Response of Cigar Tobacco Varieties to Potassium Stress and Dissecting Biochemical Determinants of Low Potassium Tolerance Mechanism

Response of Cigar Tobacco Varieties to Potassium Stress and Dissecting Biochemical Determinants of Low Potassium Tolerance Mechanism | 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 Response of Cigar Tobacco Varieties to Potassium Stress and Dissecting Biochemical Determinants of Low Potassium Tolerance Mechanism Xinying Chen, Pingping Wang, Suilong Ai, Naheeda Begum, Dezhi Kong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4432161/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 Potassium content is an important standard for measuring tobacco quality, however research on low potassium tolerance mechanism in cigar tobacco ( Nicotiana tabacum L.), which is different from regular tobacco, did not draw researchers attention. This study evaluated the tolerance to low potassium of 23 cigar varieties by studying 20 phenotypic and physiological indicators. Through principal component analysis, membership function analysis, and cluster analysis, the research comprehensively identifies the tolerance of cigar tobacco varieties to low potassium. The results indicate that the tolerance to low potassium of cigar tobacco is complex and varies with different traits and varieties. The present study identified six crucial indicators for measuring tolerance to low potassium in cigar tobacco leaves, including plant height, maximum leaf area, leaf surface area, total root absorption area, and active root absorption area. Based on the comprehensive evaluation value D of the low potassium tolerance coefficient of these six indicators, the study classified 23 cigar varieties and identified 1 low potassium-sensitive variety, 5 low potassium-low tolerance varieties, 11 low potassium-relative tolerant varieties, and 6 low potassium-tolerant varieties. The research outcomes significantly contribute to the elucidation of tolerance to low potassium in cigar tobacco, facilitating the evaluation, screening, and cultivation of cigar tobacco varieties resilient to low potassium conditions. Additionally, this laid the foundation for exploring scientific issues such as how plants can effectively utilize potassium, the mechanism of potassium ions in plants, improvement and development of cigar quality, augmentation of potassium utilization efficiency, and saving limited potassium resources to ensure long-term safety of potassium. Cigar tobacco Variety difference Potassium tolerance Comprehensive evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cigar tobacco is a type of tobacco ( Nicotiana tabacum L.), which is an annual or limited perennial herbaceous plant in the family Solanaceae and the genus Nicotiana in the order Tubiflora of the dicotyledononae(Bindler et al. 2011). Unlike regular flue-cured tobacco used in cigarette production, cigar tobacco leaves are specifically employed as raw materials for crafting cigars. They offer a more mellow and varied flavor, typically encompassing fruit, nuts, coffee, milk, and cedar aromas (Tianfei et al. 2022 ). Cigars are renowned for their profound cultural heritage and incredible taste, and its history can be traced back to ancient Maya culture. Currently, cigar tobacco is extensively cultivated globally as a primary raw material for cigars, with its main producing countries including Brazil, Cameroon, Cuba, Dominican Republic, Honduras, Indonesia, Mexico, Nicaragua, the United States, and multiple countries in China. In recent years, with the diversified development of tobacco consumption, cigar sales have skyrocketed, industrial production capacity has rapidly developed, and the demand for raw materials has become more urgent. However, there is scarce research addressing cigar tobacco cultivation techniques globally. Consequently, there is an urgent need to systematically uncover the scientific intricacies surrounding the physiological ecology, morphological anatomy, and genetic evolution of cigar tobacco, distinct from ordinary varieties. Potassium is one of the three essential elements required for plant growth, and it is also the nutrient element with the highest demand for plants. Its content in the plant body is approximately 1–5% of the total dry matter. Potassium can promote robust plant growth, tough stems and leaves, enhance the ability of plants to resist environment and diseases, and adapt to adverse external environments, and is known as a stress resistant element of plants (Adhikari et al. 2020 ). Potassium is not a structural substance that makes up the plant body, but it exists in the cell fluid through free ions or adsorption. Therefore, potassium has a high degree of mobility throughout the plant body, allowing it to regulate various metabolic reactions in the plant body (M et al. 2023). Potassium plays a crucial role in vital physiological and biochemical processes within plant body. Due to its unique physiological characteristics, potassium significantly influences growth of plants and synthesis of proteins, and sugars, and he level of essential nutrients such as fat and vitamin C therefore potassium is also known as a "quality element"(REF). Potassium salt is a mineral without substitutes, an fundamental plant nutrient, and an essential nutritional requirement for both animals and humans. Potassium fertilizer, as one of the three most important agricultural fertilizers, has a significant effect on increasing yield and income for the vast majority of crops. The global distribution of potassium resources is uneven, and production capacity is concentrated. Notably, key consumers of potassium fertilizer in Asia and Latin America have limited domestic production of potassium fertilizers. Consequently, the availability of potassium resource is a key factor in food security, and any shortage of potassium fertilizer can lead to low agricultural productivity, thereby limiting economic development. Though the global food demand is increasing yearly, the basic resource supporting food production - potassium - is becoming increasingly scarce. The potassium resources of Germany and the United States reached their peak production levels in 1980 and 1968, respectively. The predicted peak of world potassium resources in 2057 is also in sight (Al Rawashdeh 2020 ). Therefore, to ensure long-term potassium security, enhancing plant potassium utilization efficiency becomes particularly critical in environments with limited potassium resources. Over the long term, scientific issues such as how plants efficiently utilize potassium and the mechanism of potassium ion action in plants have been continuously explored by researchers. There are a large number of agricultural regions around the world lacking potassium (Cavalcante et al. 2015 ), and low potassium stress can have an impact on the morphology, physiology, and molecular level of plants. When plants lack potassium, the initial manifestation is a decrease in plant growth rate, followed by inhibition of aboveground plant growth (Henrik et al. 2022 ), slower growth and development rate, dwarfing of plants, thin and weak stems, easy lodging and bending, and weakened plant resistance, mainly decreased disease and insect resistance (Wei et al. 2022 ). Due to the high mobility of potassium ions, K + rapidly moves towards the meristem tissue during plant potassium deficiency (Shehzad et al. 2020 ), therefore symptoms of potassium deficiency in plants are generally concentrated on the old leaves, which are dark green, and yellow at the tips and edges, withered, and finally brown spots. When K + deficiency persists for a long time, young leaves also gradually turn from green to yellow (Wei et al. 2022 ), which significantly impacts crops such as cigar tobacco that harvest their leaves. Moreover, potassium is an essential nutrient for plant growth and a quality element in tobacco. In the United States, potassium content in tobacco leaves is often used to measure tobacco quality (Liao et al. 2023 ). It is generally believed that the normal value of potassium content in tobacco leaves is 2–8%. Potassium content ≥ 2% can maintain good flammability of tobacco leaves, and lower potassium content is an important factor restricting tobacco quality (Vann et al. 2013 ). Potassium efficient plants can exhibit higher nutrient utilization rates than potassium sensitive plants in the same environment (Zhenpeng et al. 2021 ). Therefore, it can be seen that evaluating the tolerance to low potassium of cigar tobacco and selecting low potassium-tolerant cigar tobacco varieties are of great significance for improving and developing cigar quality, increasing potassium utilization and saving limited potassium resources. Materials and methods Experimental Design and Plant Materials The test plants were grown in an artificial climate chamber at the School of Life Sciences, Northwest A&F University from March to May 2023, a total of 23 cigar tobacco varieties as shown in Table 1 were used as study materials. This study was a randomized complete block design with two treatments (Hoagland solution, NK and 1/4 potassium content of Hoagland solution, LK) with three replications for each treatment. Table 1 Number of tested cigar tobacco varieties Number Variety Number Variety Var 1 QX100 Var 13 2022 CX2 Var 2 QX103 Var 14 Chuan Xue No. 3 Var 3 QX104 Var 15 Hai Yan 103 Var 4 QX106 Var 16 Hai Yan 201 Var 5 QX109 Var 17 Hai Yan 204 Var 6 QX110 Var 18 Hai Yan 301 Var 7 QX200 Var 19 Hai Yan 302 Var 8 QX201 Var 20 Hai Wrapper No. 2 Var 9 QX204 Var 21 Cuba introduces No. 4 Var 10 QX206 Var 22 X Filler 2021 Var 11 QX210 Var 23 X Wrapper 2021 Var 12 GH-1 Plant Cultures Initially, seeds were surface sterilized with 5% copper sulfate soak for 15 minutes and afterward, utilize a hot water treatment and re-drying, placed the seeds in a culture dish on moist filter paper and incubated them under dark conditions at Intelligent artificial climate box (RXZ-380C-LED, Ningbo Jiangnan Instrument Factory of New Jiangnan Instrument Co., Ltd., Ningbo Science and Technology Park, China) 28°C and 85% relative humidity to induce germination. Then, the sprouted seeds were sowed in the approved sponge (V-hole, 25×25×25 mm/piece, Dongguan Aiyang Agricultural Machinery Co., Ltd., China) and placed in a black plant hydroponic box (aperture 20mm, 127×87×114 mm, Nantong Dietai Experimental Instrument Co., Ltd., China) filled with Hoagland solution to provide nutrition for the seedlings. When cigar tobacco seedling grew to three leaves stage, after 48 hours of potassium starvation with potassium free Hoagland solution, replaced with full of Hoagland solution (NK) and 1/4 potassium content of Hoagland solution (LK) separately, and cultivated in an artificial climate chamber (day and night temperature: 25/18℃, light cycle: 16/8 hours, photosynthetic photo flux density: 150µmol m − 2 s − 1 , relative humidity between 60–70%). Among them, in the LK treatment, the insufficient part of nitrogen is supplemented with NaNO 3 to maintain nitrogen concentration. During the cultivation process, an inflatable pump (ACO-318, Guangdong Hailea Group Co., Ltd., Hailia Industrial Park, Haili Road, Raoping County, Guangdong Province, China) is used to ventilate for 12 hours a day (with an intake of 75 L · min − 1 ). To maintain a relatively stable nutrient concentration in the nutrient solution, the nutrient solution is replaced every 3 days, and the pH value of each treatment nutrient solution is tested every morning and evening to ensure its stability within the range of 5.7 to 6.0. If necessary, 0.1 mmol/L NaOH or 0.1 mmol/L HCl are used for adjustment. Measuring Indicators and Methods A total of 20 indicators were investigated in this study, including morphological indicators, root activity, biomass, and indicators related to potassium utilization. To be specific, after 35 days of cultivation, use a ruler to measure plant height; measure the leaf area through photogrammetry using digital image tools(Digital image 2.11.15, China); scan the root system using a double-sided scanner EPSON 2000 and obtained root morphological parameters using the WinRHIZO Image Analysis System (WinRHIZO Pro2009b, Canada); Total root absorption area and active absorption area were also measured using the methylene blue method. Further biomass was determined by drying and weighing method. Digestion treatment was carried out using the H 2 SO 4 -H 2 O 2 external heating method, and the potassium ion concentration in the digestion solution was determined by flame photometry. The plant potassium content was calculated based on the plant sample weight and digestion solution volume, and was represented as (mg · g − 1 ) of dry matter. Calculation of Related Indicators The indicators measured in this study mainly used the following formulas: Root-shoot ratio = Root dry weight /Shoot dry weight, Root mass ratio = Root dry weight /(Shoot dry weight + Root dry weight), specific root length (m g − 1 ) = Total root length /Root dry weight, K accumulation (mg) = K content ×Shoot dry weight, K