Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal

Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal | 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 Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal Janak Adhikari, Anka Kumari Limbu, Arati Dhami, Sangita Hamal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7999882/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 Drought stress is a critical challenge to rice production, necessitating the development of drought-tolerant genotypes. This study aimed to evaluate 25 rice ( Oryza sativa L.) genotypes for drought tolerance under rainfed and irrigated conditions at, Baitadi, Nepal during June–October 2022, using an alpha lattice design. The experiment aimed to identify drought-tolerant genotypes and effective drought tolerance indices based on grain yield. Drought stress, induced at the reproductive stage, reduced yields by an average of 24.2% (range: 9.6–53.3%). Mean Productivity (MP) and Geometric Mean Productivity (GMP) emerged as the most reliable indices, showing strong positive correlations with yield under both conditions. Principal Component Analysis (PCA) and drought tolerance indices (TOL, MP, GMP, STI, YSI, SSI) identified CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B as the most drought-tolerant genotypes, with high MP and GMP values and low stress susceptibility. These genotypes are potential candidates for breeding programs in the mid-hills of Nepal, supporting sustainable rice production under water-limited conditions. Drought stress stress-resilient Figures Figure 1 Figure 2 Figure 3 Introduction Rice ( Oryza sativa L.) is a staple crop for approximately one-third of the global population, with Asia accounting for around 90% of its production and consumption (Fukagawa & Ziska, 2019 ). However, climate change poses significant challenges to rice cultivation, particularly through altered precipitation patterns and increased drought frequency, which threaten water availability and yield stability. Rising temperatures, especially elevated night temperatures, exacerbate these impacts by shortening phenological stages and reducing grain yields (Peng et al., 2004 ). The extreme climate conditions model indicates that 1% rise in the occurrence of days with extreme variations in rainfall leads to a reduction of 0.28% in rice production (Rayamajhee et al., 2021 ). In rainfed systems, drought is the primary abiotic stress, causing yield reductions of 13–35% in moderate conditions (Tiwari et al., 2019 ; Kandel et al., 2022 ) and up to 65–85% in severe cases (Kumar et al., 2012 ). These losses are particularly critical in vulnerable regions like Nepal, which is ranked fourth globally in climate change vulnerability, and where only 28% of cultivated land benefits from year-round irrigation (Poudel et al., 2020 ). In Nepal’s mid-hills, including Baitadi, rice farming relies heavily on monsoon rainfall, with terraced fields often lacking supplemental irrigation. This results in erratic water availability, leading to an average grain yield of 2.34 t ha⁻¹, below the national average of 2.71 t ha⁻¹ (Kandel et al., 2022 ). Drought stress, compounded by heat waves, increases pollen sterility and unfilled grains, further limiting productivity (Hussain et al., 2021 ). Previous studies in Nepal’s mid-hills have screened released rice varieties for drought tolerance using indices like Mean Productivity (MP) and Geometric Mean Productivity (GMP) (Adhikari et al., 2018 ; Kandel et al., 2022 ). However, evaluating unreleased pipeline varieties from the International Rice Research Institute (IRRI) in site-specific conditions, such as Baitadi, remains underexplored, presenting a critical gap for enhancing local adaptation. Tolerance level (TOL), mean productivity (MP), geometric mean productivity (GM), stress tolerance index (STI), and stress susceptibility index (SSI) are the various drought indices used to assess a cultivar's drought tolerance. In rice, cultivars with high STI values and low SSI and TOL are regarded as drought-tolerant ( Adhikari et al. 2019 ). The best indices for assessing drought tolerance are those that have high MP, GMP, and STI (Hooshmandi 2019). Previous research has identified several drought-tolerant rice genotypes.Kandel et al. ( 2022 ) evaluated 14 rice genotypes were evaluated under both drought stress and non-stress and identified NR 119 is followed by Chaite 5 and Chaite 4 as a drought-tolerant genotype. There are some drought tolerant rice varieties (DTR) such as Sukhadhan-1, Sukhadhan-2 and Sukhadhan-3 released in 2011 and Sukhadhan-4, Sukhadhan-5 and Sukhadhan-6 were released in 2014. These were released for targeting rainfed low land condition from terai to lower hills upto 1000 masl (Dhakal et al., 2020 ). Rai et al. ( 2023 ) thirty drought-tolerant spring rice ( Oryza sativa L.) genotypes using various drought tolerant indices at inner plain region and reported that genotype IRE16L1661 is stable under drought conditions based on drought tolerance indices. Previous studies have focused on released cultivars and mostly evaluated in inner plain area In order to address this, the current research accessed pipelines rice genotypes originated from IRRI and applying multiple drought tolerance indices, we sought to identify drought resilience rice genotypes for mid-hill growing condition at Baitadai, thereby contributing to sustainable rice production in drought-prone regions. Materials and Methods Research site and rice genotypes An experiment was carried out at the agronomy farm of Gokuleshwor, Baitadi, having 29⁰ 58′ 25” N latitude, 80⁰ 31′ 43” E longitude, at a height of 850 masl, from 27th of June to 12th of October, 2022. Agroclimatic details of the experiment site are shown in (Fig. 1 ). The experiment consists of twenty-five genotypes, among which twenty-three were pipeline varieties that originated from IRRI, Philippines and two were check varieties. The rice genotypes utilized in the experiment, along with their details, have been provided in table (1). Table 1 List of rice cultivars used in research. S.N. Genotypes Remarks 1 CVT BOROSVIN-312 Pipeline cultivar 2 IR 129077-2-1-21-8-B Pipeline cultivar 3 RAVI DROUGHT FR 129077-1-1-12-7-B Pipeline cultivar 4 Rato Basmati Released Variety 5 RAVI-DROUGHT IR 80991-B-330-0-1 Pipeline cultivar 6 Juwa Basmati Released Variety 7 CVT SPRING IR 86515-1-19-2-1-1-1 Pipeline cultivar 8 Sukha Dhan-3** Released Variety 9 CVT PIGMENTED-121-121 Pipeline cultivar 10 RAVI-N IR 96321-1447-651-B-1-1-2 Pipeline cultivar 11 CVT BORO-IR16L1726 Pipeline cultivar 12 RFFT-N NR 2184 Pipeline cultivar 13 Ravi control IR 129077123 1-1-55-8-B 83 Pipeline cultivar 14 RAVI DROUGHT IR 129077-2-1-42-5-B Pipeline cultivar 15 RAVI TARAHARA-105 Pipeline cultivar 16 CFFT-FAR IVR 21841871 Pipeline cultivar 17 RC IR 1611795 Pipeline cultivar 18 RAVI DROUGHT IR 129077-2-1-7-8B Pipeline cultivar 19 IR 129077:2-1-36-8-B Pipeline cultivar 20 CVT BORO IR 1621226 Pipeline cultivar 21 CVT BORO 1621004 Pipeline cultivar 22 CFFT-SPRING IR 10L192 Pipeline cultivar 23 CVT SPRING IR 17L1317 Pipeline cultivar 24 Kaljadey* Local landraces 25 Hardinath-1 Released variety *,** Local check and standard check respectively (Kaljadey is a popular landraces in the research area, whereas Sukha Dhan-3 is one of the promising drought-tolerant variety released by NARC and recommended for drought condition). Experimental field layout and crop management The field experiment was laid out in alpha-lattice design with 10 blocks and two replications (two environmental conditions each) (5 genotypes in each block). Nursery seeding took place on May 24. 