Uncovering resistance traits in rice landraces against Yellow stem borer Scirpophaga incertulas (Walker) through biochemical and Principal Component Analysis approaches

Uncovering resistance traits in rice landraces against Yellow stem borer Scirpophaga incertulas (Walker) through biochemical and Principal Component Analysis approaches | 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 Uncovering resistance traits in rice landraces against Yellow stem borer Scirpophaga incertulas (Walker) through biochemical and Principal Component Analysis approaches Divya D Mallesh, Vijaykumar Lingaraj, Shivanna Bynayakala, Lakshminarayana Reddy CN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9567632/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The yellow stem borer (YSB), Scirpophaga incertulas (Walker), is a major constraint to rice production across South and Southeast Asia, often causing yield losses of 25–70%. The present study evaluated 50 traditional rice landraces under field conditions to identify sources of resistance and elucidate their biochemical and nutritional defense mechanisms. None of the landraces exhibited complete resistance, but several landraces reacted as resistant or moderately resistant. The biochemical analyses revealed higher concentrations of phenols, tannins, and total free amino acids and were strongly associated with resistance, while elevated levels of soluble sugars, reducing sugars, and crude proteins correlated positively with susceptibility. Nutrient profiling indicated that excess nitrogen, magnesium, sulfur, and iron predisposed genotypes to higher infestation, whereas phosphorus, potassium, calcium, manganese, copper, zinc, and silicon enhanced resistance. Principal Component Analysis (PCA) confirmed a highly stable dimensional structure across growth stages, with PC1 (> 71%) reflecting an integrative macro–micro nutrient balance dominated by N, K, Ca, S, Mg, Fe, Mn, Cu, and Si, while PC2 (~ 10%) consistently isolated phosphorus and zinc as distinct determinants. Together, these findings demonstrate that resistance in rice landraces is conferred by a coordinated network of biochemical and nutritional traits, offering valuable insights for breeding and integrated pest management strategies aimed at developing YSB-resistant cultivars. Rice Scirpophaga incertulas Resistance Biochemical and nutritional defense Antibiosis PCA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Rice ( Oryza sativa L.) is a cornerstone of global food security, serving as the staple food for more than half of the world’s population and contributing nearly 20% of the total caloric intake (Fukagawa & Ziska, 2019 ). Asia accounts for over 90% of rice production and consumption, underscoring its vital role in regional economies and livelihoods (FAO, 2024 ). In India, rice is cultivated on approximately 463.79 lakh hectares, with an annual production of 130.29 million tonnes and an average productivity of 2,809 kg ha⁻¹, ranking the country second only to China in both production and consumption (Anonymous, 2023 ). However, the combined effects of population growth, increasing food demand, and declining per capita arable land pose significant challenges to sustaining and enhancing rice productivity (Giri et al. 2022 ). Rice production is constrained by numerous abiotic and biotic stresses. Among abiotic stresses, drought, salinity, and submergence significantly limit productivity, while biotic stresses are responsible for large-scale losses in both yield and grain quality. Globally, weeds account for 15–20% of yield loss, insect pests for 10–15%, and diseases for 5–10% (Ali et al. 2021 ). Within insect pests, stem borers contribute ~ 40% of total pest-induced yield losses, followed by planthoppers (25%) and gall midges (10%) (Vijaykumar et al. 2009a , 2009b ; Morya et al. 2016 ). Outbreaks of these pests can occasionally lead to complete crop failure, further exacerbating food insecurity (Reddy et al. 2010 ; Balaji and Vijaykumar, 2025 ). Among stem borers, the yellow stem borer (YSB), Scirpophaga incertulas (Walker), is the most destructive and widespread pest of rice in South and Southeast Asia. It attacks rice plants throughout their life cycle, causing 25–70% yield losses depending on the crop growth stage and severity (Catling et al. 1987 ; Pasalu et al. 2002 ). The caterpillars bore into tillers at the vegetative stage, producing “dead hearts,” while damage during the reproductive stage results in “white ears,” leading to direct loss of panicle yield (Karthikeyan & Purushothaman, 2000 ; Megha et al. 2022a ; Balaji et al. 2024 ). In India alone, annual yield losses due to YSB are estimated between 2–3 million tonnes, highlighting its national significance (Krishnaiah & Varma, 2011 ). Chemical insecticides are widely used to manage yellow stem borer; however, their effectiveness is limited due to the cryptic feeding habit of larvae inside the plant stem (Vijaykumar et al., 2012 ; Kumar et al. 2018 ). Continuous and indiscriminate pesticide use has led to multiple problems, including insecticide resistance, pest resurgence, and elimination of natural enemies, ecological imbalance, and residue hazards (Dhaliwal et al. 2010 ; Baruah et al. 2020 ). Moreover, chemical control increases production costs, which is not sustainable for smallholder farmers. These limitations highlight the urgent need for eco-friendly and durable management strategies. Host Plant Resistance (HPR) is recognized as the most economical, farmer-friendly, and environmentally safe approach for insect pest management (Sharma, 1985 ; Dhillon et al. 2006 ; Vijaykumar et al. 2015a ; 2015b ; Megha et al. 2022b ; Vijaykumar et al. 2022b ). Resistant and moderately resistant genotypes can reduce pest pressure, are compatible with other integrated pest management (IPM) strategies, and do not impose additional input costs (Pathak and Khan, 1994 ). Screening of diverse germplasm has led to the identification of promising YSB-resistant lines in earlier studies, but resistance breakdown due to evolving pest populations necessitates continuous evaluation of landraces and wild relatives (Ramesh et al. 2019 ). In addition to morphological defenses, biochemical and nutritional traits of rice plants play a pivotal role in conferring resistance to rice yellow stem borer (Vijaykumar et al. 2015a , 2015b ; Punithkumar et al. 2020; Vijaykumar et al. 2022a ; Vinutha et al. 2023 ). Plants produce a wide range of secondary metabolites and structural compounds that influence herbivore feeding, survival, and reproduction. For example, higher levels of phenols, tannins, flavonoids, and lignin are associated with reduced larval feeding and tunnelling in rice stem borer and Asian rice gall midge (Panda & Khush, 1995 ; Chavan and Patel, 2016 ; Vijaykumar et al. 2016 ; balaji et al. 2025). Silicon deposition strengthens plant cell walls and impairs insect feeding efficiency, providing both mechanical and biochemical defense (Ma and Yamaji, 2006 ; Vijaykumar et al. 2007; Balaji & Jambagi, 2024 ). Similarly, elevated protein and proline contents have been correlated with enhanced resistance, while high sugar levels often predispose plants to greater susceptibility (Kumar, 1997 ; Amsagowri et al. 2018 ; Vanitha et al. 2015 ). Recent studies have further emphasized the role of nutrient balance in rice resistance to YSB. Nutrients such as nitrogen, potassium, and micronutrients influence the expression of defense-related genes and biochemical pathways, altering the susceptibility of rice plants to stem borers (Rajareddy et al. 2024 ). Thus, investigating the interplay between biochemical constituents and nutrient levels offers new insights into host plant resistance mechanisms. Given the heavy reliance on chemical pesticides and the rising concern over environmental sustainability, exploring local landraces with inherent resistance traits holds immense potential. Landraces often harbor unique genetic and biochemical characteristics that can be exploited for breeding resistant cultivars (Sathish et al. 2020 ). Field evaluation of these landraces, coupled with detailed analysis of biochemical and nutritional traits, provides a comprehensive approach to understanding resistance mechanisms against YSB. Furthermore, multivariate tools such as Principal Component Analysis (PCA) enable the identification of key resistance traits, offering valuable guidance for future breeding programs. Therefore, the present study was designed to assess the resistance of local rice landraces against the yellow stem borer (YSB), Scirpophaga incertulas , under natural field conditions, to identify promising sources of tolerance within traditional germplasm. In addition to evaluating field-level resistance, the study emphasized understanding the biochemical and nutritional basis underlying these defense mechanisms by examining key constituents such as phenols, tannins, silica, nitrogen, and carbohydrates that influence pest feeding behavior, larval survival, and crop susceptibility. To further strengthen the analysis, principal component analysis (PCA) was employed as a multivariate tool to integrate and interpret complex biochemical and agronomic data, enabling the identification of major traits that contribute most significantly to resistance differentiation among the tested landraces. This comprehensive approach provides valuable insights into the inherent defense potential of traditional rice varieties and establishes a foundation for their future utilization in breeding programs aimed at developing YSB-resistant cultivars. Materials and methods Field evaluation of rice landraces for YSB resistance Field evaluation of local rice landraces and popular cultivars for resistance against yellow stem borer ( Scirpophaga incertulas ) was conducted at A-block, College of Agriculture, V.C. Farm, Mandya, University of Agricultural Sciences, GKVK, Karnataka during the Rabi 2023 & Summer 2024 seasons. Screening material A total of 50 local rice landraces (Table 3; Table 4) were collected from the Zonal Agricultural Research Station, V.C. Farm, Mandya, and sown separately for evaluation. Twenty-five-day-old seedlings were transplanted in three rows with a spacing of 20 cm between rows and 15 cm between plants. All entries were managed according to recommended agronomic practices, except for plant protection measures (Anonymous, 2016). Field assessment of YSB infestation Infestation by YSB was recorded during the vegetative stage (before panicle emergence) by counting the number of dead hearts relative to the total number of tillers in 10 randomly selected hills per entry at 30 and 60 days after transplanting (DAT). At pre-harvest, YSB infestation was assessed by counting the number of ear-bearing tillers and white ears in 10 randomly selected hills, and percent white ears was calculated at 100 and 120 DAT. $$\:\text{D}\text{e}\text{a}\text{d}\:\text{h}\text{e}\text{a}\text{r}\text{t}\:\left(\text{%}\right)=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{o}.\:\text{o}\text{f}\:\text{d}\text{e}\text{a}\text{d}\:\text{h}\text{e}\text{a}\text{r}\text{t}\text{s}\:\text{p}\text{e}\text{r}\:10\:\text{h}\text{i}\text{l}\text{l}\:}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{o}.\:\text{o}\text{f}\:\text{t}\text{i}\text{l}\text{l}\text{e}\text{r}\text{s}}\times\:100$$ $$\:\text{W}\text{h}\text{i}\text{t}\text{e}\:\text{e}\text{a}\text{r}\:\left(\text{%}\right)=\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{o}.\:\text{o}\text{f}\:\text{w}\text{h}\text{i}\text{t}\text{e}\:\text{e}\text{a}\text{r}\text{s}\:\text{p}\text{e}\text{r}\:10\:\text{h}\text{i}\text{l}\text{l}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{o}.\:\text{o}\text{f}\:\text{p}\text{a}\text{n}\text{i}\text{c}\text{l}\text{e}}\times\:100$$ Table 1 Standard Evaluation System for Rice against YSB (IRRI, 2013) For dead heart For white ear Scale Per cent Category Scale Per cent Category 0 No damage Highly resistance 0 No damage Highly resistance 1 1–10% Resistance 1 1–5% Resistance 3 11–20% Moderately resistance 3 6–10% Moderately resistance 5 21–30% Moderately susceptible 5 11–15% Moderately susceptible 7 31–60% Susceptible 7 16–25% Susceptible 9 61% and above Highly susceptible 9 26% and above Highly susceptible The mean and standard deviation were calculated, and genotypes were classified into different resistance categories based on infestation levels. Scoring and interpretation of YSB infestation followed the Standard Evaluation System (SES) for rice developed by the International Rice Research Institute (IRRI, 2013) (Table 1). Based on initial screening, 19 promising genotypes, representing different resistance categories along with a susceptible check (TN-1) and resistant checks (TKM6 and W1263), were re-evaluated during the Summer 2024 season. Biochemical and nutritional analysis For the selected genotypes, biochemical constituents and nutrient contents were estimated in rice stems of 30- to 60-day-old plants representing different resistance categories. The un-infested portions of each stem were analyzed for biochemical components, including total sugars, reducing sugars, total phenols, tannins, crude proteins, and total free amino acids, as well as for nutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), zinc (Zn), iron (Fe), manganese (Mn), copper (Cu), and silicon (Si). All analyses were conducted following standard procedures and protocols to elucidate the biochemical and nutritional factors underlying resistance and susceptibility among the rice landraces (Table 2). Stem samples were dried in a hot air oven at 35°C for 24–48 hours. The dried samples were then ground using a mixer grinder, and the resulting powder was stored in plastic covers until further analysis. Extraction of plant tissues in alcohol Stem samples from selected rice landraces at 30 and 60 days old were thoroughly washed with distilled water and shade-dried. Ten grams of each sample were placed in separate conical flasks, and 150 mL of 80% ethanol was added. The mixture was refluxed on a hot water bath for 30 minutes. After cooling, the tissues were ground thoroughly in a mortar and pestle with a small amount of ethanol. The supernatant was decanted into another flask, and the residue was re-extracted with a small quantity of hot ethanol and decanted again. The combined extract was filtered through Whatman No. 1 filter paper and made up to a known volume with 80% ethanol. The alcoholic extract was stored in a refrigerator at 4°C and subsequently used for the estimation of major biochemical constituents in rice stems following standard protocols. Statistical analysis The mean values of biochemical components in different rice landraces were analyzed using analysis of variance (ANOVA). Multiple comparisons of means were performed using Tukey’s honestly significant difference (HSD) test (Tukey, 1953). The relationship between YSB infestation levels and biochemical constituents was evaluated using Pearson’s correlation coefficient (R) in OriginPro 2025b, with significance assessed at p ≤ 0.01. To identify the most influential biochemical traits contributing to resistance variability, principal component analysis (PCA) was conducted using GRAPES software ( https://www.grapeshms.com ). PCA enabled the detection of key traits that significantly affect yellow stem borer resistance and provided a comprehensive understanding of the multivariate interactions among biochemical and nutritional parameters. Principal component analysis : A principal component can be defined as a linear combination of optimally weighted observed variables as suggested by Rao (1964). The goal of PCA is to reduce the number of variables of interest into a smaller set of components. Table 2 Biochemical components, plant nutrients, and their functional roles in rice defence and nutrition Component / Nutrient Standard Procedure Role in Plant Defense & Nutrition References Biochemical Components Total sugars Somogyi’s method Attract herbivores, influence plant growth and nutritional imbalances Jeandet et al. ( 2022 ); Santi et al. ( 2013 ) Reducing sugars Somogyi’s method Nutritional quality, attracting herbivores, defense responses Khatri & Chhetri ( 2020 ); Karuppiah et al. ( 2019 ) Total phenols Folin-Ciocalteau method Deterrence, antifeedant activity, reproduction inhibition, oxidative defense Dai & Mumper ( 2010 ); Sharma et al. ( 2016 ) Crude protein Micro-Kjeldahl method Nutritional quality, amino acid profiles, herbivore attraction, growth regulation Robbins et al. ( 1987 ); War et al. ( 2012 ) Total free amino acids Ninhydrin method Attract natural enemies, deterrence, signaling in defense pathways Baqir et al. ( 2019 ); Chen et al. ( 2021 ) Tannins Folin–Denis method Egg-laying deterrence, digestion inhibition, herbivore toxicity Hassani-pour et al. ( 2011 ); Barbehenn & Constabel ( 2011 ) Macronutrients (Primary) Nitrogen (N) Kjeldahl method Nutritional quality, herbivore attraction, plant physiology, protein metabolism Leghari et al. ( 2016 ); Fageria ( 2009 ) Phosphorus (P) Spectrophotometric method Plant growth, energy transfer, stress adaptation, and defense Malhotra et al. ( 2018 ); Vance et al. ( 2003 ) Potassium (K) Flame photometric method Stress tolerance, stomatal regulation, disease resistance Wang et al. ( 2013 ); Amtmann et al. ( 2008 ) Macronutrients (Secondary) Calcium (Ca) Diacid digestion method Cell wall integrity, wound healing, signaling in defense White & Broadley ( 2003 ); Hepler ( 2005 ) Magnesium (Mg) — Photosynthesis, chlorophyll formation, hormonal regulation Guo et al. ( 2016 ); Cakmak & Yazici ( 2010 ) Sulfur (S) Turbidometric method Protein synthesis, glucosinolate/secondary metabolite defense, stress tolerance Abrol et al. (2003); Bloem et al. ( 2015 ) Micronutrients Zinc (Zn) Atomic absorption spectrophotometry Enzyme activation, auxin metabolism, stress tolerance Broadley et al. ( 2007 ); Alloway ( 2008 ) Iron (Fe) Atomic absorption spectrophotometry Chlorophyll synthesis, redox balance, defense enzymes Briat et al. ( 2015 ); Connorton et al. ( 2017 ) Manganese (Mn) Atomic absorption spectrophotometry Activates antioxidant enzymes, lignin biosynthesis, stress tolerance Millaleo et al. ( 2010 ); Schmidt et al. ( 2016 ) Copper (Cu) Atomic absorption spectrophotometry Cofactor for oxidative enzymes, lignification, pathogen defense Yruela ( 2009 ); Ravet & Pilon ( 2013 ) Beneficial non-essential Silicon (Si) Spectrophotometric method Mechanical barrier, stress tolerance, enhanced resistance to insects & pathogens Deshmukh et al. ( 2017 ); Ma & Yamaji ( 2015 ) Principal Component Analysis (PCA) determines the contribution of largest character to the total variation. Let’s say that, we have N Eigen vectors, then the explained variance for each Eigen vector i.e. principal component can be expressed as ratio of related Eigen values to the total sum of Eigen values as given by Hotteling (1933). The Eigen vectors represent the principal components that contain most of the information of variance. Proportion of variance accounted for: A third criterion in solving the number of factors problem involves retaining a component, if it accounts for a specified proportion (or percentage) of variance in the data set. $$\:Proportion=\frac{Eigen\:value\:for\:thecomponent\:of\:interest}{Total\:Eigen\:values\:of\:the\:correlation\:matrix}$$ Results Field evaluation of local rice landraces against yellow stem borer, Rabi 2023 & Summer 2024 During Rabi 2023 & Summer 2024, fifty local rice landraces were screened under field conditions for their reaction to the yellow stem borer (YSB). The incidence of dead heart (DH) ranged from 0.64 to 66.07%, while white ear (WE) incidence varied between 0.61 and 27.96% (Table 3). None of the landraces expressed a highly resistant reaction (0% DH and WE; score 0). Based on the Standard Evaluation System (SES), six genotypes were classified as resistant (score 1), nine as moderately resistant (score 3), nineteen as moderately susceptible (score 5), fourteen as susceptible (score 7), and two as highly susceptible (score 9) (Table 4). At 30 DAT, resistant landraces recorded DH incidence of 0.38 ± 1.18 to 8.78 ± 4.46%, while moderately resistant entries showed 12.80 ± 7.31 to 19.92 ± 13.61% DH. Moderately susceptible landraces recorded 21.38 ± 5.66 to 28.44 ± 4.42%, and susceptible landraces had 32.56 ± 10.41 to 48.19 ± 16.57% DH. Two genotypes, Krishna Leela and Kundi Pollan , exhibited the highest incidence (63.26% and 64.48 ± 24.6%, respectively), and were categorized as highly susceptible (score 9). No genotype exhibited complete resistance (Table 3). At 60 DAT, resistant entries showed 0.63 ± 1.33 to 8.43 ± 3.16% DH, while moderately resistant ones ranged from 12.48 ± 7.14 to 19.27 ± 5.77%. Moderately susceptible landraces recorded 22.59 ± 8.75 to 28.34 ± 9.45%, and susceptible ones exhibited 34.57 ± 9.69 to 51.84 ± 18.23% DH. Two landraces, Putta Batta–2 and Kundi Pollan , showed very high infestation (62.46 ± 12.45% and 67.66 ± 19.37%) and were rated highly susceptible (score 9). None were found highly resistant (Table 3). At 100 DAT, WE incidence ranged between 0.62 ± 1.30 and 4.12 ± 2.78% in resistant genotypes, while moderately resistant landraces showed 7.08 ± 4.23 to 9.69 ± 4.08%. Moderately susceptible entries recorded 12.32 ± 16.06 to 14.31 ± 4.02%, and susceptible ones showed 16.95 ± 3.19 to 24.12 ± 16.32%. Two landraces recorded very high WE incidence (27.02 ± 12.11% and 28.89 ± 12.51%) and were classified as highly susceptible (score 9). No genotype exhibited complete resistance (Table 3). At 120 DAT, resistant landraces recorded 0.60 ± 1.25 to 4.11 ± 3.24% WE, while moderately resistant ones ranged from 6.13 ± 3.88 to 9.21 ± 3.00%. Moderately susceptible landraces had 10.73 ± 4.17 to 14.14 ± 9.68%, and susceptible ones showed 16.36 ± 3.56 to 25.76 ± 10.17% WE. Two genotypes expressed very high WE incidence (25.99 ± 8.65% and 27.03 ± 9.22%) and were categorized as highly susceptible (score 9) (Table 3). Similar to earlier observations, none of the genotypes showed complete resistance. Table 3 Reaction of local land races of paddy against yellow stem borer, S. incertulas during 2023 & 2024 (Pooled Mean ± SD) Genotypes Dead hearts at 30DAT (%) Dead hearts at 60DAT (%) White-ears at 100DAT (%) White-ears at 120DAT (%) Scale Resistance category Adri batta 8.30 ± 3.69 8.43 ± 3.16 4.12 ± 2.78 3.36 ± 2.58 1 R Bangara kolee 38.55 ± 9.29 35.33 ± 5.74 20.48 ± 9.55 22.02 ± 6.69 7 S Black sticky 14.99 ± 5.68 13.34 ± 5.39 8.25 ± 4.27 7.27 ± 3.90 3 MR Krishna leela 63.26 ± 15.8 62.46 ± 12.45 28.89 ± 12.51 27.03 ± 9.22 9 HS Karimundaga 8.78 ± 4.46 6.07 ± 3.87 3.98 ± 3.13 4.11 ± 3.24 1 R Kanakunja 33.55 ± 4.57 34.93 ± 5.11 23.08 ± 13.00 17.39 ± 3.9 7 S Karikagga 0.88 ± 1.43 0.63 ± 1.33 0.62 ± 1.30 0.60 ± 1.25 1 R Kave kantak 21.47 ± 7.46 25.90 ± 4.36 14.31 ± 4.02 12.99 ± 2.45 5 MS Kalajeera 19.92 ± 13.61 12.48 ± 7.14 9.69 ± 4.08 9.21 ± 3.00 3 MR Kundi pullan 64.48 ± 24.6 67.66 ± 19.37 27.02 ± 12.11 25.99 ± 8.65 9 HS Manjula sona 28.44 ± 4.42 27.64 ± 3.68 13.75 ± 4.25 12.17 ± 2.85 5 MS Neermullare 25.57 ± 20.52 25.96 ± 8.30 13.35 ± 5.73 12.14 ± 3.16 5 MS Naland paddy 0.38 ± 1.18 0.90 ± 1.93 0.70 ± 1.48 0.94 ± 1.97 1 R Nirga samba 18.83 ± 11.37 15.99 ± 2.84 8.50 ± 2.24 7.93 ± 2.86 3 MR Navara 32.56 ± 10.41 35.10 ± 5.98 19.23 ± 8.41 17.43 ± 3.40 7 S Neermuka 23.99 ± 5.76 26.23 ± 4.35 13.79 ± 6.52 12.25 ± 2.83 5 MS Putta batta – 2 63.48 ± 22.4 37.39 ± 9.30 17.16 ± 5.36 25.76 ± 10.17 7 S Rahodaya 17.04 ± 8.50 16.24 ± 8.29 8.70 ± 3.16 8.41 ± 2.69 3 MR Punkutt kodi – 1 33.97 ± 8.40 34.57 ± 9.69 16.95 ± 3.19 16.36 ± 3.56 7 S Kamadari 24.49 ± 11.38 26.73 ± 11.64 12.44 ± 6.22 10.73 ± 4.17 5 MS Kariga javele 27.51 ± 28.86 26.43 ± 28.65 14.14 ± 9.68 14.14 ± 9.68 5 MS Kalakoli 42.08 ± 14.94 45.01 ± 15.75 18.45 ± 5.15 18.07 ± 4.90 7 S Mapilai samba – 2 37.99 ± 15.52 39.91 ± 16.21 22.21 ± 8.38 21.39 ± 9.15 7 S Neergula batta 25.22 ± 9.86 27.83 ± 9.14 13.25 ± 3.98 12.05 ± 1.23 5 MS Narali 21.38 ± 5.66 23.63 ± 6.93 12.56 ± 2.88 11.31 ± 2.59 5 MS Rasakadam 17.17 ± 5.49 19.27 ± 5.77 7.49 ± 1.58 6.53 ± 2.63 3 MR Rathanachoodi – 1 25.18 ± 6.62 23.31 ± 5.02 12.86 ± 2.43 13.14 ± 2.43 5 MS Rathanachoodi – 2 28.11 ± 11.68 23.2 ± 6.32 12.52 ± 3.78 11.9 ± 3.10 5 MS Theerthahalli local – 1 12.8 ± 7.31 18.85 ± 4.96 8.22 ± 3.38 7.37 ± 1.33 3 MR Tai jasmine 23.56 ± 19.1 26.86 ± 16.11 13.54 ± 13.63 12.89 ± 14.17 5 MS Tornado batta – 2 15.25 ± 11.51 13.81 ± 4.26 9.57 ± 6.17 8.03 ± 3.94 3 MR TRV s Dangi red 28.2 ± 4.77 27.14 ± 5.08 13.80 ± 3.38 13.21 ± 3.70 5 MS TRV s Biladadi martiga 24.73 ± 6.16 23.77 ± 9.37 12.51 ± 5.72 11.74 ± 5.21 5 MS Raichur sanna 5.32 ± 3.27 6.43 ± 2.89 3.91 ± 2.97 3.91 ± 2.97 1 R Dunda 26.38 ± 8.95 25.51 ± 5.19 13.26 ± 4.33 12.73 ± 3.05 5 MS Dappaneya Bilijaddi 46.7 ± 18.21 47.33 ± 13.22 19.48 ± 7.36 17.72 ± 5.53 7 S Rajaboga 0.72 ± 1.52 7.52 ± 3.56 3.21 ± 1.30 0.72 ± 1.52 1 R Dodda Baikalu 39.42 ± 10.86 51.84 ± 18.23 23.61 ± 13.69 22.07 ± 15.46 7 S Dappa batta 38.00 ± 11.66 41.04 ± 17.99 18.34 ± 7.02 17.68 ± 7.82 7 S Danggaia 18.94 ± 5.48 13.89 ± 7.71 7.08 ± 4.23 6.65 ± 3.74 3 MR Doddabyra 26.59 ± 9.96 28.12 ± 2.97 12.73 ± 3.36 11.54 ± 2.96 5 MS Sanna batta – 2 26.64 ± 9.89 22.59 ± 8.75 13.88 ± 3.59 12.95 ± 4.24 5 MS Sanna rajakime 19.18 ± 11.91 18.52 ± 7.76 9.10 ± 9.32 6.13 ± 3.88 3 MR Siri sanna 43.85 ± 20.52 46.67 ± 25.42 24.12 ± 16.32 23.12 ± 17.55 7 S Sanna akki batta 27.85 ± 9.64 28.34 ± 9.45 12.58 ± 5.51 11.56 ± 5.87 5 MS Sanbag 43.8 ± 15.51 46.03 ± 13.12 21.78 ± 11.04 20.74 ± 12.29 7 S Sidda sanna 24.42 ± 8.39 27.11 ± 8.73 12.32 ± 16.06 12.32 ± 16.06 5 MS Selam sanna – 1 48.19 ± 16.57 41.38 ± 7.48 20.17 ± 12.48 16.83 ± 3.86 7 S TRV s valtgya gidda 24.43 ± 4.02 25.64 ± 5.69 13.20 ± 3.34 12.47 ± 1.51 5 MS Tagarhi 33.96 ± 6.79 36.69 ± 7.30 20.83 ± 8.60 18.43 ± 2.82 7 S Table 4 Categorization of different local landraces of rice against yellow stem borer, S. incertulas , 2023 & 2024 (Pooled Mean) Scale Percent damage Category Genotypes Total Per cent dammage observed DH(%) WE(%) DH(%) WE(%) 0 No damage No damage HR 0 0 0 0 1 1–10% 1–5% R Adri batta, Karimundaga, Karikagga, Naland paddy, Raichur sanna and Rajaboga 6 0.64–8.37 0.61–4.05 3 11–20% 6–10% MR Black sticky, Kalajeera, Nirga samba, Rahodaya, Rasakadam, Theerthahalli local – 1, Tornado batta – 2, Danggaia and Sanna rajakime 9 14.17–18.85 6.87–9.45 5 21–30% 11–15% MS Kave kantak, Manjula sona, Neermullare, Neermuka, Kamadari, Kariga javele, Neergula batta, Narali, Rathanachoodi – 1, Rathanachoodi – 2, Tai jasmine, TRV s Dangi red, TRV s Biladadi martiga, Dunda, Doddabyra, Sanna batta – 2, Sanna akki batta, Sidda sanna and TRV s valtgya gidda 19 22.51–28.10 11.59–14.14 7 31–60% 16–25% S Bangara kolee, Kanakunja, Navara, Putta batta – 2, Punkutt kodi – 1, Kalakoli, Mapilai samba – 2, Dappaneya Bilijaddi, Dodda Baikalu, Dappa batta, Siri sanna, Sanbag, Selam sanna – 1 and Tagarhi 14 33.83–50.44 16.66–23.62 9 61 and above 26 and above HS Krishna leela and Kundi pullan 2 62.86–66.07 26.51–27.96 Biochemical constituents Total phenols The