utilization efficiency (mg mg − 1 ) = Shoot dry weight /K accumulation, K utilization index (g 2 g − 1 ) = Shoot dry weight /K accumulation ×1000, Total specific surface area of root (m 2 cm − 3 ) = Total root absorption area /Root volume, Active specific surface area of root (m 2 cm − 3 ) = Active root absorption area /Root volume. The following formula was used to convert all raw indicator data measured into low potassium tolerance coefficient (LPTC): LPTC ij =LK ij /NK ij ×100%. In the formula, LPTC ij represents the LPTC of the j evaluation index for variety i, while LK ij and NK ij represent the measured values of the j evaluation index for variety i under low potassium treatment and normal potassium treatment, respectively. The membership function value U (X j ) of various comprehensive indicators for different cigar tobacco varieties was also calculated using the following formula: U (X j ) =(X j −Xmin) /(Xmax − Xmin), where X j represents j comprehensive indicators, Xmin represents the minimum value of the j comprehensive indicator, Xmax represents the maximum value of the j comprehensive indicator. The weight Wj of each comprehensive indicator was calculate using the following formula, that is, the importance of the j comprehensive indicator among all comprehensive indicators: P j represents the contribution rate of j comprehensive indicators of various cigar tobacco varieties. Calculate the comprehensive evaluation value D of tolerance to low potassium for each cigar tobacco variety using the following formula: Data Processing and Statistical Analysis Data were analyzed using IBM SPSS Statistics 20.0 software and compared the treatment means using the Least Significant Difference (LSD) test at a probability level of 0.05 And conduct principal component analysis (PCA), using the AHP membership function clustering evaluation model to perform hierarchical Euclidean distance based clustering analysis using D-values. Using Origin 2022 software, Pearson was used for correlation analysis and linear regression analysis to determine the relationship between various measurement indicators, and a correlation coefficient matrix and clustering heat map were drawn. Results Effects of Different K Levels on Growth, Physiological and Potassium Utilization Characteristics of Cigar Tobacco Seedlings A total of 20 indicators were investigated in this study, including morphological indicators, root activity, biomass, and indicators related to potassium utilization, and the results are shown in table 2. Compared to NK, plant height (PH), maximum leaf surface area (MLSA), leaf surface area (LSA), shoot dry weight (SDW), root dry weight (RDW), total root length (TRL), root surface area (RSA), root volume (RV), K content (KC), K accumulation (KA), K utilization efficiency (KUE), total root absorption area (TRAA), active root absorption area (ARAA), total specific surface area of root (TSSAOR), active specific surface area of root (ASSAOR), were found significantly different among LK. In normal K treatment, the coefficient of variation is total specific surface area of root> active specific surface area of root> root volume> root surface area> root-shoot ratio> total root length> root dry weight> root mass ratio> K utilization index> K accumulation> leaf surface area> shoot dry weight> specific root length> maximum leaf surface area> plant height> active root absorption area> total root absorption area> root average diameter> K utilization efficiency> K content, with the CV ranged from 11.35 to 116.58%. In low K treatment, the coefficient of variation is root volume> root-shoot ratio> K accumulation> root dry weight> root surface area> total specific surface area of root> active specific surface area of root> root mass ratio> total root length> K utilization index> leaf surface area> shoot dry weight> specific root length> maximum leaf surface area> plant height> total root absorption area> active root absorption area> K utilization efficiency> root average diameter> K content, with the CV ranged from 20.56 to 146.99%. It is preliminarily explained that the various indicators listed in table 2 can be used as screening indicators for the tolerance to low potassium of different cigar tobacco varieties. Table 2. Mean values and variation analysis of all indices in tested cigar tobacco seedlings in different potassium levels Trait NK LK t value Variation range Mean value CV（%） Variation range Mean value CV（%） PH (cm) 13.6-39.15 24.95 26.39 4.5-35.1 18.07 39.80 7.899*** MLSA (mm 2 ) 6262.8-24589.95 14449.97 37.32 983.3-17242.87 9318.65 43.01 7.003*** LSA (mm 2 ) 19663.4-129389 52980.52 52.64 2057.6-88488.83 32112.56 63.05 5.840*** SDW (g) 0.43-2.24 1.01 49.01 0.028-1.5 0.645 56.41 6.369*** RDW (g) 0.01-0.153 0.058 72.25 0.004-0.150 0.035 97.92 3.488** RSR 0.021-0.270 0.099 79.88 0.018-0.536 0.100 110.16 -0.037 RMR 0.020-0.211 0.083 69.71 0.018-0.349 0.081 90.4 0.086 TRL (cm) 252.64-36678.18 1309.30 77.36 84.43-2446.00 697.59 87.68 4.473*** RSA (cm 2 ) 23.50-469.84 153.02 83 7.53-322.32 81.82 97.70 4.060** RV (cm 3 ) 0.25-14.26 3.41 104.12 0.13-13.01 2.20 146.99 3.289** RAD (mm) 0.29-0.57 0.38 17.36 0.21-0.57 0.39 21.4 -0.422 SRL (m g -1 ) 21.18-351.48 188.92 44.65 14.14-281.06 174.90 44.18 0.954 KC (mg g -1 ) 51.93-87.93 77.15 11.35 36.93-77.93 57.50 20.56 11.322*** KA (mg) 28.86-276.41 84.13 63.41 1.57-225.16 42.11 99.16 7.868*** KUE (mg mg -1 ) 11.38-19.26 13.18 13.91 12.83-27.20 18.29 22.28 -8.299*** KUI (g 2 g -1 ) 4.88-40.24 14.79 66.19 0.50-39.62 13.94 78.13 0.920 TRAA (m 2 ) 0.0027-0.0075 0.0045 24.73 0.0023-0.0068 0.0041 25.73 3.158** ARAA (m 2 ) 0.0014-0.0028 0.0023 24.84 0.0012-0.0034 0.0021 25.51 3.074** TSSAOR(m 2 cm -3 ) 0.0003-0.0021 0.0038 116.58 0.0003-0.0212 0.0060 93.48 -3.232** ASSAOR (m 2 cm -3 ) 0.0002-0.0106 0.0019 116.45 0.0002-0.0107 0.0031 92.74 -3.245** NK Normal K treatment, LK Low K treatment, PH Plant height (cm), MLSA Maximum leaf surface area (mm 2 ), LSA Leaf surface area (mm 2 ), SDW Shoot dry weight (g), RDW Root dry weight (g), RSR Root-shoot ratio, RMR Root mass ratio, TRL Total root length (cm), RSA Root surface area (cm 2 ), RV Root volume (cm 3 ), RAD Root average diameter (mm), SRL Specific root length (m g -1 ), KC K content (mg g -1 ), KA K accumulation (mg), KUE K utilization efficiency (mg mg -1 ), KUI K utilization index (g 2 g -1 ), TRAA Total root absorption area (m 2 ), ARAA Active root absorption area (m 2 ), TSSAOR Total specific surface area of root (m 2 cm -3 ), ASSAOR Active specific surface area of root (m 2 cm -3 ) Note: *, ** and *** respectively indicate statistical significance in a two-tailed t-test, *indicate significant difference ( P <0.05), ** indicate very significant difference ( P <0.01), *** indicate extremely significant difference ( P <0.001) Low Potassium Tolerance Coefficient and Principal Component Analysis of Each Individual Indicator Figures 1 and figure 2 show that due to low potassium treatment, the growth of each tested varieties was inhibited to varying degrees. All varieties with only the low potassium treatment with five indicators of plant height, leaf area, root dry weight, root surface area, and potassium content were lower than the normal potassium treatment [low potassium tolerance coefficient (LPTC)<1]. Due to the differences between different cigar tobacco varieties, other indicators of some varieties under the normal potassium treatment were actually lower. Therefore, the LPTC trait is insufficient to evaluate the tolerance to low potassium of different cigar tobacco varieties. In order to supplement the deficiencies in the evaluation of the LPTC of various indicators, further principal component analysis (PCA) is employed based on LPTC. Use Origin 2022 software to conduct PCA on the LPTC of various indicators and draw PCA graphs. From figure 3, which intuitively reflects the primary and secondary effects of the LPTC of each indicator, by analyzing the feature vectors of different comprehensive indicators, it is apparent that the coefficients of PH, MLSA, LSA, SDW, KUE, RSR, RMR, TRAA, ARAA, TSSAOR and ASSAOR are relatively high. Based on the above analysis, it is further determined to use the LPTC of PH, MLSA, LSA, SDW, KUE, RSR, RMR, TRAA, ARAA, TSSAOR and ASSAOR as the screening and identification indicators for tolerance to low potassium of different cigar tobacco varieties. 23 varieties were inhibited to varying degrees due to low potassium treatment. (A) Var 1- QX100 growth, (A') Var 1- QX100 harvest; (B) Var 2- QX103 growth, (B') Var 2- QX103 harvest; (C) Var 3- QX104 growth, (C') Var 3- QX104 harvest; (D) Var 4- QX106 growth, (D') Var 4- QX106 harvest; (E )Var 5- QX109 growth, (E') Var 5- QX109 harvest; (F) Var 6- QX110 growth, (F') Var 6- QX110 harvest; (G) Var 7- QX200 growth, (G') Var 7- QX200 harvest; (H) Var 8- QX201 growth, (H') Var 8- QX201 harvest; (I) Var 9- QX204 growth, (I') Var 9- QX204 harvest; (J) Var 10- QX206 growth, (J') Var 10- QX206 harvest; (K) Var 11- QX210 growth, (K') Var 11- QX210 harvest; (L) Var 12- GH-1 growth, (L') Var 12- GH-1 harvest; (M) Var 13- 2022 CX2 growth, (M') Var 13- 2022 CX2 harvest; (N) Var 14- Chuan Xue No. 3 growth, (N') Var 14- Chuan Xue No. 3 harvest; (O) Var 15- Hai Yan 103 growth, (O') Var 15- Hai Yan 103 harvest; (P) Var 16- Hai Yan 201, (P') Var 16- Hai Yan 201 harvest; (Q) Var 17- Hai Yan 204 growth, (Q') Var 17- Hai Yan 204 harvest; (R) Var 18- Hai Yan 301 growth, R' Var 18- Hai Yan 301 harvest; S Var 19- Hai Yan 302 growth, S' Var 19- Hai Yan 302 harvest; T Var 20- Hai Wrapper No. 2 growth, T' Var 20- Hai Wrapper No. 2 harvest; U Var 21- Cuba introduces No. 4 growth, U' Var 21- QX100 harvest; V Var 22- X Filler 2021 growth, V' Var 22- X Filler 2021 harvest; W Var 23- X Wrapper 2021 growth, W' Var 23- X Wrapper 2021 harvest The main box called the interquartile range contains 50% samples in box-plot, the two sidelines mean the reasonable sample border in Tukey method, the solid line in box positions the median sample, the symbol × stands for the average, the diamond stands for the outlier Clusters indicate the LPTC of various indicators and length of the vector indicate contribution of the LPTC of various indicators Correlation Analysis and Cluster Analysis of LPTC for Main Traits Correlation analysis demonstrated that RSR, KUE and RMR have no significant correlation with other indicators, the TSSAOR and ASSAOR are significantly and negatively correlated with other indicators. However, there is a significant or highly extremely significant correlation between the LPTC of the remaining indicators, including PH, MLSA, LSA, SDW, TRAA and ARAA (Figure 4), further overlapping the information reflected in these indicators, thereby affecting the screening and identification of tolerance to low potassium in different cigar tobacco varieties. This indicates that the LPTC of PH, MLSA, LSA, SDW, TRAA and ARAA are consistent in reflecting the tolerance to low potassium ability of cigar tobacco varieties, and can be used as comprehensive identification indicators for tolerance to low potassium differentiate. Further using relative values of PH, MLSA, LSA, SDW, TRAA and ARAA consistently reflect the Euclidean squared distance metric, the 23 cigar tobacco varieties were analyzed with heatmap clustering analysis to reflect data differences by a color change gradient (Figure 5). According to the clustering results, the different cigar tobacco varieties were classified into thire categories with descending index values: the first class included one variety (Var 21- Cuba introduces No. 4); the second class included thire varieties (Var 15- Hai Yan 103, Var 9- QX204 and Var 8- QX201); the third class included nineteen varieties (Var 14- Chuan Xue No. 3, Var 11- QX210, Var 17- Hai Yan 204, Var 23- X Wrapper 2021, Var 13- 2022 CX2, Var 5- QX109, Var 4- QX106, Var 3- QX104, Var 22- X Filler 2021, Var 16- Hai Yan 201, Var 20- Hai Wrapper No. 2, Var 7- QX200, Var 12- GH-1, Var 6- QX110, Var 2- QX103, Var 18- Hai Yan 301, Var 10- QX206, Var 19- Hai Yan 302 and Var 1- QX100). Comprehensive Evaluation of Tolerance to Low Potassium and the Classification of Tolerance to Low Potassium Types in Cigar Tobacco The comprehensive identification indicators selected above is used as tolerance to low potassium screening to calculate main component scores and comprehensive scores for 23 cigar tobacco varieties (Table 3). According to the comprehensive evaluation method of membership function in fuzzy mathematics, the PC (j) values of each variety as shown in Table 4 are converted to obtain the corresponding membership function value U (j). The comprehensive evaluation value of each variety is obtained through the comprehensive evaluation formula and ranked (Table 4). The strength of the low potassium tolerance ability of 23 cigar tobacco varieties using D value to describe the stress coefficient is ranked as follows: Var 22, Var 1, Var 14, Var 20, Var 21, Var 4, Var 2, Var 10, Var 23, Var 12, Var 6, Var 16, Var 13, Var 7, Var 5, Var 3, Var 18, Var 17, Var 19, Var 11, Var 9, Var 8, Var 15. Cluster analysis was