24 hours before sowing, the seeds were initially treated with Bavistin at a rate of 2g kg − 1 of seeds. Rice seedlings were cultivated in a dry seedbed for 25 days before being transplanted into well-puddled soil with a 20cm x 20cm spacing and a 3m× 2m plot size. A total of 6 tons ha − 1 of farmyard manure (FYM) and 120:40:40 kg NPK ha − 1 of chemical fertilizer were applied to both irrigation and drought-prone regions. Half of the nitrogen dosage was given during field preparation, along with full phosphorus, potassium, and FYM doses. The other half of the dosage was divided between the tillering and panicle initiation stages. After transplantation, water was left on the field for a month to allow healthy establishment of the crop during the vegetative stage, and no further irrigation was given. At the start of the reproductive stage, extra water was removed from the field by implementing a drainage system, and drought conditions were maintained. This was done in a rainfed field maintained as a drought-stressed field. Drought tolerance indices and statistical analysis To distinguish genotypes based on drought response in terms of grain yield, drought tolerant indices based on grain yield tons ha − 1 for irrigated (Yp) and drought stress (Ys) conditions for each genotype were computed using the formula presented in table (2). MS Excel (version 19) was used to record the data, and R (4.0.5) was utilized to analyze it. R Studio was used to determine the association between drought indices and yield under stress and non-stress conditions. Principal components analysis examined the statistical relationship between genotypes under stress using R Studio. Table 2 Drought tolerance indices used in the study Indices Formula References Tolerance index (TOL) TOL = Yp-Ys (Rosielle & Hamblin, 1981 ) Mean productivity index (MP) MP=(Yp + Ys)/2 (Adhikari et al., 2019 ; Kandel et al., 2022 ) Geometrical mean productivity (GMP) GMP= (Yp*Ys )1/2 (Adhikari et al., 2019 ) Stress tolerance index (STI) STI = Yp × Ys/(Yp) 2 (Fernandez 1992; Anwaar et al. 2020) Stress susceptibility index (SSI) SSI = 1-(Ys/Yp)/SI while SI = 1 − (Ys/Yp) (Fischer & Maurer, 1978 ) Results and Discussion Drought tolerant indices and grain yield under stress and normal conditions In table (3), all six drought tolerance indices were analyzed and presented. The tolerance indices MP and GMP were noted highest on genotype CFFT-SPRING IR 10L192 (3.44, and 19.11 respectively) with the highest yield under both environmental conditions Yp and Ys. In TOL, the highest was recorded in genotype Ravi control IR 129077123 1-1-55-8-B 83 (1.78) so this leads to deterioration in the yield of rice. Whereas the STI and YSI were found maximum on genotype CVT SPRING IR 17L1317 and Sukha Dhan-3 (0.90). The highest SSI (2.08) and maximum yield loss (50.66%) were obtained on genotype Ravi control IR 129077123 1-1-55-8-B 83. In contrast, the grand mean yield under stress and non-stress conditions were discovered to be, respectively, 2.3 and 3 tonsha − 1 . The yield loss has an average of 24.2% and ranges from 9.6% to 53.3%. Our findings are by those of(Kandel et al., 2022 ), drought conditions reduced yield by an average of 27%. In drought-affected conditions, 12–46% of grain yield was lost (Ouk et al., 2006 ); and the yield reduced by 9–51%(Pantuwan et al., 2004 ). Table 3 Mean Performance of twenty-five rice genotypes using various drought tolerant indices Genotype Ys(tons ha − 1 ) Yp(tons ha − 1 ) MP TOL GMP STI YSI SSI Yield loss (%) CFFT-FAR IVR 21841871 2.20 2.60 2.40 0.40 2.39 0.64 0.85 0.66 13.33 CFFT-SPRING IR 10L192 4.10 4.80 4.45 0.70 4.44 2.19 0.85 0.63 23.33 CVT BORO 1621004 2.10 2.60 2.35 0.50 2.34 0.61 0.81 0.82 16.67 CVT BORO IR 1621226 3.30 4.00 3.65 0.70 3.63 1.47 0.83 0.75 23.33 CVT BORO-IR16L1726 1.70 2.20 1.95 0.50 1.93 0.42 0.77 0.97 16.67 CVT BOROSVIN-312 1.90 2.30 2.10 0.40 2.09 0.49 0.83 0.75 13.33 CVT PIGMENTED-121-121 1.40 1.70 1.55 0.30 1.54 0.26 0.82 0.76 10.00 CVT SPRING IR 17L1317 2.80 3.10 2.95 0.30 2.95 0.96 0.90 0.41 10.00 CVT SPRING IR 86515-1-19-2-1-1-1 2.30 3.10 2.70 0.80 2.67 0.79 0.74 1.11 26.67 Hardinath-1 2.10 3.10 2.60 1.00 2.55 0.72 0.68 1.38 33.33 IR 129077-2-1-21-8-B 1.20 2.70 1.95 1.50 1.80 0.36 0.44 2.38 50.00 IR 129077:2-1-36-8-B 1.90 2.60 2.25 0.70 2.22 0.55 0.73 1.15 23.33 Juwa Basmati 2.20 2.90 2.55 0.70 2.53 0.71 0.76 1.03 23.33 Kaljadey (Local) 2.50 3.70 3.10 1.20 3.04 1.03 0.68 1.39 40.00 Rato Basmati 3.00 3.90 3.45 0.90 3.42 1.30 0.77 0.99 30.00 Ravi control IR 129077123 1-1-55-8-B 83 1.70 3.40 2.55 1.70 2.40 0.64 0.50 2.14 56.67 RAVI DROUGHT FR 129077-1-1-12-7-B 3.10 3.70 3.40 0.60 3.39 1.27 0.84 0.70 20.00 RAVI DROUGHT IR 129077-2-1-42-5-B 2.70 3.60 3.15 0.90 3.12 1.08 0.75 1.07 30.00 RAVI DROUGHT IR 129077-2-1-7-8B 1.50 1.70 1.60 0.20 1.60 0.28 0.88 0.50 6.67 RAVI TARAHARA-105 2.10 2.30 2.20 0.20 2.20 0.54 0.91 0.37 6.67 RAVI-DROUGHT IR 80991-B-330-0-1 3.20 3.90 3.55 0.70 3.53 1.39 0.82 0.77 23.33 RAVI-N IR 96321-1447-651-B-1-1-2 1.20 2.10 1.65 0.90 1.59 0.28 0.57 1.84 30.00 RC IR 1611795 1.80 3.10 2.45 1.30 2.36 0.62 0.58 1.80 43.33 RFFT-N NR 2184 2.00 3.00 2.50 1.00 2.45 0.67 0.67 1.43 33.33 Sukha Dhan- 3 (std check) 3.00 3.40 3.20 0.40 3.19 1.13 0.88 0.50 13.33 CV (%) F-test Grand Mean 2.30 3.00 2.65 0.74 2.61 0.82 0.75 1.05 24.67 *Significant at P-value ≤ 0.05, **Significant at 0.01 level, Ys: yield under stress, Yp: yield under non-stress, MP: mean productivity, TOL: Tolerance index, GM: geometric mean productivity, STI: stress tolerance index, YSI: yield stability index, SSI: stress susceptibility index. Correlation of drought indices A correlation study of grain yield and various drought tolerance indices can be useful criteria for choosing the optimal genotypes and stress tolerance indices shown in figure (2). Ypwas found to be positive and significant association with Ys, MP, and GMP. The study ofKandel et al.(2022) has also found a similar result. In earlier studies Mousavi et al. ( 2008 );Dadbakhsh et al. ( 2011 )and Bennani et al. ( 2017 ), reported that Yp and Ys were positively correlated. The association of Yp was non-significant and negative with YSI and STI. Ys showed a positive and statistically significant relationship with STI and YSI. Adhikari et al. ( 2019 ) reported similar findings. The SSI and YL have shown significant and negative correlation with Ys whereas, in the case of Yp, there was a non-significant and positive correlation with Yp. This suggested that the selection based on SSI and YL increased yield under Yp. Due to the positive correlation of Yp with SSI and TOL,the selection based on SSI and TOL will result in increased yield under Yp(Sio-Se Mardeh et al., 2006 ). The MP was found to be highly positive correlation with the yield at both conditions. A similar result was obtained byKandel et al. ( 2022 ), that the GMP, MP, and SSI were positive and significantly correlated with yield. A positive correlation between yield under stressed and non-stressed conditions has also been reported in earlier studies (Saeed et al., 2008 ; Ahmadizadeh et al., 2011 ; Bennani et al., 2017 ). The YSI and STI have high significance levels and negative correlations with TOL, SSI, and YL. In our experiment, MP and GMP were identified as pertinent indices to select stress tolerance genotypes as these indices haveshowna highly positive correlationwith grain yield (Rahimi et al., 2013 ; Bhandari et al., 2020 )under both drought and irrigated conditions. So, to select high-yielding lines, MP and GMP can be used in functional rice breeding programs. Principal Component Analysis The cumulative variance of the first two main components exceeded 99%, with an eigenvalue larger than one. The first principal component analysis (PCA) accounted for 59.6% of the total variance in Yp, Ys, STI, MP, GMP, and STI. Thus, the first component might be labeled yield potential and stress tolerance, whilst the second PCA accounted for 39.9% of total variability. Drought tolerance indicators and genotype response were studied using principal component analysis. MP and GMP exhibited significantly positive association with the yield of grain under Yp and Ys, implying that selecting based on these indices will result in higher grain yield in both situations. Similarly, STI and YSI were the two indices that were highly positive with Ys, hence, the increasing grain yield in condition will result based on the selection of these indices. The SSI and YL were highly correlated in a positive direction, showing a close association aligned with the genotypes. The SSI, YL, and TOL indices were positively associated with Yp and Ys. The genotypes CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B were located between Yp and Ys and the indices between MP and GMP. The CVT SPRING IR 17L1317, CVT BORO 1621004, CFFT-FAR IVR 21841871, CVT BOROSVIN-312, RAVI TARAHARA-105, CVT PIGMENTED-121-121 and RAVI DROUGHT IR 129077-2-1-7-8B had considerable correlation with STI, YSI, YL, SSI and TOL figure (3). While, the genotypes Juwa Basmati, IR 129077:2-1-36-8-B, RAVI-N IR 96321-1447-651-B-1-1-2, and CVT BORO-IR16L1726 by MP, and GMP as well