Method for identifying or assisting in identifying drought resistance of sesame and application of method

Abstract

The invention relates to the technical field of drought resistance identification of plants, in particular to a method for identification or auxiliary identification of drought resistance of sesame and application of the method. According to the method, 158 representative sesame germplasm resources at home and abroad are used as materials, seedling stage drought resistance identification is carried out by utilizing a repeated drought method, and a stepwise regression equation is established by comprehensively analyzing the drought resistance of sesame germplasm: a prediction D value =-1.056 + 1.699 plant height + 0.112 underground part fresh weight + 0.057 underground part dry weight + 0.161 overground part fresh weight + 0.176 total leaf number; the predicted D value obtained according to the stepwise regression equation and the drought resistance metric value (D value) calculated according to the 13 indexes have extremely significant correlation, which indicates that the drought resistance of the sesame variety to be tested can be judged through the predicted D value. According to the method, the drought resistance of the sesame can be rapidly and accurately identified only by detecting five indexes, and the method has the advantages of being low in detection cost, simple and rapid.

Description

Technical Field

[0001] The present invention relates to the technical field of plant drought resistance identification, and particularly to a method for identifying or assisting in identifying the drought resistance of sesame and its application. Background Art

[0002] Sesame (Sesamum indicum L.) belongs to the genus Sesamum in the Pedaliaceae family and is one of the high-quality oil crops with a long cultivation history and high value, widely planted all over the world. Sesame seeds have the effects of antioxidant, anti-aging, and cholesterol reduction, and can be widely used in industries such as food, health products, and medicine.

[0003] As a crop that does not compete with food crops for land, most sesame is planted on dry and infertile land, and its production is restricted by water. The world average yield is only 481.5 kg / hm 2 . At the same time, sesame, as a shallow-rooted crop, is sensitive to abiotic stresses such as drought and waterlogging. Especially when sesame seedlings encounter drought stress, it is easy to cause seedling shortages, which in turn affects the yield of sesame. Therefore, drought is the main factor affecting sesame production. Identifying the drought resistance of sesame germplasm resources at the seedling stage and screening excellent drought-resistant sesame germplasm resources are of great significance for breeding drought-resistant sesame varieties and conducting research on drought resistance mechanisms.

[0004] Sesame drought resistance is a complex quantitative trait controlled by multiple genes, which is characterized by the ability to survive and reduce losses under drought conditions. Most of the existing methods for evaluating the drought resistance of sesame germplasm resources use more than 10 indicators for comprehensive evaluation, which are not only costly but also time-consuming. Summary of the Invention

[0005] To solve the above problems, the present invention provides a method for identifying or assisting in identifying the drought resistance of sesame and its application. Using the method of the present invention, the drought resistance of sesame can be quickly and accurately identified, and only 5 indicators including plant height, fresh weight of underground parts, dry weight of underground parts, fresh weight of aboveground parts, and total number of leaves need to be detected, with low detection cost.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for identifying or assisting in identifying the drought resistance of sesame, comprising the following steps:

[0008] Treat sesame seedlings by the repeated drought method, measure the plant height, fresh weight of underground parts, dry weight of underground parts, fresh weight of aboveground parts, and total number of leaves of the sesame seedlings after the 3rd treatment, and calculate the predicted value of the drought resistance measure of the sesame to be tested according to formula I:

[0009] Predicted D value = -1.056 + 1.699 plant height + 0.112 fresh weight of underground part + 0.057 dry weight of underground part + 0.161 fresh weight of aboveground part + 0.176 total number of leaves Formula Ⅰ;

[0010] Among them, the predicted D value is the predicted value of drought resistance measurement; the plant height, fresh weight of underground part, dry weight of underground part, fresh weight of aboveground part and total number of leaves are the measured values under defined units. Specifically: the unit of plant height is cm, the unit of fresh weight of underground part is g, the unit of dry weight of underground part is g, the unit of fresh weight of aboveground part is g, and the unit of total number of leaves is piece;

[0011] The higher the predicted value of drought resistance measurement, the stronger the drought resistance of the sesame germplasm to be tested.