further conducted using the comprehensive evaluation value D of 23 cigar tobacco varieties as a variable. The inter group connection method in system clustering was used, and the Euclidean distance was used as the genetic distance metric. Finally, the 23 cigar leaf varieties were divided into 4 categories (Figure 6). The first category is low potassium-sensitive variety, included one variety: Var 22; The second type is a low potassium-low tolerance variety, including five varieties: Var 1, Var 14, Var 20, Var 21 and Var 4; The third type is low potassium-relative tolerant variety, included eleven varieties: Var 2, Var 10, Var 23, Var 12, Var 6, Var 16, Var 13, Var 7, Var 5, Var 18 and Var 3; The fourth category is low potassium-tolerant variety, included six varieties: Var 17, Var 19, Var 11, Var 9, Var 15 and Var 8. Table 3. Main component scores and comprehensive scores of 23 tested cigar tobacco varieties Number Main factor score Comprehensive score PC1 PC2 Var 1 -0.6918 -1.5355 -0.9342 Var 2 -0.4677 -0.0896 -0.3008 Var 3 0.3075 0.4619 0.3383 Var 4 -0.9040 0.5913 -0.3148 Var 5 0.0214 0.6424 0.2364 Var 6 -0.4151 0.3716 -0.1097 Var 7 -0.1358 0.5981 0.1303 Var 8 1.5779 0.2681 1.0029 Var 9 1.4457 0.2719 0.9280 Var 10 -0.2405 -0.6767 -0.3746 Var 11 1.1766 0.2288 0.7579 Var 12 -0.5491 0.5130 -0.1376 Var 13 -0.0988 0.0625 -0.0352 Var 14 -0.2543 -2.0976 -0.8780 Var 15 1.6247 0.3462 1.0571 Var 16 -0.4090 0.6074 -0.0240 Var 17 0.7599 0.5364 0.6250 Var 18 0.2544 0.6430 0.3708 Var 19 1.0408 0.3071 0.7070 Var 20 -1.0661 0.2290 -0.5346 Var 21 0.3560 -3.3120 -0.9497 Var 22 -2.7739 0.5365 -1.4117 Var 23 -0.5585 0.4963 -0.1489 T able 4. Membership function U(j) value and low potassium tolerance ranking of 23 tested cigar tobacco varieties Number Membership function D-value Ranking μ1 μ2 Var 1 0.4733 0.4492 0.4672 2 Var 2 0.5243 0.8148 0.5980 7 Var 3 0.7005 0.9542 0.7649 16 Var 4 0.4251 0.9869 0.5677 6 Var 5 0.6355 0.9999 0.7280 15 Var 6 0.5362 0.9314 0.6365 11 Var 7 0.5997 0.9887 0.6984 14 Var 8 0.9894 0.9052 0.9680 22 Var 9 0.9593 0.9062 0.9458 21 Var 10 0.5760 0.6663 0.5989 8 Var 11 0.8981 0.8953 0.8974 20 Var 12 0.5058 0.9671 0.6229 10 Var 13 0.6082 0.8532 0.6703 13 Var 14 0.5728 0.3071 0.5054 3 Var 15 1.0000 0.9250 0.9810 23 Var 16 0.5376 0.9910 0.6527 12 Var 17 0.8034 0.9730 0.8464 18 Var 18 0.6885 1.0000 0.7675 17 Var 19 0.8672 0.9151 0.8794 19 Var 20 0.3882 0.8953 0.5169 4 Var 21 0.7116 0.0000 0.5310 5 Var 22 0.0000 0.9731 0.2469 1 Var 23 0.5037 0.9629 0.6202 9 Discussion This study evaluated the different morphological, physiological, and biochemical characteristics of 23 cigar tobacco varieties during the seedling stage to evaluate their tolerance to low potassium. The tolerance to low potassium characteristics of cigar tobacco are a comprehensive reflection of multiple traits, influenced by both genetics and environment. To eliminate genetic differences and overlap redundancy between evaluation indicators, and ensure the accuracy of comprehensive evaluation values, this study initially transformed all observed indicator values into LPTC, and then conducted a comprehensive evaluation of each indicator through PCA and membership function method. Response of Various Traits to Low Potassium Stress In this study, plant height, maximum leaf area, leaf area, stem dry weight, root dry weight, total root length, root surface area, root volume, potassium content, potassium accumulation, potassium utilization efficiency, total root absorption area, effective root absorption area, root effective specific surface area, root total specific surface area, and root effective specific surface area were all significantly or significantly reduced to varying degrees due to low potassium treatment. These findings are consistent with previous findings, the results obtained by peanut ( Arachis hypogaea L.) (Yingyan et al. 2023 ), maize( Zea mays L.) (E et al. 2020), rice( Oryza sativa L.) (Fang et al. 2015 ) and other plants as research subjects are consistent. Potassium ions are the most abundant cations in plants, accounting for up to 2–10% of the total dry matter in plants (Wengong et al. 2019 ). It plays a role in osmotic regulation, maintaining membrane potential (Chakraborty et al. 2016 ), and regulating plant enzyme activity. When potassium supply is insufficient, it can exacerbate membrane lipid peroxidation and affect material transport. Moreover, as potassium acts as an activator of various enzymes in plants, meanwhile potassium deficiency affects the activity of various enzymes and protein synthesis, thereby affecting important physiological and biochemical processes such as photosynthesis in plants, ultimately leading to leaf aging, reduced biomass, potassium content, and other traits (Römheld and Kirkby 2010 ). Application of LPTC in Tolerance to Low Potassium Screening Further, the tolerance coefficient of indicators under normal and certain stress conditions can more accurately reflect the strength of plant tolerance to this stress than their absolute values. Therefore, various tolerance coefficients are often used in plant screening and comprehensive evaluation research. Drought tolerance coefficient (DC) is used to evaluate the drought resistance of different genotypes of millet (Jibing et al. 2021 ), soybean (Chunjuan et al. 2020 ), sugar beet (Zhenwei et al. 2023 ). Waterlogging tolerance coefficient (WTC) is used to evaluate the waterlogging tolerance of different genotypes of maize (LIU et al. 2010 ), and pumpkin (Zhenwei et al. 2023 ). And in the study of phosphorus efficiency screening, the optimal regression equation was established with the comprehensive evaluation value of low phosphorus tolerance (D value) as the dependent variable and the low phosphorus tolerance coefficient of each indicator as the independent variables, which could be used for the rapid identification of low-P tolerance (Wang et al. 2021 ). Similarly, a low nitrogen tolerance coefficient was also used in the study of nitrogen efficiency screening in plants (Jianjia et al. 2022 ; Singh and Verma 2013 ; ZHANG et al. 2018 ). This study also converted the measured indicators into LPTC, based on previous research. LPTC < 1 indicates that the indicator is inhibited by low potassium treatment. The LPTC of all 23 cigar tobacco varieties, including PH, LA, RDW, RSA, and PC, is consistent < 1, while the LPTC of other indicators shows ≥ 1 in some varieties, indicating that LK is actually similar to or even higher than NK. This is the difference between different cigar tobacco varieties. Related Indices Selection of Cigar Tobacco Tolerance to Low Potassium Tolerance At present, there is no research on the evaluation and screening of tolerance to low potassium in cigar tobacco. Therefore, selecting appropriate evaluation indicators is very important. In order to accurately select screening indicators of significant importance, this study extensively measured 20 related traits of plant growth, physiology, and potassium utilization. The CV range NL of the 20 traits measured in this study is from 11.35 to 116.58%, and LK is from 20.56 to 146.99%, all of which belong to the range of 10–100% or > 100%. This indicates that the measured traits have moderate or strong variability among different varieties, indicating that all tested indicators can be used as screening indicators for tolerance to low potassium in different cigar tobacco varieties. The results indicate a significant or extremely significant correlation between the LPTC of PH, MLSA, LSA, SDW, TRAA, and ARAA, which overlap the information reflected in these indicators, thereby affecting the screening and identification of tolerance to low potassium in different cigar tobacco varieties. This indicates that the LPTC of these indicators consistently reflect the tolerance to low potassium ability of cigar tobacco varieties and can be determined as comprehensive identification indicators for tolerance to low potassium screening. Dun Xiaoling et al. studied the potassium utilization efficiency of rapeseed( Brassica napus L.) using root morphology and biomass as criteria (Dun et al. 2019 ). Plant height is also commonly used in the screening of potassium efficient rice varieties (Fang et al. 2015 ; Tingchang et al. 2023 ). The Total root absorption area and Active root absorption area are also reliable indicators for evaluating tolerance to low potassium in this study, which is a new approach that can be referenced for studying tolerance to low potassium. Comprehensive Evaluation of Tolerance to Low Potassium At present, the use of multiple indicators and methods such as PCA and membership function analysis in the comprehensive evaluation of plant resistance or efficiency has been widely applied in plant resistance or efficiency research (Niu et al. 2023 ; Rasel et al. 2021 ; Wang et al. 2021 ; Wang et al. 2022 ). This study transformed the 20 measured indicators into LPTC, representing the degree to which each indicator was affected by potassium factors. Complex traits were simplified to more accurately reflect the potassium utilization efficiency of different varieties of cigar tobacco. PCA is the most commonly used multivariate method that allows for the comparison of different genotypes, grouping similar categories of genotypes into one group (Tuhina-Khatun et al. 2015 ), and classifying genotypes from different biological backgrounds (Kim et al. 2013 ). In botany research, PCA can also be used to explore the relationship between different physiological and biochemical parameters of plants under stress conditions (Department Of Biology et al. 2016; Su et al. 2016 ), which may be one of the most convenient and effective methods for researchers to better understand plant physiological and biochemical changes that occur in issues such as tolerance to low potassium in cigar tobacco varieties. By using PCA and membership function method, the comprehensive evaluation value D value of low potassium tolerance of different cigar tobacco varieties was calculated. This study introduced D value to evaluate the tolerance to potassium stress, the higher the D value, the stronger the tolerance of the cigar tobacco variety to low potassium stress. Then cluster using the D value, and use the Euclidean distance method to use the comprehensive evaluation value D value of cigar tobacco's low potassium tolerance as a variable for cluster analysis. The cluster analysis method based on Euclidean distance is also an exoteric scheme frequently applied in botany, which can arrange genotype groups based on the similarity of the research object (Muti et al. 2021 ; Pongprayoon et al. 2019 ). This method can reduce the complexity of the studied traits and improve the accuracy of stress assessment. In addition, this method can not only quickly screen low potassium tolerance with potential tolerance, but also include as many related traits as possible to avoid the one-sidedness of a single parameter. Conclusion This study employed a conversion of phenotypic and physiological indicators into a low potassium tolerance index, assessing the tolerance to low potassium of 23 cigar tobacco varieties via their comprehensive evaluation value D value. Based on PCA and membership function analysis, it is divided into four levels: Low potassium-sensitive variety, low potassium-low tolerance variety, low potassium-relative tolerant variety, and low potassium-tolerant variety. Finally, six varieties, Hai Yan 204, Hai Yan 302, QX210, QX204, Hai Yan 103, and QX201, were identified as low potassium-tolerant varieties. Eleven varieties, QX103, QX206, X Wrapper 2021, GH-1, QX110, Hai Yan 201, 2022 CX2, QX200, QX109, Hai Yan 301, and QX104, were identified as low potassium-relative tolerant varieties, which can be used to develop potassium efficient cigar tobacco varieties in the future. In addition, we have identified six indicators to measure the tolerance to low potassium of cigar tobacco, including plant height, maximum leaf area, leaf surface area, total root absorption area, and active root absorption area. Declarations Ethics declarations Conflict of interest : X. Chen, P. Wang, S. Ai, N. Begum, D. Kong, H. A. Nanaei, M. Ahmad, S. Jabeen, L. Zhang declare that they have no competing interests. Funding This study was supported by the Major Science and Technology Project of Shaanxi Provincial Company of China National Tobacco Corporation, Research and Development of Key Production Technologies for High Quality Cigar Leaves in Shaanxi Province (SXYC-2022-KJ-01) Author Contribution Authors ContributionsX. C. and L.Z. conceived and designed the experiments. X. C. and D. K. performed the experiments. X. C. analyzed the data and wrote the paper. P.W., S. A., N. B., H. A. N., M. A. and S. J. revised the manuscript. All authors read and approved the final manuscript. Acknowledgements We acknowledge the Artificial Climate Chamber and Large Instrument Sharing Platform of the School of Life Sciences at Northwest A&F University for their support, as well as to the Shaanxi Provincial Company of China Tobacco Corporation for their funding (SXYC-2022-KJ-01). 