as the genotypes Ravi control IR 129077123 1-1-55-8-B 83, Kaljadey, RC IR 1611795, Hardinath-1, IR 129077-2-1-21-8-B, CVT SPRING IR 86515-1-19-2-1-1-1, and RFFT-N NR 2184 by SSI, YL, TOL, STI and YSI were identified as responsive to drought. In contrast, the rest genotypes were recognized as genotypes with moderate drought tolerance. While screening drought-tolerant genotypes using the drought tolerance indices and multivariate analysis, CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B genotypes were genotypes with the highest drought tolerance. Similar findings were reported by Jeyaprakash et al. ( 2016 ) and Rahimi et al. ( 2013 )in drought tolerance of rice, among all other indices, YI, GMP, MP, STI, MPI, and HM emerged as the top stress indicators for identifying drought-tolerant genotypes demonstrated by biplot graph. Conclusion According to the study's findings, water stress dramatically lowers rice output across all cultivars throughout the critical growth period of the crop. It also suggests that selecting cultivars with better drought tolerance and greater production potential and stability can be done effectively with the use of drought tolerance index selection. The CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B rice genotypes were found to have a high level of drought tolerance based on drought tolerance indices. These genotypes also displayed low SSI and TOL with high STI values. These genotypes fared better against drought stress and produced the maximum yield in irrigated situations. Consequently, these rice varieties resistant to drought can be better substitutes for drought-prone belts for rice cultivation. Declarations Acknowledgments The authors were thankful to the Directorate of Agricultural Research, Tarahara, Koshi Province, Sunsari for providing genetic materials used in the study. Consent to participate Not applicable Author’s contribution Janak Adhikari: Conceptualization, proposal development, methodology, analysis, resources, writing-original draft. Anka Kumari Limbu: Data curation, investigation, review, writing-original draft. Arati Dhami: Methodology, analysis, writing-original draft. Sangita Hamal: Data collection, review and editing Arpana Joshi: Review and editing, analysis. Bishnu Prasad Kandel: Supervision, data management, statically analysis and reviewing editing. Competing statement The authors state that they do not have any competing interests. Ethical statement This article does not include any studies involving human or animal subjects conducted by any of the authors. Funding No funding was received. Consent to Publish declaration Not applicable Data availability statement All data generated or analysed during this study are included in this published article References Adhikari BB, Mehera B, Haefele SM. Selection of drought tolerant rice varieties for the western mid hills of Nepal. J Inst Agric Anim Sci. 2018;33(0):195–206. https://doi.org/10.3126/jiaas.v33i0.20705 . Adhikari M, Adhikari NR, Sharma S, Gairhe J, Bhandari RR, Paudel S. Evaluation of Drought Tolerant Rice Cultivars Using Drought Tolerant Indices under Water Stress and Irrigated Condition. Am J Clim Change. 2019;08(02):228–36. https://doi.org/10.4236/ajcc.2019.82013 . Ahmadizadeh M, Club YR, Branch J, Dadbakhsh A, Yazdansepas A. Corresponding Author Study Drought Stress on Yield of Wheat (Triticum aestivum L.) Genotypes by Drought Tolerance Indices. Adv Environ Biology. 2011;5(7):1804–10. Bennani S, Nsarellah N, Jlibene M, Tadesse W, Birouk A, Ouabbou H. Efficiency of drought tolerance indices under different stress severities for bread wheat selection. Aust J Crop Sci. 2017;11(4). https://doi.org/10.21475/ajcs.17.11.04.pne272 . Bhandari K, Joshi LP, Bhandari N, Upadhyay K, Sharma S. Evaluation of drought tolerance indices for selection of high yielding drought tolerant rice genotypes in Lamjung, Nepal. Azarian J Agric. 2020;7(2):54–9. Bouslama M, SchapaughJr WT. Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop Sci. 1984;24(5):933–7. Fukagawa NK, Ziska LH. (2019). Rice: Importance for Global Nutrition. J NutrSciVitaminol (Tokyo).65(Supplement):S2-S3. 10.3177/jnsv.65.S2 . PMID: 31619630. Chaudhari PR, Tamrakar N, Singh L, Tandon A, Sharma D. Rice nutritional and medicinal properties: A review article. J Pharmacognosy Phytochemistry. 2018;7(2):150–6. Dadbakhsh A, Yazdansepas A, Ahmadizadeh M. Study drought stress on yield of wheat (Triticum aestivum L.) genotypes by drought tolerance indices. Adv Environ Biology. 2011;5(7):1804–10. El-Hashash EF, El-Agoury RYA, El-Absy KM, Sakr SMI. Genetic parameters, multivariate analysis and tolerance indices of rice genotypes under normal and drought stress environments. Asian J Res Crop Sci. 2018;1(3):1–18. Fischer RA, Maurer R. Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust J Agric Res. 1978;29(5):897–912. Hussain T, Hussain N, Ahmed M, Nualsri C, Duangpan S. Responses of lowland rice genotypes under terminal water stress and identification of drought tolerance to stabilize rice productivity in southern Thailand. Plants. 2021;10(12):2565. Jeyaprakash P, Ramchander S, Raveendran M, Kari B. Determination of Stress Indices for Selection of Superior Genotypes under Drought Situation in Rice (Oryza sativa L). Int J Agric Sci. 2016;8:38–1791. http://www.bioinfopublication.org/jouarchive.php?opt=&jouid=BPJ0000217 . Kandel BP, Joshi LP, Sharma S, Adhikari P, Koirala B, Shrestha K. Drought tolerance screening of rice genotypes in mid-hills of Nepal using various drought indices. ActaAgriculturaeScandinavica Sect B: Soil Plant Sci. 2022;72(1):744–50. https://doi.org/10.1080/09064710.2022.2072382 . Kumar A, Verulkar SB, Mandal NP, Variar M, Shukla VD, Dwivedi JL, Singh BN, Singh ON, Swain P, Mall AK. High-yielding, drought-tolerant, stable rice genotypes for the shallow rainfed lowland drought-prone ecosystem. Field Crops Res. 2012;133:37–47. Kumar S, Dwivedi SK, Singh SS, Bhatt BP, Mehta P, Elanchezhian R, Singh VP, Singh ON. Morpho-physiological traits associated with reproductive stage drought tolerance of rice (Oryza sativa L.) genotypes under rain-fed condition of eastern Indo-Gangetic Plain. Indian J Plant Physiol. 2014;19(2):87–93. https://doi.org/10.1007/s40502-014-0075-x . Lin H-I, Yu Y-Y, Wen F-I, Liu P-T. Status of food security in East and Southeast Asia and challenges of climate change. Climate. 2022;10(3):40. Lobell DB, Gourdji SM. Focus Issue on the Plant Physiology of Global Change: The Influence of Climate Change on Global Crop Productivity. Plant Physiol. 2012;160(4):1686. Mohanty S, Wassmann R, Nelson A, Moya P, Jagadish SVK. Rice and climate change: significance for food security and vulnerability. Int Rice Res Inst. 2013;14:1–14. Mousavi SS, YAZDI SB, Naghavi MR, Zali AA, Dashti H, Pourshahbazi A. (2008). Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes. Ouk M, Basnayake J, Tsubo M, Fukai S, Fischer KS, Cooper M, Nesbitt H. Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice. Field Crops Res. 2006;99(1):48–58. Pantuwan G, Fukai S, Cooper M, Rajatasereekul S, O’toole JC, Basnayake J. Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands: 4. Vegetative stage screening in the dry season. Field Crops Res. 2004;89(2–3):281–97. Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG. (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences, 101(27), 9971–9975. Poudel S, Funakawa S, Shinjo H, Mishra B. Understanding households’ livelihood vulnerability to climate change in the Lamjung district of Nepal. Environ Dev Sustain. 2020;22:8159–82. Rahimi M, Dehghani H, Rabiei B, Tarang AR. Evaluation of rice segregating population based on drought tolerance criteria and biplot analysis. Int J Agric Crop Sci. 2013;5(3):194. Rayamajhee V, Guo W, Bohara AK. The impact of climate change on rice production in Nepal. Econ Disasters Clim Change. 2021;5:111–34. Rosielle AA, Hamblin J. Theoretical aspects of selection for yield in stress and non-stress environment 1. Crop Sci. 1981;21(6):943–6. Saeed S, Bu-Ali M, Naghavi MR, Dashti H, Moosavi SS, Samadi BY, Naghavi MR, Zali AA, Pourshahbazi A. (2008). Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes SEE PROFILE Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes. In DESERT (Vol. 12). http://jdesert.ut.ac.ir Sio-Se Mardeh A, Ahmadi A, Poustini K, Mohammadi V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Res. 2006;98(2):222–9. https://doi.org/https://doi.org/10.1016/j.fcr.2006.02.001 . Tiwari DN, Tripathi SR, Tripathi MP, Khatri N, Bastola BR. (2019). Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Advances in Agriculture, 2019. Rai N, Thapa S, Rawal S, Jamkatel DP, Maharjan B. Yield Performance Evaluation of Thirty Spring Rice (Oryza sativa L.) Cultivars Under Terminal Drought Conditions Using Various Drought-Tolerant Indices. AgroEnvironmental Sustain. 2023;1(2):86–92. https://doi.org/10.59983/s2023010201 . Dhakal S, Adhikari BB, Kandel BP. Performance of drought tolerant rice varieties in different altitudes at Duradada, Lamjung, Nepal. J Agric Nat Resour. 2020;3(1):290–300. https://doi.org/10.3126/janr.v3i1.27199 . 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7999882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551567634,"identity":"d47875d0-3eba-4ca9-bd25-4bb77919fa0d","order_by":0,"name":"Janak Adhikari","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Janak","middleName":"","lastName":"Adhikari","suffix":""},{"id":551567635,"identity":"78e173c9-37c2-4fcc-9964-ea8d7eb9e2e0","order_by":1,"name":"Anka Kumari Limbu","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Anka","middleName":"Kumari","lastName":"Limbu","suffix":""},{"id":551567636,"identity":"e8128737-0bc8-46cb-9c05-01430cffaf05","order_by":2,"name":"Arati Dhami","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Arati","middleName":"","lastName":"Dhami","suffix":""},{"id":551567637,"identity":"7bab6e2f-9a3e-4faa-88bc-8186a576e342","order_by":3,"name":"Sangita Hamal","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Sangita","middleName":"","lastName":"Hamal","suffix":""},{"id":551567638,"identity":"6b8d5d18-35cd-469e-8bcf-2c2e0c11f0d4","order_by":4,"name":"Arpana Joshi","email":"","orcid":"","institution":"Tribhuvan University","correspondingAuthor":false,"prefix":"","firstName":"Arpana","middleName":"","lastName":"Joshi","suffix":""},{"id":551567639,"identity":"8b8a328c-70dd-47df-8c4c-0dc9eebf4f00","order_by":5,"name":"Bishnu Prasad Kandel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACAx4gwcNgYSfP3gDiWhCtRSLZsOcAiCtBvBbGhhsJID4RWsx5Dj978KZCgplx5vOrG34USDDwt3cn4NVi2dtmbjjnjAQfu3RO2c0eoMMkzpzdgN9h5xnMpHnbgLbMzkm7wQPUYiCRS0gL+zdp3n9Av9w8k3bzD1FazvYAbWkAeZ/92G2ibLHsOVMmOecYKJBz2G7LGEjwEPSLOU/6Nok3NTbAqDz+7OabPzZy/O29+LUgAR4DMEmschBgf0CK6lEwCkbBKBhBAAB2xUPMBmi08gAAAABJRU5ErkJggg==","orcid":"","institution":"Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Bishnu","middleName":"Prasad","lastName":"Kandel","suffix":""}],"badges":[],"createdAt":"2025-10-31 15:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7999882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7999882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97139663,"identity":"061c7ac6-9234-4e78-922e-4b6da727794e","added_by":"auto","created_at":"2025-12-01 10:01:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138350,"visible":true,"origin":"","legend":"","description":"","filename":"ricedroughtthesis1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/0418e9f845a3d565f9e03053.docx"},{"id":97138778,"identity":"e88d102a-7df8-410b-8ffe-9ed380134412","added_by":"auto","created_at":"2025-12-01 09:59:19","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7206,"visible":true,"origin":"","legend":"","description":"","filename":"97eade5f03be44c09dd5082600f99706.json","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/38fbd3fa367fa28de3f58db9.json"},{"id":97139859,"identity":"63c81da9-dccb-4e14-80a2-0550d6cbc945","added_by":"auto","created_at":"2025-12-01 10:02:47","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105432,"visible":true,"origin":"","legend":"","description":"","filename":"97eade5f03be44c09dd5082600f997061enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/e7a7179200a5e642745a35a6.xml"},{"id":97016522,"identity":"9cc4346e-7731-44bb-bb24-e0d69d10e982","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"emf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":53980,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.emf","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/9ba35d6b96ad4abde549e6bd.emf"},{"id":97016520,"identity":"20b51570-8e21-4c15-b7e3-eb7c37f27505","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7167,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/478d89c0b87ecb02730a36ae.png"},{"id":97016523,"identity":"9ea1a7b2-024e-4145-b5aa-e671448ffd40","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14083,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/113f43bf01448a19848b91f5.png"},{"id":97139269,"identity":"99f90382-1496-4935-b45f-6c80982a0cd3","added_by":"auto","created_at":"2025-12-01 09:59:55","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27355,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/b50f4721553b291aad91006a.png"},{"id":97016529,"identity":"21452659-90cf-42b8-9dc3-756f66a4b9f0","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102272,"visible":true,"origin":"","legend":"","description":"","filename":"97eade5f03be44c09dd5082600f997061structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/cc20b899c6d19cf70a1789e9.xml"},{"id":97016528,"identity":"fa7942ad-638f-4a66-bea3-00561c8853c7","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107955,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/7527d74654da49b94fefb0c4.html"},{"id":97016518,"identity":"e02b735d-5785-45f3-af7c-efbca0f0a9c4","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27913,"visible":true,"origin":"","legend":"\u003cp\u003eClimatic details of research area during the crop period (Jun to Oct) in 2022\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/eae5deaeee96d80d2fbf4f6a.jpeg"},{"id":97016526,"identity":"6a669d95-e52b-4a21-8fb1-da632abca913","added_by":"auto","created_at":"2025-11-28 17:09:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28764,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between drought indices of rice\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/77bd60bf7bd31d79a80e3b00.png"},{"id":97140400,"identity":"ce082af8-78a7-4b7a-8cb9-b1ad0e10b99f","added_by":"auto","created_at":"2025-12-01 10:04:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41361,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot showing drought tolerant indices and rice genotypes\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/d3dfd325d4d975f6527ce18f.jpg"},{"id":97250805,"identity":"1bd48718-92d5-4a21-b0ce-ef1b95b87eef","added_by":"auto","created_at":"2025-12-02 13:15:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":922369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7999882/v1/97bcc1c0-f90d-43fb-9f17-98ca655ec039.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) is a staple crop for approximately one-third of the global population, with Asia accounting for around 90% of its production and consumption (Fukagawa \u0026amp; Ziska, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, climate change \u003cem\u003eposes significant\u003c/em\u003e challenges to rice cultivation, particularly through altered precipitation patterns and increased drought frequency, which threaten water availability and yield stability. Rising temperatures, especially elevated night temperatures, exacerbate these impacts by shortening phenological stages and reducing grain yields (Peng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The extreme climate conditions model indicates that 1% rise in the occurrence of days with extreme variations in rainfall leads to a reduction of 0.28% in rice production (Rayamajhee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In rainfed systems, drought is the primary abiotic stress, causing yield reductions of 13\u0026ndash;35% in moderate conditions (Tiwari et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kandel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and up to 65\u0026ndash;85% in severe cases (Kumar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These losses are particularly critical in vulnerable regions like Nepal, which is ranked fourth globally in climate change vulnerability, and where only 28% of cultivated land benefits from year-round irrigation (Poudel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Nepal\u0026rsquo;s mid-hills, including Baitadi, rice farming relies heavily on monsoon rainfall, with terraced fields often lacking supplemental irrigation. This