Conferring Drought Tolerance in Rice Landraces Using Seedling Indices

Conferring Drought Tolerance in Rice Landraces Using Seedling Indices | 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 Conferring Drought Tolerance in Rice Landraces Using Seedling Indices Bibas B.K., Sneha Dahal, Nirmala Pradhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5327215/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 Landraces are a vital source of genetic diversity in crops, offering a rich pool of allelic variation that plant breeders can utilize for developing new cultivars. Although, Nepal has a huge diversity for rice landraces but their actual potentiality hasn’t been sufficiently explored yet. In this study, 25 rice landraces were tested to evaluate the drought tolerance level in completely randomized design (CRD) in 250 ml disposable cup at seedling stage. The rice landraces were tested under 60% FC, 100% FC, and saturated condition for ten quantitative traits. The analysis of variance suggested that there is a significant difference among the landraces for different root and shoot traits as well as in different moisture conditions. Correlation analysis showed that root length has significant positive correlation with shoot length, root: shoot ratio, and fresh root weight under drought condition with 60% FC. Similarly, negative correlation was observed between root: shoot ratio and root number under drought condition. However, germination percentage didn’t show discrimination over any moisture conditions. Principal component analysis showed positive connection of root length and root: shoot ratio towards 60% FC. While strong connection was observed between shoot length, fresh weight, dry weight and root number towards 100% FC and saturated condition. It was found that, Manamurey showed better performance under all studied traits but more insightful result can be obtained by further assessing at vegetative and reproductive stage respectively. rice landraces drought stress seedling stage correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Introduction Rice is the major staple food of Nepal. Regarding acreage and yield, rice surpasses all other cereal crops in Nepal, ranking third globally after wheat and maize. The total area of land under rice cultivation is estimated to be 1,477,378 ha with annual production of 5,130,625 mt and productivity of 3.47 mt/ha (MOALD 2021 ). Rice contributes about 21% of AGDP in Nepal which is equivalent to almost 10% of national GDP (Aryal et al. 2022 ). Variations in altitude, geography, physical and climatic circumstances have enriched the country with immense genetic diversity in the form of landraces or traditional cultivars. Landraces are maintained and managed by the farmers in their fields for diverse purposes, including indigenous rites and beliefs, and their immense adaptability to changing conditions over time and across different environments (Bajracharya et al. 2010 ). Landraces are adopted to marginal to high fertile soil, drought to deep water, different planting seasons, different climatic conditions, pest and disease infestation. Districts such as Kaski, Lamjung, Tanahun, Bara and Sunsari are richer in rice diversity (Joshi 2017 ). Landraces possess specific traits to adapt for better local adaptation in their environment, various socio-economic and cultural values (Rayamajhi and Thakuri 2023 ). From an irrigation perspective, out of the total agricultural land, 52% of the land remains rainfed, with only 48% of the land irrigated in which 39% of the land receives year-round irrigation (Irrigation Master Plan 2019). Lack of proper maintenance and associated regulatory mechanisms creates inadequate supply of irrigation in the irrigated areas of the country (Aryal et al. 2022 ). On average, 2500 liters of water is used, ranging from 800 liters to more than 5000 liters to produce 1 kg of rice which indicates water productivity of rice ranges from 0.6–1.6 kg/m 3 in different parts of the world (Mboyerwa et al. 2021 ). Rice is highly sensitive to moisture stress during pre-flowering and grain-filling stages (Kandel et al. 2022 ). Drought stress at various growth stages may reduce the assimilates' ability to translocate to the grains, resulting in a drop in grain weight and an increase in empty grains (Moonmoon and Islam 2017 ). Under mild water stress, grain yield is reduced by 5–38%, and under severe water stress by 25–67% (Basha and A. 2017). Drought stress reduces plant development, affecting the root system architecture, leaf surface traits, normal plant senescence, and inhibition of stem reserves (Moonmoon and Islam 2017 ). Nepal is considered one of the centers of diversity of rice (Ghimire et al. 2018 ). Landraces comprise a major component of Nepal's rice production system, accounting for about 70% of the country's total rice area (Amgai and Joshi 2004 ). Rice landraces, despite having low yield are believed to be adopted in the local environment (Rijal 2010 ), resistant to both biotic and abiotic stresses, and are considered reservoirs of genetic potential, whereas modern rice varieties don't hold such qualities (Tiwari et al. 2018 ). Preservation of local landraces helps in creating improved rice varieties and hybrids for drought tolerance. Therefore, the objective of this study is to identify local landraces adopted in drought conditions by analyzing and comparing root and shoot traits in seedling stage for its subsequent application in modern breeding programs. Materials and methods Experimental site The research was conducted in a greenhouse of the Institute of Agriculture and Animal Science, Lamjung Campus, Sundarbazar, Lamjung. The research site lies in the mid-hill region at an altitude of 610 masl, a latitude of 28.12° N, and a longitude of 84.41°E. Climatic conditions and growing season The research was conducted during July and August 2024. Landraces tested A total of 25 distinct rice landraces were tested in this experiment which were collected from the Purkot and Ghanpokhara Community Seed Bank of Tanahun and Lamjung respectively. Experimental details The experiment was conducted under a two-factor factorial completely randomized design. Factor A: 25 rice landraces Table 1 List of rice landraces tested in the experiment Treatment Landraces Treatment Landraces T1 Kattikey T14 Jarneli T2 Rambilash T15 Jungey T3 Jhini T16 Kamal T4 Darmali T17 Dalley Masino T5 Basmati T18 Gaurey T6 Aangha T19 Himali T7 Mansara T20 Jungey Kanchi T8 Kalo Jhinuwa T21 Manamurey T9 Kalokattey T22 Nouley Dalley T10 Gurdo T23 Pahele T11 Pathijharey T24 Biramphul T12 Krishnabeli T25 Seto Dalley T13 Aapjhuttey Factor B: Moisture condition Table 2 Different moisture conditions tested in the experiment S.N. Treatment Moisture Condition 1 D1 100% field capacity 2 D2 60% field capacity 3 S Saturated There were three replications of each factor. The experiment was conducted in the disposable cups of 250 ml. Total landraces: 25 Moisture condition: 3 Total replication: 3 A total of 25×3×3 i.e. 225 disposable cups were used in the study containing two plants in each cup. Cultivation practices Planting media The planting media was thoroughly prepared by mixing sand, soil (sandy loam texture), and FYM in the ratio of 1:2:1. Each cup was filled to its capacity weighing 200 g respectively. Seeding 6 seeds were sown per cup. After 5 days of seedling establishment, only two seedlings were maintained per cup. Water management Three moisture conditions were maintained in the experiment (Table 2). To determine the field capacity, 200 g of soil was saturated in a cup and left to drain for 48 hours. The opening end of the cup was covered with plastic to prevent evaporation. The computation of water required for irrigation was performed in the following manner: Weight of the disposable cups = 1.93 g Weight of the planting media = 200 g Weight of the oven-dried media at 72℃ for 24 hour = 159.97 g In 200 g of planting media, the composition of soil solid was 159.97 g and that of soil water was 200-159.97 = 40.03 g Weight of soil at FC after 48 hours = 233.92 g Soil water at FC = 233.92 – 159.97= 73.95 g Soil water at 100% FC = 100% of 73.95 = 73.95 g Soil water at 60% FC = 60% of 73.95 = 44.37 g So, the amount of water to be added at 100% FC = 73.95 – 40.03 = 33.92 g And the amount of water to be added at 60% FC = 44.37 – 40.03 = 4.34 g With the density of water being 1g/cc, 33.92 ml of water was added to the soil for 100% FC and 4.34 ml for 60% FC. Each pot was watered every three days after the seedling establishment. Data collection was performed at 21 DAS with destructive sampling. Data collection All of the traits with their method of detection are expressed in (Table 3). Fresh plant samples were oven-dried at 70±5 for about 48 hours for the detection of dry weight (Badr et al. 2020). Germination percentage was calculated by determining the proportion of seeds that successfully germinated under controlled conditions. The following formula was used to calculate the germination percentage: Germination Percentage (GP) = (Mamun et al. 2018). Table 3 Observed traits description and method employed for data collection S.N. Parameters Abbreviation Detection Techniques 1 Germination percentage GP Recorded in percentage by using a mathematical formula. 2 Root length RL The distance from the base of the plant (where it connects to the plant) to the tip of the longest root and measured in centimeters using a measuring scale. 3 Shoot length SL The distance from the base of the plant (where it connects to the root) to the tip of the longest flag leaf and measured in centimeters using measuring the scale. 4 Root: shoot ratio RL/SL Dividing root length by shoot length. 5 Fresh root weight FRW Roots were placed on precision balance to measure their fresh weight in grams immediately after washing. 6 Fresh shoot weight FSW Shoots were placed on precision balance to measure their fresh weight in grams. 7 Total plant weight TPW FRW+FSW 8 Root dry weight RDW Fresh root samples were oven-dried at 70±5℃ for about 48 hours and measured their dry weight in grams. 9 Shoot dry weight SDW Fresh shoot samples were oven-dried at 70±5℃ for about 48 hours and measured their dry weight in grams. 10 Root number RN Washed roots were counted manually. Data analysis The observed data was entered in MS Excel (2021). Visualization of the interaction effect was done through MS Excel. Analysis of variance, mean separation, and F-test were performed at 5% level of significance. Boxplot, correlation, and principal component analysis were carried out in R (4.4.1). Results Comparison of rice landraces for traits Table 4 shows the analysis of variance for 10 quantitative traits among landraces, conditions, and the interaction between landraces and conditions at 21 days. Table 4 Analysis of variance of studied traits S.N. Traits Mean sum of square Landraces (DF = 24) Condition (DF = 2) Landraces*Condition (DF = 48) Error (DF = 150) 1 Germination percentage 151.44 ** 34.57 ns 120.73 * 81.84 2 Root length 12.16 *** 203.47 *** 4.68 ns 3.98 3 Shoot length 167.7 *** 1299.4 *** 31.3 *** 12.8 4 Root: shoot ratio 0.0480 *** 1.2540 *** 0.0142 ** 0.0078 5 Fresh root weight 0.00463 *** 0.05342 *** 0.00213 *** 0.00042 6 Fresh shoot weight 0.01873 *** 0.22429 *** 0.00397 *** 0.00097 7 Total plant weight 0.0378 *** 0.4966 *** 0.0088 *** 0.0019 8 Root dry weight 0.0000640 *** 0.0003217 *** 0.0000242 *** 0.0000111 9 Shoot dry weight 0.000358 *** 0.003807 *** 0.000163 *** 0.000040 10 Root number 10.5 *** 665.6 *** 3.4 ns 2.2 *, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas ns denotes non-significant. Table 5 Mean separation table for quantitative traits in twenty-five rice landraces Factor A: Landraces Landraces GP RL SL RL/SL FRW FSW TPW RDW SDW RN Aangha 94.444 c 13.678 fgh 34.450 bcd 0.406 kl 0.140 cdef 0.246 abcd 0.386 bcde 0.017 cdef 0.047 cdefg 9.333 f Aapjhuttey 96.296 b 15.133 bcdefg 30.733 efghi 0.503 defghi 0.094 kl 0.177 efghi 0.271 jkl 0.012 hi 0.039 j 8.278 l Basmati 88.889 f 13.506 gh 25.144 lmno 0.580 bc 0.121 ghi 0.151 ghi 0.272 jkl 0.018 bcd 0.043 fghij 8.944 i Biramphul 81.481 h 16.928 ab 30.056 fghij 0.576 bc 0.106 hijkl 0.203 defgh 0.309 ghijk 0.015 defg 0.041 ghij 8.389 k Dalley Masino 96.296 b 15.017 cdefg 23.239 o 0.685 a 0.101 jkl 0.128 i 0.229 l 0.010 i 0.032 k 7.000 r Darmali 98.148 a 15.344 bcdefg 35.628 abc 0.453 hijkl 0.143 cde 0.260 abcd 0.403 bcd 0.021 a 0.054 ab 9.056 h Gaurey 94.444 c 13.800 efgh 31.639 defg 0.444 hijkl 0.142 cdef 0.208 cdefg 0.350 defg 0.016 cdefg 0.039 ij 9.833 d Gurdo 94.444 c 13.556 fgh 31.061 efgh 0.458 hijkl 0.091 l 0.209 cdefg 0.306 ghijk 0.015 defg 0.042 ghij 8.222 l Himali 94.444 c 15.144 bcdefg 29.639 ghijk 0.547 cde 0.131 defg 0.235 bcde 0.366 cdef 0.016 cdefg 0.042 ghij 9.222 g Jarneli 96.296 b 14.117 defgh 37.694 ab 0.393 l 0.145 bcd 0.280 ab 0.425 b 0.018 bcd 0.054 ab 10.000 c Jhini 98.148 a 14.372 defgh 24.728 mno 0.591 bc 0.117 ghij 0.212 cdefg 0.330 fghi 0.014 efgh 0.041 ghij 7.000 r Jungey 94.444 c 13.861 efgh 26.944 jklmn 0.551 cb 0.126 efg 0.208 cdefg 0.334 efgh 0.013 ghi 0.042 ghij 7.556 o Jungey Kanchi 90.741 e 15.400 abcdef 27.694 ijklm 0.587 bc 0.100 jkl 0.159 fghi 0.259 kl 0.015 defgh 0.039 j 7.444 p Kalo Jhinuwa 96.296 b 14.522 defgh 26.594 klmn 0.557 bcd 0.113 ghijk 0.146 hi 0.258 kl 0.013 ghi 0.045 defgh 7.944 m Kalokattey 94.444 c 14.239 defgh 27.789 hijklm 0.529 cdef 0.100 jkl 0.176 efghi 0.276 ijkl 0.015 defg 0.045 efghi 7.556 o Kamal 94.444 c 16.906 ab 28.211 hijkl 0.621 ab 0.116 ghij 0.160 fghi 0.275 ijkl 0.014 fgh 0.040 hij 7.778 n Kattikey 98.148 a 14.361 defgh 33.383 cdef 0.438 ijkl 0.105 ijkl 0.218 bcdef 0.323 fghij 0.017 bcde 0.050 bcde 9.111 h Krishnabeli 88.889 f 14.550 defgh 27.889 hijklm 0.527 cdefg 0.125 efgh 0.166 fghi 0.291 hijk 0.013 ghi 0.039 j 8.722 j Manamurey 88.889 f 17.250 a 38.694 a 0.462 ghijk 0.179 a 0.303 a 0.483 a 0.020 ab 0.057 a 9.944 c Mansara 94.444 c 14.283 defgh 35.200 bc 0.419 jkl 0.163 ab 0.266 abc 0.430 ab 0.015 defg 0.051 bc 10.611 a Nouley Dalley 92.593 d 13.689 fgh 27.856 hijklm 0.525 cdefg 0.123 fghi 0.213 cdefg 0.336 efgh 0.013 ghi 0.039 hij 9.833 d Pahele 96.296 b 15.633 abcde 34.511 bcd 0.471 fghijk 0.121 ghi 0.233 bcde 0.354 cdefg 0.019 abc 0.053 ab 10.333 b Pathijharey 90.741 e 13.156 h 23.889 no 0.580 bc 0.118 ghij 