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FRONT PLANT SCI 7: 1133. http://doi.org/10.3389/fpls.2016.01133 Tianfei Z, Qianying Z, Pinhe L, Xinying W, Yi L, Zhen Y, Dongliang L, Juan Z, Du Guocheng (2022) Analysis of Microbial Community, Volatile Flavor Compounds, and Flavor of Cigar Tobacco Leaves From Different Regions. FRONT MICROBIOL 13: 907270. Tingchang L, Liangli B, Lifang H, Donghai M (2023) NP and 9311 are excellent population parents for screening QTLs of potassium-efficient rice. PLOS ONE 18: e284510. http://doi.org/10.1371/JOURNAL.PONE.0284510 Tuhina-Khatun M, Hanafi MM, Rafii Yusop M, Wong MY, Salleh FM, Ferdous J, D Amelio S (2015) Genetic Variation, Heritability, and Diversity Analysis of Upland Rice ( Oryza sativa L.) Genotypes Based on Quantitative Traits. BIOMED RES INT 2015: 290861. http://doi.org/10.1155/2015/290861 Vann MC, Fisher LR, Jordan DL, Smith WD, Hardy DH, Stewart AM (2013) Potassium Rate and Application Effect on Flue‐Cured Tobacco. AGRON J 105: 304-310. 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HORTICULTURAE 8: 660. http://doi.org/10.3390/HORTICULTURAE8070660 Wengong H, Shuquan Z, Guangwen W, Ying Y, Chuanying R, Qinghua K, Yan L, Chunbo L, Liguo Z, Yaguang Z (2019) Transcriptome profiling of potassium starvation responsiveness in flax (Linum usitatissimum L.). PAK J BOT 51. Yingyan L, Penghao S, Yuanchun Z, Dongying Z, Qiqi D, Peiyan J, Zhenhua L, Xinhua Z, Haiqiu Y (2023) Physiological Mechanism of Photosynthetic, Nutrient, and Yield Responses of Peanut Cultivars with Different Tolerances under Low K Stress. Agronomy 13: 185.http://doi.org/10.3390/AGRONOMY13010185 Zhang H, Fu X, Wang X, Gui H, Dong Q, Pang N, Wang Z, Zhang X, Song M (2018) Identification and screening of nitrogen-efficient cotton genotypes under low and normal nitrogen environments at the seedling stage. J. Cotton Res. 1, 6. http://doi.org/10.1186/s42397-018-0006-x Zhenpeng D, Jin Y, Yuanya C, Haohao H, Xun L, Xiaoping Y, Jichun W, Changwen L (2021) Screening high potassium efficiency potato genotypes and physiological responses at different potassium levels. Not. Bot. Horti Agrobot. Cluj-Na. 49. Zhenwei L, Dandan Q, Zhenyu L, Pengwei W, Li S, Xinzheng L (2023) Evaluation of waterlogging tolerance and responses of protective enzymes to waterlogging stress in pumpkin. PEERJ 11: e15177. http://doi.org/10.7717/PEERJ.15177 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4432161","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312675678,"identity":"49b0e354-613b-4180-a40f-3c778ee7778d","order_by":0,"name":"Xinying Chen","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Chen","suffix":""},{"id":312675679,"identity":"aef6e727-a120-4e50-a4f6-2aa9a1d2ae67","order_by":1,"name":"Pingping Wang","email":"","orcid":"","institution":"Shaanxi Tobacco Scientific 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15:45:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4432161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4432161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58279853,"identity":"997c1a32-4ce2-4e47-90e3-9123f4fbe98c","added_by":"auto","created_at":"2024-06-13 10:37:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2442005,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhotos of Growth and Harvest of Different Varieties of Cigar Tobacco\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/d95552b287aa8d8cd067b4fc.png"},{"id":58279495,"identity":"2281e4d9-bb02-42b7-b0ca-c5bb2340486d","added_by":"auto","created_at":"2024-06-13 10:29:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLPTC of Single Index of Tested Cigar Tobacco Varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main box called the interquartile range contains 50% samples in box-plot, the two sidelines mean the reasonable sample border in Tukey method, the solid line in box positions the median sample, the symbol × stands for the average, the diamond stands for the outlier\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/c89971df1faaa0c76233d9c6.png"},{"id":58279499,"identity":"11c5872a-57e3-4461-9418-4095e5b82b2d","added_by":"auto","created_at":"2024-06-13 10:29:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA of LPTC of Tested Cigar Tobacco Varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClusters indicate the LPTC of various indicators and length of the vector indicate contribution of the LPTC of various indicators\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/b808e4effa913f174a451967.png"},{"id":58279852,"identity":"ff3499f5-07a9-4468-8315-0a0568f965a8","added_by":"auto","created_at":"2024-06-13 10:37:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Plot of LPTC of Main Characters of Tested Cigar Tobacco Varieties\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/228f14e97cd1614598b41e10.png"},{"id":58279497,"identity":"2e98a0f7-462a-4a90-990d-3987367966eb","added_by":"auto","created_at":"2024-06-13 10:29:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClustering Diagram of LPTC of Main Characters of Tested Cigar Tobacco Varieties\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/1ff6fe7e027f4560795033f6.png"},{"id":58279500,"identity":"bb6fee91-8feb-4a2d-93ca-36d0e6456835","added_by":"auto","created_at":"2024-06-13 10:29:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":40444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCluster Tree of 23 Tested Cigar Tobacco Varieties\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/df796d7e8a1209240f1d903b.png"},{"id":58280418,"identity":"7dec4413-811f-43a9-ba5b-6262135e9e59","added_by":"auto","created_at":"2024-06-13 10:45:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4725436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4432161/v1/8a203970-53c9-4982-9512-f3c989b5f6ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Response of Cigar Tobacco Varieties to Potassium Stress and Dissecting Biochemical Determinants of Low Potassium Tolerance Mechanism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCigar tobacco is a type of tobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e L.), which is an annual or limited perennial herbaceous plant in the family Solanaceae and the genus Nicotiana in the order Tubiflora of the dicotyledononae(Bindler et al. 2011). Unlike regular flue-cured tobacco used in cigarette production, cigar tobacco leaves are specifically employed as raw materials for crafting cigars. They offer a more mellow and varied flavor, typically encompassing fruit, nuts, coffee, milk, and cedar aromas (Tianfei et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cigars are renowned for their profound cultural heritage and incredible taste, and its history can be traced back to ancient Maya culture. Currently, cigar tobacco is extensively cultivated globally as a primary raw material for cigars, with its main producing countries including Brazil, Cameroon, Cuba, Dominican Republic, Honduras, Indonesia, Mexico, Nicaragua, the United States, and multiple countries in China. In recent years, with the diversified development of tobacco consumption, cigar sales have skyrocketed, industrial production capacity has rapidly developed, and the demand for raw materials has become more urgent. However, there is scarce research addressing cigar tobacco cultivation techniques globally. Consequently, there is an urgent need to systematically uncover the scientific intricacies surrounding the physiological ecology, morphological anatomy, and genetic evolution of cigar tobacco, distinct from ordinary varieties.\u003c/p\u003e \u003cp\u003ePotassium is one of the three essential elements required for plant growth, and it is also the nutrient element with the highest demand for plants. Its content in the plant body is approximately 1\u0026ndash;5% of the total dry matter. Potassium can promote robust plant growth, tough stems and leaves, enhance the ability of plants to resist environment and diseases, and adapt to adverse external environments, and is known as a stress resistant element of plants (Adhikari et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Potassium is not a structural substance that makes up the plant body, but it exists in the cell fluid through free ions or adsorption. Therefore, potassium has a high degree of mobility throughout the plant body, allowing it to regulate various metabolic reactions in the plant body (M et al. 2023).\u003c/p\u003e \u003cp\u003ePotassium plays a crucial role in vital physiological and biochemical processes within plant body. Due to its unique physiological characteristics, potassium significantly influences growth of plants and synthesis of proteins, and sugars, and he level of essential nutrients such as fat and vitamin C therefore potassium is also known as a \"quality element\"(REF). Potassium salt is a mineral without substitutes, an fundamental plant nutrient, and an essential nutritional requirement for both animals and humans. Potassium fertilizer, as one of the three most important agricultural fertilizers, has a significant effect on increasing yield and income for the vast majority of crops. The global distribution of potassium resources is uneven, and production capacity is concentrated. Notably, key consumers of potassium fertilizer in Asia and Latin America have limited domestic production of potassium fertilizers. Consequently, the availability of potassium resource is a key factor in food security, and any shortage of potassium fertilizer can lead to low agricultural productivity, thereby limiting economic development. Though the global food demand is increasing yearly, the basic resource supporting food production - potassium - is becoming increasingly scarce. The potassium resources of Germany and the United States reached their peak production levels in 1980 and 1968, respectively. The predicted peak of world potassium resources in 2057 is also in sight (Al Rawashdeh \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, to ensure long-term potassium security, enhancing plant potassium utilization efficiency becomes particularly critical in environments with limited potassium resources. Over the long term, scientific issues such as how plants efficiently utilize potassium and the mechanism of potassium ion action in plants have been continuously explored by researchers.\u003c/p\u003e \u003cp\u003eThere are a large number of agricultural regions around the world lacking potassium (Cavalcante et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and low potassium stress can have an impact on the morphology, physiology, and molecular level of plants. When plants lack potassium, the initial manifestation is a decrease in plant growth rate, followed by inhibition of aboveground plant growth (Henrik et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), slower growth and development rate, dwarfing of plants, thin and weak stems, easy lodging and bending, and weakened plant resistance, mainly decreased disease and insect resistance (Wei et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to the high mobility of potassium ions, K\u003csup\u003e+\u003c/sup\u003erapidly moves towards the meristem tissue during plant potassium deficiency (Shehzad et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), therefore symptoms of potassium deficiency in plants are generally concentrated on the old leaves, which are dark green, and yellow at the tips and edges, withered, and finally brown spots. When K\u0026thinsp;+\u0026thinsp;deficiency persists for a long time, young leaves also gradually turn from green to yellow (Wei et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which significantly impacts crops such as cigar tobacco that harvest their leaves. Moreover, potassium is an essential nutrient for plant growth and a quality element in tobacco. In the United States, potassium content in tobacco leaves is often used to measure tobacco quality (Liao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is generally believed that the normal value of potassium content in tobacco leaves is 2\u0026ndash;8%. Potassium content\u0026thinsp;\u0026ge;\u0026thinsp;2% can maintain good flammability of tobacco leaves, and lower potassium content is an important factor restricting tobacco quality (Vann et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Potassium efficient plants can exhibit higher nutrient utilization rates than potassium sensitive plants in the same environment (Zhenpeng et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, it can be seen that evaluating the tolerance to low potassium of cigar tobacco and selecting low potassium-tolerant cigar tobacco varieties are of great significance for improving and developing cigar quality, increasing potassium utilization and saving limited potassium resources.