[0012] Preferably, the method further includes classifying the germplasm of the sesame to be tested according to the predicted D value. The drought resistance levels from strong to weak are highly drought-resistant germplasm, drought-resistant germplasm, drought-sensitive germplasm and extremely drought-sensitive germplasm:

[0013] When the predicted D value ≥ 1.28, the germplasm of the sesame to be tested is highly drought-resistant;

[0014] When 1.28 > the predicted D value ≥ 0.92, the germplasm of the sesame to be tested is drought-resistant;

[0015] When 0.92 > the predicted D value ≥ 0.43, the germplasm of the sesame to be tested is drought-sensitive;

[0016] When 0.43 > the predicted D value, the germplasm of the sesame to be tested is extremely drought-sensitive.

[0017] Preferably, the sesame seedling is the sesame seedling when the 2nd to 3rd pairs of true leaves are unfolded.

[0018] Preferably, the sesame seedling is the initial sesame seedling grown after the sesame seeds are sown and germinated.

[0019] Preferably, the repeated drought method includes: after watering the sesame seedlings, conducting drought treatment, observing the leaf wilting situation of the seedlings, and re-watering when 50% of the leaves of the sesame seedlings show permanent wilting.

[0020] Preferably, the measurement time is before re-watering after the 3rd drought treatment.

[0021] The present invention provides the application of the method described in the above technical solution in the identification or auxiliary identification of drought resistance of sesame germplasm at the seedling stage.

[0022] The present invention provides the application of the method described in the above technical solution in sesame drought resistance breeding.

[0023] Preferably, the sesame drought resistance breeding includes: screening or assisting in screening drought-resistant sesame germplasm.

[0024] Beneficial effects:

[0025] In this invention, 158 representative sesame germplasm resources at home and abroad were used as materials, and the repeated drought method was used for drought resistance identification at the seedling stage. By investigating the relevant indexes at the seedling stage (total number of leaves, number of wilted leaves, plant height, stem diameter, conductivity, aboveground fresh weight, underground fresh weight, total fresh weight, fresh weight root-shoot ratio, aboveground dry weight, underground dry weight, total dry weight, dry weight root-shoot ratio), the drought tolerance of sesame germplasm was comprehensively analyzed, and a stepwise regression equation was established: predicted D value = -1.056 + 1.699×plant height + 0.112×underground fresh weight + 0.057×underground dry weight + 0.161×aboveground fresh weight + 0.176×total number of leaves. The predicted D value was obtained using this equation, and a correlation analysis was performed between the predicted D value and the drought resistance measurement value (D value) calculated through calculation. It was found that there was a highly significant correlation between the two, indicating that the drought resistance ability of the variety to be tested can be preliminarily judged by the predicted D value calculated by the equation. Using the method of this invention, only 5 indexes, namely plant height, underground fresh weight, underground dry weight, aboveground fresh weight, and total number of leaves, need to be detected to quickly and accurately identify the drought resistance of sesame, which has the advantages of low detection cost, simplicity, and rapidity. Description of the drawings

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments will be briefly introduced below.

[0027] Figure 1 It is the correlation analysis result of the drought resistance coefficient of each index;

[0028] Figures 2 to 3 It is the correlation analysis result of the drought resistance coefficient of the drought comprehensive evaluation D value and each index at the seedling stage; among them, (a) is the correlation analysis of the D value and the total number of leaves, (b) is the correlation analysis of the D value and the number of wilted leaves, (c) is the correlation analysis of the D value and the plant height, (d) is the correlation analysis of the D value and the stem diameter, (e) is the correlation analysis of the D value and the conductivity, (f) is the correlation analysis of the D value and the aboveground fresh weight, (g) is the correlation analysis of the D value and the underground fresh weight, (h) is the correlation analysis of the D value and the total fresh weight, (i) is the correlation analysis of the D value and the fresh weight root-shoot ratio, (j) is the correlation analysis of the D value and the aboveground dry weight, (k) is the correlation analysis of the D value and the underground dry weight, (l) is the correlation analysis of the D value and the total dry weight, (m) is the correlation analysis of the D value and the dry weight root-shoot ratio, and (n) is the correlation analysis of the D value and the predicted D value;

