germination stage. Root growth of tolerant genotypes, including Badshabhog, Ahom Sali, Joymoti, and Disang, was largely unaffected by Al stress. In contrast, genotypes such as Malbhog Bora, Joldhubi, and Bon Gathu exhibited noticeable root growth reduction, with the most severe inhibition observed in the highly susceptible genotypes Ranjit Sub-1, Jolkonwari, and Prashad Bhog (\u003cb\u003eFig.\u003c/b\u003e \u003cb\u003e1b\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEffect of aluminium toxicity on root tolerance index\u003c/h2\u003e\u003cp\u003eThe capacity of genotypes to sustain root growth under Al stress was further quantified using the Root Tolerance Index (RTI), which ranged from 0 to 1.5, with higher values reflecting greater tolerance. Based on RTI at 200 \u0026micro;M Al after 72 h, genotypes were classified as highly tolerant (\u0026ge;\u0026thinsp;1), moderately tolerant (0.5\u0026ndash;1), or susceptible (\u0026le;\u0026thinsp;0.49). Ten genotypes were highly tolerant, 23 moderately tolerant, and 20 sensitive (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eSupplementary Table S4\u003c/b\u003e). Genotypes such as Ahom Sali, Disang, Joymoti, and Badshabhog were identified as tolerant, whereas Ranjit Sub-1, Jolkonwari, Prashad Bhog, and Cheni Lahi were highly susceptible.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation analysis of root traits under aluminium stress\u003c/h2\u003e\u003cp\u003eTo identify the physiological traits most predictive of seedling aluminium tolerance, correlations among key root and shoot attributes under Al stress were examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Root length (R.L.) and root tolerance index (R.T.I.) exhibited a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.99), indicating that longer roots are associated with greater stress tolerance. Relative root reduction (R.R.R%) was strongly negatively correlated with R.L. (r = \u0026minus;\u0026thinsp;0.86) and R.T.I. (r = \u0026minus;\u0026thinsp;0.96), reflecting the impact of stress-induced root growth inhibition on overall tolerance. Conversely, relative root elongation (R.R.E%) correlated positively with R.L. (r\u0026thinsp;=\u0026thinsp;0.86) and R.T.I. (r\u0026thinsp;=\u0026thinsp;0.96), highlighting its contribution to stress adaptation. Shoot tolerance index (S.T.I.) showed moderate positive correlations with R.L. (r\u0026thinsp;=\u0026thinsp;0.60), R.T.I. (r\u0026thinsp;=\u0026thinsp;0.58), and R.R.E% (r\u0026thinsp;=\u0026thinsp;0.58), indicating that shoot performance is influenced by root adaptability. Root fresh weight (R.F.W.) and dry weight (R.D.W.) were strongly correlated (r\u0026thinsp;=\u0026thinsp;0.94) and positively associated with root length and tolerance, underscoring the importance of biomass accumulation. Relative water content (R.W.C.) exhibited a moderate positive correlation with R.F.W. (r\u0026thinsp;=\u0026thinsp;0.78), reflecting its role in maintaining root turgor under aluminium stress.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGenotypic Classification and Multivariate Analysis under Aluminium Stress\u003c/h2\u003e\u003cp\u003eHierarchical clustering analysis (HCA) of nine morpho-physiological traits across 53 rice genotypes, using Ward\u0026rsquo;s linkage and Euclidean distance, grouped the genotypes into five principal clusters, reflecting diverse responses to aluminium stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Cluster I (genotypes 1\u0026ndash;10) comprised highly tolerant genotypes, characterized by superior root traits (root length, RTI, relative root elongation) and enhanced biomass retention, indicative of robust mechanisms mitigating Al-induced growth inhibition. Cluster II (genotypes 11\u0026ndash;20) included moderately tolerant genotypes, exhibiting intermediate root and shoot growth with maintained relative water content, suggesting partial tolerance. Cluster III (genotypes 21\u0026ndash;33) represented intermediate performers, showing balanced traits between tolerance and susceptibility. Cluster IV (genotypes 34\u0026ndash;42, 53) encompassed moderately susceptible genotypes, displaying reduced root biomass and tolerance indices but some compensatory shoot growth. Cluster V (genotypes 20, 22, 25\u0026ndash;29, 31, 34, 43) contained sensitive genotypes, distinguished by substantial root loss, reduced RWC, and pronounced Al susceptibility.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was performed to further dissect the traits contributing to phenotypic variability under Al stress. The first two components accounted for 83.9% of total variation, with PC1 explaining 62.9% through root-related traits and PC2 explaining 21% via shoot characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). On PC1, root dry weight (RDW), root fresh weight (RFW), relative water content (RWC), and relative root elongation (RRE) clustered together, showing strong positive associations and an inverse relationship with relative root reduction (RRR). On PC2, shoot length (SL) and shoot tolerance index (STI) were strongly correlated, reflecting the independent contribution of shoot vigour to stress adaptation. Genotypes 1\u0026ndash;14 exhibited enhanced root development and water retention, indicating higher Al tolerance, whereas genotypes 46\u0026ndash;53 aligned with RRR, reflecting increased susceptibility. Genotypes in the lower quadrant (20\u0026ndash;28) were associated with SL and STI, suggesting moderate tolerance through shoot adaptation. These PCA results corroborated the hierarchical clustering outcomes, emphasizing that root traits are primary determinants of aluminium tolerance, while shoot vigour provides secondary support.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGenetic Diversity and Population Structure of Rice Genotypes Based on SSR Markers\u003c/h2\u003e\u003cp\u003eOf the 56 SSR markers screened, 33 were polymorphic, revealing high allelic diversity among the 53 rice genotypes, with 103 alleles detected and an average of 3.15 alleles per locus. Polymorphism information content (PIC) values ranged from 0.14 to 0.79, with a mean of 0.46, while expected heterozygosity (He) varied from 0.15 to 0.81, averaging 0.46, indicating substantial genetic variability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Shannon\u0026rsquo;s information index (I) ranged from 0.29 to 1.71, further supporting the observed diversity.\u003c/p\u003e\u003cp\u003eDistance-based cluster analysis using a neighbor-joining (N-J) tree, constructed from the 33 polymorphic SSR markers, grouped the 53 rice genotypes into three major clusters with high bootstrap support (\u0026gt;\u0026thinsp;50%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The N-J tree captured the genetic relationships among genotypes and reflected their population structure in relation to aluminium tolerance. Clusters were color-coded based on Al-toxicity response: green for highly tolerant, black for moderately tolerant, and red for susceptible genotypes. Comparison of genotypic clustering with phenotypic Root Tolerance Index (RTI) data and morphological clustering revealed that most highly tolerant genotypes grouped in Cluster I, while moderately tolerant genotypes primarily occupied Cluster III. Genotypes such as Gudumoni, Bokul Bora, Ghew Bora, Poita Bora, and Bor Chakua were accommodated in Cluster II, exhibiting mixed tolerance profiles. Cluster I included five tolerant, one susceptible, and one moderately tolerant genotype, whereas Cluster III contained susceptible genotypes like Sial Khati and Dipholu alongside moderately tolerant accessions. Jaccard\u0026rsquo;s similarity matrix indicated the highest genetic similarity (0.91) between Boka Chakua and Jolkonwari, and the greatest dissimilarity (0.38) between Kalamdini and Kanaklata, confirming substantial genetic variation within the panel.