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eExperimental Design and Plant Materials\u003c/h2\u003e\n \u003cp\u003eThe test plants were grown in an artificial climate chamber at the School of Life Sciences, Northwest A\u0026amp;F University from March to May 2023, a total of 23 cigar tobacco varieties as shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e were used as study materials. This study was a randomized complete block design with two treatments (Hoagland solution, NK and 1/4 potassium content of Hoagland solution, LK) with three replications for each treatment.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of tested cigar tobacco varieties\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022 CX2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChuan Xue No. 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Yan 103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Yan 201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Yan 204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Yan 301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Yan 302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHai Wrapper No. 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCuba introduces No. 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX Filler 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQX210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eX Wrapper 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVar 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGH-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePlant Cultures\u003c/h2\u003e\n \u003cp\u003eInitially, seeds were surface sterilized with 5% copper sulfate soak for 15 minutes and afterward, utilize a hot water treatment and re-drying, placed the seeds in a culture dish on moist filter paper and incubated them under dark conditions at Intelligent artificial climate box (RXZ-380C-LED, Ningbo Jiangnan Instrument Factory of New Jiangnan Instrument Co., Ltd., Ningbo Science and Technology Park, China) 28°C and 85% relative humidity to induce germination. Then, the sprouted seeds were sowed in the approved sponge (V-hole, 25×25×25 mm/piece, Dongguan Aiyang Agricultural Machinery Co., Ltd., China) and placed in a black plant hydroponic box (aperture 20mm, 127×87×114 mm, Nantong Dietai Experimental Instrument Co., Ltd., China) filled with Hoagland solution to provide nutrition for the seedlings. When cigar tobacco seedling grew to three leaves stage, after 48 hours of potassium starvation with potassium free Hoagland solution, replaced with full of Hoagland solution (NK) and 1/4 potassium content of Hoagland solution (LK) separately, and cultivated in an artificial climate chamber (day and night temperature: 25/18℃, light cycle: 16/8 hours, photosynthetic photo flux density: 150µmol m\u003csup\u003e− 2\u003c/sup\u003e s\u003csup\u003e− 1\u003c/sup\u003e, relative humidity between 60–70%). Among them, in the LK treatment, the insufficient part of nitrogen is supplemented with NaNO\u003csub\u003e3\u003c/sub\u003e to maintain nitrogen concentration. During the cultivation process, an inflatable pump (ACO-318, Guangdong Hailea Group Co., Ltd., Hailia Industrial Park, Haili Road, Raoping County, Guangdong Province, China) is used to ventilate for 12 hours a day (with an intake of 75 L · min\u003csup\u003e− 1\u003c/sup\u003e). To maintain a relatively stable nutrient concentration in the nutrient solution, the nutrient solution is replaced every 3 days, and the pH value of each treatment nutrient solution is tested every morning and evening to ensure its stability within the range of 5.7 to 6.0. If necessary, 0.1 mmol/L NaOH or 0.1 mmol/L HCl are used for adjustment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasuring Indicators and Methods\u003c/h2\u003e\n \u003cp\u003eA total of 20 indicators were investigated in this study, including morphological indicators, root activity, biomass, and indicators related to potassium utilization. To be specific, after 35 days of cultivation, use a ruler to measure plant height; measure the leaf area through photogrammetry using digital image tools(Digital image 2.11.15, China); scan the root system using a double-sided scanner EPSON 2000 and obtained root morphological parameters using the WinRHIZO Image Analysis System (WinRHIZO Pro2009b, Canada); Total root absorption area and active absorption area were also measured using the methylene blue method. Further biomass was determined by drying and weighing method. Digestion treatment was carried out using the H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e-H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e external heating method, and the potassium ion concentration in the digestion solution was determined by flame photometry. The plant potassium content was calculated based on the plant sample weight and digestion solution volume, and was represented as (mg · g\u003csup\u003e− 1\u003c/sup\u003e) of dry matter.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eCalculation of Related Indicators\u003c/h2\u003e\n \u003cp\u003eThe indicators measured in this study mainly used the following formulas: Root-shoot ratio = Root dry weight /Shoot dry weight, Root mass ratio = Root dry weight /(Shoot dry weight + Root dry weight), specific root length (m g\u003csup\u003e− 1\u003c/sup\u003e) = Total root length /Root dry weight, K accumulation (mg) = K content ×Shoot dry weight, K utilization efficiency (mg mg\u003csup\u003e− 1\u003c/sup\u003e) = Shoot dry weight /K accumulation, K utilization index (g\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e− 1\u003c/sup\u003e) = Shoot dry weight /K accumulation ×1000, Total specific surface area of root (m\u003csup\u003e2\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e) = Total root absorption area /Root volume, Active specific surface area of root (m\u003csup\u003e2\u003c/sup\u003e cm\u003csup\u003e− 3\u003c/sup\u003e) = Active root absorption area /Root volume.\u003c/p\u003e\n \u003cp\u003eThe following formula was used to convert all raw indicator data measured into low potassium tolerance coefficient (LPTC): LPTC\u003csub\u003eij\u003c/sub\u003e =LK\u003csub\u003eij\u003c/sub\u003e /NK\u003csub\u003eij\u003c/sub\u003e ×100%. In the formula, LPTC\u003csub\u003eij\u003c/sub\u003e represents the LPTC of the j evaluation index for variety i, while LK\u003csub\u003eij\u003c/sub\u003e and NK\u003csub\u003eij\u003c/sub\u003e represent the measured values of the j evaluation index for variety i under low potassium treatment and normal potassium treatment, respectively.\u003c/p\u003e\n \u003cp\u003eThe membership function value U (X\u003csub\u003ej\u003c/sub\u003e) of various comprehensive indicators for different cigar tobacco varieties was also calculated using the following formula: U (X\u003csub\u003ej\u003c/sub\u003e) =(X\u003csub\u003ej\u003c/sub\u003e−Xmin) /(Xmax − Xmin), where X\u003csub\u003ej\u003c/sub\u003e represents j comprehensive indicators, Xmin represents the minimum value of the j comprehensive indicator, Xmax represents the maximum value of the j comprehensive indicator. The weight Wj of each comprehensive indicator was calculate using the following formula, that is, the importance of the j comprehensive indicator among all comprehensive indicators:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"49\" width=\"166\"\u003e\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003ej\u003c/sub\u003e represents the contribution rate of j comprehensive indicators of various cigar tobacco varieties. Calculate the comprehensive evaluation value D of tolerance to low potassium for each cigar tobacco variety using the following formula:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"239\" height=\"57\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eData Processing and Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eData were analyzed using IBM SPSS Statistics 20.0 software and compared the treatment means using the Least Significant Difference (LSD) test at a probability level of 0.05 And conduct principal component analysis (PCA), using the AHP membership function clustering evaluation model to perform hierarchical Euclidean distance based clustering analysis using D-values. Using Origin 2022 software, Pearson was used for correlation analysis and linear regression analysis to determine the relationship between various measurement indicators, and a correlation coefficient matrix and clustering heat map were drawn.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEffects of Different K Levels on Growth, Physiological and Potassium Utilization Characteristics of Cigar Tobacco Seedlings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 20 indicators were investigated in this study, including morphological indicators, root activity, biomass, and indicators related to potassium utilization, and the results are shown in table 2. Compared to NK, plant height (PH), maximum leaf surface area (MLSA), leaf surface area (LSA), shoot dry weight (SDW), root dry weight (RDW), total root length (TRL), root surface area (RSA), root volume (RV), K content (KC), K accumulation (KA), K utilization efficiency (KUE), total root absorption area (TRAA), active root absorption area (ARAA), total specific surface area of root (TSSAOR), active specific surface area of root (ASSAOR), were found significantly different among LK. In normal K treatment, the coefficient of variation is total specific surface area of root\u0026gt; active specific surface area of root\u0026gt; root volume\u0026gt; root surface area\u0026gt; root-shoot ratio\u0026gt; total root length\u0026gt; root dry weight\u0026gt; root mass ratio\u0026gt; K utilization index\u0026gt; K accumulation\u0026gt; leaf surface area\u0026gt; shoot dry weight\u0026gt; specific root length\u0026gt; maximum leaf surface area\u0026gt; plant height\u0026gt; active root absorption area\u0026gt; total root absorption area\u0026gt; root average diameter\u0026gt; K utilization efficiency\u0026gt; K content, with the CV ranged from 11.35 to 116.58%. In low K treatment, the coefficient of variation is root volume\u0026gt; root-shoot ratio\u0026gt; K accumulation\u0026gt; root dry weight\u0026gt; root surface area\u0026gt; total specific surface area of root\u0026gt; active specific surface area of root\u0026gt; root mass ratio\u0026gt; total root length\u0026gt; K utilization index\u0026gt; leaf surface area\u0026gt; shoot dry weight\u0026gt; specific root length\u0026gt; maximum leaf surface area\u0026gt; plant height\u0026gt; total root absorption area\u0026gt; active root absorption area\u0026gt; K utilization efficiency\u0026gt; root average diameter\u0026gt; K content, with the CV ranged from 20.56 to 146.99%. It is preliminarily explained that the various indicators listed in table 2 can be used as screening indicators for the tolerance to low potassium of different cigar tobacco varieties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Mean values and variation analysis of all indices in tested cigar tobacco seedlings in different potassium levels\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"121%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.346938775510203%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003eVariation range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eMean value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\" valign=\"top\"\u003e\n \u003cp\u003eCV（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.058823529411764%\" valign=\"top\"\u003e\n \u003cp\u003eVariation range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.176470588235293%\" valign=\"top\"\u003e\n \u003cp\u003eMean value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.764705882352942%\" valign=\"top\"\u003e\n \u003cp\u003eCV（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003ePH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e13.6-39.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e24.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e26.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e4.5-35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e39.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7.899***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eMLSA (mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e6262.8-24589.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e14449.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e37.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e983.3-17242.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e9318.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e43.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eLSA (mm\u003csup\u003e2\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e19663.4-129389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e52980.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e52.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e2057.6-88488.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e32112.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e63.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e5.840***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eSDW (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.43-2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e49.