[0029] Figure 4 It is the clustering analysis result of the D value of sesame germplasm. Detailed implementation manners

[0030] The present invention provides a method for identifying or assisting in the identification of sesame drought resistance, comprising the following steps:

[0031] Treat sesame seedlings by the repeated drought method, measure the plant height, fresh weight of the underground part, dry weight of the underground part, fresh weight of the above-ground part and total number of leaves of the sesame seedlings after the third treatment, and calculate the predicted value of the drought resistance metric of the sesame to be tested according to formula I:

[0032] Predicted D value = -1.056 + 1.699 × plant height + 0.112 × fresh weight of the underground part + 0.057 × dry weight of the underground part + 0.161 × fresh weight of the above-ground part + 0.176 × total number of leaves Formula I;

[0033] Among them, the predicted D value is the predicted value of the drought resistance metric; the plant height, fresh weight of the underground part, dry weight of the underground part, fresh weight of the above-ground part and total number of leaves are measured values under defined units, specifically: the unit of plant height is cm, the unit of fresh weight of the underground part is g, the unit of dry weight of the underground part is g, the unit of fresh weight of the above-ground part is g, and the unit of total number of leaves is piece;

[0034] The higher the predicted value of the drought resistance metric, the stronger the drought resistance of the sesame to be tested.

[0035] As an implementation manner, the method further includes classifying the germplasm of the sesame to be tested according to the predicted D value. The drought resistance levels from strong to weak are highly drought-resistant germplasm, drought-resistant germplasm, drought-sensitive germplasm and extremely drought-sensitive germplasm:

[0036] When the predicted D value ≥ 1.28, the germplasm of the sesame to be tested is highly drought-resistant;

[0037] When 1.28 > predicted D value ≥ 0.92, the germplasm of the sesame to be tested is drought-resistant;

[0038] When 0.92 > predicted D value ≥ 0.43, the germplasm of the sesame to be tested is drought-sensitive;

[0039] When 0.43 > predicted D value, the germplasm of the sesame to be tested is extremely drought-sensitive.

[0040] As an implementation manner, the sesame seedlings are sesame seedlings when the second to third pairs of true leaves are unfolded.

[0041] As an implementation manner, the sesame seedlings are the initial sesame seedlings grown after the sesame seeds are sown and germinated.

[0042] As an implementation manner, the repeated drought method includes: after watering the sesame seedlings, performing a drought treatment, observing the leaf wilting condition of the seedlings, and re-watering when 50% of the leaves of the sesame seedlings show permanent wilting.

[0043] As an implementation manner, the measurement time is before re-watering after the third drought treatment.

[0044] Using 158 sesame germplasm resources at home and abroad as materials, the potting repeated drought method was adopted, with normal (CK) and drought stress (DS) treatments set, and 13 agronomic trait indicators such as plant height, total number of leaves, number of wilted leaves, stem diameter, and electrical conductivity were measured. Using principal component analysis, membership function, cluster analysis method, and stepwise regression analysis method, a comprehensive evaluation of the drought resistance of the tested sesame germplasm resources was carried out. Using principal component analysis, 13 single indicators were synthesized into 5 independent comprehensive indicators, representing 79.267% of the original data information. Using stepwise regression analysis, an optimal stepwise regression equation for preliminarily predicting the drought resistance of sesame was constructed, and it was obtained that plant height, fresh weight and dry weight of the underground part, fresh weight of the aboveground part, and total number of leaves could be used as the main indicators for identifying the drought resistance of sesame seedlings. The correlation analysis of the predicted D value and the D value calculated through calculation found that there was a very significant correlation between the two. The predicted D value calculated through calculation can preliminarily judge the drought resistance ability of the variety to be tested. According to the drought resistance measurement value (D, drought resistance comprehensive evaluation value), the drought resistance of 158 sesame germplasm resources was divided into 4 categories ( Figure 4 ). 11 extremely drought-resistant materials such as Fenzhi 11, Weinan Sesame, and Yu-50 were screened out.