\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\u003eDiversity statistics using SSR marker attributes of Observed number of alleles (N\u003csub\u003eA\u003c/sub\u003e), Expected no of alleles (N\u003csub\u003eE\u003c/sub\u003e) polymorphic information content (PIC), Shannon Information Index (I), and Expected heterozygosity (H\u003csub\u003eE\u003c/sub\u003e) for 33 polymorphic loci studied in 53 rice genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocus Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ena\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM17\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRM26\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\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\u003e3.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM138\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM524\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM174\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM203\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM209\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM237\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM259\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM261\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.47↻\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e12.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM262\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e13.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM341\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM430\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM475\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e16.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM482\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e17.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM526\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e18.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM556\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e19.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM573\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e20.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM3602\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e21.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM3825\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e22.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM6327\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e23.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM496\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e24.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM149\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e25.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM242\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e26.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM5442\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e27.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM180\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e28.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM205\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e29.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM214\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e30.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM247\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e31.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM257\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e32.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM271\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e33.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eRM490\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.00\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSTDEV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003ePhysiological and Biochemical Responses of Rice Genotypes under Aluminium Stress\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eChlorophyll degradation was more pronounced under Al stress, particularly in susceptible genotypes. Tolerant genotypes (Badshabhog, Ahom Sali) exhibited 38\u0026ndash;53% reductions in chlorophyll a, 28\u0026ndash;55% in chlorophyll b, and 8\u0026ndash;36% in carotenoids relative to control, whereas Jolkonwari and Ranjit Sub-1 displayed 78\u0026ndash;79% loss of chlorophyll a, 89\u0026ndash;94% of chlorophyll b, and 64\u0026ndash;78% of carotenoids (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAluminium accumulation in roots mirrored these trends, with tolerant genotypes accumulating significantly lower Al than moderately tolerant or susceptible genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), highlighting the role of Al exclusion or sequestration in stress tolerance.Al stress also induced oxidative stress in all genotypes. H₂O₂ and malondialdehyde (MDA) levels increased markedly in susceptible genotypes (Jolkonwari, Ranjit Sub-1), while tolerant genotypes (Badshabhog, Ahom Sali) exhibited lower ROS accumulation, reflecting enhanced antioxidant defense (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Proline levels rose across all genotypes, with the highest induction in tolerant cultivars (Badshabhog\u0026thinsp;+\u0026thinsp;136.35%, Ahom Sali\u0026thinsp;+\u0026thinsp;129.54%), indicating an adaptive osmoprotective response (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Antioxidant enzyme activities were also differentially modulated: SOD, POD, CAT, and APX activities increased significantly in tolerant genotypes, with Badshabhog showing the greatest induction (SOD- 67.6%, POD- 40%, CAT- 23.59%, APX- 88%), whereas susceptible genotypes especially Ranjit Sub-1 displayed lower or highest reduction in enzyme activities (SOD- 90%, POD- 42.7%, APX- 109.45%, CAT- 25.91%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef). These results highlight the coordinated role of organic acid exudation, ROS detoxification, proline accumulation, and enzymatic defense in conferring Al tolerance. Organic acid-mediated detoxification was a key tolerance mechanism. Under 200 \u0026micro;M Al, Ahom Sali showed the highest accumulation of citrate (19.06 \u0026micro;mol/g F.W.), malate (6.31 \u0026micro;mol/g F.W.), and oxalate (3.41 \u0026micro;mol/g F.W.), indicating effective chelation of toxic Al\u0026sup3;⁺ ions. Joldhubi exhibited moderate increases, while Ranjit Sub-1 showed comparatively low organic acid levels, highlighting the importance of root exudation in Al detoxification (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNutrient analysis revealed significant genotypic variation in the uptake of essential macro- and micronutrients under Al stress. Tolerant genotypes maintained relatively higher levels of key nutrients, whereas susceptible genotypes exhibited reduced accumulation, particularly of phosphorus (P), calcium (Ca), and magnesium (Mg), consistent with Al-induced disruption of root ion transport and membrane integrity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Potassium (K) and micronutrients such as iron (Fe) and manganese (Mn) showed variable responses, with some tolerant genotypes maintaining uptake or displaying increased accumulation, possibly as a compensatory mechanism against oxidative stress. Collectively, these results indicate that root growth, biomass retention, water status, chlorophyll stability, and nutrient homeostasis are closely associated with Al tolerance in rice, with early germination performance providing a predictive indicator of subsequent seedling resilience.