total phenol content in rice genotypes showed a clear decline with increasing susceptibility to yellow stem borer. At 30 DAT, phenol levels ranged from 0.21 to 0.85 mg g⁻¹, with resistant and moderately resistant genotypes recording higher values (0.49–0.85 mg g⁻¹), moderately susceptible genotypes showing intermediate levels (0.32–0.41 mg g⁻¹), and susceptible to highly susceptible ones having the lowest contents (0.10–0.31 mg g⁻¹). At 60 DAT, phenol content ranged from 0.428 to 1.12 mg g⁻¹. Resistant genotypes such as Rajbaga (1.12), TKM6 (1.073), W1263 (1.063), Karimunduga (0.915) and Karikagga (0.903) accumulated the maximum phenols, followed by moderately resistant types (0.76–0.86 mg g⁻¹). Moderately susceptible genotypes exhibited intermediate values (0.52–0.64 mg g⁻¹), while susceptible and highly susceptible genotypes recorded lower levels (0.48–0.61 mg g⁻¹). The highly susceptible Krishna Leela (0.428 mg g⁻¹) and the susceptible check TN-1 (0.483 mg g⁻¹) had the minimum phenol content, indicating greater vulnerability (Tables 5 and 6). Total soluble sugars (TSS) Total soluble sugars (TSS) showed a clear association with susceptibility to yellow stem borer. At 30 DAT, TSS ranged from 3.25 to 8.02 mg g⁻¹, with resistant genotypes recording the lowest levels (3.25–4.01 mg g⁻¹), moderately resistant types slightly higher (5.01–5.43 mg g⁻¹), and susceptible to highly susceptible genotypes the highest (6.82–8.02 mg g⁻¹). At 60 DAT, a similar trend was observed, with TSS ranging from 2.25 to 6.77 mg g⁻¹. Resistant genotypes such as TKM6, W1263, Rajbaga, Adri batta, Nagaland rice, Karimunduga and Karikagga had the lowest values (2.25–2.96 mg g⁻¹), while moderately resistant ones showed slightly higher levels (3.66–4.34 mg g⁻¹). Moderately susceptible genotypes recorded intermediate values (4.33–5.52 mg g⁻¹), whereas susceptible and highly susceptible genotypes, including TN-1 (6.77 mg g⁻¹), accumulated the maximum sugars. Overall, higher TSS consistently corresponded with greater susceptibility to yellow stem borer (Tables 5 and 6). Reducing sugars (TRS) Reducing sugars in rice genotypes showed a consistent increase with susceptibility to yellow stem borer, ranging from 7.05–13.05 mg g⁻¹ at 30 DAT and 5.21–11.51 mg g⁻¹ at 60 DAT. Resistant genotypes (Karikagga, Nagaland rice, Adri batta, Rajbaga, W1263, Karimunduga, TKM6) recorded the lowest values, followed by moderately resistant types (Nirga samba, Kala Jeera, Black sticky) with slightly higher levels. Moderately susceptible genotypes (Kave kantak, Manjula Sona, Neermullarae, Neermuka) showed intermediate contents, while susceptible and highly susceptible ones (Bangara kolee, Navara, Kankunia, Punkutt kodi-1, Putta batta-2, Kundi polan, Krishna leela) accumulated significantly higher sugars, with TN-1 consistently recording the maximum (13.05 and 11.51 mg g⁻¹ at 30 and 60 DAT, respectively) (Tables 5 and 6). Crude proteins Crude protein content in rice genotypes increased with susceptibility to yellow stem borer, ranging from 2.84–7.21 mg g⁻¹ at 30 DAT and 1.28–5.81 mg g⁻¹ at 60 DAT. Resistant genotypes (TKM6, W1263, Karikagga, Nagaland rice, Adri batta, Karimunduga, Rajboga) recorded the lowest levels, moderately resistant types (Nirga samba, Kala jeera, Black sticky) had slightly higher values, and moderately susceptible genotypes showed intermediate contents. Susceptible and highly susceptible genotypes, including TN-1, Krishna leela, and Kundi polan, accumulated the highest proteins at both stages (Tables 5 and 6). Table 5 Biochemical constituents of rice genotypes associated with infestation of yellow stem borer at 30 DAT, Summer 2024 Treatments Biochemical constituents (mg g − 1 ) Sl. No. Category Genotypes DH (%)* Total soluble Sugars Reducing Sugars Total Phenols Crude Protein Total Free Amino Acids Tannins 1 R Karikagga 5.32 jkl 3.81 ij 7.05 h 0.53 cd 3.49 g 20.5 abcd 4.31 c 2 Rajbaga 3.21 kl 3.39 j 7.28 h 0.85 a 3.92 fg 21.3 ab 5.31 ab 3 Nagaland Rice 7.21 ijk 3.62 j 7.21 h 0.62 bc 3.52 g 21.08 abc 4.01 cd 4 Karimunduga 8.1 ij 4.01 hij 7.36 h 0.64 b 3.81 fg 20.92 abc 4.61 bc 5 Adri batta 6.12 jkl 3.59 j 7.26 h 0.57 bcd 3.72 g 21.32 ab 5.09 ab 6 MR Nirga Samba 15.98 g 5.01 ghi 8.14 fgh 0.53 cd 5.07 ef 21.21 ab 3.42 def 7 Kala Jeera 14.32 gh 5.29 fgh 8.51 efgh 0.49 de 5.35 de 19.42 abcd 3.91 cd 8 Black Sticky 11.12 hi 5.43 efg 8.76 efgh 0.61 bc 5.45 de 19.9 abcd 3.53 de 9 MS Kave Kantak 21.05 f 5.99 defg 9.09 efgh 0.32 fghi 5.81 bcde 19.14 abcd 3.09 efg 10 Neermullarae 23.05 f 6.52 bcdef 9.76 cdef 0.35 fgh 5.71 cde 20.09 abcd 2.72 fgh 11 Neermuka 21.1 f 6.71 abcde 9.64 defg 0.41 ef 6.32 abcde 17.23 bcd 2.93 efgh 12 Manjula Sona 24.1 f 6.34 cdefg 9.55 defg 0.39 efg 6.1 abcde 18.01 abcd 2.82 efgh 13 S Bangara Kolee 33.21 e 7.42 abc 10.29 bcdef 0.3 ghij 6.41 abcd 16.72 cd 2.4 ghi 14 Punkutt Kodi-1 36.12 e 7.41 abc 11.39 abcd 0.23 ij 6.31 abcde 17.49 bcd 1.85 ij 15 Kankunia 42.12 c 7.08 abcd 10.29 bcdef 0.31 fghij 6.32 abcde 16.21 d 1.81 ij 16 Navara 49.21 b 7.32 abcd 10.59 bcde 0.26 hij 6.54 abcd 18.34 abcd 2.32 hi 17 Putta Batta-2 37.21 de 6.82 abcd 11.64 abcd 0.24 ij 6.75 abc 17.32 bcd 1.92 ij 18 HS Kundi polan 62.12 a 7.34 abc 11.91 abc 0.24 ij 7.01 ab 17.42 bcd 1.71 ij 19 Krishna Leela 65.26 a 7.74 ab 12.01 ab 0.23 ij 6.99 ab 17.93 abcd 1.73 ij 20 SC TN-1 42.02 cd 8.02 a 13.05 a 0.21 j 7.21 a 16.43 d 1.45 j 21 RC TKM6 3.21 kl 3.25 j 7.55 gh 0.82 a 2.84 g 21.89 a 5.49 a 22 W1263 2.35 l 3.51 j 7.34 h 0.79 a 3.04 g 22.13 a 4.09 cd SE m ± 0.88 0.25 0.40 0.02 0.23 0.81 0.14 CD @ p = 0.05 2.53 0.71 1.14 0.06 0.67 2.31 0.40 *Values in the column followed by common letters are non-significant at p = 0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No – number. Table 6 Biochemical constituents of rice genotypes associated with infestation of yellow stem borer at 60 DAT, Summer 2024 Treatments Biochemical constituents (mg g − 1 ) Sl. No. Category Genotypes DH (%)* Total soluble Sugars Reducing Sugars Total Phenols Crude Protein Total Free Amino Acids Tannins 1 R Karikagga 4.57 ij 2.56 jk 5.25 i 0.903 bc 2.12 gh 19.63 abcd 6.47 abcde 2 Rajbaga 2.46 j 2.39 k 5.368 hi 1.12 a 2.55 g 20.58 ab 7.47 ab 3 Nagaland Rice 6.46 ij 2.37 k 5.21 i 0.81 cd 2.25 gh 20.27 ab 6.24 bcdef 4 Karimunduga 7.35 hij 2.96 ijk 5.66 ghi 0.915 bc 2.5 g 20.13 abc 7.02 abcd 5 Adri batta 5.37 ij 2.34 k 5.348 hi 0.794 cde 2.35 g 20.43 ab 7.15 abc 6 MR Nirga Samba 15.23 fg 3.66 hij 6.62 fghi 0.76 cdef 3.66 f 20.39 ab 5.68 efgh 7 Kala Jeera 13.57 gh 4.04 ghi 7.17 efgh 0.863 c 3.96 ef 18.63 abcd 6.07 cdefg 8 Black Sticky 10.37 ghi 4.34 fgh 6.78 fghi 0.84 c 4.16 def 19.12 abcd 5.73 defgh 9 MS Kave Kantak 20.3 ef 4.74 efgh 7.31 efg 0.521 gh 4.47 cdef 18.251 abcd 5.17 efghi 10 Neermullarae 22.3 e 5.52 bcde 8.26 cdef 0.593 fgh 4.31 def 19.22 abcd 4.76 ghij 11 Neermuka 20.35 ef 4.96 defg 8.26 cdef 0.623 efg 5.02 abcd 16.38 bcd 5.07 fghi 12 Manjula Sona 23.35 e 4.33 fgh 7.638 def 0.643 defg 4.69 bcdef 17.22 abcd 5 fghi 13 S Bangara Kolee 32.46 d 5.92 abcd 8.95 bcde 0.61 fg 5.1 abcd 15.83 cd 4.51 hij 14 Punkutt Kodi-1 35.37 cd 6.16 abc 9.47 bcd 0.513 gh 4.98 abcde 16.66 bcd 3.9 ij 15 Kankunia 41.37 c 5.38 cdef 8.45 bcdef 0.6 fgh 4.99 abcde 15.36 d 3.86 ij 16 Navara 48.46 b 6.07 abcd 8.86 bcde 0.484 gh 5.15 abcd 17.45 abcd 4.48 hij 17 Putta Batta-2 36.46 cd 4.98 defg 9.819 abc 0.513 gh 5.4 abc 16.48 bcd 4.12 ij 18 HS Kundi polan 61.37 a 6.09 abcd 10.27 ab 0.513 gh 5.69 ab 16.59 bcd 3.91 ij 19 Krishna Leela 64.51 a 6.66 ab 10.25 ab 0.428 h 5.58 ab 17.08 abcd 3.95 ij 20 SC TN-1 41.27 c 6.77 a 11.509 a 0.483 gh 5.809 a 15.59 d 3.63 j 21 RC TKM6 2.46 j 2.25 k 5.638 ghi 1.073 ab 1.28 h 21.07 a 7.6 a 22 W1263 2.08 j 2.26 k 5.59 ghi 1.063 ab 1.67 gh 21.31 a 6.25 bcdef SE m ± 1.24 0.21 0.35 0.03 0.19 0.81 0.24 CD @ p = 0.05 3.53 0.61 1.01 0.09 0.54 2.32 0.69 Total free amino acid (TFA) Total free amino acids (TFA) in rice genotypes decreased with increasing susceptibility to yellow stem borer, ranging from 16.21–22.13 mg g⁻¹ at 30 DAT and 15.36–21.31 mg g⁻¹ at 60 DAT. Resistant genotypes (W1263, TKM6, Adri Batta, Rajboga, Nagaland rice/paddy, Karimunduga, Karikagga) recorded the highest TFA, moderately resistant types (Nirga samba, Black sticky, Kala Jeera) showed slightly lower levels, and moderately susceptible genotypes had intermediate contents. Susceptible and highly susceptible genotypes, including TN-1, Kankunia, Bangara kolee, Putta batta-2, Punkutt kodi-1, Krishna leela, and Kundi polan, accumulated the lowest TFA at both stages (Tables 5 and 6). Tannins Tannin content in rice genotypes declined with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, Rajboga, Adri Batta, Karimunduga, Karikagga, W1263, Nagaland paddy) recorded the highest tannin levels (4.01–5.49 mg g⁻¹), moderately resistant types (Kala Jeera, Black sticky, Nirga samba) had intermediate values (3.42–3.91 mg g⁻¹), and moderately susceptible genotypes (Kave kantak, Neermuka, Manjula Sona, Neermullarae) showed 2.72–3.09 mg g⁻¹. Susceptible and highly susceptible genotypes, including TN-1, Kankunia, Punkutt kodi-1, Putta batta-2, Navara, Bangara kolee, Krishna leela, and Kundi polan, recorded the lowest levels (1.45–2.40 mg g⁻¹). At 60 DAT, the trend persisted: resistant genotypes (TKM6, Rajbaga, Adri Batta, Karimunduga, Karikagga, W1263, Nagaland rice) showed 6.24–7.60 mg g⁻¹, moderately resistant types 5.68–6.07 mg g⁻¹, moderately susceptible 4.76–5.17 mg g⁻¹, and susceptible to highly susceptible genotypes 3.63–4.51 mg g⁻¹ (Tables 5 and 6). Nutrient composition Nitrogen Nitrogen content in rice genotypes increased with susceptibility to yellow stem borer. Resistant genotypes (W1263, TKM6, Karikagga, Rajboga, Adri Batta, Karimunduga, Nagaland rice) recorded the lowest levels (0.46–0.96%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) showed slightly higher values (0.83–1.18%), moderately susceptible genotypes (Neermullarae, Manjula Sona, Neermuka, Kave kantak) had intermediate levels (1.01–1.43%), and susceptible to highly susceptible genotypes (Bangara kolee, Kankunia, Navara, Punkutt kodi-1, Putta batta-2, TN-1, Krishna leela, Kundi polan) accumulated the highest N (1.12–1.56%) (Tables 7 and 8). Phosphorous Phosphorus content in rice genotypes decreased with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (Karimunduga, TN-1, Karikagga, Adri Batta, Nagaland paddy, Rajboga) recorded the highest levels (0.45–0.48%), moderately resistant types (Black sticky, Nirga samba, Kala Jeera) had 0.42–0.44%, moderately susceptible genotypes (Kave kantak, Manjula Sona, Neermuka, Neermullarae) showed 0.35–0.41%, and susceptible to highly susceptible genotypes (Putta batta-2, Bangara kolee, Kankunia, Punkutt kodi-1, Navara, Krishna leela, Kundi polan) had the lowest levels (0.20–0.34%). At 60 DAT, resistant genotypes (Karikagga, Rajbaga, Nagaland rice, Karimunduga, Adri Batta, TKM6, W1263) showed 0.49–0.73%, moderately resistant 0.65–0.658%, moderately susceptible 0.617–0.69%, and susceptible to highly susceptible genotypes 0.42–0.57% (Tables 7 and 8). Table 7 Primary, secondary nutrients and silicon constituents in the rice land races with different resistance categories at 30 DAT, Summer 2024 Treatments DH (%) Macro-Nutrients (%) Micro-Nutrients (mg kg − 1 ) Si (%) Primary Nutrients Secondary Nutrients Sl. No. Category Genotypes N P K Ca Mg S Zn Fe Mn Cu 1 R Karikagga 5.32 jkl 0.54 d 0.47 a 2.64 a 0.37 abc 0.16 ef 0.17 def 52.33 de 145.33 abc 14.35 cdefgh 8.24 a 3.82 a 2 Rajbaga 3.21 kl 0.55 d 0.45 a 2.49 ab 0.35 bcde 0.15 f 0.15 f 59.21 cd 142 bc 19.74 a 7.54 abcd 3.66 a 3 Nagaland Rice 7.21 ijk 0.63 cd 0.46 a 2.58 a 0.38 abc 0.18 cdef 0.16 ef 60.1 bcd 138.33 c 15.93 bcd 7.88 abcd 3.42 ab 4 Karimunduga 8.1 ij 0.61 cd 0.48 a 2.61 a 0.36 abcd 0.17 def 0.18 cdef 58.1 cde 141.33 bc 18.37 ab 7.77 abcd 3.76 a 5 Adri batta 6.12 jkl 0.6 cd 0.47 a 2.59 a 0.34 bcde 0.15 f 0.15 f 53.21 de 142 bc 15.38 bcdef 8.12 ab 3.33 ab 6 MR Nirga Samba 15.98 g 0.85 bc 0.43 abc 2.41 abc 0.31 cdef 0.19 bcdef 0.18 cdef 70.21 abc 148.33 abc 15.78 bcde 8.09 abc 3.26 abc 7 Kala Jeera 14.32 gh 0.83 bc 0.42 abc 2.4 abc 0.37 abc 0.18 cdef 0.17 def 82.32 a 143.33 abc 12.32 efghi 7.67 abcd 3.01 bc 8 Black Sticky 11.12 hi 0.85 bc 0.44 ab 2.37 abc 0.36 abcd 0.2 abcde 0.19 bcdef 74.32 ab 141.67 bc 14.56 cdefg 7.85 abcd 2.72 cd 9 MS Kave Kantak 21.05 f 1.07 ab 0.41 abcd 1.98 bcd 0.29 defg 0.21 abcd 0.22 abc 81.32 a 146 abc 13.22 defghi 7.32 abcd 2.18 de 10 Neermullarae 23.05 f 1.01 ab 0.35 bcdef 1.99 bcd 0.25 fgh 0.22 abc 0.21 abcd 55.28 cde 153 abc 14.82 cdefg 6.28 de 2.13 de 11 Neermuka 21.1 f 1.06 ab 0.39 abcde 1.95 bcde 0.28 efg 0.21 abcd 0.21 abcd 34.12 f 152 abc 11.31 ghij 5.23 e 2.09 ef 12 Manjula Sona 24.1 f 1.05 ab 0.41 abcd 1.94 cde 0.24 fgh 0.2 abcde 0.2 abcde 76.12 a 153.33 abc 12.43 defghi 6.34 cde 2.05 efg 13 S Bangara Kolee 33.21 e 1.13 a 0.32 defg 1.62 de 0.25 fgh 0.23 ab 0.23 ab 55 de 158 ab 10.25 ij 6.42 bcde 1.32 h 14 Punkutt Kodi-1 36.12 e 1.15 a 0.29 pp . 1.76 de 0.23 gh 0.21 abcd 0.21 abcd 54.21 de 153 abc 11.9 fghi 7.17 abcd 1.49 gh 15 Kankunia 42.12 c 1.12 a 0.31 efg 1.63 de 0.24 fgh 0.22 abc 0.22 abc 53.21 de 147 abc 11.01 hij 7.31 abcd 1.51 fgh 16 Navara 49.21 b 1.14 a 0.2 h 1.74 de 0.26 fgh 0.21 abcd 0.21 abcd 44.21 ef 159 a 12.72 defghi 6.51 abcde 1.59 efgh 17 Putta Batta-2 37.21 de 1.12 a 0.34 cdefg 1.62 de 0.24 fgh 0.23 ab 0.23 ab 53.21 de 151.33 abc 13.42 defghi 7.42 abcd 1.41 h 18 HS Kundi polan 62.12 a 1.17 a 0.28 pp . 1.42 e 0.22 gh 0.2 abcde 0.2 abcde 46.21 def 152 abc 12.41 defghi 6.32 de 1.32 h 19 Krishna Leela 65.26 a 1.19 a 0.26 pp . 1.43 e 0.23 gh 0.19 bcdef 0.19 bcdef 45.3 def 150.67 abc 10.02 ij 6.41 bcde 1.26 h 20 SC TN-1 42.02 cd 1.24 a 0.48 a 1.48 de 0.2 h 0.24 a 0.24 a 43.21 ef 159 a 8.23 j 6.32 de 1.43 h 21 RC TKM6 3.21 kl 0.53 d 0.25 g 2.83 a 0.43 a 0.19 bcdef 0.19 bcdef 79.41 a 144.67 abc 18.72 ab 8.24 a 3.76 a 22 W1263 2.35 l 0.44 d 0.21 h 2.72 a 0.39 ab 0.15 f 0.15 f 82.32 a 147.67 abc 17.41 abc 7.9 abcd 3.25 abc SE m ± 0.88 0.05 0.02 0.10 0.01 0.01 0.01 2.77 3.08 0.65 0.33 0.11 CD @ p = 0.05 2.53 0.14 0.05 0.29 0.29 0.03 0.03 7.89 8.79 1.86 0.93 0.31 *Values in the column followed by common letters are non-significant at p = 0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No – number Table 8 Primary, secondary nutrients and silicon constituents in the rice land races with different resistance categories at 60 DAT, Summer 2024 Si. No. Category Genotypes %DH Macro-Nutrients Micro-Nutrients Si Primary Nutrients Secondary Nutrients N P K Ca Mg S Zn Fe Mn Cu 1 R Karikagga 4.57 ij 0.841 gh 0.72 ab 3.86 abcd 0.6 abc 0.19 efg 0.195 cdefg 51.08 cdef 147.34 a 15.44 cdefgh 7.81 a 3.93 a 2 Rajbaga 2.46 j 0.861 fgh 0.73 a 3.73 abcde 0.62 ab 0.19 efg 0.177 g 57.946 bcd 143.7 a 20.86 a 7.12 abcd 3.78 ab 3 Nagaland Rice 6.46 ij 0.93 efgh 0.72 ab 3.83 abcd 0.6 abc 0.2 defg 0.188 efg 58.856 bc 139.65 a 17.01 bcd 7.49 abc 3.56 ab 4 Karimunduga 7.35 hij 0.96 efgh 0.7 abc 3.92 abc 0.58 abcde 0.202 cdefg 0.199 cdefg 56.866 bcde 142.97 a 19.49 ab 7.37 abcd 3.91 a 5 Adri batta 5.37 ij 0.932 efgh 0.68 abc 3.91 abc 0.56 abcdef 0.181 g 0.172 g 51.986 cdef 143.38 a 16.45 bcdef 7.65 ab 3.45 abc 6 MR Nirga Samba 15.23 fg 1.181 bcde 0.65 abcd 3.6 abcdefg 0.48 cdefg 0.218 bcdefg 0.201 cdefg 68.986 ab 149.87 a 16.84 bcde 7.65 ab 3.39 abc 7 Kala Jeera 13.57 gh 1.135 defg 0.65 abcd 3.71 abcdef 0.59 abcd 0.212 bcdefg 0.19 defg 81.106 a 144.94 a 13.44 efghi 7.27 abcd 3.13 bc 8 Black Sticky 10.37 ghi 1.166 cdef 0.658 abcd 3.66 abcdefg 0.58 abcde 0.233 abcdefg 0.215 bcdefg 73.116 a 143.23 a 15.77 cdefg 7.47 abcd 2.87 cd 9 MS Kave Kantak 20.3 ef 1.391 abcd 0.64 abcde 3.25 bcdefgh 0.51 bcdefg 0.237 abcdef 0.247 abc 80.12 a 147.02 a 14.37 defghi 6.85 abcd 2.32 de 10 Neermullarae 22.3 e 1.321 abcd 0.617 abcde 3.25 bcdefgh 0.45 fg 0.25 abcd 0.236 abcdef 54.006 cdef 154.23 a 15.9 cdefg 5.85 de 2.24 def 11 Neermuka 20.35 ef 1.431 abcd 0.66 abcd 3.14 cdefgh 0.5 bcdefg 0.24 abcde 0.232 abcdef 32.84 g 153.4 a 12.38 ghij 4.78 e 2.21 defg 12 Manjula Sona 23.35 e 1.42 abcd 0.69 abc 3.2 bcdefgh 0.46 efg 0.228 abcdefg 0.237 abcdef 74.85 a 154.93 a 13.68 defghi 5.86 cde 2.19 efg 13 S Bangara Kolee 32.46 d 1.472 abc 0.51 def 2.89 gh 0.47 defg 0.256 abc 0.259 ab 53.746 cdef 159.14 a 11.37 ij 5.96 cde 1.44 h 14 Punkutt Kodi-1 35.37 cd 1.465 abc 0.49 ef 2.99 efgh 0.45 fg 0.24 abcde 0.247 abc 52.95 cdef 154.04 a 12.95 fghi 6.68 abcd 1.61 fgh 15 Kankunia 41.37 c 1.428 abcd 0.59 abcde 2.92 efgh 0.46 efg 0.254 abcd 0.264 ab 51.97 cdef 148.54 a 12.16 hij 6.87 abcd 1.63 fgh 16 Navara 48.46 b 1.461 abc 0.42 f 3.05 defgh 0.48 cdefg 0.241 abcde 0.249 abc 42.92 fg 160.29 a 13.93 defghi 6.1 bcde 1.71 efgh 17 Putta Batta-2 36.46 cd 1.42 abcd 0.57 bcdef 2.9 fgh 0.46 efg 0.264 ab 0.241 abcde 51.98 cdef 152.87 a 14.5 defghi 7 abcd 1.549 gh 18 HS Kundi polan 61.37 a 1.478 ab 0.55 cdef 2.67 h 0.44 fg 0.229 abcdefg 0.243 abcd 44.956 defg 153 a 13.47 defghi 5.94 cde 1.46 h 19 Krishna Leela 64.51 a 1.511 a 0.51 def 2.7 h 0.4 g 0.224 abcdefg 0.223 bcdefg 44.056 efg 152.375 a 11.06 ij 5.94 cde 1.41 h 20 SC TN-1 41.27 c 1.563 a 0.72 ab 2.69 h 0.42 g 0.276 a 0.285 a 41.95 fg 160.54 a 9.35 j 5.86 cde 1.55 gh 21 RC TKM6 2.46 j 0.864 fgh 0.51 def 4.11 a 0.65 a 0.22 bcdefg 0.185 fg 78.14 a 146.24 a 19.84 ab 7.79 a 3.88 a 22 W1263 2.08 j 0.808 h 0.49 ef 3.99 ab 0.61 ab 0.182 fg 0.202 cdefg 81.066 a 149.21 a 18.49 abc 7.47 abcd 3.4 abc SEm+ 1.24 0.06 0.03 0.15 0.02 0.01 0.01 2.54 2.24 0.66 0.30 0.12 CD @ p = 0.05 3.53 0.16 0.08 0.43 0.07 0.03 0.03 7.25 6.40 1.88 0.86 0.35 Values in the column followed by common letters are non-significant at p = 0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No – number. Potassium Potassium content in rice genotypes declined with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, W1263, Karikagga, Karimunduga, Adri Batta, Nagaland paddy, Rajboga) recorded the highest K levels (2.49–2.83%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) had 2.37–2.41%, moderately susceptible genotypes (Neermullarae, Kave kantak, Neermuka, Manjula Sona) showed 1.94–1.99%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Navara, Kankunia, Bangara kolee, Putta batta-2, TN-1, Krishna leela, Kundi polan) recorded the lowest levels (1.42–1.76%). At 60 DAT, the trend persisted, with resistant genotypes at 3.73–4.11%, moderately resistant 3.60–3.71%, moderately susceptible 3.14–3.25%, and susceptible to highly susceptible 2.67–3.05% (Tables 7 and 8). Calcium Calcium content in rice genotypes decreased with susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, W1263, Nagaland paddy, Karikagga, Karimunduga, Rajboga, Adri Batta) recorded the highest levels (0.34–0.43%), moderately resistant types (Kala Jeera, Black sticky, Nirga samba) had 0.31–0.37%, moderately susceptible genotypes (Kave kantak, Neermuka, Neermullarae, Manjula Sona) showed 0.24–0.29%, and susceptible to highly susceptible genotypes (Navara, Bangara kolee, Kankunia, Putta batta-2, Punkutt kodi-1, TN-1, Krishna leela, Kundi polan) recorded the lowest levels (0.20–0.26%). At 60 DAT, the trend persisted with resistant genotypes (TKM6, W1263, Karikagga, Rajbaga, Nagaland rice, Karimunduga, Adri batta) showing 0.56–0.65%, moderately resistant types 0.48–0.59%, moderately susceptible genotypes 0.45–0.51%, and susceptible to highly susceptible genotypes 0.40–0.48%. (Tables 7 and 8). Magnesium Magnesium content in rice genotypes ranged from 0.15 to 0.24% at 30 DAT and 0.181 to 0.276% at 60 DAT, showing an increasing trend with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, W1263, Adri Batta, Karikagga, Karimunduga, Nagaland paddy, TKM6) recorded the lowest levels (0.15–0.202%), moderately resistant types (Kala Jeera, Nirga samba, Black sticky) had 0.18–0.233%, moderately susceptible genotypes (Manjula Sona, Kave kantak, Neermuka, Neermullarae) showed 0.20–0.25%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Navara, Kankunia, Bangara kolee, Putta batta-2, TN-1, Krishna leela, Kundi polan) recorded the highest levels (0.19–0.276%) (Tables 7 and 8). Sulfur Sulfur content in rice genotypes ranged from 0.15 to 0.29%, increasing with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, Adri Batta, Nagaland paddy/rice, TKM6, Karikagga, Karimunduga, W1263) recorded the lowest levels (0.15–0.20%), moderately resistant types (Kala Jeera, Nirga samba, Black sticky) had 0.17–0.22%, moderately susceptible genotypes (Neermullarae, Neermuka, Manjula Sona, Kave kantak) showed 0.21–0.25%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Putta batta-2, Bangara kolee, Navara, Kankunia, TN-1, Krishna leela, Kundi polan) recorded the highest levels (0.20–0.29%) (Tables 7 and 8). Silicon Silicon content in rice genotypes decreased with susceptibility to yellow stem borer, ranging from 1.26 to 3.82% at initial observation and 1.41 to 3.93% at 60 DAT. Resistant genotypes (Karikagga, Karimunduga, TKM6, Rajboga, Nagaland paddy, Adri Batta, W1263) recorded the highest levels (3.25–3.93%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) had 2.72–3.39%, moderately susceptible genotypes (Kave kantak, Neermullarae, Neermuka, Manjula Sona) showed 2.05–2.32%, and susceptible to highly susceptible genotypes (Navara, Kankunia, Punkutt kodi-1, TN-1, Putta batta-2, Bangara kolee, Kundi polan, Krishna leela) recorded the lowest levels (1.26–1.71%) (Tables 7 and 8). Zinc Zinc content in rice genotypes ranged from 32.84 to 82.32 mg kg⁻¹, decreasing with susceptibility to yellow stem borer. At both 30 and 60 DAT, resistant genotypes (W1263, TKM6, Nagaland paddy/rice, Rajboga, Karimunduga, Adri Batta, Karikagga) contained 51.08–81.07 mg kg⁻¹, moderately resistant types (Kala Jeera, Black Sticky, Nirga Samba) recorded 40.21–82.32 mg kg⁻¹, moderately susceptible genotypes (Kave Kantak, Manjula Sona, Neermullarae, Neermuka) had 32.84–81.32 mg kg⁻¹, and susceptible to highly susceptible genotypes (Bangara Kolee, Kankunia, Punkutt Kodi-1, Navara, Putta Batta-2, TN-1, Krishna Leela, Kundi polan) ranged 41.95–55.28 mg kg⁻¹ (Tables 7 and 8). Iron Iron content in rice genotypes ranged from 138 to 160.54 mg kg⁻¹, increasing with susceptibility to yellow stem borer. Resistant genotypes (Nagaland paddy, Karimunduga, Black Sticky, Rajboga, Adri Batta, Kala Jeera, TKM6, Karikagga) contained 138.33–147.34 mg kg⁻¹, moderately resistant types (Nirga Samba, Krishna Leela, Putta Batta-2, Neermuka, Kala Jeera, Black Sticky) had 143.23–149.87 mg kg⁻¹, moderately susceptible genotypes (Kave Kantak, Neermullarae, Neermuka, Manjula Sona, Kankunia, W1263) recorded 146.00–154.93 mg kg⁻¹, and susceptible to highly susceptible genotypes (Bangara Kolee, Kundi Polan, Punkutt Kodi-1, Navara, Putta Batta-2, Krishna Leela, Manjula Sona, Neermullarae, TN-1) showed 148.54–160.54 mg kg⁻¹ (Tables 7 and 8). Manganese Manganese content in rice genotypes ranged from 8.23 to 19.74 mg kg⁻¹ at 30 DAT and 9.35 to 20.86 mg kg⁻¹ at 60 DAT, decreasing with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, TKM6, Karimunduga, W1263, Nagaland paddy, Adri Batta, Karikagga) contained 14.35–19.74 mg kg⁻¹ at 30 DAT and 15.44–20.86 mg kg⁻¹ at 60 DAT; moderately resistant types (Nirga Samba, Black Sticky, Kala Jeera) had 12.32–15.78 mg kg⁻¹ and 13.44–16.84 mg kg⁻¹, respectively; moderately susceptible genotypes (Neermullarae, Kave Kantak, Manjula Sona, Neermuka) recorded 11.31–14.82 mg kg⁻¹ and 12.38–15.90 mg kg⁻¹; and susceptible to highly susceptible genotypes (Putta Batta-2, Navara, Punkutt Kodi-1, Kankunia, Bangara Kolee, TN-1, Kundi Polan, Krishna Leela) showed 8.23–13.42 mg kg⁻¹ and 9.35–14.50 mg kg⁻¹ at 30 and 60 DAT, respectively (Tables 7 and 8). Copper Copper content in rice genotypes ranged from 4.78 to 8.24 mg kg⁻¹, decreasing with susceptibility to yellow stem borer. Resistant genotypes (Karikagga, TKM6, Adri Batta, W1263, Nagaland paddy, Karimunduga, Rajboga) contained 7.12–8.24 mg kg⁻¹, moderately resistant types (Nirga Samba, Black Sticky, Kala Jeera) had 7.27–8.09 mg kg⁻¹, moderately susceptible genotypes (Kave Kantak, Manjula Sona, Neermullarae, Neermuka) recorded 4.78–7.32 mg kg⁻¹, and susceptible to highly susceptible genotypes (Putta Batta-2, Kankunia, Punkutt Kodi-1, Navara, Bangara Kolee, TN-1, Kundi Polan, Krishna Leela) showed 5.86–7.42 mg kg⁻¹ (Tables 7 and 8). Principal component analysis Biochemical traits and their association with pest infestation A comprehensive principal component analysis (PCA) of seven key biochemical variables measured at 30 and 60 days after transplanting (DAT) revealed a remarkably consistent pattern across both growth stages. At 30 DAT, the analysis reduced the variation among genotypes to a single dominant principal component (PC1), which captured a staggering 91.71% of the total variance (eigenvalue 6.42), demonstrating strong co-variation and integrative shifts among these biochemical traits (Table 11; Fig. 5A & 5B). The most influential contributors to PC1 were total soluble sugars (TSS, 15.18%), crude protein (CP, 14.73%), reducing sugars (RS, 14.47%), and tannins (TA, 14.65%), with significant but slightly lower contributions from days to heading (DH), total phenols (TP), and total free amino acids (TFAA) in the 13–14% range. At 60 DAT, a similarly unidimensional structure emerged: PC1 (eigenvalue 6.36) explained 90.88% of overall variance, with the largest contributions from TSS (14.93%), CP (14.83%), RS (14.69%), and TA (14.75%), again supported by the other defensive and nutritional traits (Table 11; Fig. 6A & 6B) Table 9 Presents the correlation between biochemical constituents and yellow stem borer Biochemical constituents Pearson correlation co efficient (r) [n(Σxy) − ΣxΣy] / √[n(Σx²) − (Σx)²][n(Σy²) − (Σy)²] At 30 DAT At 60 DAT Total soluble Sugars 0.888 ** 0.896 ** Reducing Sugars 0.914 ** 0.903 ** Total Phenols -0.858 ** -0.856 ** Crude Protein 0.869 ** 0.863 ** Total Free amino acid -0.798 ** -0.799 ** Tannins -0.886 ** -0.881 ** *N = 22; ** Significant at P ≤ 0.01; Table 10 Presents the correlation between nutrient content and yellow stem borer incidence Nutrient content Pearson correlation co efficient (r) [n(Σxy) − ΣxΣy] / √[n(Σx²) − (Σx)²][n(Σy²) − (Σy)²] at 30 DAT 60 DAT N 0.872 ** 0.857 ** P -0.483 * -0.498 * K -0.934 ** -0.923 ** Ca -0.854 ** -0.864 ** Mg 0.622 ** 0.638 ** S 0.750 ** 0.732 ** Zn -0.556 ** -0.555 ** Fe 0.683 ** 0.679 ** Mn -0.746 ** -0.747 ** Cu -0.643 ** -0.641 ** Si -0.898 ** -0.897 ** *N = 22; ** Significant at P ≤ 0.01; Table 11 Loading of each trait and % contribution of biochemical variables towards principal components at 30 and 60 DAT during Summer , 2024 Loadings of each variable 30 DAT 60 DAT Variables PC1 PC1 DH 0.366 0.368 TSS 0.39 0.387 RS 0.38 0.383 TP -0.375 -0.372 CP 0.384 0.385 TFAA -0.368 -0.366 TA -0.383 -0.384 % Contribution of variables on PCs Variables PC1 PC1 DH 13.36 13.555 TSS 15.182 14.939 RS 14.474 14.698 TP 14.09 13.812 CP 14.725 14.826 TFAA 13.512 13.412 TA 14.657 14.759 Eigen value 6.42 6.362 Percentage of variance 91.714 90.884 Cumulative percentage of variance 91.714 90.884 , Nutrient components and their association with pest infestation A comprehensive principal component analysis (PCA) of eleven nutrient variables quantified