[0045] Based on the above advantages, the present invention provides the application of the method described in the above technical solution in the identification or auxiliary identification of the drought resistance of sesame germplasm at the seedling stage.

[0046] Based on the above advantages, the present invention provides the application of the method described in the above technical solution in sesame drought-resistant breeding.

[0047] As an implementation method, the sesame drought-resistant breeding includes: screening or assisting in screening drought-resistant sesame germplasm.

[0048] In order to further illustrate the present invention, the following will describe in detail a method for identifying or assisting in identifying the drought resistance of sesame and its application provided by the present invention in conjunction with the drawings and embodiments, but they cannot be understood as limiting the protection scope of the present invention.

[0049] Example 1

[0050]

[0051] 1.1 Test materials

[0052] The tested materials were 158 sesame germplasm resources provided by the Sesame Research Group of the Institute of Industrial Crops, Shanxi Agricultural University, including 144 domestic resources (from 12 provinces and cities such as Shanxi and Jiangxi in China) and 14 foreign resources (from 9 countries such as Ethiopia and Tanzania). The specific information is shown in Table 1.

[0053] Table 1 Information of 158 Sesame Germplasm Resources

[0054]

[0055]

[0056]

[0057]

[0058] 1.2 Experimental Methods

[0059] The present invention adopts the potting method and is carried out in the artificial climate chamber of the Institute of Industrial Crops, Shanxi Agricultural University. Healthy and plump sesame seeds are sown in plastic pots of 10 cm × 10 cm. Each material is divided into a drought group DS and a control group CK, and each treatment group is repeated 4 times. After emergence, thin out the seedlings, and keep 4 evenly growing seedlings in each pot. Before the drought treatment, each pot is watered with 50 mL each time. When the 2nd - 3rd pair of true leaves of sesame unfold, the control group is watered normally, and the drought treatment group adopts the repeated drought method (see [Liu Wenping, Lü Wei, Li Donghua, Ren Guoxiang, Zhang Yanxin, Wen Fei, Han Junmei, Zhang Xiurong. Association analysis of drought resistance at the adult stage of sesame germplasm resources [J]. Scientia Agricultura Sinica, 2017, 50(4): 625 - 639.]), and the treatment is carried out 3 times. When 50% of the leaves of the plant show permanent wilting, re - water with 50 mL.

[0060] 1.3 Determination Indexes and Methods

[0061] Before re - watering after the third drought treatment, 13 indexes including the total number of leaves, the number of wilted leaves, plant height, stem diameter, conductivity (relative conductivity), above - ground fresh weight, underground fresh weight, total fresh weight, fresh weight root - shoot ratio, above - ground dry weight, underground dry weight, total dry weight, and dry weight root - shoot ratio are measured and calculated. For each material, 3 plants are randomly selected for measurement, with 4 replicates in total.

[0062] The plant height is measured with a ruler, and the stem diameter is measured with a vernier caliper. When measuring the biomass at the seedling stage, first take out the seedlings, and pay attention to root damage during the extraction process. After cleaning, divide the seedlings into above - ground and underground parts and then measure.

[0063] The relative conductivity is measured by the conductivity method (see [Wen Chao, Zhang Zhong, Yun Jinfeng, Jia Lixia, Liu Li, Cui Aijiao. Comparison of drought resistance of 15 Agropyron cristatum germplasm materials at the seedling stage [J]. Inner Mongolia Prataculture, 2008, 20(2): 21 - 25.]). Weigh 0.1 g of the seedlings of each material, wash them 3 times with distilled water, place them in a 15 mL centrifuge tube, add 10 mL of distilled water, shake at 160 r / min for 50 min, let it stand still fully, then measure the initial value of its conductivity. After heating in a boiling water bath for 15 min and then cooling, measure the final value of its conductivity. There are 5 replicates.