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTranscriptional Responses of Candidate Genes Associated with Aluminium Tolerance\u003c/h2\u003e\u003cp\u003eThe highly tolerant Al-resistant genotype Ahom Sali, moderately tolerant Joldhubi, and significantly susceptible Al-sensitive genotype Ranjit Sub-1 was further identified for screening the molecular transcriptional responses underlying the differences in Al-resistant and Al-sensitive responses. The change in expression of selected genes in the root system under 200 \u0026micro;M aluminium (Al) stress after a 72-h interval was analysed by quantitative real-time qRT-PCR. To investigate the impact of aluminium stress on various genotypes, we assessed the expression of candidate genes for Al tolerance (\u003cem\u003eOsNrat1, OsALS1, OsSTAR1, OsSTAR2\u003c/em\u003e, and \u003cem\u003eOsFRDL4\u003c/em\u003e) to elucidate their physiological and molecular responses under Al stress conditions. Distinct expression patterns were observed for these candidate genes among the three genotypes.In both varieties Al enhanced the expression of \u003cem\u003eNrat1\u003c/em\u003e. However, upregulation was highest (3-fold increase) in Ranjit Sub-1 compared to Ahom Sali and Joldhubi (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed, Al notably diminished the expression of \u003cem\u003eALS1\u003c/em\u003e in roots of the tolerant variety Ahom Sali, while its expression increased in the susceptible variety Ranjit Sub-1. The expression of \u003cem\u003eOsSTAR1\u003c/em\u003e and \u003cem\u003eOsSTAR2\u003c/em\u003e was significantly upregulated in the Al-tolerant rice genotype Ahom Sali and moderately tolerant genotype Joldhubi, but downregulated in the Al-sensitive genotype Ranjit Sub-1 under Al stress (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). These findings further confirm that tolerant varieties reduce Al toxicity by modifying cell wall components and reducing Al accumulation through enhanced expression of \u003cem\u003eOsSTAR1\u003c/em\u003e and \u003cem\u003eOsSTAR2\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Analysis of Morphological, Physiological, and Biochemical Traits\u003c/h2\u003e\u003cp\u003ePearson\u0026rsquo;s correlation analysis revealed significant associations among morphological, physiological, and biochemical traits under aluminium (Al) stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Al content showed strong negative correlations with root length, fresh weight, dry weight, tolerance indices, chlorophyll pigments, antioxidant enzymes (SOD, CAT, APX), and organic acids (oxalate, malate, citrate). In contrast, root biomass traits, antioxidant enzyme activities, and stress-associated metabolites such as proline and organic acids were positively correlated in tolerant genotypes. Antioxidant activity exhibited positive associations with chlorophyll content and negative associations with oxidative stress indicators (H₂O₂ and MDA). Proline showed positive correlations with antioxidant defense and chlorophyll stability. These results highlight root biomass, antioxidant enzymes, proline, and organic acids as key contributors to Al tolerance, while high Al accumulation, increased MDA and H₂O₂, and reduced root growth were indicators of Al sensitivity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSoil acidity and aluminium (Al) toxicity are major constraints to rice cultivation in acid soil regions worldwide. In neutral soils, Al exists in non-toxic oxide or silicate forms, but under acidic conditions (pH\u0026thinsp;\u0026lt;\u0026thinsp;5), it solubilizes into the phytotoxic Al\u0026sup3;⁺ ion, rapidly inhibiting root elongation and altering root morphology (Ofoe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shetty \u0026amp; Prakash \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Root growth suppression is the earliest and most sensitive symptom of Al toxicity and has been widely used as a screening criterion for tolerance (Awasthi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, 53 rice genotypes from Northeast India were evaluated under hydroponic conditions to dissect their responses to Al stress. At 200 \u0026micro;M Al, identified as the threshold concentration, root growth was markedly inhibited, consistent with earlier findings (Yang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bhattacharjee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Root growth reduction was accompanied by decreases in biomass, root tolerance index (RTI), and relative water content. Susceptible genotypes such as Ranjit Sub-1, Jolkonwari, and Prashad Bhog exhibited severe inhibition, whereas tolerant genotypes such as Ahom Sali, Disang, and Kola Bora maintained high RTI values. Occasionally, tolerant genotypes displayed paradoxical growth stimulation under cytotoxic concentrations, corroborating reports by Rout \u0026amp; Das (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Principal component analysis confirmed that root-related parameters contributed most strongly to Al tolerance, while shoot traits played a secondary, compensatory role (Ma et al. 2014; Wijayanto 2021).\u003c/p\u003e\u003cp\u003eCluster analysis grouped the genotypes into five tolerance classes, ranging from highly tolerant to susceptible, with PCA results congruent to clustering. Highly tolerant genotypes clustered together, characterized by robust root traits and biomass, whereas susceptible groups were defined by pronounced root reduction and poor water retention. Such integrated morpho-physiological characterization provides a robust framework for identifying elite donors for breeding programs.\u003c/p\u003e\u003cp\u003eMolecular profiling with 55 SSR markers revealed moderate allelic diversity, with 33 markers polymorphic and 103 alleles detected. The mean PIC value of 0.48 is comparable to previous reports in Indian rice germplasm (Das et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pradhan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, molecular clustering poorly aligned with phenotypic grouping, with tolerant and susceptible genotypes often clustered together. This inconsistency likely reflects the use of general rice SSRs rather than stress-specific markers (Verma et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saha et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar discrepancies between phenotypic and molecular classification have been reported in other crops (Sun et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings emphasize the need for high-density, Al-linked molecular markers to complement phenotypic screening.