at 30 and 60 days after transplanting (DAT) during Summer 2024 revealed a highly stable dimensional pattern across both stages. At 30 DAT, variation among the nutrient traits was largely condensed into the first principal component (PC1), which alone accounted for 71.63% of the total variance (eigenvalue 7.879), while PC2 contributed an additional 9.61%, cumulatively explaining 81.24% of observed variability (Table 12). The most influential loadings on PC1 were nitrogen (N, 11.83%), potassium (K, 11.90%), calcium (Ca, 11.31%), sulfur (S, 10.92%), magnesium (Mg, 9.13%), iron (Fe, 9.39%), manganese (Mn, 9.43%), copper (Cu, 8.54%), and silicon (Si, 11.92%), suggesting that PC1 predominantly represented a balanced axis of macro- and micro-nutrient contribution. By contrast, PC2 variance (9.61%) was heavily structured around phosphorus (P, 70.21%) and zinc (Zn, 19.25%), indicating a secondary, nutrient-specific gradient (Table 12; Fig. 7A & 7B). At 60 DAT, the pattern was strikingly consistent. PC1 (eigenvalue 7.875) explained 71.59% of total variance, with PC2 adding 9.58%, thereby capturing a cumulative 81.17% (Table 12). As at 30 DAT, PC1 was strongly defined by N (11.93%), K (11.91%), Ca (10.83%), Mg (9.32%), S (10.77%), Fe (9.36%), Mn (9.50%), Cu (8.58%), and Si (11.91%), underscoring the integrative role of these nutrients in shaping overall compositional shifts during crop establishment (Fig. 8A & 8B). PC2 at this stage was again dominated by P (63.41%) and Zn (24.79%), reaffirming a persistent but separate axis of nutrient contribution (Table 12). Table 12 Loading of each trait and % contribution of nutrient variables towards principal components at 30 and 60 DAT during Summer , 2024 Loadings of each variable 30 DAT 60 DAT Variables PC1 PC2 PC1 PC2 N 0.344 0.026 0.345 0.003 P -0.127 0.838 -0.138 -0.796 K -0.345 -0.019 -0.345 0.053 Ca -0.336 -0.116 -0.329 0.066 Mg 0.302 -0.017 0.305 0.062 S 0.33 -0.056 0.328 0.066 Zn -0.201 -0.439 -0.2 0.498 Fe 0.306 -0.187 0.306 0.165 Mn -0.307 -0.166 -0.308 0.129 Cu -0.292 -0.111 -0.293 0.213 Si -0.345 0.113 -0.345 -0.114 % Contribution of variables on PCs Variables PC1 PC2 PC1 PC2 N 11.829 0.068 11.933 0.001 P 1.606 70.213 1.911 63.406 K 11.900 0.036 11.912 0.286 Ca 11.306 1.350 10.832 0.441 Mg 9.134 0.028 9.321 0.386 S 10.92 0.310 10.772 0.441 Zn 4.030 19.247 3.984 24.786 Fe 9.387 3.508 9.356 2.734 Mn 9.432 2.746 9.496 1.667 Cu 8.536 1.225 8.575 4.543 Si 11.919 1.269 11.909 1.310 Eigenvalue 7.879 1.057 7.875 1.054 Percentage of variance 71.632 9.61 71.589 9.577 Cumulative percentage of variance 71.632 81.242 71.589 81.167 Discussion Biochemical basis of resistance Phenols Resistance in rice accessions against yellow stem borer (YSB) is closely associated with a range of biochemical traits that influence insect oviposition, feeding, and survival. Among these, phenolic compounds serve as a primary defense mechanism. Resistant entries consistently exhibit higher phenol content (6.26–7.72 mg g⁻¹) compared to susceptible ones (2.53–3.21 mg g⁻¹), which act as strong feeding deterrents, reduce nutrient utilization, and exert direct toxicity on larvae (Padhi, 2004 ; Suchita et al. 2011 ; Elanchezhyan et al. 2017 ). Stem phenolics further enhance antibiosis by increasing larval mortality (Zhu et al. 2002 ). Resistant varieties such as ACK 14004 and BRNS WP, with phenol levels of 4.08–3.83 mg g⁻¹ FW, have demonstrated anti-feedant and repellent effects (Elanchezhyan et al. 2017 ). These observations are in accordance with the current study, where resistant genotypes exhibited higher phenolic content than susceptible and moderately susceptible genotypes (Table 5). Overall, resistant and moderately resistant landraces consistently maintained elevated phenol concentrations, showing a strong negative correlation with YSB incidence (r = − 0.858** at 30 DAT and r = − 0.856** at 60 DAT) (Fig. 1; Table 9) (Fig. 3), highlighting the defensive role of phenols in enhancing resistance upon infestation. In addition, phenols participate in reactive oxygen species (ROS) scavenging, mitigating oxidative stress caused by radicals such as O₂⁻, OH⁻, H₂O₂, and singlet oxygen, thereby triggering cascades of defense-related enzyme activities (Maffei et al. 2007 ). Elevated phenol levels have also been negatively correlated with infestations by leaf folder, Asian rice gall midge, chilli black thrips and brown planthopper, confirming their central role in host plant resistance (Punithavalli et al. 2013 ; Vijaykumar et al. 2009a ; 2009b ; Kumar et al. 2012 ; Vanitha et al. 2015 ; Ashrith et al. 2017 ; Megha 2019 ; Sadafale et al. 2025 ; Balaji et al. 2025). Tannins Tannins represent a key group of defensive secondary metabolites, particularly abundant in resistant rice genotypes. Their insect-deterrent activity is multifaceted: they act as feeding inhibitors, bind dietary proteins and reduce their digestibility, chelate essential metal ions, suppress digestive enzyme activity, and directly damage the insect midgut epithelium, thereby leading to reduced larval growth, delayed development, and higher mortality (Punithavalli et al. 2013 ; Dubey et al. 2016 ). In the present study, correlation analysis clearly demonstrated a strong negative association between tannin content and YSB infestation, recorded at both 30 days after transplanting (DAT) (r = − 0.886**) and 60 DAT (r = − 0.881**) (Fig. 1; Fig. 3; Table 9), signifying the consistent contribution of tannins to resistance mechanisms across crop growth stages. Earlier studies also highlighted tannins’ effectiveness against cereal shoot fly and stem borer (Khurana & Verma, 1983 ). More recent investigations strengthened this evidence, showing that higher tannin levels are closely linked to reduced YSB damage in rice (Megha 2019 ; Ranjini 2022 ; Sadafale et al. 2025 ; Balaji et al. 2025). Beyond rice, tannins have been reported as a generalized defensive trait in cereals and legumes, conferring protection against a wide spectrum of herbivorous insects. Their quantitative expression is often environment- and genotype-dependent, influenced by soil fertility, stress conditions, and plant phenology. The ability of tannins to simultaneously target insect physiology, nutrient assimilation, and gut integrity makes them particularly important in breeding programs focused on durable pest resistance. Sugars (Soluble and reducing sugars) Carbohydrate metabolism plays a critical role in determining host susceptibility to insect pests. Susceptible rice genotypes generally accumulate higher levels of total and reducing sugars, which provide readily available energy that supports larval development, oviposition, and survival. For example, under YSB infestation, the susceptible check TN 1 recorded 8.02–8.33 mg g⁻¹ of total sugars, while the resistant check W 1263 maintained only 4.43 mg g⁻¹ (Thakur, 2022 ). Vanitha et al. ( 2015 ) similarly reported a strong positive correlation (r = 0.88**) between total sugars and leaf folder damage. In the present study, total soluble sugars showed a significant positive correlation with YSB incidence, with r = 0.888 at 30 DAT and r = 0.896** at 60 DAT, while reducing sugars showed comparable correlations with dead heart incidence (r = 0.914 and r = 0.903**, respectively) (Fig. 1; Table 9). The decline in sugar content after infestation may result from activation of phenylpropanoid pathway enzymes, which convert sugars into phenols, flavonoids, and lignin to reinforce resistance (Kumar et al. 2012 ; Rajadurai & Kumar 2017 ; Megha 2019 ). This diversion reflects a metabolic trade-off, where primary sugars serve as precursors for defense-related secondary metabolites that deter feeding and interfere with insect digestion. Resistant genotypes thus sustain lower sugar levels while channeling them into defensive pathways post-infestation. Furthermore, carbohydrate partitioning is linked to signaling pathways involving jasmonic acid (JA) and salicylic acid (SA), which coordinate the induction of secondary metabolites and systemic defense responses. Therefore, carbohydrate dynamics are not just nutritional determinants of pest success but also key biochemical markers for breeding rice with enhanced resistance to YSB. Crude proteins Crude protein content shows a strong positive correlation with susceptibility to YSB. Susceptible entries such as TN 1 (8.08 mg g⁻¹) and IR 36 (8.05 mg g⁻¹) provide a rich nitrogen source for larvae, enhancing their survival and development, whereas resistant varieties like TKM 6 maintain lower protein levels (6.30 mg g⁻¹), thereby restricting insect growth (Punithavalli et al. 2013 ; Megha 2019 ; Ranjini 2022 ). Protein content typically declines after infestation, particularly in susceptible genotypes, either due to direct consumption by larvae or its mobilization into defense-related pathways (Lokesh & Mehla 2017 ). In the present investigation, correlation analysis confirmed a significant positive association between crude protein content and dead heart incidence, with values of r = 0.869** at 30 DAT and r = 0.863** at 60 DAT (Fig. 1; Table 9). These results are consistent with earlier findings by Punithavalli et al. ( 2013 ), who reported reduced soluble protein concentrations following infestation. In their study, TN 1 (8.08 mg g⁻¹) and IR 36 (8.05 mg g⁻¹) recorded the highest protein contents in healthy plants, while resistant genotypes PTB 33 (6.49 mg g⁻¹) and TKM 6 (6.30 mg g⁻¹) maintained much lower levels. Following infestation, protein content decreased across all genotypes, with TN 1 showing 7.61 mg g⁻¹ and TKM 6 recording 6.21 mg g⁻¹. Similarly, in the present study, the resistant check TKM 6 maintained the lowest crude protein levels at 30 DAT (2.95 mg g⁻¹). Reports by Megha ( 2019 ) and Ranjini ( 2022 ) further support that protein content declines in infested rice genotypes and that crude proteins are positively and significantly associated with insect growth, development, and life cycle progression (Lokesh & Mehla 2017 ). Free amino acids Interestingly, free amino acids (FAAs) exhibit an opposite trend to crude proteins, with resistant entries such as W 1263 and TKM 6 maintaining significantly higher levels (22.13 mg g⁻¹) than susceptible checks like TN 1 (16.21 mg g⁻¹). Although amino acids serve as essential nutrients for insect growth and reproduction, in resistant plants they are often redirected into the phenylpropanoid pathway, contributing to the biosynthesis of phenolics and other defense compounds. In the present study, correlation analysis confirmed a significant negative association between total free amino acids and YSB incidence, with r = − 0.798** at 30 DAT and r = − 0.799** at 60 DAT (Fig. 1; Table 9). These findings accord with Megha 2019 , who reported higher FAA concentrations in resistant genotypes. For instance, W 1263 recorded 22.13 mg g⁻¹ with a strong negative influence on dead heart incidence, while susceptible genotypes like TN 1 and Kankunia maintained lower levels (~ 16.2 mg g⁻¹), showing significant negative correlations (up to r = − 0.982**). Similarly, Kumar et al. 2012 noted higher FAA levels in resistant lines such as JGL 13595 (34.82 mg g⁻¹), compared with much lower values in TN 1 (16.45 mg g⁻¹). Overall, FAAs function not only as nutrients involved in protein synthesis and development but also as precursors for defensive secondary metabolites. Their reduction following infestation reflects diversion into the phenylpropanoid pathway, enhancing the formation of phenols and lignin, which strengthen plant resistance against both biotic and abiotic stress. Collectively, these biochemical traits—including elevated phenols, tannins, and free amino acids, alongside lower total sugars and soluble proteins—form a robust defense network. Nutritional components modulate host suitability, while secondary metabolites confer antibiosis, antixenosis, and deterrence, establishing a strong biochemical basis for developing insect-resistant rice varieties through breeding and metabolic manipulation. Nutritional basis of resistance against Yellow stem borer Plant nutrition plays a central role in determining crop susceptibility or resistance to insect pests and diseases. Both macro- and micro-nutrients, along with beneficial elements such as silicon, influence host defense through structural reinforcement, metabolic adjustments, and activation of defense signalling pathways. The present investigation highlights the multifaceted roles of essential nutrients in modulating resistance against yellow stem borer ( Scirpophaga incertulas ), supported by correlation analysis with infestation parameters. Nitrogen, while indispensable for plant growth, exhibits a dual effect on pest interactions. Excess nitrogen enhances leaf succulence, chlorophyll content, and soluble nitrogen compounds, increasing tissue nutritional value for herbivores (Mattson, 1980 ). At the same time, it suppresses the biosynthesis of phenolics, lignin, and silica, weakening structural and biochemical defenses (Huber et al. 2012 ; Tripathi et al. 2022). In rice, higher nitrogen levels significantly increased yellow stem borer, brown planthopper, leaf folder, and rice hispa infestations (Prasad & Prasad, 1994 ; Sogawa, 1992 ; Heinrichs & Aquino, 1982 ). The present study corroborates this, with a strong positive correlation between nitrogen content and yellow stem borer infestation at 30 DAT (r = 0.872**) and 60 DAT (r = 0.857**) (Fig. 2; Table 10). Similar trends were observed across wheat, cotton, maize, and sorghum, emphasizing that excessive or imbalanced nitrogen application favors pest proliferation. This highlights the need for precision nutrient management integrated with pest control. In contrast, certain nutrients enhance host resistance by strengthening structural components and activating secondary metabolism. Phosphorus, for example, promotes lignin and phenolic synthesis, increasing tissue toughness and reducing palatability to herbivores. Adequate phosphorus also reinforces stalks and roots, limiting susceptibility to boring insects. In this study, phosphorus content was negatively correlated with yellow stem borer infestation (r = − 0.483** at 30 DAT; r = − 0.498* at 60 DAT) (Fig. 2; Table 10), consistent with reports in sugarcane, maize, rice, and soybean where higher phosphorus reduced stalk and stem borer incidence by enhancing lignification and optimizing the tissue carbon–phosphorus balance (Hall, 2002 ; Shahzad et al. 2017 ; Facknath & Lalljee, 2005 ; Johnson et al. 2007 ). Potassium further supports plant defenses by regulating osmotic balance and facilitating polyphenol accumulation, thereby reducing tissue suitability for herbivores. In the present study, a strong negative association with yellow stem borer infestation was observed at both 30 DAT (r = − 0.934**) and 60 DAT (r = − 0.923**) (Fig. 2; Table 10; Fig. 4). Adequate potassium similarly reduced pest damage in rice, cotton, and maize, lowering dead heart incidence, bollworm feeding, aphid populations, and larval growth of stem borers and fall armyworm (Voleti et al. 2008 ; Pettigrew, 2008 ; Bala et al. 2018 ; Amtmann et al. 2008 ). Conversely, potassium deficiency predisposed plants to higher pest damage due to weakened tissues and diminished biochemical defenses Calcium and magnesium exemplify nutrients with contrasting but complementary roles. Calcium strengthens cell walls through pectin cross-linking and mediates signaling pathways such as salicylic and jasmonic acid, which regulate defensive protein and metabolite synthesis. A significant negative correlation with yellow stem borer infestation was observed (r = − 0.854** at 30 DAT; r = − 0.864** at 60 DAT) (Fig. 2; Table 10), confirming its protective role, as also reported in cotton and grapevine (Srinivasan et al. 2006 ; Volpe et al. 2018 ). Magnesium, essential for chlorophyll biosynthesis and enzyme activation, showed a positive association with infestation (r = 0.622** at 30 DAT; r = 0.638** at 60 DAT) (Fig. 2; Table 10), suggesting that higher magnesium levels may increase tissue nutritional quality, inadvertently supporting larval development. Similar context-dependent effects have been reported in wheat and soybean (Cakmak and Yazici 2010 ; Cheng et al. 2017 ; Marschner 2012). Sulfur, zinc, and iron influence both plant metabolism and pest interactions. While sulfur is vital for defense-related compounds such as glucosinolates, cysteine, and methionine, it was positively correlated with yellow stem borer infestation (r = 0.750** at 30 DAT; r = 0.732** at 60 DAT) (Fig. 2; Table 10), likely due to improved tissue nutritional quality under certain conditions. Zinc contributed to membrane stability and antioxidant enzyme function, exhibiting a negative correlation with infestation (r = − 0.555**) and supporting reduced dead heart and white ear incidence. Iron, despite its role in lignification and oxidative defense, showed a positive association with infestation (r = 0.682 at 30 DAT; r = 0.679 at 60 DAT), suggesting elevated iron may favor larval development (Datnoff et al. 2007 ; Zheng et al. 2009 ; Suri et al. 2019 ; Prasanna et al. 2011 ). Manganese and copper consistently enhanced resistance through structural reinforcement and activation of secondary metabolism. Manganese activated phenylalanine ammonia-lyase, promoting lignin and phenolic synthesis, with a strong negative correlation with yellow stem borer infestation (r = − 0.746** at 30 DAT; r = − 0.747** at 60 DAT) (Fig. 2; Table 10). Copper, as a cofactor for polyphenol oxidases, similarly reinforced defenses (r = − 0.643** at 30 DAT; r = − 0.641** at 60 DAT), strengthening both structural and enzymatic barriers against herbivory. Silicon, although not considered an essential nutrient, plays a unique and multifaceted role in pest resistance. Silicon deposits in epidermal tissues act as a mechanical barrier, physically restricting larval penetration. In addition, it primes biochemical defenses by enhancing the synthesis of phenolics, phytoalexins, and antioxidative enzymes. In the present study, silicon content showed a strong negative correlation with yellow stem borer infestation at initial observation (r = − 0.898**) and 60 DAT (r = − 0.897**) (Fig. 2; Fig. 4; Table 10), which translated into significantly reduced dead heart and white ear incidence. These observations are consistent with previous studies in rice, where silicon supplementation decreased stem borer damage, blast, and sheath blight severity (Datnoff et al. 1997 ; Voleti et al. 2008 ; Ma and Takahashi 2002 ), highlighting its dual mechanical and biochemical contribution to resistance. Collectively, these results demonstrate that nutrient availability exerts profound effects on yellow stem borer infestation, largely through modulation of structural integrity, secondary metabolism, and enzymatic defenses. While nitrogen, magnesium, sulfur, and iron may, under certain conditions, favor pest development, nutrients such as phosphorus, potassium, calcium, manganese, copper, zinc, and silicon consistently enhance resistance. These findings underscore the critical importance of balanced and integrated nutrient management as a key strategy for sustainable pest suppression in rice and other cropping systems. Principal component analysis Biochemical traits and their association with pest infestation The PCA individual and biplot analysis at 30 and 60 DAT revealed the biochemical variability underlying rice genotypes’ responses to yellow stem borer (YSB) infestation. At 30 DAT, the first principal component (PC1) explained the majority of the variation, whereas the second component (PC2) contributed minimally, providing slight vertical separation. Genotypes positioned on the right, such as Krishna Leela and Kundi Polan, were closely associated with rightward-directed traits, while those on the left, including TKM6 and Rajboga, exhibited contrasting biochemical profiles aligned with leftward traits. Clusters of genotypes reflected shared biochemical characteristics, whereas genotypes further apart, for instance Krishna Leela and Neermuka, demonstrated distinct profiles. These patterns suggested that variation was governed by the combined influence of multiple biochemical factors rather than a single dominant trait (Figs. 5A and 5B). A similar pattern was observed at 60 DAT. PC1 again captured the majority of variation, with PC2 contributing slightly to vertical differentiation. Right-side genotypes, such as Kundi Polan and TN-1, were strongly associated with Dead Heart (DH), Total Soluble Sugars (TSS), and Feeding Preference (FP), whereas left-side genotypes, including W1263 and Nirga Samba, were linked with Total Free Amino Acids (TFA) and Total Amino Acids (TA). Slight vertical separation was evident among genotypes such as TKM6 and Manjula Sona. Distant genotypes, for example Kundi Polan and Neermuka, showed clear biochemical divergence, while clusters represented genotypes with similar biochemical characteristics. The balanced contributions of multiple traits indicated that biochemical diversity arose from complex interactions rather than dominance of a single factor (Figs. 6A and 6B). The present study further identified total soluble sugars, reducing sugars, crude protein, and tannins as the dominant contributors to biochemical variability associated with YSB incidence at both 30 and 60 DAT. These findings align with earlier studies showing that primary metabolites such as sugars and proteins often promote pest infestation, whereas secondary metabolites like phenolic compounds and tannins act defensively. For example, Batra et al. 2018 demonstrated that elevated nutrient contents, particularly sugars and proteins, enhanced aphid colonization in barley. Similarly, Kumar et al. 2019 reported that higher protein levels in black gram supported larval growth of Callosobruchus maculatus , and in sugarcane, reducing sugars correlated positively with top borer incidence, while proteinase inhibitor activity, a defense-related protein, showed negative association (Bharti et al. 2019 ). Conversely, resistant genotypes often contained higher concentrations of phenolic compounds. In muskmelon, resistance to Bactrocera cucurbitae was associated with elevated phenols, tannins, and flavonoids, whereas susceptible lines exhibited higher sugar content (Bhat et al. 2014 ). Similarly, compensatory induction of phenolics and condensed tannins in aspen reduced aphid populations (Bandau et al. 2022 ). Studies in stored grains showed that high protein and soluble sugar levels favored development of Rhyzopertha dominica , further confirming the role of primary metabolites in susceptibility (Jaiswal et al. 2024 ). Taken together, these studies reinforce the PCA results in the present investigation, where sugars and crude protein loaded strongly on PC1, explaining a major proportion of variance and aligning with susceptibility traits. Phenols and tannins, although contributing less to variance, represented defense-associated metabolites conferring tolerance. The consistent clustering of these biochemical traits across crops highlights their potential as reliable biochemical markers for resistance breeding and integrated pest management strategies against YSB. Nutrient components and their association with pest infestation The PCA individual and biplots (Fig. 7 and Fig. 8) at 30 and 60 DAT during Summer 2024 revealed a consistent nutrient association pattern, with PC1 (71.6%) driven predominantly by macronutrients including N, K, Ca, Mg, S, Fe, Mn, Cu, and Si, while PC2 (9.6%) was mainly influenced by P and Zn. Genotypes such as W1263 and TKM6 were strongly associated with macronutrients, whereas TN-1 and Neermuka showed contrasting nutrient profiles. Rajboga, Naland Paddy, Karikagga, Kari Munduga, and Adri Batta clustered under the influence of P and Zn, while genotypes like Bangara Kolee, Puttabatta, and Neermullarae remained close to the origin, indicating balanced nutrient associations. The vectors of N, Mg, S, and Fe exhibited positive correlation, K and Si acted in the opposite direction, and Zn, Mn, and Cu jointly influenced genotypes like Kala Jeera, Black Sticky, and Vijaya Samba. Overall, both Fig. 7B and Fig. 8B confirm that genotypic variation is largely explained by a broad macro–micro nutrient balance (PC1), with P and Zn serving as distinct determinants of separation along PC2. The present PCA results demonstrated a highly stable dimensional structure across crop growth stages, with PC1 consistently explaining over 71% of the variation and reflecting a broad nutrient balance shaped by N, K, Ca, S, Mg, Fe, Mn, Cu, and Si. This integrative nutrient axis indicates that both macro- and micro-elements act in coordination to define the compositional landscape of rice during establishment, a trend also reported in earlier multivariate studies where N and K emerged as central drivers of growth, photosynthetic efficiency, and yield stability (Ahmad et al. 2016 ; Ali et al. 2019 ). The co-loading of Ca, S, and Mg supports their synergistic role in structural integrity and metabolic processes, while the association of Fe, Mn, and Cu underscores their involvement in energy metabolism, enzyme activation, and stress defense (Broadley et al. 2012 ; Farooq et al. 2020 ). By contrast, PC2 consistently isolated phosphorus and zinc across both stages, accounting for about 10% of the variance, highlighting a nutrient-specific gradient. This separation aligns with previous findings that P and Zn interactions often diverge due to competitive uptake and antagonistic behavior in soil–plant systems (Singh et al. 2017 ; Kumar et al. 2021 ). Overall, the genotypic divergence observed at 30 and 60 DAT was driven not only by a broad nutrient balance (PC1) but also by distinct P–Zn dynamics (PC2), emphasizing that while rice genotypes primarily differ along a stable macro–micro nutrient balance axis, P and Zn consistently emerge as independent determinants of variability, with important implications for nutrient use efficiency, stress resilience, and breeding strategies. Conclusion The study highlights the significant role of both biochemical and nutritional traits in shaping resistance to the yellow stem borer in rice landraces. Elevated phenols, tannins, and free amino acids served as reliable defensive markers, whereas higher sugars and proteins were linked to greater susceptibility. Among nutrients, nitrogen, magnesium, sulfur, and iron favored infestation, while phosphorus, potassium, calcium, manganese, copper, zinc, and silicon consistently enhanced resistance through structural reinforcement and metabolic regulation. PCA further revealed a stable nutrient–resistance framework across crop stages, with a broad macro–micro nutrient balance driving most genotypic variation and phosphorus–zinc dynamics forming a distinct secondary axis. These results emphasize the importance of balanced nutrient management and the potential of resistant landraces as donors in breeding programs. Harnessing their inherent biochemical defenses and favorable nutrient interactions can contribute to the development of durable YSB-resistant rice cultivars, thereby reducing dependence on chemical control and supporting sustainable rice production. Declarations Competing Interests . The authors declare no competing interests. Ethics Approval and Consent to Participate. This article does not contain any studies with human or animal subjects. Consent for Publication . Obtained from all authors. Author Contribution Divya DM - Conceptualization; Data curation; Formal analysis; Investigation; Writing-original draft. Vijaykumar L – Conceptualization; Writing; review & editing; Validation; Methodology; Investigation. Shivanna B, Lakshminarayana Reddy CN- Conceptualization; Methodology; Supervision; Review & editing; Visualization; Validation; Methodology. Acknowledgements The authors express their sincere gratitude to the authorities of the University of Agricultural Sciences, Bangalore, for their support. Special thanks are extended to the Director of Research for their guidance and facility. The second author gratefully acknowledges the Science and Engineering Research Board (SERB), Ministry of Science and Technology, Government of India, New Delhi, for providing moral support. Materials Availability. Not applicable. Code Availability. Not applicable. Data availability. The datasets generated during and/or analysed during the current study are available from the corresponding authors upon reasonable request. 