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.028-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e56.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e6.369***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eRDW (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.01-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e72.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.004-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e97.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3.488**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eRSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.021-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e79.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.018-0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e110.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.020-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e69.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.018-0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e90.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eTRL (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e252.64-36678.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e1309.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e77.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e84.43-2446.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e697.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e87.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e4.473***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;RSA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e23.50-469.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e153.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e7.53-322.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e81.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e97.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e4.060**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;RV (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.25-14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e104.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.13-13.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e146.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3.289**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eRAD (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.29-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.21-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e-0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eSRL (m g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e21.18-351.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e188.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e44.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e14.14-281.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e174.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e44.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eKC (mg g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e51.93-87.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e77.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e11.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e36.93-77.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e57.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e20.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e11.322***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;KA (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e28.86-276.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e84.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e63.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e1.57-225.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e42.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e99.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e7.868***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;KUE (mg mg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e11.38-19.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e12.83-27.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e18.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e-8.299***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eKUI (g\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e4.88-40.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e14.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e66.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.50-39.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e13.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e78.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eTRAA (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0027-0.0075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e24.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023-0.0068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e25.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3.158**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eARAA (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0014-0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e24.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0012-0.0034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e25.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e3.074**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eTSSAOR(m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ecm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0003-0.0021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e116.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0003-0.0212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e93.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e-3.232**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\" valign=\"top\"\u003e\n \u003cp\u003eASSAOR (m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ecm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002-0.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e116.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0.0002-0.0107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e0.0031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e92.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\" valign=\"top\"\u003e\n \u003cp\u003e-3.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eNK\u003c/em\u003e Normal K treatment, \u003cem\u003eLK\u003c/em\u003e Low K treatment, \u003cem\u003ePH\u003c/em\u003e Plant height (cm), \u003cem\u003eMLSA\u003c/em\u003e Maximum leaf surface area (mm\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eLSA\u003c/em\u003e Leaf surface area (mm\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eSDW\u003c/em\u003e Shoot dry weight (g), \u003cem\u003eRDW\u003c/em\u003e Root dry weight (g), \u003cem\u003eRSR\u003c/em\u003e Root-shoot ratio, \u003cem\u003eRMR\u003c/em\u003e Root mass ratio, \u003cem\u003eTRL\u003c/em\u003e Total root length (cm), \u003cem\u003eRSA\u003c/em\u003e Root surface area (cm\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eRV\u003c/em\u003e Root volume (cm\u003csup\u003e3\u003c/sup\u003e), \u003cem\u003eRAD\u003c/em\u003e Root average diameter (mm), \u003cem\u003eSRL\u003c/em\u003e Specific root length (m g\u003csup\u003e-1\u003c/sup\u003e), \u003cem\u003eKC\u003c/em\u003e K content (mg g\u003csup\u003e-1\u003c/sup\u003e), \u003cem\u003eKA\u003c/em\u003e K accumulation (mg), \u003cem\u003eKUE\u003c/em\u003e K utilization efficiency (mg mg\u003csup\u003e-1\u003c/sup\u003e), \u003cem\u003eKUI\u003c/em\u003e K utilization index (g\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e), \u003cem\u003eTRAA\u003c/em\u003e Total root absorption area (m\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eARAA\u003c/em\u003e Active root absorption area (m\u003csup\u003e2\u003c/sup\u003e), \u003cem\u003eTSSAOR\u003c/em\u003e Total specific surface area of root (m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ecm\u003csup\u003e-3\u003c/sup\u003e), \u003cem\u003eASSAOR\u003c/em\u003e Active specific surface area of root (m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ecm\u003csup\u003e-3\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eNote: *, ** and *** respectively indicate statistical significance in a two-tailed t-test, *indicate significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), ** indicate very significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), *** indicate extremely significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow Potassium Tolerance Coefficient and Principal Component Analysis of Each Individual Indicator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures 1 and figure 2 show that due to low potassium treatment, the growth of each tested varieties was inhibited to varying degrees. All varieties with only the low potassium treatment with five indicators of plant height, leaf area, root dry weight, root surface area, and potassium content were lower than the normal potassium treatment [low potassium tolerance coefficient (LPTC)\u0026lt;1]. Due to the differences between different cigar tobacco varieties, other indicators of some varieties under the normal potassium treatment were actually lower. Therefore, the LPTC trait is insufficient to evaluate the tolerance to low potassium of different cigar tobacco varieties. In order to supplement the deficiencies in the evaluation of the LPTC of various indicators, further principal component analysis (PCA) is employed based on LPTC.\u003c/p\u003e\n\u003cp\u003eUse Origin 2022 software to conduct PCA on the LPTC of various indicators and draw PCA graphs. From figure 3, which intuitively reflects the primary and secondary effects of the LPTC of each indicator, by analyzing the feature vectors of different comprehensive indicators, it is apparent that the coefficients of PH, MLSA, LSA, SDW, KUE, RSR, RMR, TRAA, ARAA, TSSAOR and ASSAOR are relatively high. Based on the above analysis, it is further determined to use the LPTC of PH, MLSA, LSA, SDW, KUE, RSR, RMR, TRAA, ARAA, TSSAOR and ASSAOR as the screening and identification indicators for tolerance to low potassium of different cigar tobacco varieties.\u003c/p\u003e\n\u003cp\u003e23 varieties were inhibited to varying degrees due to low potassium treatment. (A) Var 1- QX100 growth, (A\u0026apos;) Var 1- QX100 harvest; (B) Var 2- QX103 growth, (B\u0026apos;) Var 2- QX103 harvest; (C) Var 3- QX104 growth, (C\u0026apos;) Var 3- QX104 harvest; (D) Var 4- QX106 growth, (D\u0026apos;) Var 4- QX106 harvest; (E )Var 5- QX109 growth, (E\u0026apos;) Var 5- QX109 harvest; (F) Var 6- QX110 growth, (F\u0026apos;) Var 6- QX110 harvest; (G) Var 7- QX200 growth, (G\u0026apos;) Var 7- QX200 harvest; (H) Var 8- QX201 growth, (H\u0026apos;) Var 8- QX201 harvest; (I) Var 9- QX204 growth, (I\u0026apos;) Var 9- QX204 harvest; (J) Var 10- QX206 growth, (J\u0026apos;) Var 10- QX206 harvest; (K) Var 11- QX210 growth, (K\u0026apos;) Var 11- QX210 harvest; (L) Var 12- GH-1 growth, (L\u0026apos;) Var 12- GH-1 harvest; (M) Var 13- 2022 CX2 growth, (M\u0026apos;) Var 13- 2022 CX2 harvest; (N) Var 14- Chuan Xue No. 3 growth, (N\u0026apos;) Var 14- Chuan Xue No. 3 harvest; (O) Var 15- Hai Yan 103 growth, (O\u0026apos;) Var 15- Hai Yan 103 harvest; (P) Var 16- Hai Yan 201, (P\u0026apos;) Var 16- Hai Yan 201 harvest; (Q) Var 17- Hai Yan 204 growth, (Q\u0026apos;) Var 17- Hai Yan 204 harvest; (R) Var 18- Hai Yan 301 growth, R\u0026apos; Var 18- Hai Yan 301 harvest; S Var 19- Hai Yan 302 growth, S\u0026apos; Var 19- Hai Yan 302 harvest; T Var 20- Hai Wrapper No. 2 growth, T\u0026apos; Var 20- Hai Wrapper No. 2 harvest; U Var 21- Cuba introduces No. 4 growth, U\u0026apos; Var 21- QX100 harvest; V Var 22- X Filler 2021 growth, V\u0026apos; Var 22- X Filler 2021 harvest; W Var 23- X Wrapper 2021 growth, W\u0026apos; Var 23- X Wrapper 2021 harvest\u003c/p\u003e\n\u003cp\u003eThe main box called the interquartile range contains 50% samples in box-plot, the two sidelines mean the reasonable sample border in Tukey method, the solid line in box positions the median sample, the symbol \u0026times; stands for the average, the diamond stands for the outlier\u003c/p\u003e\n\u003cp\u003eClusters indicate the LPTC of various indicators and length of the vector indicate contribution of the LPTC of various indicators\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis and Cluster Analysis of LPTC for Main Traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analysis demonstrated that RSR, KUE and RMR have no significant correlation with other indicators, the TSSAOR and ASSAOR are significantly and negatively correlated with other indicators. However, there is a significant or highly extremely significant correlation between the LPTC of the remaining indicators, including PH, MLSA, LSA, SDW, TRAA and ARAA (Figure\u0026nbsp;4), further overlapping the information reflected in these indicators, thereby affecting the screening and identification of tolerance to low potassium in different cigar tobacco varieties. This indicates that the LPTC of PH, MLSA, LSA, SDW, TRAA and ARAA are consistent in reflecting the tolerance to low potassium ability of cigar tobacco varieties, and can be used as comprehensive identification indicators for tolerance to low potassium differentiate.