0.182 efghi 0.300 ghijk 0.016 defg 0.039 j 7.333 q Rambilash 92.593 d 16.572 abc 32.950 cdefg 0.506 defgh 0.149 bcd 0.258 abcd 0.407 bc 0.016 cdefg 0.049 bcdef 9.556 e Seto Dalley 85.185 g 15.728 abcd 33.828 cde 0.48 efghij 0.156 bc 0.248 abcd 0.405 bcd 0.021 a 0.051 bcd 9.333 f F-test ** *** *** *** *** *** *** *** *** *** Mean 93.259 14.829 30.377 0.515 0.125 0.210 0.335 0.0157 0.0444 8.733 LSD 0.0417 1.858 3.332 0.0665 0.019 0.0624 0.0551 0.003 0.00589 0.0838 CV (%) 9.679 13.453 11.777 13.852 16.397 31.905 17.64 21.124 14.240 1.030 SEM 0.820 0.232 0.863 0.014 0.004 0.009 0.012 0.0005 0.0012 0.216 Factor B: Moisture condition Conditions GP RL SL RL/SL FRW FSW TPW RDW SDW RN D1 93.111 a 15.086 b 31.224 b 0.504 b 0.123 b 0.205 b 0.328 b 0.016 b 0.046 b 8.033 b D2 94.000 a 16.333 a 25.857 c 0.649 a 0.099 c 0.157 c 0.257 c 0.013 c 0.036 c 6.166 c S 92.666 a 13.069 c 34.052 a 0.391 c 0.152 a 0.267 a 0.419 a 0.017 a 0.049 a 12.00 a F test NS *** *** *** *** *** *** *** *** *** Mean 93.259 14.829 30.377 0.515 0.124 0.210 0.335 0.157 0.04441 8.733 LSD 2.912 0.643 1.154 0.023 0.006 0.021 0.019 0.001 0.00204 0.0290 CV (%) 9.679 13.452 11.777 13.852 16.397 31.905 17.647 21.124 14.240 1.030 SEM 0.391 0.950 2.403 0.074 0.015 0.031 0.046 0.001 0.004 1.719 Interaction (A*B) * NS *** ** *** *** *** *** *** NS LSD: Least Significant Difference; CV: Coefficient of Variation; SEM: Standard Error of Mean *, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas NS denotes non-significant. a−r means with the same set of letters are not significantly different. Mean performance comparison of the study parameters Germination percentage Significant difference was observed between germination in 25 different rice landraces. Among tested landraces, the highest germination was achieved in Darmali, Jhini, and Kattikey with a germination percentage of 98.148. Whereas, the lowest germination was achieved in Biramphul (81.481%) followed by Seto Dalley (81.185%) respectively. Root length The interaction between landraces and moisture conditions exhibited statistical non-significance. Under the saturated condition, Biramphul displayed an extensive root length of 15.62 cm and was statistically similar to Himali (14.5), Seto Dalley (14.47), and Manamurey (14.43). Under 100% FC, Kamal exhibited a greater root length of 19.18 cm with statistical similarity to Biramphul (19.02), Manamurey (17.82), and Rambilash (17.07) respectively. Similarly, Kamal exhibited an extensive root length of 20.43 cm under drought conditions with 60% FC and was statistically consistent with the performance of Manamurey (19.5), Rambilash (18.48), and Jungey Kanchi (17.73). As for the main effects, the landraces showed highly significant differences in terms of root length. As illustrated in (Table 5 ), Manamurey exhibited the highest root length of 17.25 cm mean value which was statistically similar with Biramphul, Rambilash, Pahele, Kamal, Jungey Kanchi, and Seto Dalley. Substantial variability in the root length was evident between the three moisture conditions: S (13.069), D 1 (15.086), and D 2 (16.333). Greater root length under drought stress is similar with the findings of Kaysar et al. ( 2023 ). Shoot length Significant difference was observed between the shoot length in 25 different rice landraces. The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, the most significant shoot length was observed in Manamurey with 43.28 cm followed by the Mansara (41.92), Jarneli (40.7), Seto Dalley (40.37), and Himali (39.25). Under 100% FC, Jarneli was found to incur the greatest shoot length with mean value of 42.65 cm which was statistically at par with Darmali (42.5), Manamurey (41.35) and Pahele (40.45). Under drought condition with 60% FC, Rambilash displayed extensive shoot length with a mean value of 32 cm which was statistically consistent with the performance of Manamurey (31.45), Mansara (30.48), and Aangha (30.42). As for the main effects, the landraces showed highly significant differences in terms of shoot length. As shown in (Table 5 ), shoot length was found to be highest in Manamurey with a mean value of 38.694 cm, which was statistically at par with Jarneli and Darmali. Similarly, the lowest shoot length was achieved in Dalley Masino with a mean value of 23.239 cm. Likewise, for the moisture condition, S, D 1, and D 2 exhibited highly significant differences with 34.052 cm, 31.224 cm, and 25.857 cm respectively. Patel et al. ( 2021 ) reported a decrease in shoot length in drought conditions. Root: shoot ratio The interaction between landraces and moisture conditions exhibited statistical significance. As demonstrated in (Fig. 4 ), in saturated condition Pathijharey exhibited the highest ratio with mean value of 0.502 which was statistically at par with Dalley Masino (0.488) and Biramphul (0.47). Under 100% FC, Kamal showed the highest ratio with mean value of 0.687 statistically similar with Dalley Masino (0.673), Kalo Jhinuwa (0.658), and Biramphul (0.637). Likewise, Dalley Masino exhibited the highest ratio under 60% FC with mean value of 0.893. Significant variances were observed among the landraces concerning the root: shoot ratio. The highest root: shoot ratio was recorded in Dalley Masino (0.685) whereas the lowest was in Jarneli with 0.393 respectively. Similarly, Substantial variability in the ratio was evident between the three watering conditions: S (0.391), D 1 (0.504), and D 2 (0.649). These results indicated that drought stress condition induces longer roots in the rice landraces which ultimately increase the root shoot ratio. These findings are consistent with the study of Hou et al. ( 2022 ). Fresh root weight Noticeable variations were identified among the different landraces concerning the fresh root weight as presented in (Table 5 ). The interaction between landraces and moisture conditions also exhibited statistical significance. Under saturated conditions, Seto Dalley showed the highest weight with mean value of 0.218 g which was statistically similar with Manamurey (0.208), Himali (0.206), Rambilash (0.196), and Jarneli (0.19). Manamurey exhibited the highest fresh root weight under 100% FC, with mean value of 0.201 g which was statistically at par with Darmali (0.178) and Mansara (0.173). Under drought conditions with 60% FC, Mansara showed the highest weight with mean value of 0.156 g which was statistically consistent with Kamal (0.131) and Manamurey (0.128). As illustrated in (Table 5 ), Manamurey showed the highest fresh root weight with 0.179 g and was statistically at par with Mansara (0.163). Likewise, for the moisture condition, S, D 1, and D 2 exhibited highly significant differences with 0.152 g, 0.123 g, and 0.099 g respectively. The findings of this experiment are consistent with Dien et al. ( 2017 ), which has indicated that plants grown in saturated conditions have more fresh root weight in contrast to drought conditions. Fresh shoot weight The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey demonstrated the highest fresh shoot weight with mean value of 0.396 g which was statistically significant with Himali (0.385) and Jarneli (0.37). Under 100% FC, Darmali was found to possess highest fresh shoot weight with mean value of 0.341 g which was statistically consistent with Manamurey (0.32). Similarly, at drought conditions with 60% FC, Aangha exhibited highest weight with a mean value of 0.221 g which was statistically consistent with Rambilash (0.211) and Mansara (0.203) respectively. As for the main effects, the landraces showed highly significant differences in terms of fresh shoot weight. As illustrated in (Table 5 ), Manamurey showed highest fresh shoot weight with 0.303 g and was statistically at par with Jarneli (0.280), Mansara (0.266), Darmali (0.260), Rambilash (0.258), Seto Dalley (0.248) and Aangha (0.246). Likewise, for the moisture condition, S, D 1, and D 2 exhibited highly significant differences with 0.267 g, 0.205 g, and 0.157 g respectively. Similarly, Saha et al. ( 2019 ) reported decrease in fresh weight of shoots in drought conditions. Total plant weight The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey exhibited highest total plant weight with mean value of 0.605 g which was statistically similar with Himali (0.591), Jarneli (0.56), and Seto Dalley (0.553). Under 100% FC, Manamurey demonstrated highest plant weight with a mean value of 0.521 g and was statistically consistent with Darmali (0.52). At drought conditions with 60% FC, Mansara was found to incur the highest plant weight with mean value of 0.36 g which was statistically similar with Rambilash (0.333), Aangha (0.331), and Manamurey (0.323). Regarding main effects, the landraces showed highly significant differences in terms of total plant weight, and highest plant weight was achieved in Manamurey with a mean value of 0.483 g. Substantial variability in the weight was evident between the three moisture conditions: S (0.419), D 1 (0.328), and D 2 (0.257). Root dry weight A significant difference was observed in the root dry weight among the 25 different rice landraces. Landraces showed significant impact with the moisture conditions, as shown in (Fig. 8 ). Under saturated condition, Kalokattey showed highest root dry weight with mean value of 0.0218 g which was statistically similar with Seto Dalley (0.0215), Manamurey (0.0214), and Kattikey (0.0211). Manamurey exhibited highest root dry weight under 100% FC with mean value of 0.0249 g which was statistically consistent with Seto Dalley (0.0241) and Darmali (0.0215). Under drought condition at 60% FC, highest root dry weight was recorded for Pahele with mean value of 0.0205 g. As illustrated in the (Table 5 ), Seto Dalley and Darmali exhibited highest root dry weight with 0.021 g which was statistically similar with Manamurey (0.02) and Pahele (0.019). For the moisture condition, S, D 1, and D 2 exhibited highly significant differences with 0.017 g, 0.016 g, and 0.013 g respectively. Decline in the root dry weight with the increase in water stress is in agreement with the findings of Patel et al. ( 2021 ). Shoot dry weight Noticeable variations were identified among the different landraces concerning the shoot dry weight as presented in (Table 5 ). The interaction between landraces and moisture conditions is also statistically significant. As demonstrated in (Fig. 9 ), under saturated condition, Jarneli showed highest shoot dry weight with mean value of 0.0636 g which was statistically similar with Kalokattey (0.0632) and Rambilash (0.0622). Under 100% FC, Manamurey exhibited highest dry weight with mean value of 0.0713 g and was statistically similar with Darmali (0.0647). Under drought condition at 60% FC, highest shoot dry weight was recorded for Pahele with a mean value of 0.0503 g. Regarding main effects, the landraces showed highly significant differences in terms of shoot dry weight. As illustrated in (Table 5 ), shoot dry weight was found to be highest for the landrace Manamurey with 0.057 g and was statistically at par with Darmali (0.054), Jarneli (0.054) and Pahele (0.053). For the moisture condition, S, D 1, and D 2 exhibited highly significant differences with 0.049 g, 0.046 g, and 0.036 g respectively. Saha et al. ( 2019 ), Patel et al. ( 2021 ) reported a significant reduction in the dry matter of shoots when exposed to stress conditions. Root number A significant difference was observed in the root number among the 25 different rice landraces. Landraces showed a significant impact with the moisture conditions, as shown in (Fig. 10 ). The most significant root number was observed in Aangha with mean value of 14.33 in saturated condition, Mansara with 10.83 in 100% FC, and Pahele with 8.83 in drought condition with 60% FC. Among the 25 rice landraces, highest root number was observed in Mansara with 10.611 and was significantly different from other landraces. On the contrary, lowest root number was observed in Dalley Masino and Jhini with mean value of 7.00. Substantial variability in the ratio was evident between the three watering conditions: S (12.00), D 1 (8.033), and D 2 (6.166).Kaysar et al. ( 2023 ) reported prominent number of roots in rice landraces under saturated conditions. Correlation between the traits Correlation ( r -value) was determined using a correlation matrix to identify the inter-relationship of studied traits. The significant r values among different traits varied from 32, 29, and 25 under Saturated, 100% FC, and 60% FC respectively. Root length showed a significant positive correlation with root: shoot ratio (0.49, 0.54) under saturated and 100% FC while, a positive correlation was observed with shoot length (0.32), root dry weight (0.29), and root number (0.33) under saturated condition and fresh shoot weight (0.25) and root dry weight (0.32) in 100% FC. Under 60% FC, it showed a positive correlation with shoot length (0.36), root: shoot ratio (0.31), fresh root weight (0.36), and total plant weight (0.26). However, no significant correlation was observed for root length and root number in 100 and 60% FC. Notably, shoot length scored a highly significant positive correlation with all the traits in a saturated condition, except fresh root weight in 100% FC and root dry weight in 60% FC. Likewise, root: shoot ratio was negatively correlated with shoot length under all conditions (-0.65, -0.77, -0.75). There was a significant correlation observed between root: shoot ratio with fresh shoot weight (-0.27, -0.47, -0.45) including shoot dry weight (-0.40) under saturated condition, total plant weight (-0.42) under 100% FC and root number (-0.38) in case of 60% FC. However, a negative correlation was observed with fresh root weight (-0.27), total plant weight (-0.36), root dry weight (-0.27, -0.25), and root number (-0.28, -0.30) under saturated condition and 100% FC while, total plant weight (-0.34) and shoot dry weight (-0.27, -0.32) under 60% FC and 100% FC respectively. Fresh root weight was significantly correlated with all the traits except root length in saturated condition and root length, shoot length, and root: shoot ratio in 100% FC along with root dry weight and root: shoot ratio under 60% FC condition. Fresh shoot weight showed significant positive correlation with total plant weight (0.98, 0.91, 0.91), root dry weight (0.47, 