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Voleti SR, Nanda A, Patra SK (2008) Influence of nutrient management on rice stem borer incidence and silicon accumulation. Int Rice Res Notes 33(2):37–39. Volpe V, Dell’Aglio E, Fontana A (2018) Calcium signaling in grapevine resistance. Plant Physiol Biochem 132:221–230. https://doi.org/10.1016/j.plaphy.2018.08.030 Wang M, Zheng Q, Shen Q, Guo S (2013) The critical role of potassium in plant stress response. Int J Mol Sci 14:7370–7390. https://doi.org/10.3390/ijms14047370 War AR, Paulraj MG, War MY, Ignacimuthu S (2012) Role of salicylic acid in induction of plant defense system in chickpea (Cicer arietinum L.). Plant Signal Behav 7(3):235–241. https://doi.org/10.4161/psb.18892 White PJ, Broadley MR (2003) Calcium in plants. Ann Bot 92(4):487–511. https://doi.org/10.1093/aob/mcg164 Yruela I (2009) Copper in plants: Acquisition, transport and interactions. Funct Plant Biol 36(5):409–430. https://doi.org/10.1071/FP08288 Zheng L, Yamaji N, Yokosho K, Ma JF (2009) Element uptake and distribution in rice. Plant Cell Physiol 50(1):188–198. https://doi.org/10.1093/pcp/pcn180 Zhu J, Chen H, Yu Y (2002) Phenolic-mediated antibiosis in rice against stem borers. Entomol Exp Appl 105(3):249–256. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9567632","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634818746,"identity":"67658a2b-8300-49d5-9bc9-0420679bb978","order_by":0,"name":"Divya D Mallesh","email":"","orcid":"","institution":"University of Agricultural Sciences, Bangalore","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"D","lastName":"Mallesh","suffix":""},{"id":634818747,"identity":"610f4e4d-3d22-481d-8c0e-e855fea3890e","order_by":1,"name":"Vijaykumar Lingaraj","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACHhhDgvkAiJQhRQtbAojkwa0UUwuPAQofJzA4c8Z0w889h+XlZ/d8fnWjxoKHgf3w0Q14tZztMbvZ8+yw4YY7Z7dZ5xwDOownLe0GPi2S/TxmN3gOHGbcIJG7zTiHDahFAihCSMvNPwcO28+fkfPMOOcfEVr4eXvMbgNtSWy4kcP8OLeNGC08x8puyxxIT95w55gZc26fBA8bIb+w8SRvu/nmgLXt/NnNjz/nfKuT42c/fAyvFhTtEmCSWOUgwPyBFNWjYBSMglEwcgAAzk5KEvj9EiEAAAAASUVORK5CYII=","orcid":"","institution":"University of Agricultural Sciences, Bangalore","correspondingAuthor":true,"prefix":"","firstName":"Vijaykumar","middleName":"","lastName":"Lingaraj","suffix":""},{"id":634818748,"identity":"5bf0d2bf-f0ad-4991-a911-b5f156d173a3","order_by":2,"name":"Shivanna Bynayakala","email":"","orcid":"","institution":"University of Agricultural Sciences, Bangalore","correspondingAuthor":false,"prefix":"","firstName":"Shivanna","middleName":"","lastName":"Bynayakala","suffix":""},{"id":634818749,"identity":"94885019-9d07-4acd-aee6-2e6589eebcef","order_by":3,"name":"Lakshminarayana Reddy CN","email":"","orcid":"","institution":"University of Agricultural Sciences, Bangalore","correspondingAuthor":false,"prefix":"","firstName":"Lakshminarayana","middleName":"Reddy","lastName":"CN","suffix":""}],"badges":[],"createdAt":"2026-04-29 15:09:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9567632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9567632/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109186684,"identity":"e65fefc9-549a-461d-9bd4-ba44fe1218b4","added_by":"auto","created_at":"2026-05-13 11:14:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103340,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrix heat maps showing the relationship between pest infestation and Biochemical constituents at 30 DAT (A) and 60 DAT (B).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/2db554452fc2b4d228896634.png"},{"id":109186710,"identity":"4246de74-ee19-4e18-871b-187a070e6b18","added_by":"auto","created_at":"2026-05-13 11:15:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135912,"visible":true,"origin":"","legend":"\u003cp\u003e(A–D). Pearson correlation matrix heat maps showing the relationship between pest infestation and nutrient content at 30 DAT (A–B) and 60 DAT (C–D).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/e074f2f1eb35534cf1476f96.png"},{"id":109186682,"identity":"0a01327e-b471-4148-8d72-272111d04650","added_by":"auto","created_at":"2026-05-13 11:14:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305195,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between pest infestation and tannin, phenol content at 30 and 60 DAT during, \u003cem\u003eSummer\u003c/em\u003e2024\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/8047fc6076405f896bdd4a78.png"},{"id":109186711,"identity":"0a21fc34-8f8d-4141-8b66-113c76236824","added_by":"auto","created_at":"2026-05-13 11:15:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":295023,"visible":true,"origin":"","legend":"\u003cp\u003eRelation between pest infestation and Si, K content at 30 and 60 DAT during \u003cem\u003eSummer\u003c/em\u003e2024\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/3711843488efc7791a5deae1.png"},{"id":109186709,"identity":"7f3ad9cb-0f1a-4f07-8b31-b307d984842c","added_by":"auto","created_at":"2026-05-13 11:15:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":211554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Individual contribution of biochemical constituent variables to principal components at 30 DAT during \u003cem\u003eSummer\u003c/em\u003e2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e PCA biplot showing the contribution of biochemical constituent variables to principal components at 30 DAT during \u003cem\u003eSummer\u003c/em\u003e 2024.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/c97cc8c36187759f0f331a82.png"},{"id":109186687,"identity":"4dce06f0-b364-4432-ae62-8bf54b7f46c2","added_by":"auto","created_at":"2026-05-13 11:14:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":204356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Individual contribution of biochemical constituent variables to principal components at 60 DAT during \u003cem\u003eSummer\u003c/em\u003e2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e PCA biplot showing the contribution of biochemical constituent variables to principal components at 60 DAT during \u003cem\u003eSummer\u003c/em\u003e 2024.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/35d22da7a6c809c691b5c324.png"},{"id":109186685,"identity":"2c8aed1c-b732-407f-93e6-bb7cb77013cb","added_by":"auto","created_at":"2026-05-13 11:14:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. PCA showing the individual contribution of nutrient variables to principal components at 30 DAT during \u003cem\u003eSummer\u003c/em\u003e2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e PCA biplot illustrating the contribution of nutrient constituent variables to principal components at 30 DAT during summer 2024.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/eea9c0f1ef916ba63d713fa5.png"},{"id":109186669,"identity":"ba995b9f-d83c-4c90-ab94-dc11425b82ad","added_by":"auto","created_at":"2026-05-13 11:14:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e8A\u003c/strong\u003e. PCA showing the individual contribution of nutrient variables to principal components at 60 DAT during \u003cem\u003eSummer\u003c/em\u003e2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. PCA biplot illustrating the contribution of nutrient constituent variables to principal components at 60 DAT during summer 2024.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/bedd8cac5214c9ea63d67f3c.png"},{"id":109186737,"identity":"5aeaddbf-9a23-47a7-a4fd-e7b46e3d2926","added_by":"auto","created_at":"2026-05-13 11:15:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3298416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9567632/v1/4f0d88b9-0b69-4aad-8bd8-6574d05bea25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering resistance traits in rice landraces against Yellow stem borer Scirpophaga incertulas (Walker) through biochemical and Principal Component Analysis approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) is a cornerstone of global food security, serving as the staple food for more than half of the world\u0026rsquo;s population and contributing nearly 20% of the total caloric intake (Fukagawa \u0026amp; Ziska, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Asia accounts for over 90% of rice production and consumption, underscoring its vital role in regional economies and livelihoods (FAO, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In India, rice is cultivated on approximately 463.79 lakh hectares, with an annual production of 130.29\u0026nbsp;million tonnes and an average productivity of 2,809 kg ha⁻\u0026sup1;, ranking the country second only to China in both production and consumption (Anonymous, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the combined effects of population growth, increasing food demand, and declining per capita arable land pose significant challenges to sustaining and enhancing rice productivity (Giri et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRice production is constrained by numerous abiotic and biotic stresses. Among abiotic stresses, drought, salinity, and submergence significantly limit productivity, while biotic stresses are responsible for large-scale losses in both yield and grain quality. Globally, weeds account for 15\u0026ndash;20% of yield loss, insect pests for 10\u0026ndash;15%, and diseases for 5\u0026ndash;10% (Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Within insect pests, stem borers contribute\u0026thinsp;~\u0026thinsp;40% of total pest-induced yield losses, followed by planthoppers (25%) and gall midges (10%) (Vijaykumar et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e; Morya et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Outbreaks of these pests can occasionally lead to complete crop failure, further exacerbating food insecurity (Reddy et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Balaji and Vijaykumar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Among stem borers, the yellow stem borer (YSB), \u003cem\u003eScirpophaga incertulas\u003c/em\u003e (Walker), is the most destructive and widespread pest of rice in South and Southeast Asia. It attacks rice plants throughout their life cycle, causing 25\u0026ndash;70% yield losses depending on the crop growth stage and severity (Catling et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Pasalu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The caterpillars bore into tillers at the vegetative stage, producing \u0026ldquo;dead hearts,\u0026rdquo; while damage during the reproductive stage results in \u0026ldquo;white ears,\u0026rdquo; leading to direct loss of panicle yield (Karthikeyan \u0026amp; Purushothaman, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Megha et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Balaji et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In India alone, annual yield losses due to YSB are estimated between 2\u0026ndash;3\u0026nbsp;million tonnes, highlighting its national significance (Krishnaiah \u0026amp; Varma, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChemical insecticides are widely used to manage yellow stem borer; however, their effectiveness is limited due to the cryptic feeding habit of larvae inside the plant stem (Vijaykumar et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Continuous and indiscriminate pesticide use has led to multiple problems, including insecticide resistance, pest resurgence, and elimination of natural enemies, ecological imbalance, and residue hazards (Dhaliwal et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Baruah et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, chemical control increases production costs, which is not sustainable for smallholder farmers. These limitations highlight the urgent need for eco-friendly and durable management strategies. Host Plant Resistance (HPR) is recognized as the most economical, farmer-friendly, and environmentally safe approach for insect pest management (Sharma, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Dhillon et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vijaykumar et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e; \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Megha et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Vijaykumar et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Resistant and moderately resistant genotypes can reduce pest pressure, are compatible with other integrated pest management (IPM) strategies, and do not impose additional input costs (Pathak and Khan, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Screening of diverse germplasm has led to the identification of promising YSB-resistant lines in earlier studies, but resistance breakdown due to evolving pest populations necessitates continuous evaluation of landraces and wild relatives (Ramesh et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to morphological defenses, biochemical and nutritional traits of rice plants play a pivotal role in conferring resistance to rice yellow stem borer (Vijaykumar et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Punithkumar et al. 2020; Vijaykumar et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Vinutha et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plants produce a wide range of secondary metabolites and structural compounds that influence herbivore feeding, survival, and reproduction. For example, higher levels of phenols, tannins, flavonoids, and lignin are associated with reduced larval feeding and tunnelling in rice stem borer and Asian rice gall midge (Panda \u0026amp; Khush, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Chavan and Patel, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vijaykumar et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; balaji et al. 2025). Silicon deposition strengthens plant cell walls and impairs insect feeding efficiency, providing both mechanical and biochemical defense (Ma and Yamaji, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vijaykumar et al. 2007; Balaji \u0026amp; Jambagi, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, elevated protein and proline contents have been correlated with enhanced resistance, while high sugar levels often predispose plants to greater susceptibility (Kumar, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Amsagowri et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vanitha et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Recent studies have further emphasized the role of nutrient balance in rice resistance to YSB. Nutrients such as nitrogen, potassium, and micronutrients influence the expression of defense-related genes and biochemical pathways, altering the susceptibility of rice plants to stem borers (Rajareddy et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, investigating the interplay between biochemical constituents and nutrient levels offers new insights into host plant resistance mechanisms.\u003c/p\u003e \u003cp\u003eGiven the heavy reliance on chemical pesticides and the rising concern over environmental sustainability, exploring local landraces with inherent resistance traits holds immense potential. Landraces often harbor unique genetic and biochemical characteristics that can be exploited for breeding resistant cultivars (Sathish et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Field evaluation of these landraces, coupled with detailed analysis of biochemical and nutritional traits, provides a comprehensive approach to understanding resistance mechanisms against YSB. Furthermore, multivariate tools such as Principal Component Analysis (PCA) enable the identification of key resistance traits, offering valuable guidance for future breeding programs.\u003c/p\u003e \u003cp\u003eTherefore, the present study was designed to assess the resistance of local rice landraces against the yellow stem borer (YSB), \u003cem\u003eScirpophaga incertulas\u003c/em\u003e, under natural field conditions, to identify promising sources of tolerance within traditional germplasm. In addition to evaluating field-level resistance, the study emphasized understanding the biochemical and nutritional basis underlying these defense mechanisms by examining key constituents such as phenols, tannins, silica, nitrogen, and carbohydrates that influence pest feeding behavior, larval survival, and crop susceptibility. To further strengthen the analysis, principal component analysis (PCA) was employed as a multivariate tool to integrate and interpret complex biochemical and agronomic data, enabling the identification of major traits that contribute most significantly to resistance differentiation among the tested landraces. This comprehensive approach provides valuable insights into the inherent defense potential of traditional rice varieties and establishes a foundation for their future utilization in breeding programs aimed at developing YSB-resistant cultivars.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField evaluation of rice landraces for YSB resistance\u003c/h2\u003e \u003cp\u003eField evaluation of local rice landraces and popular cultivars for resistance against yellow stem borer (\u003cem\u003eScirpophaga incertulas\u003c/em\u003e) was conducted at A-block, College of Agriculture, V.C. Farm, Mandya, University of Agricultural Sciences, GKVK, Karnataka during the Rabi 2023 \u0026amp; Summer 2024 seasons.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScreening material\u003c/h3\u003e\n\u003cp\u003eA total of 50 local rice landraces (Table\u0026nbsp;3; Table\u0026nbsp;4) were collected from the Zonal Agricultural Research Station, V.C. Farm, Mandya, and sown separately for evaluation. Twenty-five-day-old seedlings were transplanted in three rows with a spacing of 20 cm between rows and 15 cm between plants. All entries were managed according to recommended agronomic practices, except for plant protection measures (Anonymous, 2016).\u003c/p\u003e\n\u003ch3\u003eField assessment of YSB infestation\u003c/h3\u003e\n\u003cp\u003eInfestation by YSB was recorded during the vegetative stage (before panicle emergence) by counting the number of dead hearts relative to the total number of tillers in 10 randomly selected hills per entry at 30 and 60 days after transplanting (DAT). At pre-harvest, YSB infestation was assessed by counting the number of ear-bearing tillers and white ears in 10 randomly selected hills, and percent white ears was calculated at 100 and 120 DAT.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{D}\\text{e}\\text{a}\\text{d}\\:\\text{h}\\text{e}\\text{a}\\text{r}\\text{t}\\:\\left(\\text{%}\\right)=\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{o}.\\:\\text{o}\\text{f}\\:\\text{d}\\text{e}\\text{a}\\text{d}\\:\\text{h}\\text{e}\\text{a}\\text{r}\\text{t}\\text{s}\\:\\text{p}\\text{e}\\text{r}\\:10\\:\\text{h}\\text{i}\\text{l}\\text{l}\\:}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{o}.\\:\\text{o}\\text{f}\\:\\text{t}\\text{i}\\text{l}\\text{l}\\text{e}\\text{r}\\text{s}}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{h}\\text{i}\\text{t}\\text{e}\\:\\text{e}\\text{a}\\text{r}\\:\\left(\\text{%}\\right)=\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{o}.\\:\\text{o}\\text{f}\\:\\text{w}\\text{h}\\text{i}\\text{t}\\text{e}\\:\\text{e}\\text{a}\\text{r}\\text{s}\\:\\text{p}\\text{e}\\text{r}\\:10\\:\\text{h}\\text{i}\\text{l}\\text{l}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{o}.\\:\\text{o}\\text{f}\\:\\text{p}\\text{a}\\text{n}\\text{i}\\text{c}\\text{l}\\text{e}}\\times\\:100$$\u003c/div\u003e\u003c/div\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\u003eStandard Evaluation System for Rice against YSB (IRRI, 2013)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFor dead heart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eFor white ear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer cent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePer cent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighly resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHighly resistance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eResistance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModerately resistance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerately susceptible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u0026ndash;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModerately susceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u0026ndash;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61% and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighly susceptible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26% and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHighly susceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean and standard deviation were calculated, and genotypes were classified into different resistance categories based on infestation levels. Scoring and interpretation of YSB infestation followed the Standard Evaluation System (SES) for rice developed by the International Rice Research Institute (IRRI, 2013) (Table\u0026nbsp;1). Based on initial screening, 19 promising genotypes, representing different resistance categories along with a susceptible check (TN-1) and resistant checks (TKM6 and W1263), were re-evaluated during the \u003cem\u003eSummer\u003c/em\u003e 2024 season.\u003c/p\u003e\n\u003ch3\u003eBiochemical and nutritional analysis\u003c/h3\u003e\n\u003cp\u003eFor the selected genotypes, biochemical constituents and nutrient contents were estimated in rice stems of 30- to 60-day-old plants representing different resistance categories. The un-infested portions of each stem were analyzed for biochemical components, including total sugars, reducing sugars, total phenols, tannins, crude proteins, and total free amino acids, as well as for nutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), zinc (Zn), iron (Fe), manganese (Mn), copper (Cu), and silicon (Si). All analyses were conducted following standard procedures and protocols to elucidate the biochemical and nutritional factors underlying resistance and susceptibility among the rice landraces (Table\u0026nbsp;2). Stem samples were dried in a hot air oven at 35\u0026deg;C for 24\u0026ndash;48 hours. The dried samples were then ground using a mixer grinder, and the resulting powder was stored in plastic covers until further analysis.\u003c/p\u003e\n\u003ch3\u003eExtraction of plant tissues in alcohol\u003c/h3\u003e\n\u003cp\u003eStem samples from selected rice landraces at 30 and 60 days old were thoroughly washed with distilled water and shade-dried. Ten grams of each sample were placed in separate conical flasks, and 150 mL of 80% ethanol was added. The mixture was refluxed on a hot water bath for 30 minutes. After cooling, the tissues were ground thoroughly in a mortar and pestle with a small amount of ethanol. The supernatant was decanted into another flask, and the residue was re-extracted with a small quantity of hot ethanol and decanted again. The combined extract was filtered through Whatman No. 1 filter paper and made up to a known volume with 80% ethanol. The alcoholic extract was stored in a refrigerator at 4\u0026deg;C and subsequently used for the estimation of major biochemical constituents in rice stems following standard protocols.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe mean values of biochemical components in different rice landraces were analyzed using analysis of variance (ANOVA). Multiple comparisons of means were performed using Tukey\u0026rsquo;s honestly significant difference (HSD) test (Tukey, 1953). The relationship between YSB infestation levels and biochemical constituents was evaluated using Pearson\u0026rsquo;s correlation coefficient (R) in OriginPro 2025b, with significance assessed at p\u0026thinsp;\u0026le;\u0026thinsp;0.01. To identify the most influential biochemical traits contributing to resistance variability, principal component analysis (PCA) was conducted using GRAPES software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grapeshms.com\u003c/span\u003e\u003cspan address=\"https://www.grapeshms.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). PCA enabled the detection of key traits that significantly affect yellow stem borer resistance and provided a comprehensive understanding of the multivariate interactions among biochemical and nutritional parameters. \u003cb\u003ePrincipal component analysis\u003c/b\u003e: A principal component can be defined as a linear combination of optimally weighted observed variables as suggested by Rao (1964). The goal of PCA is to reduce the number of variables of interest into a smaller set of components.\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\u003eBiochemical components, plant nutrients, and their functional roles in rice defence and nutrition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent / Nutrient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard Procedure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRole in Plant Defense \u0026amp; Nutrition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBiochemical Components\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal sugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomogyi\u0026rsquo;s method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttract herbivores, influence plant growth and nutritional imbalances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJeandet et al. 