\u003c/p\u003e\n\u003cp\u003eFurther using relative values of PH, MLSA, LSA, SDW, TRAA and ARAA consistently reflect the Euclidean squared distance metric, the\u0026nbsp;23 cigar tobacco varieties\u0026nbsp;were analyzed with heatmap clustering analysis to reflect data differences by a color change gradient (Figure\u0026nbsp;5). According to the clustering results, the different cigar\u0026nbsp;tobacco varieties\u0026nbsp;were classified into thire categories with descending index values: the first class included one variety (Var 21-\u0026nbsp;Cuba introduces No. 4); the second class included thire varieties (Var 15-\u0026nbsp;Hai Yan 103,\u0026nbsp;Var 9- QX204 and Var 8- QX201); the third class included nineteen varieties (Var 14-\u0026nbsp;Chuan Xue No. 3, Var 11- QX210, Var 17-\u0026nbsp;Hai Yan\u0026nbsp;204, Var 23-\u0026nbsp;X Wrapper 2021,\u0026nbsp;Var 13- 2022 CX2, Var 5- QX109, Var 4- QX106, Var 3- QX104, Var 22- X Filler 2021, Var 16-\u0026nbsp;Hai Yan\u0026nbsp;201, Var 20-\u0026nbsp;Hai Wrapper No. 2, Var 7- QX200, Var 12- GH-1, Var 6- QX110, Var 2- QX103, Var 18-\u0026nbsp;Hai Yan\u0026nbsp;301, Var 10- QX206, Var 19-\u0026nbsp;Hai Yan\u0026nbsp;302 and Var 1- QX100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive Evaluation of Tolerance to Low Potassium and the Classification of Tolerance to Low Potassium Types in Cigar Tobacco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comprehensive identification indicators selected above is used as tolerance to low potassium screening to calculate main component scores and comprehensive scores for 23 cigar tobacco varieties (Table 3). According to the comprehensive evaluation \u0026nbsp;method of membership function in fuzzy mathematics, the PC (j) values of each variety as shown in Table 4 are converted to obtain the corresponding membership function value U (j). The comprehensive evaluation value of each variety is obtained through the comprehensive evaluation formula and ranked (Table 4). The strength of the low potassium tolerance ability of 23 cigar tobacco varieties using D value to describe the stress coefficient is ranked as follows: Var 22, Var 1, Var 14, Var 20, Var 21, Var 4, Var 2, Var 10, Var 23, Var 12, Var 6, Var 16, Var 13, Var 7, Var 5, Var 3, Var 18, Var 17, Var 19, Var 11, Var 9, Var 8, Var 15.\u003c/p\u003e\n\u003cp\u003eCluster analysis was further conducted using the comprehensive evaluation value D of 23 cigar\u0026nbsp;tobacco varieties\u0026nbsp;as a variable. The inter group connection method in system clustering was used, and the Euclidean distance was used as the genetic distance metric. Finally, the 23 cigar leaf varieties were divided into 4 categories (Figure 6). The first category is low potassium-sensitive variety,\u0026nbsp;included one variety:\u0026nbsp;Var 22; The second type is a low potassium-low tolerance variety, including five varieties:\u0026nbsp;Var 1, Var 14, Var 20, Var 21 and Var 4; The third type is low potassium-relative tolerant variety,\u0026nbsp;included eleven varieties: Var 2, Var 10, Var 23, Var 12, Var 6, Var 16, Var 13, Var 7, Var 5, Var 18 and Var 3; The fourth category is low potassium-tolerant variety, included six varieties: Var 17, Var 19, Var 11, Var 9, Var 15 and Var 8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Main component scores and comprehensive scores of 23 tested cigar tobacco varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMain factor score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eComprehensive score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.6918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-1.5355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.9342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.4677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.0896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.3008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.3075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.4619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.3383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.9040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.5913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.3148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.0214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.6424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.2364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.4151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.3716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.1097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.1358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.5981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.1303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e1.5779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.2681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e1.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e1.4457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.2719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.9280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.2405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.6767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.3746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e1.1766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.2288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.7579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.5491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.5130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.1376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.0988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.0625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.0352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.2543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-2.0976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.8780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e1.6247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.3462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e1.0571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.4090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.6074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.0240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.7599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.5364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.6250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.2544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.6430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.3708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e1.0408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.3071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e0.7070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-1.0661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.2290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.5346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.3560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-3.3120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.9497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-2.7739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.5365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-1.4117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eVar 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e-0.5585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e0.4963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.242424242424242%\"\u003e\n \u003cp\u003e-0.1489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4. Membership function U(j) value and low potassium tolerance ranking of 23 tested cigar tobacco varieties\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.916666666666668%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.708333333333336%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMembership function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.875%\" valign=\"top\"\u003e\n \u003cp\u003eD-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.0833333333333335%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.397260273972602%\"\u003e\n \u003cp\u003e\u0026mu;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.397260273972602%\"\u003e\n \u003cp\u003e\u0026mu;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.767123287671232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.698630136986301%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.73972602739726%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.4733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.4492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.4672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.7005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.7649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.4251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.6355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.7280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.9680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.9458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.6663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.8974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.6082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.3071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.9810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.8464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.6885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.7675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.8794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.3882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.8953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.7116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.5310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.2469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.157894736842106%\" valign=\"top\"\u003e\n \u003cp\u003eVar 23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.5037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e0.9629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.105263157894736%\"\u003e\n \u003cp\u003e0.6202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1052631578947367%\"\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\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the different morphological, physiological, and biochemical characteristics of 23 cigar tobacco varieties during the seedling stage to evaluate their tolerance to low potassium. The tolerance to low potassium characteristics of cigar tobacco are a comprehensive reflection of multiple traits, influenced by both genetics and environment. To eliminate genetic differences and overlap redundancy between evaluation indicators, and ensure the accuracy of comprehensive evaluation values, this study initially transformed all observed indicator values into LPTC, and then conducted a comprehensive evaluation of each indicator through PCA and membership function method.