results in erratic water availability, leading to an average grain yield of 2.34 t ha⁻\u0026sup1;, below the national average of 2.71 t ha⁻\u0026sup1; (Kandel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Drought stress, compounded by heat waves, increases pollen sterility and unfilled grains, further limiting productivity (Hussain et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies in Nepal\u0026rsquo;s mid-hills have screened released rice varieties for drought tolerance using indices like Mean Productivity (MP) and Geometric Mean Productivity (GMP) (Adhikari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kandel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, evaluating unreleased pipeline varieties from the International Rice Research Institute (IRRI) in site-specific conditions, such as Baitadi, remains underexplored, presenting a critical gap for enhancing local adaptation. Tolerance level (TOL), mean productivity (MP), geometric mean productivity (GM), stress tolerance index (STI), and stress susceptibility index (SSI) are the various drought indices used to assess a cultivar's drought tolerance. In rice, cultivars with high STI values and low SSI and TOL are regarded as drought-tolerant \u003cem\u003e(\u003c/em\u003eAdhikari et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The best indices for assessing drought tolerance are those that have high MP, GMP, and STI \u003cem\u003e(Hooshmandi 2019).\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePrevious research has identified several drought-tolerant rice genotypes.Kandel et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated 14 rice genotypes were evaluated under both drought stress and non-stress and identified NR 119 is followed by Chaite 5 and Chaite 4 as a drought-tolerant genotype. There are some drought tolerant rice varieties (DTR) such as Sukhadhan-1, Sukhadhan-2 and Sukhadhan-3 released in 2011 and Sukhadhan-4, Sukhadhan-5 and Sukhadhan-6 were released in 2014. These were released for targeting rainfed low land condition from terai to lower hills upto 1000 masl (Dhakal et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rai et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) thirty drought-tolerant spring rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) genotypes using various drought tolerant indices at inner plain region and reported that genotype IRE16L1661 is stable under drought conditions based on drought tolerance indices. Previous studies have focused on released cultivars and mostly evaluated in inner plain area\u003c/p\u003e\u003cp\u003eIn order to address this, the current research accessed pipelines rice genotypes originated from IRRI and applying multiple drought tolerance indices, we sought to identify drought resilience rice genotypes for mid-hill growing condition at Baitadai, thereby contributing to sustainable rice production in drought-prone regions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch site and rice genotypes\u003c/h2\u003e\u003cp\u003eAn experiment was carried out at the agronomy farm of Gokuleshwor, Baitadi, having 29⁰ 58\u0026prime; 25\u0026rdquo; N latitude, 80⁰ 31\u0026prime; 43\u0026rdquo; E longitude, at a height of 850 masl, from 27th of June to 12th of October, 2022. Agroclimatic details of the experiment site are shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The experiment consists of twenty-five genotypes, among which twenty-three were pipeline varieties that originated from IRRI, Philippines and two were check varieties. The rice genotypes utilized in the experiment, along with their details, have been provided in table (1).\u003c/p\u003e\u003cp\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\u003eList of rice cultivars used in research.\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\"\u003e\u003cp\u003eS.N.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRemarks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCVT BOROSVIN-312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIR 129077-2-1-21-8-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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\u003eRAVI DROUGHT FR 129077-1-1-12-7-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRato Basmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReleased Variety\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\u003eRAVI-DROUGHT IR 80991-B-330-0-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuwa Basmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReleased Variety\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\u003eCVT SPRING IR 86515-1-19-2-1-1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSukha Dhan-3**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReleased Variety\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\u003eCVT PIGMENTED-121-121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAVI-N IR 96321-1447-651-B-1-1-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCVT BORO-IR16L1726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRFFT-N NR 2184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRavi control IR 129077123 1-1-55-8-B 83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAVI DROUGHT IR 129077-2-1-42-5-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAVI TARAHARA-105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFFT-FAR IVR 21841871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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=\"c2\"\u003e\u003cp\u003eRC IR 1611795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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\"\u003e\u003cp\u003eRAVI DROUGHT IR 129077-2-1-7-8B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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=\"c2\"\u003e\u003cp\u003eIR 129077:2-1-36-8-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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\u003eCVT BORO IR 1621226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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\"\u003e\u003cp\u003eCVT BORO 1621004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\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=\"c2\"\u003e\u003cp\u003eCFFT-SPRING IR 10L192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCVT SPRING IR 17L1317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePipeline cultivar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKaljadey*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLocal landraces\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHardinath-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReleased variety\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*,** Local check and standard check respectively (Kaljadey is a popular landraces in the research area, whereas Sukha Dhan-3 is one of the promising drought-tolerant variety released by NARC and recommended for drought condition).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExperimental field layout and crop management\u003c/h3\u003e\n\u003cp\u003eThe field experiment was laid out in alpha-lattice design with 10 blocks and two replications (two environmental conditions each) (5 genotypes in each block). Nursery seeding took place on May 24. 24 hours before sowing, the seeds were initially treated with Bavistin at a rate of 2g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of seeds. Rice seedlings were cultivated in a dry seedbed for 25 days before being transplanted into well-puddled soil with a 20cm x 20cm spacing and a 3m\u0026times; 2m plot size. A total of 6 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of farmyard manure (FYM) and 120:40:40 kg NPK ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of chemical fertilizer were applied to both irrigation and drought-prone regions. Half of the nitrogen dosage was given during field preparation, along with full phosphorus, potassium, and FYM doses. The other half of the dosage was divided between the tillering and panicle initiation stages. After transplantation, water was left on the field for a month to allow healthy establishment of the crop during the vegetative stage, and no further irrigation was given. At the start of the reproductive stage, extra water was removed from the field by implementing a drainage system, and drought conditions were maintained. This was done in a rainfed field maintained as a drought-stressed field.