0.57), shoot dry weight (0.46, 0.58, 0.37), and root number (0.58) under saturated, 100% FC and 60% FC respectively. There was a positive correlation of total plant weight with root dry weight (0.48, 0.51), shoot dry weight (0.45, 0.58, 0.38), and root number (0.59, 0.23, 0.24). Root dry weight showed a highly significant correlation with shoot dry weight (0.65, 0.75, 0.67) under all conditions while a positive correlation was observed with root number (0.43, 0.24) under saturated and 60% FC conditions. Shoot dry weight positively correlated with root number (0.26, 0.24, 0.27) at all moisture conditions. Principal Component Analysis 25 rice landraces were studied through principal component analysis (PCA) biplot, where the landraces and traits are mapped based on their relationships across the first two principal components (PC1, PC2). Under the 60% FC, both axes of components (PC1 and PC2) explain a significant portion of the variance in the dataset with an eigenvalue greater than one. PC1 accounted for 46.7% while, PC2 accounted for 17.4%, totaling 64.1% of the variability (Supplementary Table 1). Furthermore, traits like RL, SL, FRW, FSW, TPW, and RN are highly associated with PC1 whereas, RDW and SDW are highly associated with PC2 (Supplementary Table 4). Thus, interpreting (Fig. 18 ), Jungey Kanchi, Basmati, and Kamal are landraces located far from the origin, representing extreme or distinct characteristics compared to other landraces. Likewise, traits like FRW, TPW, FSW, RN, and SL are closely aligned, indicating that these traits are strongly and positively correlated. Similarly, under 100% FC and Saturated conditions, both components explain a total of 67.7% and 70.9% of the variability in the dataset with an eigenvalue greater than one (Supplementary Table 1). Under 100% FC, traits RL, SL, FRW, FSW, TPW, RDW, and SDW are highly associated with PC1 whereas, GP and RN are highly associated with PC2 (Supplementary Table 3). Likewise, under saturated condition, all traits except GP and SDW are associated with PC2 (Supplementary Table 2). Thus, interpreting (Fig. 16 , 17 ) traits like RDW, SDW, FRW, FSW, TPW, RN, and SL are aligned closely suggesting a significant positive correlation. This implies that landraces with higher shoot and root weight also tend to have higher root numbers and shoot length. Under all conditions, RL/SL have arrows pointing opposite to FSW, TPW, and SL, indicating a negative correlation which means that landraces with higher shoot biomass (FSW, TPW, SL) tend to have lower root-to-shoot ratios. Interestingly, RL and RN have almost perpendicular arrows, indicating little to no correlation between these two traits (Fig. 17 ). Similarly, the PCA-Biplot shown in (Fig. 19 ), indicates the contribution of traits to overall variation in the dataset. PC1 explains more than half (53.8%) of the variance in the data while PC2 explains a smaller portion of the variance (12.7%) in the dataset. Together, PC1 and PC2 account for 66.5% of the total variation. Unlike the above PCA Biplots, traits like RDW, SDW, FRW, FSW, TPW, SL, and RN suggest a strong correlation between them. Likewise, RL/SL and RN were directed towards opposite directions, indicating a negative correlation between them. Traits with an arrow directed toward the environment indicate a strong association with that tested environment. Therefore, RL and RL/SL are directed towards the D2 condition (60% FC), suggesting these traits have a positive association with 60% FC. Traits like: RDW, SDW, FRW, TPW, FSW, SL, and RN are directed towards D1 (100% FC) and saturated condition suggesting these traits have a positive association with this moisture condition. However, GP falls in all moisture conditions which indicates that GP doesn’t have discrimination over any moisture conditions. Discussions The performance of rice landraces for drought-tolerant traits may be best assessed by analyzing the presence of variability among the tested landraces with the evaluated traits. Numerous experts across the globe have conducted similar studies on rice landraces and have found agro-morphological diversification linked to vegetative traits (Mishra et al. 2018; Ndikuryayo et al. 2023). In nearly every trait under study, the ANOVA findings showed significant differences between the genotypes under S, D1, and D2 conditions that suggest evident indication of genetic variability among the landraces under study. Under saturated conditions, highest shoot biomass (FSW, TPW, SL) can be attributed to the ability of plants to uptake more nutrients as they are more readily available in moist conditions, allowing for optimal growth of shoot (Ros et al. 2003 ). According to (Fig. 9 ), the findings indicated that under saturated condition, Manamurey incurred highest values of shoot fresh weight than other landraces. The observed outcome may derive from the enhanced capacity of the rice landrace to absorb water and nutrients, as well as their increased stomatal conductance, which in turn leads to enhanced photosynthesis (Kamarudin et al. 2018 ). However, it has been demonstrated that in drought condition, chemical and hydraulic signals transmitted from the drying roots to the shoots undertake to regulate stomatal closure, resulting in lower CO2 assimilation and net photosynthetic rates (Hasanuzzaman et al. 2013 ). This could be attributed for the reduction in shoot biomass in drought condition. The results were similarly consistent with those of (Zubaer et al. 2007 ), who noted that rice shoot dry matter of Aman rice genotypes decreased as water stress increased. Different rice genotypes exhibit varying responses to drought stress, with some showing enhanced root growth as a strategy to avoid drought. Water stress influences roots to grow toward areas with higher water content inducing to form a deeper and thinner root system and increasing the total absorption surface area favoring the uptake of water and nutrients (Kou et al. 2022 ). Hassan et al. ( 2023 ) concluded that extreme drought can limit secondary root growth and cause primary roots to become thicker but less branched, resulting in fewer overall roots. Deeper root system is facilitated with the rise in abscisic acid concentration in the roots (Panda et al. 2021). Signaling of ABA during water stress leads to the auxin biosynthesis modifying root morphology and root system architecture ensuring water uptake (Kalra et al. 2024 ). Drought stress condition may induce longer roots in the rice landraces which ultimately increase the root shoot ratio (Hou et al. 2022 ). Studies have shown that different rice genotypes exhibit varying levels of DRO1 expression that plays significant role in down streaming of auxin signaling, which is crucial for root development and gravitropic responses when subjected to drought stress (Uga et al. 2015 ; Zubaer et al. 2007 ). The increase in root length under drought conditions may be attributed to the enhanced expression and functional variations of the DRO1 gene (Uga et al. 2013 ). Root: shoot ratio (RL/SL) is an important indicator of drought tolerance (Xu et al. 2015 ). A higher root: shoot ratio indicates that a plant allocates more resources to its root system to exploit available moisture. When drought stress occurs, plants often reduce shoot growth to conserve resources for root development (Takahashi et al. 2020 ). This mechanism can help to maintain water uptake efficiency, as roots continue to grow and explore for moisture even when above-ground growth slows down (Kou et al. 2022 ). As per (Fig. 4 ), the highest value of root: shoot ratio was observed in Dalley Masino at 60% FC. Varieties with a favorable root: shoot ratio generally exhibit better yield performance and maintain physiological stability under drought conditions (Hassan et al. 2023 ). Ultimately, optimizing the RL/SL can be a crucial strategy for breeding programs aimed at enhancing drought resilience in various crop species. Plants may initially increase root biomass to seek moisture; however, as they adapt to prolonged stress, they may allocate more resources toward maintaining existing shoots rather than expanding roots further (Sainju et al. 2017 ). This shift can lead to an overall increase in shoot biomass with a corresponding decrease in the root biomass. This adaptive strategy highlights the complex balance must be maintained between root and shoot growth in response to environmental stresses (Numajiri et al. 2024 ). A negative correlation was observed between shoot biomass (FSW, TPW, SL) and root: shoot ratio in the landraces under study. This could be reasoned with altered carbohydrate partitioning exhibited by rice seedlings, favoring either root growth over shoot growth or vice-versa (Bui et al. 2019 ). It is reported that drought stress may lead to a significant increase in the proportion of soluble sugars and starch in roots, while the amount in stems decreased proportionally as a result of an increase in the activity of root invertase and leaf sucrose-phosphate synthase (Xu et al. 2015 ). This trade-off of resources may favor roots to improve water uptake efficiency and enhance root: shoot ratio. The results concluded that certain cultivars develop elongated roots without a corresponding increase in root number, thus demonstrating a negative correlation. This elongation occurred at the expense of root number, as resources are diverted towards extending existing roots rather than producing new ones which could be depicted as the adaptive mechanism by certain genotypes under drought conditions (Yang et al. 2022 ). Deeper roots can penetrate lower soil layers where moisture may still be available, thus optimizing water absorption and enhancing drought resistance (Huang et al. 2019 ; Shafi et al. 2023 ). Several QTLs were identified as common between root and shoot traits, indicating a genetic basis for their interdependence, particularly suggesting the correlation between root and shoot weight for two subgroups indica and japonica (Zhao et al. 2019 ). The presence of pleiotropic QTLs suggests that certain genetic factors regulate the growth of both root and shoot systems simultaneously, promoting coordinated biomass accumulation. These QTLs may explain the positive correlation exhibited by root and shoot weight by different landraces under study. For assessing drought-tolerant landraces, longer root length (Zhao et al. 2019 ; Kim et al. 2020 ), less reduction in shoot length (Islam et al. 2022 ; Das et al. 2024 ) and higher root: shoot ratio (Hussain et al. 2022 ) suggest that rice is capable of thriving in water-scarce conditions. Taking into account root and shoot traits, Manamurey emerges as a robust competitor under drought conditions. Given that this study focuses primarily on the seedling stage, it is also essential to place considerable emphasis on reproductive traits. Furthermore, the selection of drought-tolerant genotypes should consider various other factors, such as biochemical responses, environmental conditions, anatomical changes, extensive field trials, and overall plant performance. While Manamurey demonstrates great potential, more thorough testing under real-world conditions is necessary. Additionally, future research should concentrate on its ability to withstand reproductive drought, which would be valuable for advancing its use in breeding programs aimed at enhancing drought resistance Conclusion Drought stress greatly affects the growth and development of rice roots and shoots, both of which play a vital role in the plant's ability to adapt drought conditions. Therefore, it’s necessary to explore the inherent genetic potential of existing germplasm which shows promising performance under drought conditions in seedling stage. Assessing the performance of landraces under the seedling stage is one of the primary steps in developing drought-resistant cultivar after evaluation in vegetative and reproductive stage successively. This study shows a significant difference among the landraces for different root and shoot traits. Similarly, significant difference was observed among the performance of landraces in different moisture conditions. Among studied traits, the mean value of root length and root: shoot ratio was observed highest in drought condition with 60% FC. However, other traits like shoot length, fresh weight, dry weight and root number were highest mean value under saturated condition. Likewise, drought condition with 60% FC, showed positive correlation between root length and shoot length. While 100% FC and saturated condition showed positive correlation between shoot length, fresh root weight and root number. Notably, negative correlation was observed between root: shoot ratio and root number under drought condition. Furthermore, principal component analysis showed that root length and root: shoot ratio showed strong connection towards drought condition with 60% FC while shoot length, fresh weight, dry weight and root number showed strong connection towards 100%FC and saturated condition. Interestingly, germination percentage didn’t show discrimination over any moisture conditions. These findings can be used as selection criteria for drought stress conditions to develop better drought tolerant cultivar however, further study on their tolerance ability in vegetative and reproductive stage would be more fruitful. Declarations Author Contribution All the authors contributed equally in conception and design of experiment, data collection, data analysis, and manuscript writing. All authors read, proofread, and approved the submitted version and agreed to publish the final version of the manuscript. Funding No funding or financial support was received during the experiment and preparation of this manuscript. Acknowledgment The authors are grateful towards Purkot and Ghanpokhara community seed bank for providing the rice landraces for research. In addition, deeply indebted towards Institute of Agriculture and Animal Science, Lamjung Campus for providing research materials during research period. Conflict of interest The author declares no conflict of interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5327215","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370533207,"identity":"953accdf-5886-44b1-addb-55532685a02e","order_by":0,"name":"Bibas