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(\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper (Cu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtomic absorption spectrophotometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCofactor for oxidative enzymes, lignification, pathogen defense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYruela (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); Ravet \u0026amp; Pilon (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBeneficial non-essential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilicon (Si)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectrophotometric method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMechanical barrier, stress tolerance, enhanced resistance to insects \u0026amp; pathogens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeshmukh et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Ma \u0026amp; Yamaji (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\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\u003ePrincipal Component Analysis (PCA) determines the contribution of largest character to the total variation. Let\u0026rsquo;s say that, we have N Eigen vectors, then the explained variance for each Eigen vector i.e. principal component can be expressed as ratio of related Eigen values to the total sum of Eigen values as given by Hotteling (1933). The Eigen vectors represent the principal components that contain most of the information of variance.\u003c/p\u003e \u003cp\u003eProportion of variance accounted for: A third criterion in solving the number of factors problem involves retaining a component, if it accounts for a specified proportion (or percentage) of variance in the data set.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Proportion=\\frac{Eigen\\:value\\:for\\:thecomponent\\:of\\:interest}{Total\\:Eigen\\:values\\:of\\:the\\:correlation\\:matrix}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eField evaluation of local rice landraces against yellow stem borer, Rabi 2023 \u0026amp; Summer 2024\u003c/h2\u003e \u003cp\u003eDuring Rabi 2023 \u0026amp; Summer 2024, fifty local rice landraces were screened under field conditions for their reaction to the yellow stem borer (YSB). The incidence of dead heart (DH) ranged from 0.64 to 66.07%, while white ear (WE) incidence varied between 0.61 and 27.96% (Table\u0026nbsp;3). None of the landraces expressed a highly resistant reaction (0% DH and WE; score 0). Based on the Standard Evaluation System (SES), six genotypes were classified as resistant (score 1), nine as moderately resistant (score 3), nineteen as moderately susceptible (score 5), fourteen as susceptible (score 7), and two as highly susceptible (score 9) (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eAt 30 DAT, resistant landraces recorded DH incidence of 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18 to 8.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.46%, while moderately resistant entries showed 12.80\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31 to 19.92\u0026thinsp;\u0026plusmn;\u0026thinsp;13.61% DH. Moderately susceptible landraces recorded 21.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66 to 28.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42%, and susceptible landraces had 32.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41 to 48.19\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57% DH. Two genotypes, \u003cem\u003eKrishna Leela\u003c/em\u003e and \u003cem\u003eKundi Pollan\u003c/em\u003e, exhibited the highest incidence (63.26% and 64.48\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6%, respectively), and were categorized as highly susceptible (score 9). No genotype exhibited complete resistance (Table\u0026nbsp;3). At 60 DAT, resistant entries showed 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33 to 8.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16% DH, while moderately resistant ones ranged from 12.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14 to 19.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77%. Moderately susceptible landraces recorded 22.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75 to 28.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45%, and susceptible ones exhibited 34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69 to 51.84\u0026thinsp;\u0026plusmn;\u0026thinsp;18.23% DH. Two landraces, \u003cem\u003ePutta Batta\u0026ndash;2\u003c/em\u003e and \u003cem\u003eKundi Pollan\u003c/em\u003e, showed very high infestation (62.46\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45% and 67.66\u0026thinsp;\u0026plusmn;\u0026thinsp;19.37%) and were rated highly susceptible (score 9). None were found highly resistant (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eAt 100 DAT, WE incidence ranged between 0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 and 4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78% in resistant genotypes, while moderately resistant landraces showed 7.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23 to 9.69\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08%. Moderately susceptible entries recorded 12.32\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06 to 14.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02%, and susceptible ones showed 16.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19 to 24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32%. Two landraces recorded very high WE incidence (27.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11% and 28.89\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51%) and were classified as highly susceptible (score 9). No genotype exhibited complete resistance (Table\u0026nbsp;3). At 120 DAT, resistant landraces recorded 0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 to 4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24% WE, while moderately resistant ones ranged from 6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88 to 9.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00%. Moderately susceptible landraces had 10.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17 to 14.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68%, and susceptible ones showed 16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56 to 25.76\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17% WE. Two genotypes expressed very high WE incidence (25.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65% and 27.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22%) and were categorized as highly susceptible (score 9) (Table\u0026nbsp;3). Similar to earlier observations, none of the genotypes showed complete resistance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReaction of local land races of paddy against yellow stem borer, \u003cem\u003eS. incertulas\u003c/em\u003e during 2023 \u0026amp; 2024 (Pooled Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\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\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDead hearts at 30DAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead hearts at 60DAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhite-ears at 100DAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWhite-ears at 120DAT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eResistance category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdri batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBangara kolee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.02\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack sticky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKrishna leela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.26\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKave kantak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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pullan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.48\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.66\u0026thinsp;\u0026plusmn;\u0026thinsp;19.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManjula sona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePutta batta \u0026ndash; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.48\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.39\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.76\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRahodaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.04\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunkutt kodi \u0026ndash; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamadari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.49\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKariga javele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.51\u0026thinsp;\u0026plusmn;\u0026thinsp;28.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.43\u0026thinsp;\u0026plusmn;\u0026thinsp;28.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.14\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalakoli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.08\u0026thinsp;\u0026plusmn;\u0026thinsp;14.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.01\u0026thinsp;\u0026plusmn;\u0026thinsp;15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMapilai samba \u0026ndash; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.91\u0026thinsp;\u0026plusmn;\u0026thinsp;16.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.39\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeergula batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.83\u0026thinsp;\u0026plusmn;\u0026thinsp;9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRasakadam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRathanachoodi \u0026ndash; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.18\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRathanachoodi \u0026ndash; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.11\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheerthahalli local \u0026ndash; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTai jasmine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.86\u0026thinsp;\u0026plusmn;\u0026thinsp;16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.54\u0026thinsp;\u0026plusmn;\u0026thinsp;13.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.89\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTornado batta \u0026ndash; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.25\u0026thinsp;\u0026plusmn;\u0026thinsp;11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.03\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRV s Dangi red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRV s Biladadi martiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaichur sanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDunda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.38\u0026thinsp;\u0026plusmn;\u0026thinsp;8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.26\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDappaneya Bilijaddi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.33\u0026thinsp;\u0026plusmn;\u0026thinsp;13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRajaboga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDodda Baikalu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.42\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.84\u0026thinsp;\u0026plusmn;\u0026thinsp;18.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.07\u0026thinsp;\u0026plusmn;\u0026thinsp;15.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDappa batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.04\u0026thinsp;\u0026plusmn;\u0026thinsp;17.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDanggaia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoddabyra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.59\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanna batta \u0026ndash; 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanna rajakime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.18\u0026thinsp;\u0026plusmn;\u0026thinsp;11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.52\u0026thinsp;\u0026plusmn;\u0026thinsp;7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiri sanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.85\u0026thinsp;\u0026plusmn;\u0026thinsp;20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.67\u0026thinsp;\u0026plusmn;\u0026thinsp;25.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.12\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.12\u0026thinsp;\u0026plusmn;\u0026thinsp;17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanna akki batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.34\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSanbag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.03\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSidda sanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.11\u0026thinsp;\u0026plusmn;\u0026thinsp;8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.32\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.32\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelam sanna \u0026ndash; 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.19\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.38\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRV s valtgya gidda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTagarhi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCategorization of different local landraces of rice against yellow stem borer, \u003cem\u003eS. incertulas\u003c/em\u003e, 2023 \u0026amp; 2024 (Pooled Mean)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePercent damage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ePer cent dammage observed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDH(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWE(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDH(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWE(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdri batta, Karimundaga, Karikagga, Naland paddy, Raichur sanna and Rajaboga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u0026ndash;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u0026ndash;4.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBlack sticky, Kalajeera, Nirga samba, Rahodaya, Rasakadam, Theerthahalli local \u0026ndash; 1, Tornado batta \u0026ndash; 2, Danggaia and Sanna rajakime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.17\u0026ndash;18.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.87\u0026ndash;9.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026ndash;15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKave kantak, Manjula sona, Neermullare, Neermuka, Kamadari, Kariga javele, Neergula batta, Narali, Rathanachoodi \u0026ndash; 1, Rathanachoodi \u0026ndash; 2, Tai jasmine, TRV s Dangi red, TRV s Biladadi martiga, Dunda, Doddabyra, Sanna batta \u0026ndash; 2, Sanna akki batta, Sidda sanna and TRV s valtgya gidda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.51\u0026ndash;28.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.59\u0026ndash;14.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u0026ndash;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBangara kolee, Kanakunja, Navara, Putta batta \u0026ndash; 2, Punkutt kodi \u0026ndash; 1, Kalakoli, Mapilai samba \u0026ndash; 2, Dappaneya Bilijaddi, Dodda Baikalu, Dappa batta, Siri sanna, Sanbag, Selam sanna \u0026ndash; 1 and Tagarhi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.83\u0026ndash;50.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.66\u0026ndash;23.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKrishna leela and Kundi pullan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.86\u0026ndash;66.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.51\u0026ndash;27.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical constituents\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eTotal phenols\u003c/h2\u003e \u003cp\u003eThe total phenol content in rice genotypes showed a clear decline with increasing susceptibility to yellow stem borer. At 30 DAT, phenol levels ranged from 0.21 to 0.85 mg g⁻\u0026sup1;, with resistant and moderately resistant genotypes recording higher values (0.49\u0026ndash;0.85 mg g⁻\u0026sup1;), moderately susceptible genotypes showing intermediate levels (0.32\u0026ndash;0.41 mg g⁻\u0026sup1;), and susceptible to highly susceptible ones having the lowest contents (0.10\u0026ndash;0.31 mg g⁻\u0026sup1;). At 60 DAT, phenol content ranged from 0.428 to 1.12 mg g⁻\u0026sup1;. Resistant genotypes such as Rajbaga (1.12), TKM6 (1.073), W1263 (1.063), Karimunduga (0.915) and Karikagga (0.903) accumulated the maximum phenols, followed by moderately resistant types (0.76\u0026ndash;0.86 mg g⁻\u0026sup1;). Moderately susceptible genotypes exhibited intermediate values (0.52\u0026ndash;0.64 mg g⁻\u0026sup1;), while susceptible and highly susceptible genotypes recorded lower levels (0.48\u0026ndash;0.61 mg g⁻\u0026sup1;). The highly susceptible Krishna Leela (0.428 mg g⁻\u0026sup1;) and the susceptible check TN-1 (0.483 mg g⁻\u0026sup1;) had the minimum phenol content, indicating greater vulnerability (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTotal soluble sugars (TSS)\u003c/h2\u003e \u003cp\u003eTotal soluble sugars (TSS) showed a clear association with susceptibility to yellow stem borer. At 30 DAT, TSS ranged from 3.25 to 8.02 mg g⁻\u0026sup1;, with resistant genotypes recording the lowest levels (3.25\u0026ndash;4.01 mg g⁻\u0026sup1;), moderately resistant types slightly higher (5.01\u0026ndash;5.43 mg g⁻\u0026sup1;), and susceptible to highly susceptible genotypes the highest (6.82\u0026ndash;8.02 mg g⁻\u0026sup1;). At 60 DAT, a similar trend was observed, with TSS ranging from 2.25 to 6.77 mg g⁻\u0026sup1;. Resistant genotypes such as TKM6, W1263, Rajbaga, Adri batta, Nagaland rice, Karimunduga and Karikagga had the lowest values (2.25\u0026ndash;2.96 mg g⁻\u0026sup1;), while moderately resistant ones showed slightly higher levels (3.66\u0026ndash;4.34 mg g⁻\u0026sup1;). Moderately susceptible genotypes recorded intermediate values (4.33\u0026ndash;5.52 mg g⁻\u0026sup1;), whereas susceptible and highly susceptible genotypes, including TN-1 (6.77 mg g⁻\u0026sup1;), accumulated the maximum sugars. Overall, higher TSS consistently corresponded with greater susceptibility to yellow stem borer (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReducing sugars (TRS)\u003c/h2\u003e \u003cp\u003eReducing sugars in rice genotypes showed a consistent increase with susceptibility to yellow stem borer, ranging from 7.05\u0026ndash;13.05 mg g⁻\u0026sup1; at 30 DAT and 5.21\u0026ndash;11.51 mg g⁻\u0026sup1; at 60 DAT. Resistant genotypes (Karikagga, Nagaland rice, Adri batta, Rajbaga, W1263, Karimunduga, TKM6) recorded the lowest values, followed by moderately resistant types (Nirga samba, Kala Jeera, Black sticky) with slightly higher levels. Moderately susceptible genotypes (Kave kantak, Manjula Sona, Neermullarae, Neermuka) showed intermediate contents, while susceptible and highly susceptible ones (Bangara kolee, Navara, Kankunia, Punkutt kodi-1, Putta batta-2, Kundi polan, Krishna leela) accumulated significantly higher sugars, with TN-1 consistently recording the maximum (13.05 and 11.51 mg g⁻\u0026sup1; at 30 and 60 DAT, respectively) (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCrude proteins\u003c/h2\u003e \u003cp\u003eCrude protein content in rice genotypes increased with susceptibility to yellow stem borer, ranging from 2.84\u0026ndash;7.21 mg g⁻\u0026sup1; at 30 DAT and 1.28\u0026ndash;5.81 mg g⁻\u0026sup1; at 60 DAT. Resistant genotypes (TKM6, W1263, Karikagga, Nagaland rice, Adri batta, Karimunduga, Rajboga) recorded the lowest levels, moderately resistant types (Nirga samba, Kala jeera, Black sticky) had slightly higher values, and moderately susceptible genotypes showed intermediate contents. Susceptible and highly susceptible genotypes, including TN-1, Krishna leela, and Kundi polan, accumulated the highest proteins at both stages (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical constituents of rice genotypes associated with infestation of yellow stem borer at 30 DAT, \u003cem\u003eSummer\u003c/em\u003e 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e \u003cp\u003eBiochemical constituents (mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl.\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDH (%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal soluble \u003c/p\u003e \u003cp\u003eSugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReducing \u003c/p\u003e \u003cp\u003eSugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal \u003c/p\u003e \u003cp\u003ePhenols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCrude \u003c/p\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal Free\u003c/p\u003e \u003cp\u003eAmino Acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTannins\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarikagga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.32\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.81\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.05\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.49\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.5\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.31\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRajbaga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.39\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.28\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.92\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.3\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.31\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagaland Rice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21\u003csup\u003eijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.62\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.21\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.52\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.08\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.01\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarimunduga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.01\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.36\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.81\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.92\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.61\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdri batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.12\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.26\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.72\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.32\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.09\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNirga Samba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.98\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.01\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.14\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.07\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.21\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.42\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKala Jeera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.32\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.51\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.35\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.42\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.91\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack Sticky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.12\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.43\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.76\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.45\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.9\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.53\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKave Kantak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.05\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.99\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.09\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.81\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.14\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.09\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermullarae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.05\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.52\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.76\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.71\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.09\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.72\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.71\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.64\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.32\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.23\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.93\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e 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colname=\"c10\"\u003e \u003cp\u003e2.82\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBangara Kolee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.21\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.42\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.29\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003csup\u003eghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.41\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.72\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.4\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePunkutt Kodi-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.12\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.41\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.39\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.31\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.49\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.85\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKankunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.12\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.08\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.29\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003csup\u003efghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.32\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.21\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.81\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.32\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.59\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.54\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.34\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.32\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutta Batta-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.21\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.82\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.64\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.75\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.32\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.92\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKundi polan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.34\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.91\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.01\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.42\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.71\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKrishna Leela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.26\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.74\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.01\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.99\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.93\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.73\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.02\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.43\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTKM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.55\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.84\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.34\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.04\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.09\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSE m \u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD @ p\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.40\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*Values in the column followed by common letters are non-significant at p\u0026thinsp;=\u0026thinsp;0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No \u0026ndash; number.