\u003c/p\u003e\n\u003ch3\u003eDrought tolerance indices and statistical analysis\u003c/h3\u003e\n\u003cp\u003eTo distinguish genotypes based on drought response in terms of grain yield, drought tolerant indices based on grain yield tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for irrigated (Yp) and drought stress (Ys) conditions for each genotype were computed using the formula presented in table (2). MS Excel (version 19) was used to record the data, and R (4.0.5) was utilized to analyze it. R Studio was used to determine the association between drought indices and yield under stress and non-stress conditions. Principal components analysis examined the statistical relationship between genotypes under stress using R Studio.\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\u003eDrought tolerance indices used in the study\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\"\u003e\u003cp\u003eIndices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTolerance index (TOL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTOL\u0026thinsp;=\u0026thinsp;Yp-Ys\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Rosielle \u0026amp; Hamblin, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean productivity index (MP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMP=(Yp\u0026thinsp;+\u0026thinsp;Ys)/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Adhikari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kandel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeometrical mean productivity (GMP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGMP= (Yp*Ys )1/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Adhikari et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress tolerance index (STI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTI\u0026thinsp;=\u0026thinsp;Yp \u0026times; Ys/(Yp)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Fernandez 1992; Anwaar et al. 2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStress susceptibility index (SSI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSI\u0026thinsp;=\u0026thinsp;1-(Ys/Yp)/SI\u003c/p\u003e\u003cp\u003ewhile SI\u0026thinsp;=\u0026thinsp;1\u0026thinsp;\u0026minus;\u0026thinsp;(Ys/Yp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Fischer \u0026amp; Maurer, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\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"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eDrought tolerant indices and grain yield under stress and normal conditions\u003c/h2\u003e\u003cp\u003eIn table (3), all six drought tolerance indices were analyzed and presented. The tolerance indices MP and GMP were noted highest on genotype CFFT-SPRING IR 10L192 (3.44, and 19.11 respectively) with the highest yield under both environmental conditions Yp and Ys. In TOL, the highest was recorded in genotype Ravi control IR 129077123 1-1-55-8-B 83 (1.78) so this leads to deterioration in the yield of rice. Whereas the STI and YSI were found maximum on genotype CVT SPRING IR 17L1317 and Sukha Dhan-3 (0.90). The highest SSI (2.08) and maximum yield loss (50.66%) were obtained on genotype Ravi control IR 129077123 1-1-55-8-B 83. In contrast, the grand mean yield under stress and non-stress conditions were discovered to be, respectively, 2.3 and 3 tonsha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The yield loss has an average of 24.2% and ranges from 9.6% to 53.3%. Our findings are by those of(Kandel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), drought conditions reduced yield by an average of 27%. In drought-affected conditions, 12\u0026ndash;46% of grain yield was lost (Ouk et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); and the yield reduced by 9\u0026ndash;51%(Pantuwan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean Performance of twenty-five rice genotypes using various drought tolerant indices\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYs(tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYp(tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTOL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGMP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSTI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYSI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSSI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYield loss (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFFT-FAR IVR 21841871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFFT-SPRING IR 10L192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT BORO 1621004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT BORO IR 1621226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT BORO-IR16L1726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT BOROSVIN-312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT PIGMENTED-121-121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT SPRING IR 17L1317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVT SPRING IR 86515-1-19-2-1-1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e26.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHardinath-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR 129077-2-1-21-8-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIR 129077:2-1-36-8-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJuwa Basmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaljadey (Local)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e40.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRato Basmati\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRavi control IR 129077123 1-1-55-8-B 83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e56.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI DROUGHT FR 129077-1-1-12-7-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI DROUGHT IR 129077-2-1-42-5-B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI DROUGHT IR 129077-2-1-7-8B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI TARAHARA-105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI-DROUGHT IR 80991-B-330-0-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRAVI-N IR 96321-1447-651-B-1-1-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRC IR 1611795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e43.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFFT-N NR 2184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSukha Dhan- 3 (std check)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrand Mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e24.67\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*Significant at P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, **Significant at 0.01 level, Ys: yield under stress, Yp: yield under non-stress, MP: mean productivity, TOL: Tolerance index, GM: geometric mean productivity, STI: stress tolerance index, YSI: yield stability index, SSI: stress susceptibility index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation of drought indices\u003c/h2\u003e\u003cp\u003eA correlation study of grain yield and various drought tolerance indices can be useful criteria for choosing the optimal genotypes and stress tolerance indices shown in figure (2). Ypwas found to be positive and significant association with Ys, MP, and GMP. The study ofKandel et al.