B.K.","email":"data:image/png;base64,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","orcid":"","institution":"Tribhuvan University, Institute of Agriculture and Animal Science, Lamjung Campus","correspondingAuthor":true,"prefix":"","firstName":"Bibas","middleName":"","lastName":"B.K.","suffix":""},{"id":370533208,"identity":"a1267d5e-e9bc-4111-9acb-da775a55d7bb","order_by":1,"name":"Sneha Dahal","email":"","orcid":"","institution":"Tribhuvan University, Institute of Agriculture and Animal Science, Lamjung Campus","correspondingAuthor":false,"prefix":"","firstName":"Sneha","middleName":"","lastName":"Dahal","suffix":""},{"id":370533209,"identity":"237db99e-dcd8-4047-b0ed-d9fbb991a1cb","order_by":2,"name":"Nirmala Pradhan","email":"","orcid":"","institution":"Tribhuvan University, Institute of Agriculture and Animal Science, Lamjung Campus","correspondingAuthor":false,"prefix":"","firstName":"Nirmala","middleName":"","lastName":"Pradhan","suffix":""}],"badges":[],"createdAt":"2024-10-24 15:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5327215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5327215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67740992,"identity":"dba2251a-81ea-4ee9-b36b-aa8c6754479d","added_by":"auto","created_at":"2024-10-29 08:52:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18349,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironment condition during the research period\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/64c2f68df5b08027055d05c5.png"},{"id":67740994,"identity":"fa74e672-f945-4a3a-a599-2574e9f8dd50","added_by":"auto","created_at":"2024-10-29 08:52:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33525,"visible":true,"origin":"","legend":"\u003cp\u003eMean performance comparison of 25 rice landraces in different moisture conditions. Interaction is non-significant\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/3f02f92a94351a28df888e72.png"},{"id":67740993,"identity":"42422af4-b5d3-459e-b102-21b0e4ecb5a9","added_by":"auto","created_at":"2024-10-29 08:52:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36351,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture conditions in terms of shoot length\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/c5551de05f2fa33cc983baff.png"},{"id":67740999,"identity":"2cbf9351-8530-4654-b9f7-c38e4b68dde4","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":33634,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture conditions in terms of root: shoot ratio\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/f13b8de697dcc22e96adf1fd.png"},{"id":67740996,"identity":"e3310efe-27d1-4477-b0c8-45f9b76c06a0","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35600,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture conditions in terms of fresh root weight\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/21ea64550e8b9a67f0beec65.png"},{"id":67742186,"identity":"4715a067-ccf6-43be-9de1-0297832ea0be","added_by":"auto","created_at":"2024-10-29 09:00:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37385,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture conditions in terms of fresh shoot weight\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/41581728c9da43a18331307c.png"},{"id":67744752,"identity":"f0ebfa8b-f35f-4139-8def-cc4849f52aea","added_by":"auto","created_at":"2024-10-29 09:24:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37190,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture conditions in terms of total plant weight\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/8a52613fc85793bff08c228d.png"},{"id":67742183,"identity":"ed622103-c41d-4346-8160-38f3e9e412bf","added_by":"auto","created_at":"2024-10-29 09:00:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":36989,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture condition in terms of root dry weight\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/da93a49e3fc7121bef4412bb.png"},{"id":67742773,"identity":"fb811cd3-cdcf-4a7f-b96c-c4c8a8fd2542","added_by":"auto","created_at":"2024-10-29 09:08:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37094,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction effect between landraces and moisture condition in terms of shoot dry weight\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/9dca1912523aa83ad143450d.png"},{"id":67743785,"identity":"7029bc42-f373-41d3-a096-86147e33bee9","added_by":"auto","created_at":"2024-10-29 09:16:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":34066,"visible":true,"origin":"","legend":"\u003cp\u003eMean performance comparison of rice landraces in different moisture conditions. Interaction is non-significant\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/3c55275d15cd9e2a8c8d4676.png"},{"id":67741012,"identity":"43a495da-7b7d-4819-9e9d-3b92240e1e98","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":431441,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographs illustrating the response of landraces in different moisture conditions at 21 days. (A) Nouley Dalley (B) Basmati. The description of S, D1, and D2 are expressed in (Table 2).\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/83fab1568767d673f80fc13f.png"},{"id":67741004,"identity":"26d6adc8-721f-4131-afa7-83935edb7d1b","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":162262,"visible":true,"origin":"","legend":"\u003cp\u003eBox and whisker charts showing variation among landraces for the tested quantitative traits. The blue box, pink box, and red box explain the boxplot value under 100% FC, 60% FC, and saturated condition respectively. The horizontal line inside each boxplot represents the median value. The full names of studied traits along with their description are in (Table 3).\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/a449bf62144ec15cd7838476.png"},{"id":67742187,"identity":"4c6bdaad-da0a-4783-bdec-a5abf152ebed","added_by":"auto","created_at":"2024-10-29 09:00:44","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":384372,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between studied traits under saturated condition\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/b65d73087ca6dc237bfd8903.png"},{"id":67744751,"identity":"721f3c52-9fef-4361-aa72-86d55fb599f8","added_by":"auto","created_at":"2024-10-29 09:24:44","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":380518,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between studied traits under 100% FC\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/a1fe0e8bff985e4764681c1a.png"},{"id":67741011,"identity":"0ed23a2e-4c77-4284-afbb-5e42cb21651e","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":367442,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between studied traits under 60% FC\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/f9c0ec0a66761e722bc512e5.png"},{"id":67742191,"identity":"d1264663-d790-44e5-95b2-88ae28677505","added_by":"auto","created_at":"2024-10-29 09:00:44","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":174558,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Biplot analysis for saturated condition\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/c5fb9837fa20262efe2b4ec0.png"},{"id":67742193,"identity":"883447bc-f82e-46b1-83bb-465d80be6d25","added_by":"auto","created_at":"2024-10-29 09:00:44","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":172225,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Biplot analysis for 100% FC\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/2becbe350825f1cc1332aad6.png"},{"id":67744753,"identity":"cafe47b6-2d7e-408a-a350-3ead6fdab8a8","added_by":"auto","created_at":"2024-10-29 09:24:44","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":170133,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Biplot analysis for 60% FC\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/4afe2b3671a5ed5f682eaa59.png"},{"id":67741008,"identity":"3fcb3a31-dd06-4eac-9d26-63a1c6626c54","added_by":"auto","created_at":"2024-10-29 08:52:44","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":365330,"visible":true,"origin":"","legend":"\u003cp\u003ePCA among studied traits over 60% FC, 100% FC and Saturated Condition\u003c/p\u003e","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/788059c0e3c2c09565b32a89.png"},{"id":68762218,"identity":"af2aee46-c7d8-4cf5-9c82-82fc1e9a8e2b","added_by":"auto","created_at":"2024-11-11 18:46:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3626922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/2ff6e60e-0e95-4df6-aca8-3cd9dc4ed901.pdf"},{"id":67742774,"identity":"a6aac1f6-752d-42f8-966a-297d49acef0d","added_by":"auto","created_at":"2024-10-29 09:08:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19514,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5327215/v1/ea2fd6d64e15e16b7c179238.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conferring Drought Tolerance in Rice Landraces Using Seedling Indices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice is the major staple food of Nepal. Regarding acreage and yield, rice surpasses all other cereal crops in Nepal, ranking third globally after wheat and maize. The total area of land under rice cultivation is estimated to be 1,477,378 ha with annual production of 5,130,625 mt and productivity of 3.47 mt/ha (MOALD \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rice contributes about 21% of AGDP in Nepal which is equivalent to almost 10% of national GDP (Aryal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Variations in altitude, geography, physical and climatic circumstances have enriched the country with immense genetic diversity in the form of landraces or traditional cultivars. Landraces are maintained and managed by the farmers in their fields for diverse purposes, including indigenous rites and beliefs, and their immense adaptability to changing conditions over time and across different environments (Bajracharya et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Landraces are adopted to marginal to high fertile soil, drought to deep water, different planting seasons, different climatic conditions, pest and disease infestation. Districts such as Kaski, Lamjung, Tanahun, Bara and Sunsari are richer in rice diversity (Joshi \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Landraces possess specific traits to adapt for better local adaptation in their environment, various socio-economic and cultural values (Rayamajhi and Thakuri \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom an irrigation perspective, out of the total agricultural land, 52% of the land remains rainfed, with only 48% of the land irrigated in which 39% of the land receives year-round irrigation (Irrigation Master Plan 2019). Lack of proper maintenance and associated regulatory mechanisms creates inadequate supply of irrigation in the irrigated areas of the country (Aryal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On average, 2500 liters of water is used, ranging from 800 liters to more than 5000 liters to produce 1 kg of rice which indicates water productivity of rice ranges from 0.6\u0026ndash;1.6 kg/m\u003csup\u003e3\u003c/sup\u003e in different parts of the world (Mboyerwa et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rice is highly sensitive to moisture stress during pre-flowering and grain-filling stages (Kandel et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Drought stress at various growth stages may reduce the assimilates' ability to translocate to the grains, resulting in a drop in grain weight and an increase in empty grains (Moonmoon and Islam \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Under mild water stress, grain yield is reduced by 5\u0026ndash;38%, and under severe water stress by 25\u0026ndash;67% (Basha and A. 2017). Drought stress reduces plant development, affecting the root system architecture, leaf surface traits, normal plant senescence, and inhibition of stem reserves (Moonmoon and Islam \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNepal is considered one of the centers of diversity of rice (Ghimire et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Landraces comprise a major component of Nepal's rice production system, accounting for about 70% of the country's total rice area (Amgai and Joshi \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Rice landraces, despite having low yield are believed to be adopted in the local environment (Rijal \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), resistant to both biotic and abiotic stresses, and are considered reservoirs of genetic potential, whereas modern rice varieties don't hold such qualities (Tiwari et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Preservation of local landraces helps in creating improved rice varieties and hybrids for drought tolerance. Therefore, the objective of this study is to identify local landraces adopted in drought conditions by analyzing and comparing root and shoot traits in seedling stage for its subsequent application in modern breeding programs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003ch2\u003e\u003cstrong\u003eExperimental site\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe research was conducted in a greenhouse of the Institute of Agriculture and Animal Science, Lamjung Campus,\u0026nbsp;Sundarbazar, Lamjung.\u0026nbsp;The research site lies in the mid-hill region at an altitude of 610 masl, a latitude of 28.12\u0026deg; N, and a\u0026nbsp;longitude of 84.41\u0026deg;E.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eClimatic conditions and growing season\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe research was conducted during July and August 2024.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLandraces tested\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA total of 25 distinct rice landraces were tested in this experiment which were collected from the Purkot and Ghanpokhara Community Seed Bank of Tanahun and Lamjung respectively.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eExperimental details\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe experiment was conducted under a\u0026nbsp;two-factor factorial completely randomized design.