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical constituents of rice genotypes associated with infestation of yellow stem borer at 60 DAT, \u003cem\u003eSummer\u003c/em\u003e 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c10\" namest=\"c4\"\u003e \u003cp\u003eBiochemical constituents (mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl.\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDH (%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal soluble \u003c/p\u003e \u003cp\u003eSugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReducing \u003c/p\u003e \u003cp\u003eSugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal \u003c/p\u003e \u003cp\u003ePhenols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCrude \u003c/p\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal Free\u003c/p\u003e \u003cp\u003eAmino Acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTannins\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarikagga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56\u003csup\u003ejk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.25\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.903\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.12\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.63\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.47\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRajbaga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.39\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.368\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.55\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.58\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.47\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagaland Rice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.21\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.25\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.27\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.24\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarimunduga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.35\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96\u003csup\u003eijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.66\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.915\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.13\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.02\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdri batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.348\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.794\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.35\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.43\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.15\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNirga Samba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.23\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.66\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.62\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.66\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.39\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.68\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKala Jeera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.57\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.04\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.17\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.863\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.96\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.63\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e 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colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBangara Kolee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.46\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.92\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.83\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.51\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePunkutt Kodi-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.37\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.16\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.47\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.98\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.66\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.9\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKankunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.37\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.38\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.45\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.99\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.36\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.86\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.07\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.86\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.484\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.15\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.45\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.48\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutta Batta-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.46\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.819\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.4\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.48\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.12\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKundi polan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.09\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.27\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.69\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.59\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.91\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKrishna Leela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.66\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.58\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.08\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.95\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.27\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.509\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.483\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.809\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.59\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.63\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTKM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.638\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.073\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.6\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.26\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.59\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.063\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.67\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.25\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSE m \u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD @ p\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTotal free amino acid (TFA)\u003c/h2\u003e \u003cp\u003eTotal free amino acids (TFA) in rice genotypes decreased with increasing susceptibility to yellow stem borer, ranging from 16.21\u0026ndash;22.13 mg g⁻\u0026sup1; at 30 DAT and 15.36\u0026ndash;21.31 mg g⁻\u0026sup1; at 60 DAT. Resistant genotypes (W1263, TKM6, Adri Batta, Rajboga, Nagaland rice/paddy, Karimunduga, Karikagga) recorded the highest TFA, moderately resistant types (Nirga samba, Black sticky, Kala Jeera) showed slightly lower levels, and moderately susceptible genotypes had intermediate contents. Susceptible and highly susceptible genotypes, including TN-1, Kankunia, Bangara kolee, Putta batta-2, Punkutt kodi-1, Krishna leela, and Kundi polan, accumulated the lowest TFA at both stages (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTannins\u003c/h2\u003e \u003cp\u003eTannin content in rice genotypes declined with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, Rajboga, Adri Batta, Karimunduga, Karikagga, W1263, Nagaland paddy) recorded the highest tannin levels (4.01\u0026ndash;5.49 mg g⁻\u0026sup1;), moderately resistant types (Kala Jeera, Black sticky, Nirga samba) had intermediate values (3.42\u0026ndash;3.91 mg g⁻\u0026sup1;), and moderately susceptible genotypes (Kave kantak, Neermuka, Manjula Sona, Neermullarae) showed 2.72\u0026ndash;3.09 mg g⁻\u0026sup1;. Susceptible and highly susceptible genotypes, including TN-1, Kankunia, Punkutt kodi-1, Putta batta-2, Navara, Bangara kolee, Krishna leela, and Kundi polan, recorded the lowest levels (1.45\u0026ndash;2.40 mg g⁻\u0026sup1;). At 60 DAT, the trend persisted: resistant genotypes (TKM6, Rajbaga, Adri Batta, Karimunduga, Karikagga, W1263, Nagaland rice) showed 6.24\u0026ndash;7.60 mg g⁻\u0026sup1;, moderately resistant types 5.68\u0026ndash;6.07 mg g⁻\u0026sup1;, moderately susceptible 4.76\u0026ndash;5.17 mg g⁻\u0026sup1;, and susceptible to highly susceptible genotypes 3.63\u0026ndash;4.51 mg g⁻\u0026sup1; (Tables\u0026nbsp;5 and 6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eNutrient composition\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eNitrogen\u003c/h2\u003e \u003cp\u003eNitrogen content in rice genotypes increased with susceptibility to yellow stem borer. Resistant genotypes (W1263, TKM6, Karikagga, Rajboga, Adri Batta, Karimunduga, Nagaland rice) recorded the lowest levels (0.46\u0026ndash;0.96%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) showed slightly higher values (0.83\u0026ndash;1.18%), moderately susceptible genotypes (Neermullarae, Manjula Sona, Neermuka, Kave kantak) had intermediate levels (1.01\u0026ndash;1.43%), and susceptible to highly susceptible genotypes (Bangara kolee, Kankunia, Navara, Punkutt kodi-1, Putta batta-2, TN-1, Krishna leela, Kundi polan) accumulated the highest N (1.12\u0026ndash;1.56%) (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePhosphorous\u003c/h2\u003e \u003cp\u003ePhosphorus content in rice genotypes decreased with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (Karimunduga, TN-1, Karikagga, Adri Batta, Nagaland paddy, Rajboga) recorded the highest levels (0.45\u0026ndash;0.48%), moderately resistant types (Black sticky, Nirga samba, Kala Jeera) had 0.42\u0026ndash;0.44%, moderately susceptible genotypes (Kave kantak, Manjula Sona, Neermuka, Neermullarae) showed 0.35\u0026ndash;0.41%, and susceptible to highly susceptible genotypes (Putta batta-2, Bangara kolee, Kankunia, Punkutt kodi-1, Navara, Krishna leela, Kundi polan) had the lowest levels (0.20\u0026ndash;0.34%). At 60 DAT, resistant genotypes (Karikagga, Rajbaga, Nagaland rice, Karimunduga, Adri Batta, TKM6, W1263) showed 0.49\u0026ndash;0.73%, moderately resistant 0.65\u0026ndash;0.658%, moderately susceptible 0.617\u0026ndash;0.69%, and susceptible to highly susceptible genotypes 0.42\u0026ndash;0.57% (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary, secondary nutrients and silicon constituents in the rice land races with different resistance categories at 30 DAT, \u003cem\u003eSummer\u003c/em\u003e 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDH \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c10\" namest=\"c5\"\u003e \u003cp\u003eMacro-Nutrients (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" morerows=\"1\" nameend=\"c14\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eMicro-Nutrients (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSi \u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePrimary Nutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eSecondary Nutrients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl. \u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarikagga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.32\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.33\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e145.33\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.35\u003csup\u003ecdefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.82\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRajbaga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.49\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e59.21\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e142\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.54\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagaland Rice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21\u003csup\u003eijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60.1\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e138.33\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.93\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.88\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.42\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarimunduga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58.1\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e141.33\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18.37\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.77\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdri batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.12\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e53.21\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e142\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.38\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.12\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNirga Samba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.98\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70.21\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e148.33\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.78\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.09\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.26\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKala Jeera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.32\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e143.33\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.32\u003csup\u003eefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.67\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.01\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack Sticky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.12\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.37\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e74.32\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e141.67\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.56\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.85\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e 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\u003cp\u003e0.29\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e146\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.22\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.32\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.18\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermullarae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.05\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.28\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.82\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.28\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.13\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34.12\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.94\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153.33\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e 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colname=\"c14\"\u003e \u003cp\u003e6.42\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.32\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePunkutt Kodi-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.12\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003csup\u003epp\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e54.21\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.9\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.17\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.49\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e 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colname=\"c4\"\u003e \u003cp\u003e49.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.74\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.21\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTKM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.83\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e79.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e144.67\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18.72\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.72\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.39\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e147.67\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e17.41\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.9\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.25\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSE m \u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD @ p\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.31\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*Values in the column followed by common letters are non-significant at p\u0026thinsp;=\u0026thinsp;0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No \u0026ndash; number\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary, secondary nutrients and silicon constituents in the rice land races with different resistance categories at 60 DAT, \u003cem\u003eSummer\u003c/em\u003e 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSi. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e%DH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c10\" namest=\"c5\"\u003e \u003cp\u003eMacro-Nutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" morerows=\"1\" nameend=\"c14\" namest=\"c11\" rowspan=\"2\"\u003e \u003cp\u003eMicro-Nutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePrimary Nutrients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eSecondary Nutrients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKarikagga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.86\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.195\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.08\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e147.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.44\u003csup\u003ecdefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRajbaga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.73\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.177\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e57.946\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e143.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e20.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.12\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.78\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagaland Rice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.83\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.188\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58.856\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e 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\u003cp\u003e19.49\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.37\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdri batta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.45\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNirga Samba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.23\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.181\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6\u003csup\u003eabcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.218\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.201\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e68.986\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e149.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e16.84\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.65\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.39\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKala Jeera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.57\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.135\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.71\u003csup\u003eabcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.212\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e 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align=\"left\" colname=\"c12\"\u003e \u003cp\u003e147.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.37\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.85\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.32\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermullarae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.3\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.321\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.25\u003csup\u003ebcdefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.236\u003csup\u003eabcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e54.006\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e154.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e15.9\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.85\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.24\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeermuka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.35\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.431\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.14\u003csup\u003ecdefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.232\u003csup\u003eabcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.84\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153.4\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.38\u003csup\u003eghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.78\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.21\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManjula Sona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.35\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003csup\u003ebcdefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.228\u003csup\u003eabcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.237\u003csup\u003eabcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e74.85\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e154.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.68\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.86\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.19\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c3\"\u003e \u003cp\u003ePunkutt Kodi-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.37\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.465\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.99\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.247\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.95\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e154.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.95\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.68\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.61\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKankunia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.37\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.428\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.92\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.254\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.264\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.97\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e148.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12.16\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.87\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.63\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNavara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.46\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.461\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003csup\u003edefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.241\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.249\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.92\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e160.29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.93\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.1\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.71\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutta Batta-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.46\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003csup\u003ebcdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.264\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.241\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51.98\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e152.