\u003c/p\u003e\u003cp\u003eAt the physiological and biochemical level, Al accumulation disrupted nutrient uptake, decreased biomass, and induced oxidative stress, as also noted by Phukunkamkaew et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e. At the cellular level, Al binds to negatively charged phospholipid bilayers, disrupting H⁺-ATPase function and nutrient transport (Zhang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Sensitive genotypes exhibited reduced chlorophyll and carotenoids, coupled with impaired photosynthetic function, consistent with reports of Al-induced membrane lipid peroxidation and ion imbalance (Ofoe et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Elevated Al content triggered ROS accumulation, lipid peroxidation, and mitochondrial dysfunction (Ribeiro et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with MDA accumulation confirming membrane damage. Reduced root hydraulic conductivity and ABA-mediated stomatal closure further impaired water uptake (Gavassi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAntioxidant defense systems were more strongly induced in tolerant genotypes, aligning with studies linking enhanced SOD, CAT, and APX activities to stress resilience (Fiza et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lum et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, proline accumulation was higher in tolerant genotypes, highlighting its role as an osmoprotectant and ROS scavenger. Organic acid exudation, particularly citrate secretion, played a central role in external detoxification, consistent with the function of \u003cem\u003eOsFRDL4\u003c/em\u003e (Yokosho et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Ahom Sali exhibited high citrate and malate secretion, conferring effective Al exclusion, while susceptible genotypes secreted significantly less.\u003c/p\u003e\u003cp\u003eAt the molecular level, tolerance was associated with upregulation of \u003cem\u003eOsSTAR1\u003c/em\u003e and \u003cem\u003eOsSTAR2\u003c/em\u003e, which modify root cell walls to reduce Al binding (Huang et al. 2011; Zhu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Ahom Sali and Joldhubi upregulated both genes, whereas Ranjit Sub-1 showed downregulation, highlighting their importance in exclusion mechanisms. In contrast, \u003cem\u003eOsNRAT1\u003c/em\u003e, which mediates Al\u0026sup3;⁺ influx, was highly expressed in Ranjit Sub-1, consistent with elevated Al accumulation and sensitivity (Li et al. 2013; Rosell\u0026oacute; et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Although \u003cem\u003eOsALS1\u003c/em\u003e sequesters Al into vacuoles, its induction in Ranjit Sub-1 was insufficient to counteract high Al uptake. These findings underscore that internal detoxification alone is less effective compared to combined exclusion strategies.\u003c/p\u003e\u003cp\u003eOverall, our results highlight a dual mechanism of Al tolerance in rice. Tolerant genotypes primarily employ exclusion strategies, including cell wall modification and organic acid exudation, supported by strong antioxidant responses. Sensitive genotypes rely more on internal detoxification via \u003cem\u003eOsNRAT1\u0026ndash; OsALS1\u003c/em\u003e pathways, which appears less efficient under high Al stress. The divergence in molecular and physiological strategies explains the observed variability in tolerance among Northeast Indian landraces.\u003c/p\u003e\u003cp\u003eThis integrated morpho-physiological, biochemical, and molecular framework provides valuable insights into Al tolerance mechanisms. By combining phenotypic screening with stress-specific molecular markers, it will be possible to identify and exploit elite tolerant donors such as Ahom Sali and Disang for breeding programs. Such approaches will be critical for developing rice cultivars resilient to acid soils and sustaining productivity in Al-affected regions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study revealed clear genotype-specific differences in aluminium tolerance among 53 rice landraces from Northeast India. Tolerant genotypes such as \u003cem\u003eAhom Sali\u003c/em\u003e, \u003cem\u003eDisang\u003c/em\u003e, and \u003cem\u003eKola Bora\u003c/em\u003e sustained root vigor and physiological stability under 200 \u0026micro;M Al\u0026sup3;⁺, while sensitive genotypes like \u003cem\u003eRanjit Sub-1\u003c/em\u003e, \u003cem\u003eJolkonwari\u003c/em\u003e, and \u003cem\u003eCheni Lahi\u003c/em\u003e showed severe inhibition and oxidative damage. Tolerance mechanisms in these indigenous varieties was associated with enhanced expression of exclusion-related genes (\u003cem\u003eOsSTAR1/2, OsFRDL4\u003c/em\u003e) and suppression of \u003cem\u003eOsNRAT1\u003c/em\u003e, while sensitive genotypes relied on less effective internal detoxification via \u003cem\u003eOsNRAT1\u003c/em\u003e and \u003cem\u003eOsALS1.\u003c/em\u003e The findings indicate that indigenous cultivars not only have strong mechanisms to mitigate Al toxicity but may also have supplementary genes that improve stress resilience.\u003c/p\u003e\u003cp\u003eMoreover, morphological clustering aligned with tolerance classes, but SSR markers showed weak correlation, highlighting the genetic complexity of Al tolerance. Overall, the findings underscore exclusion mechanisms as central to Al resistance and identify Ahom Sali and Disang as promising donor lines for breeding rice suited to acid soils. Therefore, conservation and detailed genetic exploration of these landraces are essential for breeding stress-tolerant rice varieties and ensuring sustainable agriculture in acid soil regions. The exhibited tolerance mechanisms and the potential for unrecognised genetic resources render the conservation and systematic characterisation of Northeast India\u0026rsquo;s indigenous rice varieties. These landraces constitute a vital gene pool for abiotic stress resistance and promote sustainable and resilient agricultural methods in the face of shifting climatic conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors certify that they have no competing interests to declare which are relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study received funding from student and research facility of Assam Agricultural University, Jorhat, Assam.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eMB, DS, SSB: Conceptualization; DS: Investigation, Data collection, Visualization and Manuscript writing; MB: Funding acquisition and Supervision; SSB: Data curation and Methodology; RK: SSR marker analysis; PCD: Plant material acquisition; IS: Formal statistical analysis; MB: Writing-review, editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their gratitude to Dr. Bidyut Kumar Sarmah, Director, DBT-NECAB and HoD, Department of Agricultural University, Assam Agricultural University, Jorhat, Assam.