\u003c/p\u003e\n\u003cp\u003eFactor A: 25 rice landraces\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1\u0026nbsp;List of rice landraces tested in the experiment\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eLandraces\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eLandraces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eKattikey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eJarneli\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eRambilash\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eJungey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eJhini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eKamal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eDarmali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eDalley Masino\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eGaurey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eAangha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eHimali\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eMansara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eJungey Kanchi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eKalo Jhinuwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eManamurey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eKalokattey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eNouley Dalley\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eGurdo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003ePahele\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003ePathijharey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eBiramphul\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eKrishnabeli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eSeto Dalley\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003eT13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003eAapjhuttey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.9169%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0831%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFactor B: Moisture condition\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2\u0026nbsp;Different moisture conditions tested in the experiment\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003eS.N.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8246%;\"\u003e\n \u003cp\u003eTreatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.3684%;\"\u003e\n \u003cp\u003eMoisture Condition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8246%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.3684%;\"\u003e\n \u003cp\u003e100% field capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8246%;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.3684%;\"\u003e\n \u003cp\u003e60% field capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.807%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8246%;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47.3684%;\"\u003e\n \u003cp\u003eSaturated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThere were three replications of each factor. The experiment was conducted in the disposable cups of 250 ml.\u003c/p\u003e\n\u003cp\u003eTotal landraces: 25\u003c/p\u003e\n\u003cp\u003eMoisture condition: 3\u003c/p\u003e\n\u003cp\u003eTotal replication: 3\u003c/p\u003e\n\u003cp\u003eA total of 25\u0026times;3\u0026times;3 i.e. 225 disposable cups were used in the study containing two plants in each cup.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCultivation practices\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cstrong\u003ePlanting media\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe planting media was thoroughly prepared by mixing sand, soil (sandy loam texture), and FYM in the ratio of 1:2:1. Each cup was filled to its capacity weighing 200 g respectively.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSeeding\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e6 seeds were sown per cup. After 5 days of seedling establishment, only two seedlings were maintained per cup.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eWater management\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThree moisture conditions were maintained in the experiment (Table 2). To determine the\u0026nbsp;field capacity, 200 g of soil was saturated in a cup and left to drain for 48 hours. The opening end of the cup was covered with plastic to prevent evaporation. The computation of water required for irrigation was performed in the following manner:\u003c/p\u003e\n\u003cp\u003eWeight of the disposable cups = 1.93 g\u003c/p\u003e\n\u003cp\u003eWeight of the planting media = 200 g\u003c/p\u003e\n\u003cp\u003eWeight of the oven-dried media at 72℃ for 24 hour = 159.97 g\u003c/p\u003e\n\u003cp\u003eIn 200 g of planting media, the composition of soil solid was 159.97 g and that of soil water was 200-159.97 = 40.03 g\u003c/p\u003e\n\u003cp\u003eWeight of soil at FC after 48 hours = 233.92 g\u003c/p\u003e\n\u003cp\u003eSoil water at FC = 233.92 \u0026ndash; 159.97= 73.95 g\u003c/p\u003e\n\u003cp\u003eSoil water at 100% FC = 100% of 73.95 = 73.95 g\u003c/p\u003e\n\u003cp\u003eSoil water at 60% FC = 60% of 73.95 = 44.37 g\u003c/p\u003e\n\u003cp\u003eSo, the\u0026nbsp;amount of water to be added at 100% FC = 73.95 \u0026ndash; 40.03 = 33.92 g\u003c/p\u003e\n\u003cp\u003eAnd the\u0026nbsp;amount of water to be added at 60% FC = 44.37 \u0026ndash; 40.03 = 4.34 g\u003c/p\u003e\n\u003cp\u003eWith the density of water being 1g/cc, 33.92 ml of water was added to the soil for 100% FC and 4.34 ml for 60% FC.\u003c/p\u003e\n\u003cp\u003eEach pot was watered every three days after the seedling establishment. Data collection was performed at 21 DAS with destructive sampling.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData collection\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll of the traits with their method of detection are expressed in (Table 3). Fresh plant samples were oven-dried at 70\u0026plusmn;5 for about 48 hours for the detection of dry weight\u0026nbsp;(Badr et al. 2020). \u0026nbsp;Germination percentage was calculated by determining the proportion of seeds that successfully germinated under controlled conditions. The following formula was used to calculate the germination percentage:\u003c/p\u003e\n\u003cp\u003eGermination Percentage (GP) =\u0026nbsp;\u0026nbsp;(Mamun et al. 2018).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3\u0026nbsp;Observed traits description and method employed for data collection\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.N.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDetection Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eGermination percentage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eRecorded in percentage by using a\u0026nbsp;mathematical formula.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eRoot length\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eThe distance from the base of the plant (where it connects to the plant) to the tip of the longest root and measured in centimeters using a measuring scale.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eShoot length\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eThe distance from the base of the plant (where it connects to the root) to the tip of the\u0026nbsp;longest flag leaf and measured in centimeters using measuring the\u0026nbsp;scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eRoot: shoot ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eRL/SL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eDividing root length by shoot length.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eFresh root weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eFRW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eRoots were placed on precision balance to measure their fresh weight in grams immediately after washing.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eFresh shoot weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eFSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eShoots were placed on precision balance to measure their fresh weight in grams.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eTotal plant weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eTPW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eFRW+FSW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eRoot dry weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eFresh root samples were oven-dried at 70\u0026plusmn;5℃ for about 48 hours and measured their dry weight in grams.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eShoot dry weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eSDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eFresh shoot samples were oven-dried at 70\u0026plusmn;5℃ for about 48 hours and measured their dry weight in grams.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.69231%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2692%;\"\u003e\n \u003cp\u003eRoot number\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2308%;\"\u003e\n \u003cp\u003eRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54.8077%;\"\u003e\n \u003cp\u003eWashed roots were counted manually.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe observed data was entered in MS Excel (2021). Visualization of the interaction effect was done through MS Excel. Analysis of variance, mean separation, and F-test were performed at 5% level of significance. Boxplot, correlation, and principal component analysis were carried out in R (4.4.1).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of rice landraces for traits\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the analysis of variance for 10 quantitative traits among landraces, conditions, and the interaction between landraces and conditions at 21 days.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance of studied traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.N.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMean sum of square\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandraces (DF\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCondition (DF\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLandraces*Condition (DF\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eError (DF\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermination percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.57\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.73\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.84\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\u003eRoot length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.68\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.98\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\u003eShoot length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.7\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1299.4\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.3\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.8\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\u003eRoot: shoot ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0480\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2540\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0142\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0078\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\u003eFresh root weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00463\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05342\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00213\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00042\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\u003eFresh shoot weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01873\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22429\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00397\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00097\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\u003eTotal plant weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0378\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4966\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0088\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0019\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\u003eRoot dry weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000640\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0003217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000242\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000111\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\u003eShoot dry weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003807\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000163\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000040\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\u003eRoot number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665.6\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\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*, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas ns denotes non-significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean separation table for quantitative traits in twenty-five rice landraces\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eFactor A: Landraces\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandraces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRL/SL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFRW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAangha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.444\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.678\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.450\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.140\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.246\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.386\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.047\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.333\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAapjhuttey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.296\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.133\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.733\u003csup\u003eefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003csup\u003edefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003csup\u003ekl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003csup\u003eefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.271\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.278\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasmati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.889\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.506\u003csup\u003egh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.144\u003csup\u003elmno\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.151\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.272\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.043\u003csup\u003efghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.944\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiramphul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.481\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.928\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.056\u003csup\u003efghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003csup\u003ehijkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003csup\u003edefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.309\u003csup\u003eghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003csup\u003eghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.389\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDalley Masino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.296\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.017\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.239\u003csup\u003eo\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.128\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.229\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.032\u003csup\u003ek\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.000\u003csup\u003er\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDarmali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.148\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.344\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.628\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003csup\u003ehijkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.260\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.403\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.054\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.056\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaurey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.444\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.800\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.639\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003csup\u003ehijkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003csup\u003ecdef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.208\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.350\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.833\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGurdo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.444\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.556\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.061\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003csup\u003ehijkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.091\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.306\u003csup\u003eghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.042\u003csup\u003eghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.222\u003csup\u003el\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHimali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.444\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.144\u003csup\u003ebcdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.639\u003csup\u003eghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.527\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.166\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.291\u003csup\u003ehijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.722\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManamurey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.889\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.250\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.694\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003csup\u003eghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.303\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.483\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.944\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMansara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.444\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.283\u003csup\u003edefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.200\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.266\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.430\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.051\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.611\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNouley Dalley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.593\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.689\u003csup\u003efgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.856\u003csup\u003ehijklm\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003csup\u003efghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.336\u003csup\u003eefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.833\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePahele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.296\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.633\u003csup\u003eabcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.511\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.471\u003csup\u003efghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.121\u003csup\u003eghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.233\u003csup\u003ebcde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.354\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.019\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.333\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathijharey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.741\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.156\u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.889\u003csup\u003eno\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003csup\u003eghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003csup\u003eefghi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.300\u003csup\u003eghijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003csup\u003edefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.333\u003csup\u003eq\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRambilash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.593\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.572\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.950\u003csup\u003ecdefg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003csup\u003edefgh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.828\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003csup\u003eefghij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.405\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.051\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.333\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \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 \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0838\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 \u003cp\u003e9.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFactor B: Moisture condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRL/SL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFRW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSDW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.111\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.086\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.224\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.328\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.046\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.033\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.333\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.857\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.157\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.257\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.036\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.166\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.666\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.069\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.052\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.267\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.419\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.049\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \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 \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0290\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 \u003cp\u003e9.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction (A*B)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eLSD: Least Significant Difference; CV: Coefficient of Variation; SEM: Standard Error of Mean\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*, **, and *** denote significance at 5%, 1%, and 0.1%, respectively, whereas NS denotes non-significant.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u0026minus;r\u003c/sup\u003e means with the same set of letters are not significantly different.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMean performance comparison of the study parameters\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eGermination percentage\u003c/h2\u003e \u003cp\u003eSignificant difference was observed between germination in 25 different rice landraces. Among tested landraces, the highest germination was achieved in Darmali, Jhini, and Kattikey with a germination percentage of 98.148. Whereas, the lowest germination was achieved in Biramphul (81.481%) followed by Seto Dalley (81.185%) respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRoot length\u003c/h2\u003e \u003cp\u003eThe interaction between landraces and moisture conditions exhibited statistical non-significance. Under the saturated condition, Biramphul displayed an extensive root length of 15.62 cm and was statistically similar to Himali (14.5), Seto Dalley (14.47), and Manamurey (14.43). Under 100% FC, Kamal exhibited a greater root length of 19.18 cm with statistical similarity to Biramphul (19.02), Manamurey (17.82), and Rambilash (17.07) respectively. Similarly, Kamal exhibited an extensive root length of 20.43 cm under drought conditions with 60% FC and was statistically consistent with the performance of Manamurey (19.5), Rambilash (18.48), and Jungey Kanchi (17.73). As for the main effects, the landraces showed highly significant differences in terms of root length. As illustrated in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), Manamurey exhibited the highest root length of 17.25 cm mean value which was statistically similar with Biramphul, Rambilash, Pahele, Kamal, Jungey Kanchi, and Seto Dalley. Substantial variability in the root length was evident between the three moisture conditions: S (13.069), D\u003csub\u003e1\u003c/sub\u003e(15.086), and D\u003csub\u003e2\u003c/sub\u003e(16.333). Greater root length under drought stress is similar with the findings of Kaysar et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eShoot length\u003c/h2\u003e \u003cp\u003eSignificant difference was observed between the shoot length in 25 different rice landraces. The interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, the most significant shoot length was observed in Manamurey with 43.28 cm followed by the Mansara (41.92), Jarneli (40.7), Seto Dalley (40.37), and Himali (39.25). Under 100% FC, Jarneli was found to incur the greatest shoot length with mean value of 42.65 cm which was statistically at par with Darmali (42.5), Manamurey (41.35) and Pahele (40.45). Under drought condition with 60% FC, Rambilash displayed extensive shoot length with a mean value of 32 cm which was statistically consistent with the performance of Manamurey (31.45), Mansara (30.48), and Aangha (30.42). As for the main effects, the landraces showed highly significant differences in terms of shoot length. As shown in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), shoot length was found to be highest in Manamurey with a mean value of 38.694 cm, which was statistically at par with Jarneli and Darmali. Similarly, the lowest shoot length was achieved in Dalley Masino with a mean value of 23.239 cm. Likewise, for the moisture condition, S, D\u003csub\u003e1,\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e exhibited highly significant differences with 34.052 cm, 31.224 cm, and 25.857 cm respectively. Patel et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a decrease in shoot length in drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRoot: shoot ratio\u003c/h2\u003e \u003cp\u003eThe interaction between landraces and moisture conditions exhibited statistical significance. As demonstrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), in saturated condition Pathijharey exhibited the highest ratio with mean value of 0.502 which was statistically at par with Dalley Masino (0.488) and Biramphul (0.47). Under 100% FC, Kamal showed the highest ratio with mean value of 0.687 statistically similar with Dalley Masino (0.673), Kalo Jhinuwa (0.658), and Biramphul (0.637). Likewise, Dalley Masino exhibited the highest ratio under 60% FC with mean value of 0.893. Significant variances were observed among the landraces concerning the root: shoot ratio. The highest root: shoot ratio was recorded in Dalley Masino (0.685) whereas the lowest was in Jarneli with 0.393 respectively. Similarly, Substantial variability in the ratio was evident between the three watering conditions: S (0.391), D\u003csub\u003e1\u003c/sub\u003e (0.504), and D\u003csub\u003e2\u003c/sub\u003e (0.649). These results indicated that drought stress condition induces longer roots in the rice landraces which ultimately increase the root shoot ratio. These findings are consistent with the study of Hou