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14.5\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.549\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKundi polan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.478\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.67\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.229\u003csup\u003eabcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.243\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.956\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e153\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e13.47\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.94\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.46\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKrishna Leela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.511\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.224\u003csup\u003eabcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.223\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.056\u003csup\u003eefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e152.375\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11.06\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.94\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.41\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTN-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.27\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.563\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.69\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.276\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.285\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.95\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e160.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.35\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.86\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.55\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTKM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.185\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e78.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e146.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.84\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.88\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.99\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.182\u003csup\u003efg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.202\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.066\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e149.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18.49\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.47\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.4\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSEm+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCD @ p\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.35\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\u003eValues in the column followed by common letters are non-significant at p\u0026thinsp;=\u0026thinsp;0.05 as per Tukey's HSD (Tukey, 1965); R- Resistant; MR- Moderately resistant; MS- Moderately susceptible; S- Susceptible; RC- Resistant check; SC- Susceptible check; No \u0026ndash; number.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePotassium\u003c/h2\u003e \u003cp\u003ePotassium content in rice genotypes declined with increasing susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, W1263, Karikagga, Karimunduga, Adri Batta, Nagaland paddy, Rajboga) recorded the highest K levels (2.49\u0026ndash;2.83%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) had 2.37\u0026ndash;2.41%, moderately susceptible genotypes (Neermullarae, Kave kantak, Neermuka, Manjula Sona) showed 1.94\u0026ndash;1.99%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Navara, Kankunia, Bangara kolee, Putta batta-2, TN-1, Krishna leela, Kundi polan) recorded the lowest levels (1.42\u0026ndash;1.76%). At 60 DAT, the trend persisted, with resistant genotypes at 3.73\u0026ndash;4.11%, moderately resistant 3.60\u0026ndash;3.71%, moderately susceptible 3.14\u0026ndash;3.25%, and susceptible to highly susceptible 2.67\u0026ndash;3.05% (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCalcium\u003c/h2\u003e \u003cp\u003eCalcium content in rice genotypes decreased with susceptibility to yellow stem borer. At 30 DAT, resistant genotypes (TKM6, W1263, Nagaland paddy, Karikagga, Karimunduga, Rajboga, Adri Batta) recorded the highest levels (0.34\u0026ndash;0.43%), moderately resistant types (Kala Jeera, Black sticky, Nirga samba) had 0.31\u0026ndash;0.37%, moderately susceptible genotypes (Kave kantak, Neermuka, Neermullarae, Manjula Sona) showed 0.24\u0026ndash;0.29%, and susceptible to highly susceptible genotypes (Navara, Bangara kolee, Kankunia, Putta batta-2, Punkutt kodi-1, TN-1, Krishna leela, Kundi polan) recorded the lowest levels (0.20\u0026ndash;0.26%). At 60 DAT, the trend persisted with resistant genotypes (TKM6, W1263, Karikagga, Rajbaga, Nagaland rice, Karimunduga, Adri batta) showing 0.56\u0026ndash;0.65%, moderately resistant types 0.48\u0026ndash;0.59%, moderately susceptible genotypes 0.45\u0026ndash;0.51%, and susceptible to highly susceptible genotypes 0.40\u0026ndash;0.48%. (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMagnesium\u003c/h2\u003e \u003cp\u003eMagnesium content in rice genotypes ranged from 0.15 to 0.24% at 30 DAT and 0.181 to 0.276% at 60 DAT, showing an increasing trend with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, W1263, Adri Batta, Karikagga, Karimunduga, Nagaland paddy, TKM6) recorded the lowest levels (0.15\u0026ndash;0.202%), moderately resistant types (Kala Jeera, Nirga samba, Black sticky) had 0.18\u0026ndash;0.233%, moderately susceptible genotypes (Manjula Sona, Kave kantak, Neermuka, Neermullarae) showed 0.20\u0026ndash;0.25%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Navara, Kankunia, Bangara kolee, Putta batta-2, TN-1, Krishna leela, Kundi polan) recorded the highest levels (0.19\u0026ndash;0.276%) (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSulfur\u003c/h2\u003e \u003cp\u003eSulfur content in rice genotypes ranged from 0.15 to 0.29%, increasing with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, Adri Batta, Nagaland paddy/rice, TKM6, Karikagga, Karimunduga, W1263) recorded the lowest levels (0.15\u0026ndash;0.20%), moderately resistant types (Kala Jeera, Nirga samba, Black sticky) had 0.17\u0026ndash;0.22%, moderately susceptible genotypes (Neermullarae, Neermuka, Manjula Sona, Kave kantak) showed 0.21\u0026ndash;0.25%, and susceptible to highly susceptible genotypes (Punkutt kodi-1, Putta batta-2, Bangara kolee, Navara, Kankunia, TN-1, Krishna leela, Kundi polan) recorded the highest levels (0.20\u0026ndash;0.29%) (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eSilicon\u003c/h2\u003e \u003cp\u003eSilicon content in rice genotypes decreased with susceptibility to yellow stem borer, ranging from 1.26 to 3.82% at initial observation and 1.41 to 3.93% at 60 DAT. Resistant genotypes (Karikagga, Karimunduga, TKM6, Rajboga, Nagaland paddy, Adri Batta, W1263) recorded the highest levels (3.25\u0026ndash;3.93%), moderately resistant types (Nirga samba, Kala Jeera, Black sticky) had 2.72\u0026ndash;3.39%, moderately susceptible genotypes (Kave kantak, Neermullarae, Neermuka, Manjula Sona) showed 2.05\u0026ndash;2.32%, and susceptible to highly susceptible genotypes (Navara, Kankunia, Punkutt kodi-1, TN-1, Putta batta-2, Bangara kolee, Kundi polan, Krishna leela) recorded the lowest levels (1.26\u0026ndash;1.71%) (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eZinc\u003c/h2\u003e \u003cp\u003eZinc content in rice genotypes ranged from 32.84 to 82.32 mg kg⁻\u0026sup1;, decreasing with susceptibility to yellow stem borer. At both 30 and 60 DAT, resistant genotypes (W1263, TKM6, Nagaland paddy/rice, Rajboga, Karimunduga, Adri Batta, Karikagga) contained 51.08\u0026ndash;81.07 mg kg⁻\u0026sup1;, moderately resistant types (Kala Jeera, Black Sticky, Nirga Samba) recorded 40.21\u0026ndash;82.32 mg kg⁻\u0026sup1;, moderately susceptible genotypes (Kave Kantak, Manjula Sona, Neermullarae, Neermuka) had 32.84\u0026ndash;81.32 mg kg⁻\u0026sup1;, and susceptible to highly susceptible genotypes (Bangara Kolee, Kankunia, Punkutt Kodi-1, Navara, Putta Batta-2, TN-1, Krishna Leela, Kundi polan) ranged 41.95\u0026ndash;55.28 mg kg⁻\u0026sup1; (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eIron\u003c/h2\u003e \u003cp\u003eIron content in rice genotypes ranged from 138 to 160.54 mg kg⁻\u0026sup1;, increasing with susceptibility to yellow stem borer. Resistant genotypes (Nagaland paddy, Karimunduga, Black Sticky, Rajboga, Adri Batta, Kala Jeera, TKM6, Karikagga) contained 138.33\u0026ndash;147.34 mg kg⁻\u0026sup1;, moderately resistant types (Nirga Samba, Krishna Leela, Putta Batta-2, Neermuka, Kala Jeera, Black Sticky) had 143.23\u0026ndash;149.87 mg kg⁻\u0026sup1;, moderately susceptible genotypes (Kave Kantak, Neermullarae, Neermuka, Manjula Sona, Kankunia, W1263) recorded 146.00\u0026ndash;154.93 mg kg⁻\u0026sup1;, and susceptible to highly susceptible genotypes (Bangara Kolee, Kundi Polan, Punkutt Kodi-1, Navara, Putta Batta-2, Krishna Leela, Manjula Sona, Neermullarae, TN-1) showed 148.54\u0026ndash;160.54 mg kg⁻\u0026sup1; (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eManganese\u003c/h2\u003e \u003cp\u003eManganese content in rice genotypes ranged from 8.23 to 19.74 mg kg⁻\u0026sup1; at 30 DAT and 9.35 to 20.86 mg kg⁻\u0026sup1; at 60 DAT, decreasing with susceptibility to yellow stem borer. Resistant genotypes (Rajboga, TKM6, Karimunduga, W1263, Nagaland paddy, Adri Batta, Karikagga) contained 14.35\u0026ndash;19.74 mg kg⁻\u0026sup1; at 30 DAT and 15.44\u0026ndash;20.86 mg kg⁻\u0026sup1; at 60 DAT; moderately resistant types (Nirga Samba, Black Sticky, Kala Jeera) had 12.32\u0026ndash;15.78 mg kg⁻\u0026sup1; and 13.44\u0026ndash;16.84 mg kg⁻\u0026sup1;, respectively; moderately susceptible genotypes (Neermullarae, Kave Kantak, Manjula Sona, Neermuka) recorded 11.31\u0026ndash;14.82 mg kg⁻\u0026sup1; and 12.38\u0026ndash;15.90 mg kg⁻\u0026sup1;; and susceptible to highly susceptible genotypes (Putta Batta-2, Navara, Punkutt Kodi-1, Kankunia, Bangara Kolee, TN-1, Kundi Polan, Krishna Leela) showed 8.23\u0026ndash;13.42 mg kg⁻\u0026sup1; and 9.35\u0026ndash;14.50 mg kg⁻\u0026sup1; at 30 and 60 DAT, respectively (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCopper\u003c/h2\u003e \u003cp\u003eCopper content in rice genotypes ranged from 4.78 to 8.24 mg kg⁻\u0026sup1;, decreasing with susceptibility to yellow stem borer. Resistant genotypes (Karikagga, TKM6, Adri Batta, W1263, Nagaland paddy, Karimunduga, Rajboga) contained 7.12\u0026ndash;8.24 mg kg⁻\u0026sup1;, moderately resistant types (Nirga Samba, Black Sticky, Kala Jeera) had 7.27\u0026ndash;8.09 mg kg⁻\u0026sup1;, moderately susceptible genotypes (Kave Kantak, Manjula Sona, Neermullarae, Neermuka) recorded 4.78\u0026ndash;7.32 mg kg⁻\u0026sup1;, and susceptible to highly susceptible genotypes (Putta Batta-2, Kankunia, Punkutt Kodi-1, Navara, Bangara Kolee, TN-1, Kundi Polan, Krishna Leela) showed 5.86\u0026ndash;7.42 mg kg⁻\u0026sup1; (Tables\u0026nbsp;7 and 8).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrincipal component analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical traits and their association with pest infestation\u003c/h2\u003e \u003cp\u003eA comprehensive principal component analysis (PCA) of seven key biochemical variables measured at 30 and 60 days after transplanting (DAT) revealed a remarkably consistent pattern across both growth stages. At 30 DAT, the analysis reduced the variation among genotypes to a single dominant principal component (PC1), which captured a staggering 91.71% of the total variance (eigenvalue 6.42), demonstrating strong co-variation and integrative shifts among these biochemical traits (Table\u0026nbsp;11; Fig.\u0026nbsp;5A \u0026amp; 5B). The most influential contributors to PC1 were total soluble sugars (TSS, 15.18%), crude protein (CP, 14.73%), reducing sugars (RS, 14.47%), and tannins (TA, 14.65%), with significant but slightly lower contributions from days to heading (DH), total phenols (TP), and total free amino acids (TFAA) in the 13\u0026ndash;14% range. At 60 DAT, a similarly unidimensional structure emerged: PC1 (eigenvalue 6.36) explained 90.88% of overall variance, with the largest contributions from TSS (14.93%), CP (14.83%), RS (14.69%), and TA (14.75%), again supported by the other defensive and nutritional traits (Table\u0026nbsp;11; Fig.\u0026nbsp;6A \u0026amp; 6B)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePresents the correlation between biochemical constituents and yellow stem borer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiochemical constituents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePearson correlation co efficient (r)\u003c/p\u003e \u003cp\u003e[n(Σxy) \u0026minus; ΣxΣy] / \u0026radic;[n(Σx\u0026sup2;) \u0026minus; (Σx)\u0026sup2;][n(Σy\u0026sup2;) \u0026minus; (Σy)\u0026sup2;]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt 30 DAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt 60 DAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal soluble Sugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReducing Sugars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.914\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Phenols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.858\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.856\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude Protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.869\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Free amino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.798\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.799\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTannins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.886\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.881\u003csup\u003e**\u003c/sup\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*N\u0026thinsp;=\u0026thinsp;22; ** Significant at P\u0026thinsp;\u0026le;\u0026thinsp;0.01;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePresents the correlation between nutrient content and yellow stem borer incidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutrient content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePearson correlation co efficient (r)\u003c/p\u003e \u003cp\u003e[n(Σxy) \u0026minus; ΣxΣy] / \u0026radic;[n(Σx\u0026sup2;) \u0026minus; (Σx)\u0026sup2;][n(Σy\u0026sup2;) \u0026minus; (Σy)\u0026sup2;]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eat 30 DAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 DAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.483\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.498\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.934\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.923\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.854\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.864\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.638\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.732\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.556\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.555\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.683\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.746\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.747\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.643\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.641\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.898\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.897\u003csup\u003e**\u003c/sup\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*N\u0026thinsp;=\u0026thinsp;22; ** Significant at P\u0026thinsp;\u0026le;\u0026thinsp;0.01;\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLoading of each trait and % contribution of biochemical variables towards principal components at 30 and 60 DAT during \u003cem\u003eSummer\u003c/em\u003e, 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLoadings of each variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 DAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 DAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e% Contribution of variables on PCs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigen value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative percentage of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.884\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=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eNutrient components and their association with pest infestation\u003c/h2\u003e \u003cp\u003eA comprehensive principal component analysis (PCA) of eleven nutrient variables quantified at 30 and 60 days after transplanting (DAT) during Summer 2024 revealed a highly stable dimensional pattern across both stages. At 30 DAT, variation among the nutrient traits was largely condensed into the first principal component (PC1), which alone accounted for 71.63% of the total variance (eigenvalue 7.879), while PC2 contributed an additional 9.61%, cumulatively explaining 81.24% of observed variability (Table\u0026nbsp;12). The most influential loadings on PC1 were nitrogen (N, 11.83%), potassium (K, 11.90%), calcium (Ca, 11.31%), sulfur (S, 10.92%), magnesium (Mg, 9.13%), iron (Fe, 9.39%), manganese (Mn, 9.43%), copper (Cu, 8.54%), and silicon (Si, 11.92%), suggesting that PC1 predominantly represented a balanced axis of macro- and micro-nutrient contribution. By contrast, PC2 variance (9.61%) was heavily structured around phosphorus (P, 70.21%) and zinc (Zn, 19.25%), indicating a secondary, nutrient-specific gradient (Table\u0026nbsp;12; Fig.\u0026nbsp;7A \u0026amp; 7B). At 60 DAT, the pattern was strikingly consistent. PC1 (eigenvalue 7.875) explained 71.59% of total variance, with PC2 adding 9.58%, thereby capturing a cumulative 81.17% (Table\u0026nbsp;12). As at 30 DAT, PC1 was strongly defined by N (11.93%), K (11.91%), Ca (10.83%), Mg (9.32%), S (10.77%), Fe (9.36%), Mn (9.50%), Cu (8.58%), and Si (11.91%), underscoring the integrative role of these nutrients in shaping overall compositional shifts during crop establishment (Fig.\u0026nbsp;8A \u0026amp; 8B). PC2 at this stage was again dominated by P (63.41%) and Zn (24.79%), reaffirming a persistent but separate axis of nutrient contribution (Table\u0026nbsp;12).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLoading of each trait and % contribution of nutrient variables towards principal components at 30 and 60 DAT during \u003cem\u003eSummer\u003c/em\u003e, 2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLoadings of each variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e30 DAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e60 DAT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e% Contribution of variables on PCs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative percentage of variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical basis of resistance\u003c/h2\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003ePhenols\u003c/h2\u003e \u003cp\u003eResistance in rice accessions against yellow stem borer (YSB) is closely associated with a range of biochemical traits that influence insect oviposition, feeding, and survival. Among these, phenolic compounds serve as a primary defense mechanism. Resistant entries consistently exhibit higher phenol content (6.26\u0026ndash;7.72 mg g⁻\u0026sup1;) compared to susceptible ones (2.53\u0026ndash;3.21 mg g⁻\u0026sup1;), which act as strong feeding deterrents, reduce nutrient utilization, and exert direct toxicity on larvae (Padhi, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Suchita et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Elanchezhyan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Stem phenolics further enhance antibiosis by increasing larval mortality (Zhu et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Resistant varieties such as ACK 14004 and BRNS WP, with phenol levels of 4.08\u0026ndash;3.83 mg g⁻\u0026sup1; FW, have demonstrated anti-feedant and repellent effects (Elanchezhyan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These observations are in accordance with the current study, where resistant genotypes exhibited higher phenolic content than susceptible and moderately susceptible genotypes (Table\u0026nbsp;5). Overall, resistant and moderately resistant landraces consistently maintained elevated phenol concentrations, showing a strong negative correlation with YSB incidence (r = \u0026minus;\u0026thinsp;0.858** at 30 DAT and r = \u0026minus;\u0026thinsp;0.856** at 60 DAT) (Fig.\u0026nbsp;1; Table\u0026nbsp;9) (Fig.\u0026nbsp;3), highlighting the defensive role of phenols in enhancing resistance upon infestation. In addition, phenols participate in reactive oxygen species (ROS) scavenging, mitigating oxidative stress caused by radicals such as O₂⁻, OH⁻, H₂O₂, and singlet oxygen, thereby triggering cascades of defense-related enzyme activities (Maffei et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Elevated phenol levels have also been negatively correlated with infestations by leaf folder, Asian rice gall midge, chilli black thrips and brown planthopper, confirming their central role in host plant resistance (Punithavalli et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vijaykumar et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e; \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vanitha et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ashrith et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Megha \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sadafale et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Balaji et al. 2025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTannins\u003c/b\u003eTannins represent a key group of defensive secondary metabolites, particularly abundant in resistant rice genotypes. Their insect-deterrent activity is multifaceted: they act as feeding inhibitors, bind dietary proteins and reduce their digestibility, chelate essential metal ions, suppress digestive enzyme activity, and directly damage the insect midgut epithelium, thereby leading to reduced larval growth, delayed development, and higher mortality (Punithavalli et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dubey et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the present study, correlation analysis clearly demonstrated a strong negative association between tannin content and YSB infestation, recorded at both 30 days after transplanting (DAT) (r = \u0026minus;\u0026thinsp;0.886**) and 60 DAT (r = \u0026minus;\u0026thinsp;0.881**) (Fig.\u0026nbsp;1; Fig.\u0026nbsp;3; Table\u0026nbsp;9), signifying the consistent contribution of tannins to resistance mechanisms across crop growth stages. Earlier studies also highlighted tannins\u0026rsquo; effectiveness against cereal shoot fly and stem borer (Khurana \u0026amp; Verma, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). More recent investigations strengthened this evidence, showing that higher tannin levels are closely linked to reduced YSB damage in rice (Megha \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ranjini \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sadafale et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Balaji et al. 2025).\u003c/p\u003e \u003cp\u003eBeyond rice, tannins have been reported as a generalized defensive trait in cereals and legumes, conferring protection against a wide spectrum of herbivorous insects. Their quantitative expression is often environment- and genotype-dependent, influenced by soil fertility, stress conditions, and plant phenology. The ability of tannins to simultaneously target insect physiology, nutrient assimilation, and gut integrity makes them particularly important in breeding programs focused on durable pest resistance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eSugars (Soluble and reducing sugars)\u003c/h3\u003e\n\u003cp\u003eCarbohydrate metabolism plays a critical role in determining host susceptibility to insect pests. Susceptible rice genotypes generally accumulate higher levels of total and reducing sugars, which provide readily available energy that supports larval development, oviposition, and survival. For example, under YSB infestation, the susceptible check TN 1 recorded 8.02\u0026ndash;8.33 mg g⁻\u0026sup1; of total sugars, while the resistant check W 1263 maintained only 4.43 mg g⁻\u0026sup1; (Thakur, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Vanitha et al. (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) similarly reported a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.88**) between total sugars and leaf folder damage. In the present study, total soluble sugars showed a significant positive correlation with YSB incidence, with r\u0026thinsp;=\u0026thinsp;0.888 at 30 DAT and r\u0026thinsp;=\u0026thinsp;0.896** at 60 DAT, while reducing sugars showed comparable correlations with dead heart incidence (r\u0026thinsp;=\u0026thinsp;0.914 and r\u0026thinsp;=\u0026thinsp;0.903**, respectively) (Fig.\u0026nbsp;1; Table\u0026nbsp;9).\u003c/p\u003e \u003cp\u003eThe decline in sugar content after infestation may result from activation of phenylpropanoid pathway enzymes, which convert sugars into phenols, flavonoids, and lignin to reinforce resistance (Kumar et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rajadurai \u0026amp; Kumar \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Megha \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This diversion reflects a metabolic trade-off, where primary sugars serve as precursors for defense-related secondary metabolites that deter feeding and interfere with insect digestion. Resistant genotypes thus sustain lower sugar levels while channeling them into defensive pathways post-infestation. Furthermore, carbohydrate partitioning is linked to signaling pathways involving jasmonic acid (JA) and salicylic acid (SA), which coordinate the induction of secondary metabolites and systemic defense responses. Therefore, carbohydrate dynamics are not just nutritional determinants of pest success but also key biochemical markers for breeding rice with enhanced resistance to YSB.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eCrude proteins\u003c/h2\u003e \u003cp\u003eCrude protein content shows a strong positive correlation with susceptibility to YSB. Susceptible entries such as TN 1 (8.08 mg g⁻\u0026sup1;) and IR 36 (8.05 mg g⁻\u0026sup1;) provide a rich nitrogen source for larvae, enhancing their survival and development, whereas resistant varieties like TKM 6 maintain lower protein levels (6.30 mg g⁻\u0026sup1;), thereby restricting insect growth (Punithavalli et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Megha \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ranjini \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Protein content typically declines after infestation, particularly in susceptible genotypes, either due to direct consumption by larvae or its mobilization into defense-related pathways (Lokesh \u0026amp; Mehla \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present investigation, correlation analysis confirmed a significant positive association between crude protein content and dead heart incidence, with values of r\u0026thinsp;=\u0026thinsp;0.869** at 30 DAT and r\u0026thinsp;=\u0026thinsp;0.863** at 60 DAT (Fig.