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResponse of Various Traits to Low Potassium Stress\u003c/h2\u003e \u003cp\u003eIn this study, plant height, maximum leaf area, leaf area, stem dry weight, root dry weight, total root length, root surface area, root volume, potassium content, potassium accumulation, potassium utilization efficiency, total root absorption area, effective root absorption area, root effective specific surface area, root total specific surface area, and root effective specific surface area were all significantly or significantly reduced to varying degrees due to low potassium treatment. These findings are consistent with previous findings, the results obtained by peanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) (Yingyan et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), maize(\u003cem\u003eZea mays\u003c/em\u003e L.) (E et al. 2020), rice(\u003cem\u003eOryza sativa\u003c/em\u003e L.) (Fang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and other plants as research subjects are consistent. Potassium ions are the most abundant cations in plants, accounting for up to 2\u0026ndash;10% of the total dry matter in plants (Wengong et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It plays a role in osmotic regulation, maintaining membrane potential (Chakraborty et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and regulating plant enzyme activity. When potassium supply is insufficient, it can exacerbate membrane lipid peroxidation and affect material transport. Moreover, as potassium acts as an activator of various enzymes in plants, meanwhile potassium deficiency affects the activity of various enzymes and protein synthesis, thereby affecting important physiological and biochemical processes such as photosynthesis in plants, ultimately leading to leaf aging, reduced biomass, potassium content, and other traits (R\u0026ouml;mheld and Kirkby \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eApplication of LPTC in Tolerance to Low Potassium Screening\u003c/h2\u003e \u003cp\u003eFurther, the tolerance coefficient of indicators under normal and certain stress conditions can more accurately reflect the strength of plant tolerance to this stress than their absolute values. Therefore, various tolerance coefficients are often used in plant screening and comprehensive evaluation research. Drought tolerance coefficient (DC) is used to evaluate the drought resistance of different genotypes of millet (Jibing et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), soybean (Chunjuan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), sugar beet (Zhenwei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Waterlogging tolerance coefficient (WTC) is used to evaluate the waterlogging tolerance of different genotypes of maize (LIU et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and pumpkin (Zhenwei et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). And in the study of phosphorus efficiency screening, the optimal regression equation was established with the comprehensive evaluation value of low phosphorus tolerance (D value) as the dependent variable and the low phosphorus tolerance coefficient of each indicator as the independent variables, which could be used for the rapid identification of low-P tolerance (Wang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, a low nitrogen tolerance coefficient was also used in the study of nitrogen efficiency screening in plants (Jianjia et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Singh and Verma \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; ZHANG et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study also converted the measured indicators into LPTC, based on previous research. LPTC\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates that the indicator is inhibited by low potassium treatment. The LPTC of all 23 cigar tobacco varieties, including PH, LA, RDW, RSA, and PC, is consistent\u0026thinsp;\u0026lt;\u0026thinsp;1, while the LPTC of other indicators shows\u0026thinsp;\u0026ge;\u0026thinsp;1 in some varieties, indicating that LK is actually similar to or even higher than NK. This is the difference between different cigar tobacco varieties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelated Indices Selection of Cigar Tobacco Tolerance to Low Potassium Tolerance\u003c/h2\u003e \u003cp\u003eAt present, there is no research on the evaluation and screening of tolerance to low potassium in cigar tobacco. Therefore, selecting appropriate evaluation indicators is very important. In order to accurately select screening indicators of significant importance, this study extensively measured 20 related traits of plant growth, physiology, and potassium utilization. The CV range NL of the 20 traits measured in this study is from 11.35 to 116.58%, and LK is from 20.56 to 146.99%, all of which belong to the range of 10\u0026ndash;100% or \u0026gt;\u0026thinsp;100%. This indicates that the measured traits have moderate or strong variability among different varieties, indicating that all tested indicators can be used as screening indicators for tolerance to low potassium in different cigar tobacco varieties. The results indicate a significant or extremely significant correlation between the LPTC of PH, MLSA, LSA, SDW, TRAA, and ARAA, which overlap the information reflected in these indicators, thereby affecting the screening and identification of tolerance to low potassium in different cigar tobacco varieties. This indicates that the LPTC of these indicators consistently reflect the tolerance to low potassium ability of cigar tobacco varieties and can be determined as comprehensive identification indicators for tolerance to low potassium screening. Dun Xiaoling et al. studied the potassium utilization efficiency of rapeseed(\u003cem\u003eBrassica napus\u003c/em\u003e L.) using root morphology and biomass as criteria (Dun et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Plant height is also commonly used in the screening of potassium efficient rice varieties (Fang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tingchang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Total root absorption area and Active root absorption area are also reliable indicators for evaluating tolerance to low potassium in this study, which is a new approach that can be referenced for studying tolerance to low potassium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive Evaluation of Tolerance to Low Potassium\u003c/h2\u003e \u003cp\u003eAt present, the use of multiple indicators and methods such as PCA and membership function analysis in the comprehensive evaluation of plant resistance or efficiency has been widely applied in plant resistance or efficiency research (Niu et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rasel et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study transformed the 20 measured indicators into LPTC, representing the degree to which each indicator was affected by potassium factors. Complex traits were simplified to more accurately reflect the potassium utilization efficiency of different varieties of cigar tobacco. PCA is the most commonly used multivariate method that allows for the comparison of different genotypes, grouping similar categories of genotypes into one group (Tuhina-Khatun et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and classifying genotypes from different biological backgrounds (Kim et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In botany research, PCA can also be used to explore the relationship between different physiological and biochemical parameters of plants under stress conditions (Department Of Biology et al. 2016; Su et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which may be one of the most convenient and effective methods for researchers to better understand plant physiological and biochemical changes that occur in issues such as tolerance to low potassium in cigar tobacco varieties. By using PCA and membership function method, the comprehensive evaluation value D value of low potassium tolerance of different cigar tobacco varieties was calculated. This study introduced D value to evaluate the tolerance to potassium stress, the higher the D value, the stronger the tolerance of the cigar tobacco variety to low potassium stress. Then cluster using the D value, and use the Euclidean distance method to use the comprehensive evaluation value D value of cigar tobacco's low potassium tolerance as a variable for cluster analysis. The cluster analysis method based on Euclidean distance is also an exoteric scheme frequently applied in botany, which can arrange genotype groups based on the similarity of the research object (Muti et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pongprayoon et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This method can reduce the complexity of the studied traits and improve the accuracy of stress assessment. In addition, this method can not only quickly screen low potassium tolerance with potential tolerance, but also include as many related traits as possible to avoid the one-sidedness of a single parameter.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed a conversion of phenotypic and physiological indicators into a low potassium tolerance index, assessing the tolerance to low potassium of 23 cigar tobacco varieties via their comprehensive evaluation value D value. Based on PCA and membership function analysis, it is divided into four levels: Low potassium-sensitive variety, low potassium-low tolerance variety, low potassium-relative tolerant variety, and low potassium-tolerant variety. Finally, six varieties, Hai Yan 204, Hai Yan 302, QX210, QX204, Hai Yan 103, and QX201, were identified as low potassium-tolerant varieties. Eleven varieties, QX103, QX206, X Wrapper 2021, GH-1, QX110, Hai Yan 201, 2022 CX2, QX200, QX109, Hai Yan 301, and QX104, were identified as low potassium-relative tolerant varieties, which can be used to develop potassium efficient cigar tobacco varieties in the future. In addition, we have identified six indicators to measure the tolerance to low potassium of cigar tobacco, including plant height, maximum leaf area, leaf surface area, total root absorption area, and active root absorption area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e X.\u0026nbsp;Chen,\u0026nbsp;P. Wang,\u0026nbsp;S. Ai,\u0026nbsp;N.\u0026nbsp;Begum, \u0026nbsp;D. Kong,\u0026nbsp;H.\u0026nbsp;A.\u0026nbsp;Nanaei,\u0026nbsp;M.\u0026nbsp;Ahmad, S. Jabeen, L. Zhang\u0026nbsp;declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Major Science and Technology Project of Shaanxi Provincial Company of China National Tobacco Corporation, Research and Development of Key Production Technologies for High Quality Cigar Leaves in Shaanxi Province (SXYC-2022-KJ-01)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors ContributionsX. C. and L.Z. conceived and designed the experiments. X. C. and D. K. performed the experiments. X. C. analyzed the data and wrote the paper. P.W., S. A., N. B., H. A. N., M. A. and S. J. revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledge the Artificial Climate Chamber and Large Instrument Sharing Platform of the School of Life Sciences at Northwest A\u0026amp;F University for their support, as well as to the Shaanxi Provincial Company of China Tobacco Corporation for their funding (SXYC-2022-KJ-01).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdhikari B, Dhungana SK, Kim ID, Shin D (2020) Effect of foliar application of potassium fertilizers on soybean plants under salinity stress. Journal of the Saudi Society of Agricultural Sciences.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAl Rawashdeh R (2020) World peak potash: An analytical study. 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PEERJ 11: e15177. http://doi.org/10.7717/PEERJ.15177\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Cigar tobacco, Variety difference, Potassium tolerance, Comprehensive evaluation","lastPublishedDoi":"10.21203/rs.3.rs-4432161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4432161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePotassium content is an important standard for measuring tobacco quality, however research on low potassium tolerance mechanism in cigar tobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e L.), which is different from regular tobacco, did not draw researchers attention. This study evaluated the tolerance to low potassium of 23 cigar varieties by studying 20 phenotypic and physiological indicators. Through principal component analysis, membership function analysis, and cluster analysis, the research comprehensively identifies the tolerance of cigar tobacco varieties to low potassium. The results indicate that the tolerance to low potassium of cigar tobacco is complex and varies with different traits and varieties. The present study identified six crucial indicators for measuring tolerance to low potassium in cigar tobacco leaves, including plant height, maximum leaf area, leaf surface area, total root absorption area, and active root absorption area. Based on the comprehensive evaluation value D of the low potassium tolerance coefficient of these six indicators, the study classified 23 cigar varieties and identified 1 low potassium-sensitive variety, 5 low potassium-low tolerance varieties, 11 low potassium-relative tolerant varieties, and 6 low potassium-tolerant varieties. The research outcomes significantly contribute to the elucidation of tolerance to low potassium in cigar tobacco, facilitating the evaluation, screening, and cultivation of cigar tobacco varieties resilient to low potassium conditions. Additionally, this laid the foundation for exploring scientific issues such as how plants can effectively utilize potassium, the mechanism of potassium ions in plants, improvement and development of cigar quality, augmentation of potassium utilization efficiency, and saving limited potassium resources to ensure long-term safety of potassium.\u003c/p\u003e","manuscriptTitle":"Response of Cigar Tobacco Varieties to Potassium Stress and Dissecting Biochemical Determinants of Low Potassium Tolerance Mechanism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-13 10:29:28","doi":"10.21203/rs.3.rs-4432161/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":"e7b6ded9-b414-47bd-bdf4-e2280bef2a31","owner":[],"postedDate":"June 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-19T11:51:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-13 10:29:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4432161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4432161","identity":"rs-4432161","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}