et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFresh root weight\u003c/h2\u003e \u003cp\u003eNoticeable variations were identified among the different landraces concerning the fresh root weight as presented in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The interaction between landraces and moisture conditions also exhibited statistical significance. Under saturated conditions, Seto Dalley showed the highest weight with mean value of 0.218 g which was statistically similar with Manamurey (0.208), Himali (0.206), Rambilash (0.196), and Jarneli (0.19). Manamurey exhibited the highest fresh root weight under 100% FC, with mean value of 0.201 g which was statistically at par with Darmali (0.178) and Mansara (0.173). Under drought conditions with 60% FC, Mansara showed the highest weight with mean value of 0.156 g which was statistically consistent with Kamal (0.131) and Manamurey (0.128). As illustrated in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), Manamurey showed the highest fresh root weight with 0.179 g and was statistically at par with Mansara (0.163). Likewise, for the moisture condition, S, D\u003csub\u003e1,\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e exhibited highly significant differences with 0.152 g, 0.123 g, and 0.099 g respectively. The findings of this experiment are consistent with Dien et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which has indicated that plants grown in saturated conditions have more fresh root weight in contrast to drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFresh shoot weight\u003c/h2\u003e \u003cp\u003eThe interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey demonstrated the highest fresh shoot weight with mean value of 0.396 g which was statistically significant with Himali (0.385) and Jarneli (0.37). Under 100% FC, Darmali was found to possess highest fresh shoot weight with mean value of 0.341 g which was statistically consistent with Manamurey (0.32). Similarly, at drought conditions with 60% FC, Aangha exhibited highest weight with a mean value of 0.221 g which was statistically consistent with Rambilash (0.211) and Mansara (0.203) respectively. As for the main effects, the landraces showed highly significant differences in terms of fresh shoot weight. As illustrated in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), Manamurey showed highest fresh shoot weight with 0.303 g and was statistically at par with Jarneli (0.280), Mansara (0.266), Darmali (0.260), Rambilash (0.258), Seto Dalley (0.248) and Aangha (0.246). Likewise, for the moisture condition, S, D\u003csub\u003e1,\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e exhibited highly significant differences with 0.267 g, 0.205 g, and 0.157 g respectively. Similarly, Saha et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported decrease in fresh weight of shoots in drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTotal plant weight\u003c/h2\u003e \u003cp\u003eThe interaction between landraces and moisture conditions exhibited statistical significance. Under saturated conditions, Manamurey exhibited highest total plant weight with mean value of 0.605 g which was statistically similar with Himali (0.591), Jarneli (0.56), and Seto Dalley (0.553). Under 100% FC, Manamurey demonstrated highest plant weight with a mean value of 0.521 g and was statistically consistent with Darmali (0.52). At drought conditions with 60% FC, Mansara was found to incur the highest plant weight with mean value of 0.36 g which was statistically similar with Rambilash (0.333), Aangha (0.331), and Manamurey (0.323). Regarding main effects, the landraces showed highly significant differences in terms of total plant weight, and highest plant weight was achieved in Manamurey with a mean value of 0.483 g. Substantial variability in the weight was evident between the three moisture conditions: S (0.419), D\u003csub\u003e1\u003c/sub\u003e(0.328), and D\u003csub\u003e2\u003c/sub\u003e (0.257).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eRoot dry weight\u003c/h2\u003e \u003cp\u003eA significant difference was observed in the root dry weight among the 25 different rice landraces. Landraces showed significant impact with the moisture conditions, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Under saturated condition, Kalokattey showed highest root dry weight with mean value of 0.0218 g which was statistically similar with Seto Dalley (0.0215), Manamurey (0.0214), and Kattikey (0.0211). Manamurey exhibited highest root dry weight under 100% FC with mean value of 0.0249 g which was statistically consistent with Seto Dalley (0.0241) and Darmali (0.0215). Under drought condition at 60% FC, highest root dry weight was recorded for Pahele with mean value of 0.0205 g. As illustrated in the (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), Seto Dalley and Darmali exhibited highest root dry weight with 0.021 g which was statistically similar with Manamurey (0.02) and Pahele (0.019). For the moisture condition, S, D\u003csub\u003e1,\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e exhibited highly significant differences with 0.017 g, 0.016 g, and 0.013 g respectively. Decline in the root dry weight with the increase in water stress is in agreement with the findings of Patel et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eShoot dry weight\u003c/h2\u003e \u003cp\u003eNoticeable variations were identified among the different landraces concerning the shoot dry weight as presented in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The interaction between landraces and moisture conditions is also statistically significant. As demonstrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), under saturated condition, Jarneli showed highest shoot dry weight with mean value of 0.0636 g which was statistically similar with Kalokattey (0.0632) and Rambilash (0.0622). Under 100% FC, Manamurey exhibited highest dry weight with mean value of 0.0713 g and was statistically similar with Darmali (0.0647). Under drought condition at 60% FC, highest shoot dry weight was recorded for Pahele with a mean value of 0.0503 g. Regarding main effects, the landraces showed highly significant differences in terms of shoot dry weight. As illustrated in (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), shoot dry weight was found to be highest for the landrace Manamurey with 0.057 g and was statistically at par with Darmali (0.054), Jarneli (0.054) and Pahele (0.053). For the moisture condition, S, D\u003csub\u003e1,\u003c/sub\u003e and D\u003csub\u003e2\u003c/sub\u003e exhibited highly significant differences with 0.049 g, 0.046 g, and 0.036 g respectively. Saha et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Patel et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a significant reduction in the dry matter of shoots when exposed to stress conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eRoot number\u003c/h2\u003e \u003cp\u003eA significant difference was observed in the root number among the 25 different rice landraces. Landraces showed a significant impact with the moisture conditions, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The most significant root number was observed in Aangha with mean value of 14.33 in saturated condition, Mansara with 10.83 in 100% FC, and Pahele with 8.83 in drought condition with 60% FC. Among the 25 rice landraces, highest root number was observed in Mansara with 10.611 and was significantly different from other landraces. On the contrary, lowest root number was observed in Dalley Masino and Jhini with mean value of 7.00. Substantial variability in the ratio was evident between the three watering conditions: S (12.00), D\u003csub\u003e1\u003c/sub\u003e(8.033), and D\u003csub\u003e2\u003c/sub\u003e (6.166).Kaysar et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported prominent number of roots in rice landraces under saturated conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation between the traits\u003c/h2\u003e \u003cp\u003eCorrelation (\u003cem\u003er\u003c/em\u003e-value) was determined using a correlation matrix to identify the inter-relationship of studied traits. The significant r values among different traits varied from 32, 29, and 25 under Saturated, 100% FC, and 60% FC respectively.\u003c/p\u003e \u003cp\u003eRoot length showed a significant positive correlation with root: shoot ratio (0.49, 0.54) under saturated and 100% FC while, a positive correlation was observed with shoot length (0.32), root dry weight (0.29), and root number (0.33) under saturated condition and fresh shoot weight (0.25) and root dry weight (0.32) in 100% FC. Under 60% FC, it showed a positive correlation with shoot length (0.36), root: shoot ratio (0.31), fresh root weight (0.36), and total plant weight (0.26). However, no significant correlation was observed for root length and root number in 100 and 60% FC. Notably, shoot length scored a highly significant positive correlation with all the traits in a saturated condition, except fresh root weight in 100% FC and root dry weight in 60% FC. Likewise, root: shoot ratio was negatively correlated with shoot length under all conditions (-0.65, -0.77, -0.75). There was a significant correlation observed between root: shoot ratio with fresh shoot weight (-0.27, -0.47, -0.45) including shoot dry weight (-0.40) under saturated condition, total plant weight (-0.42) under 100% FC and root number (-0.38) in case of 60% FC. However, a negative correlation was observed with fresh root weight (-0.27), total plant weight (-0.36), root dry weight (-0.27, -0.25), and root number (-0.28, -0.30) under saturated condition and 100% FC while, total plant weight (-0.34) and shoot dry weight (-0.27, -0.32) under 60% FC and 100% FC respectively. Fresh root weight was significantly correlated with all the traits except root length in saturated condition and root length, shoot length, and root: shoot ratio in 100% FC along with root dry weight and root: shoot ratio under 60% FC condition. Fresh shoot weight showed significant positive correlation with total plant weight (0.98, 0.91, 0.91), root dry weight (0.47, 0.57), shoot dry weight (0.46, 0.58, 0.37), and root number (0.58) under saturated, 100% FC and 60% FC respectively. There was a positive correlation of total plant weight with root dry weight (0.48, 0.51), shoot dry weight (0.45, 0.58, 0.38), and root number (0.59, 0.23, 0.24). Root dry weight showed a highly significant correlation with shoot dry weight (0.65, 0.75, 0.67) under all conditions while a positive correlation was observed with root number (0.43, 0.24) under saturated and 60% FC conditions. Shoot dry weight positively correlated with root number (0.26, 0.24, 0.27) at all moisture conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003ePrincipal Component Analysis\u003c/h2\u003e \u003cp\u003e25 rice landraces were studied through principal component analysis (PCA) biplot, where the landraces and traits are mapped based on their relationships across the first two principal components (PC1, PC2). Under the 60% FC, both axes of components (PC1 and PC2) explain a significant portion of the variance in the dataset with an eigenvalue greater than one. PC1 accounted for 46.7% while, PC2 accounted for 17.4%, totaling 64.1% of the variability (Supplementary Table\u0026nbsp;1). Furthermore, traits like RL, SL, FRW, FSW, TPW, and RN are highly associated with PC1 whereas, RDW and SDW are highly associated with PC2 (Supplementary Table\u0026nbsp;4). Thus, interpreting (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e), Jungey Kanchi, Basmati, and Kamal are landraces located far from the origin, representing extreme or distinct characteristics compared to other landraces. Likewise, traits like FRW, TPW, FSW, RN, and SL are closely aligned, indicating that these traits are strongly and positively correlated.\u003c/p\u003e \u003cp\u003eSimilarly, under 100% FC and Saturated conditions, both components explain a total of 67.7% and 70.9% of the variability in the dataset with an eigenvalue greater than one (Supplementary Table\u0026nbsp;1). Under 100% FC, traits RL, SL, FRW, FSW, TPW, RDW, and SDW are highly associated with PC1 whereas, GP and RN are highly associated with PC2 (Supplementary Table\u0026nbsp;3). Likewise, under saturated condition, all traits except GP and SDW are associated with PC2 (Supplementary Table\u0026nbsp;2). Thus, interpreting (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e,\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e) traits like RDW, SDW, FRW, FSW, TPW, RN, and SL are aligned closely suggesting a significant positive correlation. This implies that landraces with higher shoot and root weight also tend to have higher root numbers and shoot length. Under all conditions, RL/SL have arrows pointing opposite to FSW, TPW, and SL, indicating a negative correlation which means that landraces with higher shoot biomass (FSW, TPW, SL) tend to have lower root-to-shoot ratios. Interestingly, RL and RN have almost perpendicular arrows, indicating little to no correlation between these two traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, the PCA-Biplot shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e19\u003c/span\u003e), indicates the contribution of traits to overall variation in the dataset. PC1 explains more than half (53.8%) of the variance in the data while PC2 explains a smaller portion of the variance (12.7%) in the dataset. Together, PC1 and PC2 account for 66.5% of the total variation. Unlike the above PCA Biplots, traits like RDW, SDW, FRW, FSW, TPW, SL, and RN suggest a strong correlation between them. Likewise, RL/SL and RN were directed towards opposite directions, indicating a negative correlation between them. Traits with an arrow directed toward the environment indicate a strong association with that tested environment. Therefore, RL and RL/SL are directed towards the D2 condition (60% FC), suggesting these traits have a positive association with 60% FC. Traits like: RDW, SDW, FRW, TPW, FSW, SL, and RN are directed towards D1 (100% FC) and saturated condition suggesting these traits have a positive association with this moisture condition. However, GP falls in all moisture conditions which indicates that GP doesn\u0026rsquo;t have discrimination over any moisture conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eThe performance of rice landraces for drought-tolerant traits may be best assessed by analyzing the presence of variability among the tested landraces with the evaluated traits. Numerous experts across the globe have conducted similar studies on rice landraces and have found agro-morphological diversification linked to vegetative traits (Mishra et al. 2018; Ndikuryayo et al. 2023). In nearly every trait under study, the ANOVA findings showed significant differences between the genotypes under S, D1, and D2 conditions that suggest evident indication of genetic variability among the landraces under study.