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript and supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbeysekara I, Rathnayake I (2024) Global Trends in Rice Production, Consumption and Trade. SSRN J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2139/ssrn.4948477\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.4948477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAebi H (1984) Catalase in vitro. Methods Enzymol 105:121\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0076-6879(84)05016-3\u003c/span\u003e\u003cspan address=\"10.1016/S0076-6879(84)05016-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdzigbe J, Frimpong F, Danquah A, Danquah EY, Asante IK, Abebrese SO, Dormatey R, Afriyie-Debrah C, Ribeiro PF, Owusu Danquah E, Agyeman K, Bam RK, Asante MD (2025) The responses and adaptations of rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) to drought stress: A review. Clim Smart Agric 2(4):100080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.csag.2025.100080\u003c/span\u003e\u003cspan address=\"10.1016/j.csag.2025.100080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAwasthi JP, Saha B, Regon P, Sahoo S, Chowra U, Pradhan A, Roy A, Panda SK (2017) Morpho-physiological analysis of tolerance to aluminum toxicity in rice varieties of North East India. PLoS ONE 12(4):e0176357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0176357\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0176357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBates LS, Waldren RP, Teare ID (1973) Rapid determination of free proline for water-stress studies. Plant Soil 39(1):205\u0026ndash;207. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF00018060\u003c/span\u003e\u003cspan address=\"10.1007/BF00018060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBera S, De AK, Adak MK (2019) Modulation of Glycine Betaine Accumulation with Oxidative Stress Induced by Aluminium Toxicity in Rice. Proc Natl Acad Sci India Sect B Biol Sci 89(1):291\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40011-017-0948-7\u003c/span\u003e\u003cspan address=\"10.1007/s40011-017-0948-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhattacharjee B, Ali A, Tuteja N, Gill S, Pattanayak A (2023) Identification and expression pattern of aluminium-responsive genes in roots of rice genotype with reference to Al-sensitivity. Sci Rep 13(1):12184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-39238-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-39238-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoj\u0026oacute;rquez-Quintal E, Escalante-Maga\u0026ntilde;a C, Echevarr\u0026iacute;a-Machado I, Mart\u0026iacute;nez-Est\u0026eacute;vez M (2017) Aluminum, a Friend or Foe of Higher Plants in Acid Soils. Front Plant Sci 8:1767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01767\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBotstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32(3):314\u0026ndash;331\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen L, Lin Q, Nose A (2002) A comparative study on diurnal changes in metabolite levels in the leaves of three crassulacean acid metabolism (CAM) species, Ananas comosus, Kalancho\u0026euml; daigremontiana and K. pinnata. J Exp Bot 53(367):341\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jexbot/53.367.341\u003c/span\u003e\u003cspan address=\"10.1093/jexbot/53.367.341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoudhury B, Khan ML, Dayanandan S (2013) Genetic structure and diversity of indigenous rice (\u003cem\u003eOryza sativa)\u003c/em\u003e varieties in the Eastern Himalayan region of Northeast India. SpringerPlus 2(1):228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2193-1801-2-228\u003c/span\u003e\u003cspan address=\"10.1186/2193-1801-2-228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChowra U, Yanase E, Koyama H, Panda SK (2017) Aluminium-induced excessive ROS causes cellular damage and metabolic shifts in black gram \u003cem\u003eVigna mungo\u003c/em\u003e (L.) Hepper. Protoplasma 254(1):293\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00709-016-0943-5\u003c/span\u003e\u003cspan address=\"10.1007/s00709-016-0943-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas B, Sengupta S, Parida SK, Roy B, Ghosh M, Prasad M, Ghose TK (2013) Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genet 14(1):71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2156-14-71\u003c/span\u003e\u003cspan address=\"10.1186/1471-2156-14-71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas J, Rangappa K, Kalita MC, Thakuria D (2020) Endophyte Bacteria Alter Physiological Traits and Promote Growth of Rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) in Aluminium Toxic and Phosphorus Deficient Acid Inceptisols. J Pure Appl Microbiol 14(1):627\u0026ndash;639. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22207/JPAM.14.1.65\u003c/span\u003e\u003cspan address=\"10.22207/JPAM.14.1.65\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevi NT, Devi AS, Singh KR (2025) Impact of chemical fertilizer and composts application on growth and yield of rice in Northeast India. Sci Rep 15(1):8575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-93684-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-93684-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFernandes T, Melo F, Vieira MB, Louren\u0026ccedil;o TF, Pucciariello C, Saibo NJM, Abreu IA, Oliveira MM (2022) Screening for Abiotic Stress Response in Rice. In: Duque P, Szakonyi D (eds) Environmental Responses in Plants. Springer US, New York, NY, pp 161\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-0716-2297-1_12\u003c/span\u003e\u003cspan address=\"10.1007/978-1-0716-2297-1_12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiza A, Chugh V, Mishra AC, Mishra V, Purwar S, Dwivedi SV, Mishra A, Nagar S, Kharor N, Kumar V (2025) Physiological and biochemical adaptations for salt stress tolerance in eggplant (\u003cem\u003eSolanum melongena\u003c/em\u003e). Plant Physiol Rep 30(2):352\u0026ndash;368. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40502-025-00863-2\u003c/span\u003e\u003cspan address=\"10.1007/s40502-025-00863-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGavassi MA, Dodd IC, Pu\u0026eacute;rtolas J, Silva GS, Carvalho RF, Habermann G (2020) Aluminum-induced stomatal closure is related to low root hydraulic conductance and high ABA accumulation. Environ Exp Bot 179:104233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envexpbot.2020.104233\u003c/span\u003e\u003cspan address=\"10.1016/j.envexpbot.2020.104233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiannopolitis CN, Ries SK (1977) Superoxide Dismutases: II. Purification and Quantitative Relationship with Water-soluble Protein in Seedlings. Plant Physiol 59(2):315\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.59.2.315\u003c/span\u003e\u003cspan address=\"10.1104/pp.59.2.315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGogoi N, Pathak B, Rehman R, Upadhyaya S, Mahanta P, Borah A, Baishya KJ, Bhuyan K (2025) Rice yield responses to climate variability in Northeast India using machine learning approach. Theor Appl Climatol 156(4):233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-025-05452-9\u003c/span\u003e\u003cspan address=\"10.1007/s00704-025-05452-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHajiboland R, Panda CK, Lastochkina O, Gavassi MA, Habermann G, Pereira JF (2023) Aluminum Toxicity in Plants: Present and Future. J Plant Growth Regul 42(7):3967\u0026ndash;3999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00344-022-10866-0\u003c/span\u003e\u003cspan address=\"10.1007/s00344-022-10866-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayes KL, Mui J, Song B, Sani ES, Eisenman SW, Sheffield JB, Kim B (2020) Effects, uptake, and translocation of aluminum oxide nanoparticles in lettuce: A comparison study to phytotoxic aluminum ions. Sci Total Environ 719:137393. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2020.137393\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2020.137393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeath RL, Packer L (1968) Photoperoxidation in isolated chloroplasts. Arch Biochem Biophys 125(1):189\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0003-9861(68)90654-1\u003c/span\u003e\u003cspan address=\"10.1016/0003-9861(68)90654-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang C, Yamaji N, Chen Z, Ma JF (2012) A tonoplast-localized half‐size ABC transporter is required for internal detoxification of aluminum in rice. Plant J 69(5):857\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-313X.2011.04837.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2011.04837.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang CF, Yamaji N, Mitani N, Yano M, Nagamura Y, Ma JF (2009) A Bacterial-Type ABC Transporter Is Involved in Aluminum Tolerance in Rice. Plant Cell 21(2):655\u0026ndash;667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1105/tpc.108.064543\u003c/span\u003e\u003cspan address=\"10.1105/tpc.108.064543\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIrsyadi MB, Sari SK, Oktiastuti E, Rineksane IA (2024) Rapid genomic DNA extraction for Soybean (Glycine max L. Merr) using modified CTAB protocol to obtain high-quality DNA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56042/ijbb.v61i2.7603\u003c/span\u003e\u003cspan address=\"10.56042/ijbb.v61i2.7603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. IJBB\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaiswal S, Kumar A, Choudhury BU, Thangam R, Lal MK, Shettigar N, Kumar R, Verma H, Bhattacharjee B, Mishra VK (2022) Development of ammonium nitrate free nutrient media for aluminium toxicity tolerance screening of rice genotypes from North-Eastern India. J Plant Nutr 46(8):1766\u0026ndash;1776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01904167.2022.2099892\u003c/span\u003e\u003cspan address=\"10.1080/01904167.2022.2099892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaskowiak J, Tkaczyk O, Slota M, Kwasniewska J, Szarejko I (2018) Analysis of aluminum toxicity in Hordeum vulgare roots with an emphasis on DNA integrity and cell cycle. PLoS ONE 13(2):e0193156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0193156\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0193156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKikui S, Sasaki T, Maekawa M, Miyao A, Hirochika H, Matsumoto H, Yamamoto Y (2005) Physiological and genetic analyses of aluminium tolerance in rice, focusing on root growth during germination. J Inorg Biochem 99(9):1837\u0026ndash;1844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jinorgbio.2005.06.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jinorgbio.2005.06.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKocjan A, Kwasniewska J, Szurman-Zubrzycka M (2025) Understanding plant tolerance to aluminum: exploring mechanisms and perspectives. Plant Soil 507(1\u0026ndash;2):195\u0026ndash;219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-024-06745-0\u003c/span\u003e\u003cspan address=\"10.1007/s11104-024-06745-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrstic D, Djalovic I, Nikezic D, Bjelic D (2012) Aluminium in Acid Soils: Chemistry, Toxicity and Impact on Maize Plants. In: Aladjadjiyan A (ed) Food Production - Approaches, Challenges and Tasks. InTech, pp 231\u0026ndash;242\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaenoi S, Phattarakul N, Jamjod S, Yimyam N, Dell B, Rerkasem B (2015) Genotypic variation in adaptation to soil acidity in local upland rice varieties. Plant Genet Resour 13(3):206\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1479262114000896\u003c/span\u003e\u003cspan address=\"10.1017/S1479262114000896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandi S, Hausman J-F, Guerriero G, Esposito S (2017) Poaceae vs. Abiotic Stress: Focus on Drought and Salt Stress, Recent Insights and Perspectives. Front Plant Sci 8:1214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01214\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J-Y, Liu J, Dong D, Jia X, McCouch SR, Kochian LV (2014) Natural variation underlies alterations in Nramp aluminum transporter (\u003cem\u003eNRAT1\u003c/em\u003e) expression and function that play a key role in rice aluminum tolerance. Proc Natl Acad Sci USA 111(17):6503\u0026ndash;6508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1318975111\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1318975111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H-I, Yu Y-Y, Wen F-I, Liu P-T (2022) Status of Food Security in East and Southeast Asia and Challenges of Climate Change. Climate 10(3):40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cli10030040\u003c/span\u003e\u003cspan address=\"10.3390/cli10030040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLum M, Hanafi M, Rafii Y, Akmar A (2014) Effect of drought stress on growth, proline and antioxidant enzyme activities of upland rice. JAPS: J Anim Plant Sci 24(5)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa JF, Chen ZC, Shen RF (2014b) Molecular mechanisms of Al tolerance in gramineous plants. Plant Soil 381(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-014-2073-1\u003c/span\u003e\u003cspan address=\"10.1007/s11104-014-2073-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMainkar P, Jayaswal D, Kumar D, Jayaswall K, Jaiswal S, Singh AN, Kumar S, Kansal R (2023) Development of modified CTAB and Trizol protocols to isolate high molecular weight (HMW) RNA from polyphenol and polysaccharides rich pigeonpea (\u003cem\u003eCajanus cajan\u003c/em\u003e (L.) Millsp. PLoS ONE 18(12):e0291949. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0291949\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0291949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMajumdar S, Wanniang SK, Behera U (2022) Acid soil management in north eastern hill region of India Authors. Indian Farming 72(3)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartins N, Gon\u0026ccedil;alves S, Romano A (2013) Aluminum inhibits root growth and induces hydrogen peroxide accumulation in Plantago algarbiensis and P. almogravensis seedlings. Protoplasma 250(6):1295\u0026ndash;1302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00709-013-0511-1\u003c/span\u003e\u003cspan address=\"10.1007/s00709-013-0511-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMishra P (2025) Agri Business India climbs the Great Wall to emerge as world\u0026rsquo;s top rice produce\u003cem\u003er\u003c/em\u003e. Business Line. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thehindubusinessline.com/economy/agri-business/india-climbs-the-great-wall-to-emerge-as-worlds-top-rice-producer/article69589877.ece#\u003c/span\u003e\u003cspan address=\"https://www.thehindubusinessline.com/economy/agri-business/india-climbs-the-great-wall-to-emerge-as-worlds-top-rice-producer/article69589877.ece#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 30th May 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNakano Y, Asada K (1981) Hydrogen Peroxide is Scavenged by Ascorbate-specific Peroxidase in Spinach Chloroplasts. Plant Cell Physiol 22(5):867\u0026ndash;880. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oxfordjournals.pcp.a076232\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.pcp.a076232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOfoe R, Thomas RH, Asiedu SK, Wang-Pruski G, Fofana B, Abbey L (2023) Aluminum in plant: Benefits, toxicity and tolerance mechanisms. Front Plant Sci 13:1085998. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.1085998\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.1085998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhukunkamkaew S, Tisarum R, Pipatsitee P, Samphumphuang T, Maksup S, Cha-um S (2021) Morpho-physiological responses of indica rice (\u003cem\u003eOryza sativa\u003c/em\u003e sub. indica) to aluminum toxicity at seedling stage. Environ Sci Pollut Res 28(23):29321\u0026ndash;29331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-12804-1\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-12804-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePradhan SK, Barik SR, Sahoo A, Mohapatra S, Nayak DK, Mahender A, Meher J, Anandan A, Pandit E (2016) Population Structure, Genetic Diversity and Molecular Marker-Trait Association Analysis for High Temperature Stress Tolerance in Rice. PLoS ONE 11(8):e0160027. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0160027\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0160027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamesh P, Mallikarjuna G, Sameena S, Kumar A, Gurulakshmi K, Reddy BV, Reddy PCO, Sekhar AC (2020) Advancements in molecular marker technologies and their applications in diversity studies. J Biosci 45(1):123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12038-020-00089-4\u003c/span\u003e\u003cspan address=\"10.1007/s12038-020-00089-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRavikiran KT, Krishnamurthy SL, Warraich AS, Sharma PC (2018) Diversity and haplotypes of rice genotypes for seedling stage salinity tolerance analyzed through morpho-physiological and SSR markers. Field Crops Res 220:10\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fcr.2017.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2017.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibeiro C, De Marcos Lapaz A, De Freitas-Silva L, Ribeiro KVG, Yoshida CHP, Dal-Bianco M, Cambraia J (2022) Aluminum promotes changes in rice root structure and ascorbate and glutathione metabolism. Physiol Mol Biol Plants 28(11\u0026ndash;12):2085\u0026ndash;2098. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12298-022-01262-9\u003c/span\u003e\u003cspan address=\"10.1007/s12298-022-01262-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosell\u0026oacute; M, Poschenrieder C, Guns\u0026eacute; B, Barcel\u0026oacute; J, Llugany M (2015) Differential activation of genes related to aluminium tolerance in two contrasting rice cultivars. J Inorg Biochem 152:160\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jinorgbio.2015.08.021\u003c/span\u003e\u003cspan address=\"10.1016/j.jinorgbio.2015.08.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRout GR, Das P (2002) Rapid hydroponic screening for molybdenum tolerance in rice through morphological and biochemical analysis. Plant Soil Environ 48(11):505\u0026ndash;512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17221/4404-PSE\u003c/span\u003e\u003cspan address=\"10.17221/4404-PSE\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaha D, Mishra UN, Sahoo C, Tripathy S, Behera UK, Das S, Sahu C, Datt S, Rout MK, Mohanty TL, Mohanty SP, Barik SR, Mohanty IC, Pradhan SK (2024) Donor Identification, Genetic Diversity, Population Structure and Marker\u0026ndash;Trait Association Analyses for Iron Toxicity Tolerance Using Rice Landraces. Diversity 17(1):33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d17010033\u003c/span\u003e\u003cspan address=\"10.3390/d17010033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShetty R, Prakash NB (2020) Effect of different biochars on acid soil and growth parameters of rice plants under aluminium toxicity. Sci Rep 10(1):12249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-69262-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-69262-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh KM, Ahmad N, Pandey VV, Kumari T, Ritambhara S (2021) Growth Performance and Profitability of Rice Production in India: An Assertive Analysis. Economic Affairs 66(3):481\u0026ndash;486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.46852/0424-2513.3.2021.18\u003c/span\u003e\u003cspan address=\"10.46852/0424-2513.3.2021.18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh NU, Roy A, Sharma PM, Yumnam A, Paul P, Singh B (2025) Addressing Soil Acidity for Agricultural Growth in Northeast India. Vigyan Varta 6(2):204\u0026ndash;206\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun X, Xie Y, Bi Y, Liu J, Amombo E, Hu T, Fu J (2015) Comparative study of diversity based on heat tolerant-related morpho-physiological traits and molecular markers in tall fescue accessions. Sci Rep 5(1):18213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep18213\u003c/span\u003e\u003cspan address=\"10.1038/srep18213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabassum R, Tahjib-Ul-Arif Md H, Md, Sohag AAM, Islam MS, Shafi SMSH, Islam MM, Hassan L (2021) Screening salt-tolerant rice at the seedling and reproductive stages: An effective and reliable approach. Environ Exp Bot 192:104629. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envexpbot.2021.104629\u003c/span\u003e\u003cspan address=\"10.1016/j.envexpbot.2021.104629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUr Rahman S, Han J-C, Ahmad M, Ashraf MN, Khaliq MA, Yousaf M, Wang Y, Yasin G, Nawaz MF, Khan KA, Du Z (2024) Aluminum phytotoxicity in acidic environments: A comprehensive review of plant tolerance and adaptation strategies. Ecotoxicol Environ Saf 269:115791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoenv.2023.115791\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoenv.2023.115791\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVelikova V, Loreto F (2005) On the relationship between isoprene emission and thermotolerance in \u003cem\u003ePhragmites australis\u003c/em\u003e leaves exposed to high temperatures and during the recovery from a heat stress. Plant Cell Environ 28(3):318\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3040.2004.01314.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3040.2004.01314.