\u0026nbsp;1; Table\u0026nbsp;9). These results are consistent with earlier findings by Punithavalli et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who reported reduced soluble protein concentrations following infestation. In their study, TN 1 (8.08 mg g⁻\u0026sup1;) and IR 36 (8.05 mg g⁻\u0026sup1;) recorded the highest protein contents in healthy plants, while resistant genotypes PTB 33 (6.49 mg g⁻\u0026sup1;) and TKM 6 (6.30 mg g⁻\u0026sup1;) maintained much lower levels. Following infestation, protein content decreased across all genotypes, with TN 1 showing 7.61 mg g⁻\u0026sup1; and TKM 6 recording 6.21 mg g⁻\u0026sup1;. Similarly, in the present study, the resistant check TKM 6 maintained the lowest crude protein levels at 30 DAT (2.95 mg g⁻\u0026sup1;). Reports by Megha (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Ranjini (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) further support that protein content declines in infested rice genotypes and that crude proteins are positively and significantly associated with insect growth, development, and life cycle progression (Lokesh \u0026amp; Mehla \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eFree amino acids\u003c/h2\u003e \u003cp\u003eInterestingly, free amino acids (FAAs) exhibit an opposite trend to crude proteins, with resistant entries such as W 1263 and TKM 6 maintaining significantly higher levels (22.13 mg g⁻\u0026sup1;) than susceptible checks like TN 1 (16.21 mg g⁻\u0026sup1;). Although amino acids serve as essential nutrients for insect growth and reproduction, in resistant plants they are often redirected into the phenylpropanoid pathway, contributing to the biosynthesis of phenolics and other defense compounds. In the present study, correlation analysis confirmed a significant negative association between total free amino acids and YSB incidence, with r = \u0026minus;\u0026thinsp;0.798** at 30 DAT and r = \u0026minus;\u0026thinsp;0.799** at 60 DAT (Fig.\u0026nbsp;1; Table\u0026nbsp;9). These findings accord with Megha \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, who reported higher FAA concentrations in resistant genotypes. For instance, W 1263 recorded 22.13 mg g⁻\u0026sup1; with a strong negative influence on dead heart incidence, while susceptible genotypes like TN 1 and Kankunia maintained lower levels (~\u0026thinsp;16.2 mg g⁻\u0026sup1;), showing significant negative correlations (up to r = \u0026minus;\u0026thinsp;0.982**). Similarly, Kumar et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e noted higher FAA levels in resistant lines such as JGL 13595 (34.82 mg g⁻\u0026sup1;), compared with much lower values in TN 1 (16.45 mg g⁻\u0026sup1;).\u003c/p\u003e \u003cp\u003eOverall, FAAs function not only as nutrients involved in protein synthesis and development but also as precursors for defensive secondary metabolites. Their reduction following infestation reflects diversion into the phenylpropanoid pathway, enhancing the formation of phenols and lignin, which strengthen plant resistance against both biotic and abiotic stress. Collectively, these biochemical traits\u0026mdash;including elevated phenols, tannins, and free amino acids, alongside lower total sugars and soluble proteins\u0026mdash;form a robust defense network. Nutritional components modulate host suitability, while secondary metabolites confer antibiosis, antixenosis, and deterrence, establishing a strong biochemical basis for developing insect-resistant rice varieties through breeding and metabolic manipulation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eNutritional basis of resistance against Yellow stem borer\u003c/h2\u003e \u003cp\u003ePlant nutrition plays a central role in determining crop susceptibility or resistance to insect pests and diseases. Both macro- and micro-nutrients, along with beneficial elements such as silicon, influence host defense through structural reinforcement, metabolic adjustments, and activation of defense signalling pathways. The present investigation highlights the multifaceted roles of essential nutrients in modulating resistance against yellow stem borer (\u003cem\u003eScirpophaga incertulas\u003c/em\u003e), supported by correlation analysis with infestation parameters.\u003c/p\u003e \u003cp\u003eNitrogen, while indispensable for plant growth, exhibits a dual effect on pest interactions. Excess nitrogen enhances leaf succulence, chlorophyll content, and soluble nitrogen compounds, increasing tissue nutritional value for herbivores (Mattson, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). At the same time, it suppresses the biosynthesis of phenolics, lignin, and silica, weakening structural and biochemical defenses (Huber et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tripathi et al. 2022). In rice, higher nitrogen levels significantly increased yellow stem borer, brown planthopper, leaf folder, and rice hispa infestations (Prasad \u0026amp; Prasad, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sogawa, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Heinrichs \u0026amp; Aquino, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The present study corroborates this, with a strong positive correlation between nitrogen content and yellow stem borer infestation at 30 DAT (r\u0026thinsp;=\u0026thinsp;0.872**) and 60 DAT (r\u0026thinsp;=\u0026thinsp;0.857**) (Fig.\u0026nbsp;2; Table\u0026nbsp;10). Similar trends were observed across wheat, cotton, maize, and sorghum, emphasizing that excessive or imbalanced nitrogen application favors pest proliferation. This highlights the need for precision nutrient management integrated with pest control.\u003c/p\u003e \u003cp\u003eIn contrast, certain nutrients enhance host resistance by strengthening structural components and activating secondary metabolism. Phosphorus, for example, promotes lignin and phenolic synthesis, increasing tissue toughness and reducing palatability to herbivores. Adequate phosphorus also reinforces stalks and roots, limiting susceptibility to boring insects. In this study, phosphorus content was negatively correlated with yellow stem borer infestation (r = \u0026minus;\u0026thinsp;0.483** at 30 DAT; r = \u0026minus;\u0026thinsp;0.498* at 60 DAT) (Fig.\u0026nbsp;2; Table\u0026nbsp;10), consistent with reports in sugarcane, maize, rice, and soybean where higher phosphorus reduced stalk and stem borer incidence by enhancing lignification and optimizing the tissue carbon\u0026ndash;phosphorus balance (Hall, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Shahzad et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Facknath \u0026amp; Lalljee, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Johnson et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotassium further supports plant defenses by regulating osmotic balance and facilitating polyphenol accumulation, thereby reducing tissue suitability for herbivores. In the present study, a strong negative association with yellow stem borer infestation was observed at both 30 DAT (r = \u0026minus;\u0026thinsp;0.934**) and 60 DAT (r = \u0026minus;\u0026thinsp;0.923**) (Fig.\u0026nbsp;2; Table\u0026nbsp;10; Fig.\u0026nbsp;4). Adequate potassium similarly reduced pest damage in rice, cotton, and maize, lowering dead heart incidence, bollworm feeding, aphid populations, and larval growth of stem borers and fall armyworm (Voleti et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pettigrew, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bala et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Amtmann et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Conversely, potassium deficiency predisposed plants to higher pest damage due to weakened tissues and diminished biochemical defenses\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCalcium and magnesium exemplify nutrients with contrasting but complementary roles. Calcium strengthens cell walls through pectin cross-linking and mediates signaling pathways such as salicylic and jasmonic acid, which regulate defensive protein and metabolite synthesis. A significant negative correlation with yellow stem borer infestation was observed (r = \u0026minus;\u0026thinsp;0.854** at 30 DAT; r = \u0026minus;\u0026thinsp;0.864** at 60 DAT) (Fig.\u0026nbsp;2; Table\u0026nbsp;10), confirming its protective role, as also reported in cotton and grapevine (Srinivasan et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Volpe et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Magnesium, essential for chlorophyll biosynthesis and enzyme activation, showed a positive association with infestation (r\u0026thinsp;=\u0026thinsp;0.622** at 30 DAT; r\u0026thinsp;=\u0026thinsp;0.638** at 60 DAT) (Fig.\u0026nbsp;2; Table\u0026nbsp;10), suggesting that higher magnesium levels may increase tissue nutritional quality, inadvertently supporting larval development. Similar context-dependent effects have been reported in wheat and soybean (Cakmak and Yazici \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cheng et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Marschner 2012).\u003c/p\u003e \u003cp\u003eSulfur, zinc, and iron influence both plant metabolism and pest interactions. While sulfur is vital for defense-related compounds such as glucosinolates, cysteine, and methionine, it was positively correlated with yellow stem borer infestation (r\u0026thinsp;=\u0026thinsp;0.750** at 30 DAT; r\u0026thinsp;=\u0026thinsp;0.732** at 60 DAT) (Fig.\u0026nbsp;2; Table\u0026nbsp;10), likely due to improved tissue nutritional quality under certain conditions. Zinc contributed to membrane stability and antioxidant enzyme function, exhibiting a negative correlation with infestation (r = \u0026minus;\u0026thinsp;0.555**) and supporting reduced dead heart and white ear incidence. Iron, despite its role in lignification and oxidative defense, showed a positive association with infestation (r\u0026thinsp;=\u0026thinsp;0.682 at 30 DAT; r\u0026thinsp;=\u0026thinsp;0.679 at 60 DAT), suggesting elevated iron may favor larval development (Datnoff et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Suri et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Prasanna et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eManganese and copper consistently enhanced resistance through structural reinforcement and activation of secondary metabolism. Manganese activated phenylalanine ammonia-lyase, promoting lignin and phenolic synthesis, with a strong negative correlation with yellow stem borer infestation (r = \u0026minus;\u0026thinsp;0.746** at 30 DAT; r = \u0026minus;\u0026thinsp;0.747** at 60 DAT) (Fig.\u0026nbsp;2; Table\u0026nbsp;10). Copper, as a cofactor for polyphenol oxidases, similarly reinforced defenses (r = \u0026minus;\u0026thinsp;0.643** at 30 DAT; r = \u0026minus;\u0026thinsp;0.641** at 60 DAT), strengthening both structural and enzymatic barriers against herbivory.\u003c/p\u003e \u003cp\u003eSilicon, although not considered an essential nutrient, plays a unique and multifaceted role in pest resistance. Silicon deposits in epidermal tissues act as a mechanical barrier, physically restricting larval penetration. In addition, it primes biochemical defenses by enhancing the synthesis of phenolics, phytoalexins, and antioxidative enzymes. In the present study, silicon content showed a strong negative correlation with yellow stem borer infestation at initial observation (r = \u0026minus;\u0026thinsp;0.898**) and 60 DAT (r = \u0026minus;\u0026thinsp;0.897**) (Fig.\u0026nbsp;2; Fig.\u0026nbsp;4; Table\u0026nbsp;10), which translated into significantly reduced dead heart and white ear incidence. These observations are consistent with previous studies in rice, where silicon supplementation decreased stem borer damage, blast, and sheath blight severity (Datnoff et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Voleti et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ma and Takahashi \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), highlighting its dual mechanical and biochemical contribution to resistance.\u003c/p\u003e \u003cp\u003eCollectively, these results demonstrate that nutrient availability exerts profound effects on yellow stem borer infestation, largely through modulation of structural integrity, secondary metabolism, and enzymatic defenses. While nitrogen, magnesium, sulfur, and iron may, under certain conditions, favor pest development, nutrients such as phosphorus, potassium, calcium, manganese, copper, zinc, and silicon consistently enhance resistance. These findings underscore the critical importance of balanced and integrated nutrient management as a key strategy for sustainable pest suppression in rice and other cropping systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrincipal component analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBiochemical traits and their association with pest infestation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe PCA individual and biplot analysis at 30 and 60 DAT revealed the biochemical variability underlying rice genotypes\u0026rsquo; responses to yellow stem borer (YSB) infestation. At 30 DAT, the first principal component (PC1) explained the majority of the variation, whereas the second component (PC2) contributed minimally, providing slight vertical separation. Genotypes positioned on the right, such as Krishna Leela and Kundi Polan, were closely associated with rightward-directed traits, while those on the left, including TKM6 and Rajboga, exhibited contrasting biochemical profiles aligned with leftward traits. Clusters of genotypes reflected shared biochemical characteristics, whereas genotypes further apart, for instance Krishna Leela and Neermuka, demonstrated distinct profiles. These patterns suggested that variation was governed by the combined influence of multiple biochemical factors rather than a single dominant trait (Figs.\u0026nbsp;5A and 5B).\u003c/p\u003e \u003cp\u003eA similar pattern was observed at 60 DAT. PC1 again captured the majority of variation, with PC2 contributing slightly to vertical differentiation. Right-side genotypes, such as Kundi Polan and TN-1, were strongly associated with Dead Heart (DH), Total Soluble Sugars (TSS), and Feeding Preference (FP), whereas left-side genotypes, including W1263 and Nirga Samba, were linked with Total Free Amino Acids (TFA) and Total Amino Acids (TA). Slight vertical separation was evident among genotypes such as TKM6 and Manjula Sona. Distant genotypes, for example Kundi Polan and Neermuka, showed clear biochemical divergence, while clusters represented genotypes with similar biochemical characteristics. The balanced contributions of multiple traits indicated that biochemical diversity arose from complex interactions rather than dominance of a single factor (Figs.\u0026nbsp;6A and 6B).\u003c/p\u003e \u003cp\u003eThe present study further identified total soluble sugars, reducing sugars, crude protein, and tannins as the dominant contributors to biochemical variability associated with YSB incidence at both 30 and 60 DAT. These findings align with earlier studies showing that primary metabolites such as sugars and proteins often promote pest infestation, whereas secondary metabolites like phenolic compounds and tannins act defensively. For example, Batra et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e demonstrated that elevated nutrient contents, particularly sugars and proteins, enhanced aphid colonization in barley. Similarly, Kumar et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e reported that higher protein levels in black gram supported larval growth of \u003cem\u003eCallosobruchus maculatus\u003c/em\u003e, and in sugarcane, reducing sugars correlated positively with top borer incidence, while proteinase inhibitor activity, a defense-related protein, showed negative association (Bharti et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, resistant genotypes often contained higher concentrations of phenolic compounds. In muskmelon, resistance to \u003cem\u003eBactrocera cucurbitae\u003c/em\u003e was associated with elevated phenols, tannins, and flavonoids, whereas susceptible lines exhibited higher sugar content (Bhat et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, compensatory induction of phenolics and condensed tannins in aspen reduced aphid populations (Bandau et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies in stored grains showed that high protein and soluble sugar levels favored development of \u003cem\u003eRhyzopertha dominica\u003c/em\u003e, further confirming the role of primary metabolites in susceptibility (Jaiswal et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, these studies reinforce the PCA results in the present investigation, where sugars and crude protein loaded strongly on PC1, explaining a major proportion of variance and aligning with susceptibility traits. Phenols and tannins, although contributing less to variance, represented defense-associated metabolites conferring tolerance. The consistent clustering of these biochemical traits across crops highlights their potential as reliable biochemical markers for resistance breeding and integrated pest management strategies against YSB.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNutrient components and their association with pest infestation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe PCA individual and biplots (Fig.\u0026nbsp;7 and Fig.\u0026nbsp;8) at 30 and 60 DAT during Summer 2024 revealed a consistent nutrient association pattern, with PC1 (71.6%) driven predominantly by macronutrients including N, K, Ca, Mg, S, Fe, Mn, Cu, and Si, while PC2 (9.6%) was mainly influenced by P and Zn. Genotypes such as W1263 and TKM6 were strongly associated with macronutrients, whereas TN-1 and Neermuka showed contrasting nutrient profiles. Rajboga, Naland Paddy, Karikagga, Kari Munduga, and Adri Batta clustered under the influence of P and Zn, while genotypes like Bangara Kolee, Puttabatta, and Neermullarae remained close to the origin, indicating balanced nutrient associations. The vectors of N, Mg, S, and Fe exhibited positive correlation, K and Si acted in the opposite direction, and Zn, Mn, and Cu jointly influenced genotypes like Kala Jeera, Black Sticky, and Vijaya Samba. Overall, both Fig.\u0026nbsp;7B and Fig.\u0026nbsp;8B confirm that genotypic variation is largely explained by a broad macro\u0026ndash;micro nutrient balance (PC1), with P and Zn serving as distinct determinants of separation along PC2.\u003c/p\u003e \u003cp\u003eThe present PCA results demonstrated a highly stable dimensional structure across crop growth stages, with PC1 consistently explaining over 71% of the variation and reflecting a broad nutrient balance shaped by N, K, Ca, S, Mg, Fe, Mn, Cu, and Si. This integrative nutrient axis indicates that both macro- and micro-elements act in coordination to define the compositional landscape of rice during establishment, a trend also reported in earlier multivariate studies where N and K emerged as central drivers of growth, photosynthetic efficiency, and yield stability (Ahmad et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The co-loading of Ca, S, and Mg supports their synergistic role in structural integrity and metabolic processes, while the association of Fe, Mn, and Cu underscores their involvement in energy metabolism, enzyme activation, and stress defense (Broadley et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Farooq et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By contrast, PC2 consistently isolated phosphorus and zinc across both stages, accounting for about 10% of the variance, highlighting a nutrient-specific gradient. This separation aligns with previous findings that P and Zn interactions often diverge due to competitive uptake and antagonistic behavior in soil\u0026ndash;plant systems (Singh et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, the genotypic divergence observed at 30 and 60 DAT was driven not only by a broad nutrient balance (PC1) but also by distinct P\u0026ndash;Zn dynamics (PC2), emphasizing that while rice genotypes primarily differ along a stable macro\u0026ndash;micro nutrient balance axis, P and Zn consistently emerge as independent determinants of variability, with important implications for nutrient use efficiency, stress resilience, and breeding strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study highlights the significant role of both biochemical and nutritional traits in shaping resistance to the yellow stem borer in rice landraces. Elevated phenols, tannins, and free amino acids served as reliable defensive markers, whereas higher sugars and proteins were linked to greater susceptibility. Among nutrients, nitrogen, magnesium, sulfur, and iron favored infestation, while phosphorus, potassium, calcium, manganese, copper, zinc, and silicon consistently enhanced resistance through structural reinforcement and metabolic regulation. PCA further revealed a stable nutrient\u0026ndash;resistance framework across crop stages, with a broad macro\u0026ndash;micro nutrient balance driving most genotypic variation and phosphorus\u0026ndash;zinc dynamics forming a distinct secondary axis. These results emphasize the importance of balanced nutrient management and the potential of resistant landraces as donors in breeding programs. Harnessing their inherent biochemical defenses and favorable nutrient interactions can contribute to the development of durable YSB-resistant rice cultivars, thereby reducing dependence on chemical control and supporting sustainable rice production.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003e \u003cb\u003eCompeting Interests\u003c/b\u003e.\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate.\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human or animal subjects.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eConsent for Publication\u003c/b\u003e.\u003c/strong\u003e \u003cp\u003eObtained from all authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDivya DM - Conceptualization; Data curation; Formal analysis; Investigation; Writing-original draft. Vijaykumar L \u0026ndash; Conceptualization; Writing; review \u0026amp; editing; Validation; Methodology; Investigation. Shivanna B, Lakshminarayana Reddy CN- Conceptualization; Methodology; Supervision; Review \u0026amp; editing; Visualization; Validation; Methodology.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors express their sincere gratitude to the authorities of the University of Agricultural Sciences, Bangalore, for their support. Special thanks are extended to the Director of Research for their guidance and facility. The second author gratefully acknowledges the Science and Engineering Research Board (SERB), Ministry of Science and Technology, Government of India, New Delhi, for providing moral support.\u003c/p\u003e\u003ch2\u003eMaterials Availability.\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCode Availability.\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData availability.\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad I, Maqsood MA, Kanwal S, Ahmad A (2016) Nitrogen and potassium interaction improves growth, yield and nutrient uptake of wheat. 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Entomol Exp Appl 105(3):249\u0026ndash;256.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rice, Scirpophaga incertulas, Resistance, Biochemical and nutritional defense, Antibiosis, PCA","lastPublishedDoi":"10.21203/rs.3.rs-9567632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9567632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe yellow stem borer (YSB), \u003cem\u003eScirpophaga incertulas\u003c/em\u003e (Walker), is a major constraint to rice production across South and Southeast Asia, often causing yield losses of 25\u0026ndash;70%. The present study evaluated 50 traditional rice landraces under field conditions to identify sources of resistance and elucidate their biochemical and nutritional defense mechanisms. None of the landraces exhibited complete resistance, but several landraces reacted as resistant or moderately resistant. The biochemical analyses revealed higher concentrations of phenols, tannins, and total free amino acids and were strongly associated with resistance, while elevated levels of soluble sugars, reducing sugars, and crude proteins correlated positively with susceptibility. Nutrient profiling indicated that excess nitrogen, magnesium, sulfur, and iron predisposed genotypes to higher infestation, whereas phosphorus, potassium, calcium, manganese, copper, zinc, and silicon enhanced resistance. Principal Component Analysis (PCA) confirmed a highly stable dimensional structure across growth stages, with PC1 (\u0026gt;\u0026thinsp;71%) reflecting an integrative macro\u0026ndash;micro nutrient balance dominated by N, K, Ca, S, Mg, Fe, Mn, Cu, and Si, while PC2 (~\u0026thinsp;10%) consistently isolated phosphorus and zinc as distinct determinants. Together, these findings demonstrate that resistance in rice landraces is conferred by a coordinated network of biochemical and nutritional traits, offering valuable insights for breeding and integrated pest management strategies aimed at developing YSB-resistant cultivars.\u003c/p\u003e","manuscriptTitle":"Uncovering resistance traits in rice landraces against Yellow stem borer Scirpophaga incertulas (Walker) through biochemical and Principal Component Analysis approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 11:11:18","doi":"10.21203/rs.3.rs-9567632/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33431c4d-e463-41ee-a1ec-35a13ed1cba3","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T09:43:28+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"263941601406470423500431989929010161680","date":"2026-05-16T20:42:42+00:00","index":32,"fulltext":""},{"type":"reviewerAgreed","content":"249428334292453264092999936594532612985","date":"2026-05-16T18:37:01+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"280040076108492313005287851275463016085","date":"2026-05-16T11:13:59+00:00","index":29,"fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-05T08:51:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-02T07:20:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-02T07:19:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Crop Health","date":"2026-04-29T15:03:07+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:11:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 11:11:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9567632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9567632","identity":"rs-9567632","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}