\u003c/p\u003e \u003cp\u003eUnder saturated conditions, highest shoot biomass (FSW, TPW, SL) can be attributed to the ability of plants to uptake more nutrients as they are more readily available in moist conditions, allowing for optimal growth of shoot (Ros et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). According to (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), the findings indicated that under saturated condition, Manamurey incurred highest values of shoot fresh weight than other landraces. The observed outcome may derive from the enhanced capacity of the rice landrace to absorb water and nutrients, as well as their increased stomatal conductance, which in turn leads to enhanced photosynthesis (Kamarudin et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, it has been demonstrated that in drought condition, chemical and hydraulic signals transmitted from the drying roots to the shoots undertake to regulate stomatal closure, resulting in lower CO2 assimilation and net photosynthetic rates (Hasanuzzaman et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This could be attributed for the reduction in shoot biomass in drought condition. The results were similarly consistent with those of (Zubaer et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who noted that rice shoot dry matter of Aman rice genotypes decreased as water stress increased.\u003c/p\u003e \u003cp\u003eDifferent rice genotypes exhibit varying responses to drought stress, with some showing enhanced root growth as a strategy to avoid drought. Water stress influences roots to grow toward areas with higher water content inducing to form a deeper and thinner root system and increasing the total absorption surface area favoring the uptake of water and nutrients (Kou et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hassan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) concluded that extreme drought can limit secondary root growth and cause primary roots to become thicker but less branched, resulting in fewer overall roots. Deeper root system is facilitated with the rise in abscisic acid concentration in the roots (Panda et al. 2021). Signaling of ABA during water stress leads to the auxin biosynthesis modifying root morphology and root system architecture ensuring water uptake (Kalra et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Drought stress condition may induce longer roots in the rice landraces which ultimately increase the root shoot ratio (Hou et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have shown that different rice genotypes exhibit varying levels of DRO1 expression that plays significant role in down streaming of auxin signaling, which is crucial for root development and gravitropic responses when subjected to drought stress (Uga et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zubaer et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The increase in root length under drought conditions may be attributed to the enhanced expression and functional variations of the DRO1 gene (Uga et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoot: shoot ratio (RL/SL) is an important indicator of drought tolerance (Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A higher root: shoot ratio indicates that a plant allocates more resources to its root system to exploit available moisture. When drought stress occurs, plants often reduce shoot growth to conserve resources for root development (Takahashi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This mechanism can help to maintain water uptake efficiency, as roots continue to grow and explore for moisture even when above-ground growth slows down (Kou et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As per (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the highest value of root: shoot ratio was observed in Dalley Masino at 60% FC. Varieties with a favorable root: shoot ratio generally exhibit better yield performance and maintain physiological stability under drought conditions (Hassan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ultimately, optimizing the RL/SL can be a crucial strategy for breeding programs aimed at enhancing drought resilience in various crop species.\u003c/p\u003e \u003cp\u003ePlants may initially increase root biomass to seek moisture; however, as they adapt to prolonged stress, they may allocate more resources toward maintaining existing shoots rather than expanding roots further (Sainju et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This shift can lead to an overall increase in shoot biomass with a corresponding decrease in the root biomass. This adaptive strategy highlights the complex balance must be maintained between root and shoot growth in response to environmental stresses (Numajiri et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA negative correlation was observed between shoot biomass (FSW, TPW, SL) and root: shoot ratio in the landraces under study. This could be reasoned with altered carbohydrate partitioning exhibited by rice seedlings, favoring either root growth over shoot growth or vice-versa (Bui et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is reported that drought stress may lead to a significant increase in the proportion of soluble sugars and starch in roots, while the amount in stems decreased proportionally as a result of an increase in the activity of root invertase and leaf sucrose-phosphate synthase (Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This trade-off of resources may favor roots to improve water uptake efficiency and enhance root: shoot ratio.\u003c/p\u003e \u003cp\u003eThe results concluded that certain cultivars develop elongated roots without a corresponding increase in root number, thus demonstrating a negative correlation. This elongation occurred at the expense of root number, as resources are diverted towards extending existing roots rather than producing new ones which could be depicted as the adaptive mechanism by certain genotypes under drought conditions (Yang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Deeper roots can penetrate lower soil layers where moisture may still be available, thus optimizing water absorption and enhancing drought resistance (Huang et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shafi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral QTLs were identified as common between root and shoot traits, indicating a genetic basis for their interdependence, particularly suggesting the correlation between root and shoot weight for two subgroups \u003cem\u003eindica\u003c/em\u003e and \u003cem\u003ejaponica\u003c/em\u003e (Zhao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The presence of pleiotropic QTLs suggests that certain genetic factors regulate the growth of both root and shoot systems simultaneously, promoting coordinated biomass accumulation. These QTLs may explain the positive correlation exhibited by root and shoot weight by different landraces under study.\u003c/p\u003e \u003cp\u003eFor assessing drought-tolerant landraces, longer root length (Zhao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), less reduction in shoot length (Islam et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and higher root: shoot ratio (Hussain et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggest that rice is capable of thriving in water-scarce conditions. Taking into account root and shoot traits, Manamurey emerges as a robust competitor under drought conditions.\u003c/p\u003e \u003cp\u003eGiven that this study focuses primarily on the seedling stage, it is also essential to place considerable emphasis on reproductive traits. Furthermore, the selection of drought-tolerant genotypes should consider various other factors, such as biochemical responses, environmental conditions, anatomical changes, extensive field trials, and overall plant performance. While Manamurey demonstrates great potential, more thorough testing under real-world conditions is necessary. Additionally, future research should concentrate on its ability to withstand reproductive drought, which would be valuable for advancing its use in breeding programs aimed at enhancing drought resistance\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDrought stress greatly affects the growth and development of rice roots and shoots, both of which play a vital role in the plant's ability to adapt drought conditions. Therefore, it\u0026rsquo;s necessary to explore the inherent genetic potential of existing germplasm which shows promising performance under drought conditions in seedling stage. Assessing the performance of landraces under the seedling stage is one of the primary steps in developing drought-resistant cultivar after evaluation in vegetative and reproductive stage successively. This study shows a significant difference among the landraces for different root and shoot traits. Similarly, significant difference was observed among the performance of landraces in different moisture conditions. Among studied traits, the mean value of root length and root: shoot ratio was observed highest in drought condition with 60% FC. However, other traits like shoot length, fresh weight, dry weight and root number were highest mean value under saturated condition. Likewise, drought condition with 60% FC, showed positive correlation between root length and shoot length. While 100% FC and saturated condition showed positive correlation between shoot length, fresh root weight and root number. Notably, negative correlation was observed between root: shoot ratio and root number under drought condition. Furthermore, principal component analysis showed that root length and root: shoot ratio showed strong connection towards drought condition with 60% FC while shoot length, fresh weight, dry weight and root number showed strong connection towards 100%FC and saturated condition. Interestingly, germination percentage didn\u0026rsquo;t show discrimination over any moisture conditions. These findings can be used as selection criteria for drought stress conditions to develop better drought tolerant cultivar however, further study on their tolerance ability in vegetative and reproductive stage would be more fruitful.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors contributed equally in conception and design of experiment, data collection, data analysis, and manuscript writing. All authors read, proofread, and approved the submitted version and agreed to publish the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding or financial support was received during the experiment and preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful towards Purkot and Ghanpokhara community seed bank for providing the rice landraces for research. In addition, deeply indebted towards Institute of Agriculture and Animal Science, Lamjung Campus for providing research materials during research period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmgai RB, Joshi BK (2004) Realities on Nepalese Rice Landraces. 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Crop Prod 2(6): 25-30\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rice, landraces, drought stress, seedling stage, correlation","lastPublishedDoi":"10.21203/rs.3.rs-5327215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5327215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandraces are a vital source of genetic diversity in crops, offering a rich pool of allelic variation that plant breeders can utilize for developing new cultivars. Although, Nepal has a huge diversity for rice landraces but their actual potentiality hasn\u0026rsquo;t been sufficiently explored yet. In this study, 25 rice landraces were tested to evaluate the drought tolerance level in completely randomized design (CRD) in 250 ml disposable cup at seedling stage. The rice landraces were tested under 60% FC, 100% FC, and saturated condition for ten quantitative traits. The analysis of variance suggested that there is a significant difference among the landraces for different root and shoot traits as well as in different moisture conditions. Correlation analysis showed that root length has significant positive correlation with shoot length, root: shoot ratio, and fresh root weight under drought condition with 60% FC. Similarly, negative correlation was observed between root: shoot ratio and root number under drought condition. However, germination percentage didn\u0026rsquo;t show discrimination over any moisture conditions. Principal component analysis showed positive connection of root length and root: shoot ratio towards 60% FC. While strong connection was observed between shoot length, fresh weight, dry weight and root number towards 100% FC and saturated condition. It was found that, Manamurey showed better performance under all studied traits but more insightful result can be obtained by further assessing at vegetative and reproductive stage respectively.\u003c/p\u003e","manuscriptTitle":"Conferring Drought Tolerance in Rice Landraces Using Seedling Indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-29 08:52:38","doi":"10.21203/rs.3.rs-5327215/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":"ec240a78-b9f9-4a85-8dd0-ce7f4dfdc135","owner":[],"postedDate":"October 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T18:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-29 08:52:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5327215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5327215","identity":"rs-5327215","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}