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma H, Borah JL, Sarma RN (2019) Variability Assessment for Root and Drought Tolerance Traits and Genetic Diversity Analysis of Rice Germplasm using SSR Markers. Sci Rep 9(1):16513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-52884-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-52884-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVimala T, Poonghuzhali T (2013) Estimation of pigments from seaweeds by using acetone and DMSO. Int J Sci Res 4(10):1850\u0026ndash;1854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13140/RG.2.2.35655.47524\u003c/span\u003e\u003cspan address=\"10.13140/RG.2.2.35655.47524\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeatherley P (1950) Studies in the water relations of the cotton plant. I. The field measurement of water deficits in leaves. New Phytologist:81\u0026ndash;97\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWijayanto T, Susanti Y, Rahni NM, Tufaila M, Rembon FS (2021) Seedling-stage screening method for tolerance of upland rice genotypes to low pH stress. IOP Conf Ser: Earth Environ Sci 782(3):032030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1755-1315/782/3/032030\u003c/span\u003e\u003cspan address=\"10.1088/1755-1315/782/3/032030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang J, Fan W, Zheng S (2019) Mechanisms and regulation of aluminum-induced secretion of organic acid anions from plant roots. J Zhejiang Univ Sci B 20(6):513\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1631/jzus.B1900188\u003c/span\u003e\u003cspan address=\"10.1631/jzus.B1900188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L-T, Hu N-J, Fu Q-X, Chen X-Y, Ren Y-M, Ye X, Lai N-W, Chen L-S (2024) Effects of aluminum (Al) stress on nitrogen (N) metabolism of leaves and roots in two Citrus species with different Al tolerance. Sci Hort 334:113331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scienta.2024.113331\u003c/span\u003e\u003cspan address=\"10.1016/j.scienta.2024.113331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin J, Zhao H, Wu X, Ma Y, Zhang J, Li Y, Shao G, Chen H, Han R, Xu Z (2023) SSR marker-based analysis for identification and of genetic diversity of non-heading Chinese cabbage varieties. Front Plant Sci 14:1112748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1112748\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1112748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYokosho K, Yamaji N, Mitani-Ueno N, Shen RF, Ma JF (2016) An Aluminum-Inducible IREG Gene is Required for Internal Detoxification of Aluminum in Buckwheat. Plant Cell Physiol 57(6):1169\u0026ndash;1178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/pcp/pcw065\u003c/span\u003e\u003cspan address=\"10.1093/pcp/pcw065\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Kirkham MB (1996) Antioxidant responses to drought in sunflower and sorghum seedlings. New Phytol 132(3):361\u0026ndash;373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.1996.tb01856.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.1996.tb01856.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Wei J, Li D, Kong X, Rengel Z, Chen L, Yang Y, Cui X, Chen Q (2017) The Role of the Plasma Membrane H+-ATPase in Plant Responses to Aluminum Toxicity. Front Plant Sci 8:1757. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2017.01757\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2017.01757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Yang J, Lv Y, Zhang X, Xia C, Zhao H, Wen C (2023) Genetic diversity analysis and variety identification using SSR and SNP markers in melon. BMC Plant Biol 23(1):39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12870-023-04056-7\u003c/span\u003e\u003cspan address=\"10.1186/s12870-023-04056-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu CQ, Cao XC, Zhu LF, Hu WJ, Hu AY, Abliz B, Bai ZG, Huang J, Liang QD, Sajid H, Li YF, Wang LP, Jin QY, Zhang JH (2019) Boron reduces cell wall aluminum content in rice (\u003cem\u003eOryza sativa\u003c/em\u003e) roots by decreasing H2O2 accumulation. Plant Physiol Biochem 138:80\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2019.02.022\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2019.02.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Aluminium toxicity, rice genotypes, SSR markers, oxidative stress, organic acid, Al tolerance genes","lastPublishedDoi":"10.21203/rs.3.rs-7626626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7626626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Aluminium (Al) toxicity affects rice cultivation in acidic soils, largely by hindering root development and diminishing crop yield. This study investigated 53 rice genotypes from Northeast India under hydroponic conditions to assess their morpho-physiological and molecular responses to aluminium stress. Nine characteristics, including as root length, root tolerance index, relative root elongation, and biomass, were measured to categorise genotypes into tolerant, moderately tolerant, intermediate, moderately susceptible, and susceptible classifications. Root characteristics proved to be the most sensitive predictors of tolerance, with cluster and principal component analyses reliably distinguishing tolerant from susceptible genotypes. SSR marker study (33 polymorphic markers, 103 alleles, mean PIC = 0.48) indicated substantial genetic variety, although did not entirely align with phenotypic grouping. Gene expression profiling revealed divergent molecular strategies: tolerant genotypes upregulated OsSTAR1, OsSTAR2, and OsFRDL4, facilitating aluminium exclusion through cell wall modification and citrate efflux, whereas susceptible genotypes demonstrated increased expression of OsNRAT1 and OsALS1, indicating dependence on internal sequestration. These findings highlight root-based exclusion mechanisms as the principal factor influencing Al tolerance in rice.","manuscriptTitle":"Exclusion versus Detoxification: Contrasting Molecular Strategies of Aluminium Tolerance in Rice Landraces of Northeast India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 21:27:15","doi":"10.21203/rs.3.rs-7626626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2025-11-03T19:53:28+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-22T09:35:50+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T01:29:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T18:26:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Cell Reports","date":"2025-09-16T02:31:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-cell-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pcre","sideBox":"Learn more about [Plant Cell Reports](https://www.springer.com/journal/299)","snPcode":"299","submissionUrl":"https://submission.nature.com/new-submission/299/3","title":"Plant Cell Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"118e6bde-f110-448c-84ae-d94f95354083","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T14:32:14+00:00","versionOfRecord":{"articleIdentity":"rs-7626626","link":"https://doi.org/10.1007/s00299-026-03791-z","journal":{"identity":"plant-cell-reports","isVorOnly":false,"title":"Plant Cell Reports"},"publishedOn":"2026-04-02 00:00:00","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-10-03 21:27:15","video":"","vorDoi":"10.1007/s00299-026-03791-z","vorDoiUrl":"https://doi.org/10.1007/s00299-026-03791-z","workflowStages":[]},"version":"v1","identity":"rs-7626626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7626626","identity":"rs-7626626","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}