(2022) has also found a similar result. In earlier studies Mousavi et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e);Dadbakhsh et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)and Bennani et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reported that Yp and Ys were positively correlated. The association of Yp was non-significant and negative with YSI and STI. Ys showed a positive and statistically significant relationship with STI and YSI. Adhikari et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported similar findings. The SSI and YL have shown significant and negative correlation with Ys whereas, in the case of Yp, there was a non-significant and positive correlation with Yp. This suggested that the selection based on SSI and YL increased yield under Yp. Due to the positive correlation of Yp with SSI and TOL,the selection based on SSI and TOL will result in increased yield under Yp(Sio-Se Mardeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The MP was found to be highly positive correlation with the yield at both conditions. A similar result was obtained byKandel et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), that the GMP, MP, and SSI were positive and significantly correlated with yield. A positive correlation between yield under stressed and non-stressed conditions has also been reported in earlier studies (Saeed et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ahmadizadeh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bennani et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The YSI and STI have high significance levels and negative correlations with TOL, SSI, and YL. In our experiment, MP and GMP were identified as pertinent indices to select stress tolerance genotypes as these indices haveshowna highly positive correlationwith grain yield (Rahimi et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bhandari et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)under both drought and irrigated conditions. So, to select high-yielding lines, MP and GMP can be used in functional rice breeding programs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrincipal Component Analysis\u003c/h3\u003e\n\u003cp\u003eThe cumulative variance of the first two main components exceeded 99%, with an eigenvalue larger than one. The first principal component analysis (PCA) accounted for 59.6% of the total variance in Yp, Ys, STI, MP, GMP, and STI. Thus, the first component might be labeled yield potential and stress tolerance, whilst the second PCA accounted for 39.9% of total variability. Drought tolerance indicators and genotype response were studied using principal component analysis. MP and GMP exhibited significantly positive association with the yield of grain under Yp and Ys, implying that selecting based on these indices will result in higher grain yield in both situations.\u003c/p\u003e\u003cp\u003eSimilarly, STI and YSI were the two indices that were highly positive with Ys, hence, the increasing grain yield in condition will result based on the selection of these indices. The SSI and YL were highly correlated in a positive direction, showing a close association aligned with the genotypes. The SSI, YL, and TOL indices were positively associated with Yp and Ys. The genotypes CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B were located between Yp and Ys and the indices between MP and GMP. The CVT SPRING IR 17L1317, CVT BORO 1621004, CFFT-FAR IVR 21841871, CVT BOROSVIN-312, RAVI TARAHARA-105, CVT PIGMENTED-121-121 and RAVI DROUGHT IR 129077-2-1-7-8B had considerable correlation with STI, YSI, YL, SSI and TOL figure (3). While, the genotypes Juwa Basmati, IR 129077:2-1-36-8-B, RAVI-N IR 96321-1447-651-B-1-1-2, and CVT BORO-IR16L1726 by MP, and GMP as well as the genotypes Ravi control IR 129077123 1-1-55-8-B 83, Kaljadey, RC IR 1611795, Hardinath-1, IR 129077-2-1-21-8-B, CVT SPRING IR 86515-1-19-2-1-1-1, and RFFT-N NR 2184 by SSI, YL, TOL, STI and YSI were identified as responsive to drought. In contrast, the rest genotypes were recognized as genotypes with moderate drought tolerance. While screening drought-tolerant genotypes using the drought tolerance indices and multivariate analysis, CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B genotypes were genotypes with the highest drought tolerance.\u003c/p\u003e\u003cp\u003eSimilar findings were reported by Jeyaprakash et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Rahimi et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)in drought tolerance of rice, among all other indices, YI, GMP, MP, STI, MPI, and HM emerged as the top stress indicators for identifying drought-tolerant genotypes demonstrated by biplot graph.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to the study's findings, water stress dramatically lowers rice output across all cultivars throughout the critical growth period of the crop. It also suggests that selecting cultivars with better drought tolerance and greater production potential and stability can be done effectively with the use of drought tolerance index selection. The CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B rice genotypes were found to have a high level of drought tolerance based on drought tolerance indices. These genotypes also displayed low SSI and TOL with high STI values. These genotypes fared better against drought stress and produced the maximum yield in irrigated situations. Consequently, these rice varieties resistant to drought can be better substitutes for drought-prone belts for rice cultivation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors were thankful to the Directorate of Agricultural Research, Tarahara, Koshi Province, Sunsari for providing genetic materials used in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJanak Adhikari: Conceptualization, proposal development, methodology, analysis, resources, writing-original draft.\u003c/p\u003e\n\u003cp\u003eAnka Kumari Limbu: Data curation, investigation, review, writing-original draft.\u003c/p\u003e\n\u003cp\u003eArati Dhami: Methodology, analysis, writing-original draft.\u003c/p\u003e\n\u003cp\u003eSangita Hamal: Data collection, review and editing\u003c/p\u003e\n\u003cp\u003eArpana Joshi: Review and editing, analysis.\u003c/p\u003e\n\u003cp\u003eBishnu Prasad Kandel: Supervision, data management, statically analysis and reviewing editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they do not have any competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not include any studies involving human or animal subjects conducted by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdhikari BB, Mehera B, Haefele SM. Selection of drought tolerant rice varieties for the western mid hills of Nepal. J Inst Agric Anim Sci. 2018;33(0):195\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/jiaas.v33i0.20705\u003c/span\u003e\u003cspan address=\"10.3126/jiaas.v33i0.20705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdhikari M, Adhikari NR, Sharma S, Gairhe J, Bhandari RR, Paudel S. Evaluation of Drought Tolerant Rice Cultivars Using Drought Tolerant Indices under Water Stress and Irrigated Condition. Am J Clim Change. 2019;08(02):228\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4236/ajcc.2019.82013\u003c/span\u003e\u003cspan address=\"10.4236/ajcc.2019.82013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhmadizadeh M, Club YR, Branch J, Dadbakhsh A, Yazdansepas A. Corresponding Author Study Drought Stress on Yield of Wheat (Triticum aestivum L.) Genotypes by Drought Tolerance Indices. Adv Environ Biology. 2011;5(7):1804\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBennani S, Nsarellah N, Jlibene M, Tadesse W, Birouk A, Ouabbou H. Efficiency of drought tolerance indices under different stress severities for bread wheat selection. Aust J Crop Sci. 2017;11(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21475/ajcs.17.11.04.pne272\u003c/span\u003e\u003cspan address=\"10.21475/ajcs.17.11.04.pne272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhandari K, Joshi LP, Bhandari N, Upadhyay K, Sharma S. Evaluation of drought tolerance indices for selection of high yielding drought tolerant rice genotypes in Lamjung, Nepal. Azarian J Agric. 2020;7(2):54\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouslama M, SchapaughJr WT. Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop Sci. 1984;24(5):933\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFukagawa NK, Ziska LH. (2019). Rice: Importance for Global Nutrition. J NutrSciVitaminol (Tokyo).65(Supplement):S2-S3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3177/jnsv.65.S2\u003c/span\u003e\u003cspan address=\"10.3177/jnsv.65.S2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31619630.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaudhari PR, Tamrakar N, Singh L, Tandon A, Sharma D. Rice nutritional and medicinal properties: A review article. J Pharmacognosy Phytochemistry. 2018;7(2):150\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDadbakhsh A, Yazdansepas A, Ahmadizadeh M. Study drought stress on yield of wheat (Triticum aestivum L.) genotypes by drought tolerance indices. Adv Environ Biology. 2011;5(7):1804\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEl-Hashash EF, El-Agoury RYA, El-Absy KM, Sakr SMI. Genetic parameters, multivariate analysis and tolerance indices of rice genotypes under normal and drought stress environments. Asian J Res Crop Sci. 2018;1(3):1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer RA, Maurer R. Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust J Agric Res. 1978;29(5):897\u0026ndash;912.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHussain T, Hussain N, Ahmed M, Nualsri C, Duangpan S. Responses of lowland rice genotypes under terminal water stress and identification of drought tolerance to stabilize rice productivity in southern Thailand. Plants. 2021;10(12):2565.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeyaprakash P, Ramchander S, Raveendran M, Kari B. Determination of Stress Indices for Selection of Superior Genotypes under Drought Situation in Rice (Oryza sativa L). Int J Agric Sci. 2016;8:38\u0026ndash;1791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinfopublication.org/jouarchive.php?opt=\u0026amp;jouid=BPJ0000217\u003c/span\u003e\u003cspan address=\"http://www.bioinfopublication.org/jouarchive.php?opt=\u0026amp;jouid=BPJ0000217\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKandel BP, Joshi LP, Sharma S, Adhikari P, Koirala B, Shrestha K. Drought tolerance screening of rice genotypes in mid-hills of Nepal using various drought indices. ActaAgriculturaeScandinavica Sect B: Soil Plant Sci. 2022;72(1):744\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09064710.2022.2072382\u003c/span\u003e\u003cspan address=\"10.1080/09064710.2022.2072382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar A, Verulkar SB, Mandal NP, Variar M, Shukla VD, Dwivedi JL, Singh BN, Singh ON, Swain P, Mall AK. High-yielding, drought-tolerant, stable rice genotypes for the shallow rainfed lowland drought-prone ecosystem. Field Crops Res. 2012;133:37\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Dwivedi SK, Singh SS, Bhatt BP, Mehta P, Elanchezhian R, Singh VP, Singh ON. Morpho-physiological traits associated with reproductive stage drought tolerance of rice (Oryza sativa L.) genotypes under rain-fed condition of eastern Indo-Gangetic Plain. Indian J Plant Physiol. 2014;19(2):87\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40502-014-0075-x\u003c/span\u003e\u003cspan address=\"10.1007/s40502-014-0075-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H-I, Yu Y-Y, Wen F-I, Liu P-T. Status of food security in East and Southeast Asia and challenges of climate change. Climate. 2022;10(3):40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLobell DB, Gourdji SM. Focus Issue on the Plant Physiology of Global Change: The Influence of Climate Change on Global Crop Productivity. Plant Physiol. 2012;160(4):1686.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohanty S, Wassmann R, Nelson A, Moya P, Jagadish SVK. Rice and climate change: significance for food security and vulnerability. Int Rice Res Inst. 2013;14:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMousavi SS, YAZDI SB, Naghavi MR, Zali AA, Dashti H, Pourshahbazi A. (2008). Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOuk M, Basnayake J, Tsubo M, Fukai S, Fischer KS, Cooper M, Nesbitt H. Use of drought response index for identification of drought tolerant genotypes in rainfed lowland rice. Field Crops Res. 2006;99(1):48\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePantuwan G, Fukai S, Cooper M, Rajatasereekul S, O\u0026rsquo;toole JC, Basnayake J. Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands: 4. Vegetative stage screening in the dry season. Field Crops Res. 2004;89(2\u0026ndash;3):281\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG. (2004). Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences, 101(27), 9971\u0026ndash;9975.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePoudel S, Funakawa S, Shinjo H, Mishra B. Understanding households\u0026rsquo; livelihood vulnerability to climate change in the Lamjung district of Nepal. Environ Dev Sustain. 2020;22:8159\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahimi M, Dehghani H, Rabiei B, Tarang AR. Evaluation of rice segregating population based on drought tolerance criteria and biplot analysis. Int J Agric Crop Sci. 2013;5(3):194.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRayamajhee V, Guo W, Bohara AK. The impact of climate change on rice production in Nepal. Econ Disasters Clim Change. 2021;5:111\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosielle AA, Hamblin J. Theoretical aspects of selection for yield in stress and non-stress environment 1. Crop Sci. 1981;21(6):943\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaeed S, Bu-Ali M, Naghavi MR, Dashti H, Moosavi SS, Samadi BY, Naghavi MR, Zali AA, Pourshahbazi A. (2008). Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes SEE PROFILE Introduction of new indices to identify relative drought tolerance and resistance in wheat genotypes. In \u003cem\u003eDESERT\u003c/em\u003e (Vol. 12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://jdesert.ut.ac.ir\u003c/span\u003e\u003cspan address=\"http://jdesert.ut.ac.ir\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSio-Se Mardeh A, Ahmadi A, Poustini K, Mohammadi V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Res. 2006;98(2):222\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.fcr.2006.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.fcr.2006.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTiwari DN, Tripathi SR, Tripathi MP, Khatri N, Bastola BR. (2019). Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Advances in Agriculture, 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRai N, Thapa S, Rawal S, Jamkatel DP, Maharjan B. Yield Performance Evaluation of Thirty Spring Rice (Oryza sativa L.) Cultivars Under Terminal Drought Conditions Using Various Drought-Tolerant Indices. AgroEnvironmental Sustain. 2023;1(2):86\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.59983/s2023010201\u003c/span\u003e\u003cspan address=\"10.59983/s2023010201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhakal S, Adhikari BB, Kandel BP. Performance of drought tolerant rice varieties in different altitudes at Duradada, Lamjung, Nepal. J Agric Nat Resour. 2020;3(1):290\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/janr.v3i1.27199\u003c/span\u003e\u003cspan address=\"10.3126/janr.v3i1.27199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Drought stress, stress-resilient","lastPublishedDoi":"10.21203/rs.3.rs-7999882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7999882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress is a critical challenge to rice production, necessitating the development of drought-tolerant genotypes. This study aimed to evaluate 25 rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) genotypes for drought tolerance under rainfed and irrigated conditions at, Baitadi, Nepal during June\u0026ndash;October 2022, using an alpha lattice design. The experiment aimed to identify drought-tolerant genotypes and effective drought tolerance indices based on grain yield. Drought stress, induced at the reproductive stage, reduced yields by an average of 24.2% (range: 9.6\u0026ndash;53.3%). Mean Productivity (MP) and Geometric Mean Productivity (GMP) emerged as the most reliable indices, showing strong positive correlations with yield under both conditions. Principal Component Analysis (PCA) and drought tolerance indices (TOL, MP, GMP, STI, YSI, SSI) identified CFFT-SPRING IR 10L192, CVT BORO IR 1621226, Rato Basmati, RAVI-DROUGHT IR 80991-B-330-0-1, and RAVI DROUGHT IR 129077-2-1-42-5-B as the most drought-tolerant genotypes, with high MP and GMP values and low stress susceptibility. These genotypes are potential candidates for breeding programs in the mid-hills of Nepal, supporting sustainable rice production under water-limited conditions.\u003c/p\u003e","manuscriptTitle":"Yield Based Drought Tolerance Indices Used To Identify Drought Resilient Rice Genotypes In Gokuleshwor, Baitadi, Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 17:09:14","doi":"10.21203/rs.3.rs-7999882/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":"0a08c13d-e7f0-437e-b088-da9c2a2bd83a","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T11:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 17:09:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7999882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7999882","identity":"rs-7999882","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}