[0064] Relative conductivity = Initial conductivity value / Final conductivity value × 100%.

[0065] Total fresh weight = Shoot fresh weight + Root fresh weight.

[0066] Fresh weight root - shoot ratio = Root fresh weight / Shoot fresh weight × 100%.

[0067] Total dry weight = Shoot dry weight + Root dry weight.

[0068] Dry weight root - shoot ratio = Root dry weight / Shoot dry weight × 100%.

[0069] 1.4 Data processing

[0070] Based on the data average as the basic data, the drought resistance of the tested materials was identified and evaluated using the drought resistance coefficient (DTC) and the drought resistance measurement value (D value). The calculation formulas are as follows:

[0071] Drought resistance coefficient DTC = Drought - stressed trait value / Control trait value (1);

[0072] Comprehensive drought resistance coefficient CDTC = (Sum of drought resistance coefficients of n indicators) / n (2);

[0073]

[0074] Based on the principal component analysis, the membership function analysis was used to calculate the membership function value of the comprehensive index for the drought resistance coefficients of 158 materials according to formula (3). In the formula, μ(X j ) represents the membership function value of the j - th comprehensive index of the tested variety, X j is the measured value of the j - th comprehensive index of the tested variety, X max is the maximum value among the measured values of the j - th comprehensive index of all tested varieties, X min is the minimum value among the measured values of the j - th comprehensive index of all tested varieties.

[0075]

[0076] The principal component analysis calculates the weight according to formula (4). In the formula, Wj represents the weight of the j - th comprehensive index, and Pj represents the variance contribution rate of the j - th comprehensive index.

[0077]

[0078] According to formula (5), the drought resistance measurement value was calculated to comprehensively evaluate the drought resistance of sesame germplasm resources at the seedling stage. μ(X j ) represents the membership function value of the j - th comprehensive index of the tested variety, and Wj represents the weight of the j - th comprehensive index.

[0079] 1.5 Data Analysis

[0080] Data were sorted and analyzed using Excel, principal component analysis and stepwise regression analysis were performed using SPSS 26.0, and correlation analysis and cluster analysis were performed using Origin and R studio.

[0081] 2 Results and Analysis

[0082] 2.1 The representativeness of sesame resources and the analysis results of their measured values are shown in Table 2.

[0083] The results showed that under different treatment conditions, among the 13 indicators of total leaf number, leaf wilting number, plant height, stem diameter, electrical conductivity, aboveground fresh weight, underground fresh weight, total fresh weight, fresh weight root-shoot ratio, aboveground dry weight, underground dry weight, total dry weight, and dry weight root-shoot ratio, significant differences were shown among different germplasms. Under the two treatments, the coefficient of variation was 8.35% - 65.11%, indicating that the selected sesame germplasm resources were rich in types and could be used for drought resistance identification. Compared with the control, under drought stress, except for the leaf wilting number, electrical conductivity, and aboveground dry weight, the other 10 measured indicators decreased significantly. Paired-sample t-test analysis found that the measured indicators under the two treatment conditions were significantly different. At the same time, the coefficients of variation of the underground fresh weight and dry weight of the tested sesame germplasm resources under the control and drought treatment conditions were both greater than 50%, which were 58.00%, 50.44% and 57.52%, 56.86% respectively, and were the most sensitive to drought stress.

[0084] Table 2 Analysis of differences in measured indicators of sesame germplasm resources under normal and drought stress treatment conditions

[0085]

[0086]

[0087] Note: *P < 0.05; **P < 0.01; ***P < 0.001. DS - Drought stress, CK - Normal watering, and the data in the table are the average values of 4 - 5 repeated measurements. The same applies to the following tables.

[0088] 2.2 The drought resistance coefficients and correlation analysis results of each trait are shown in Table 3 and Figure 1 .

[0089] The results showed that there were significant differences in the drought resistance coefficients of the same trait indicators among different materials. In addition, the values of the drought resistance coefficients of different indicators varied greatly, and the coefficient of variation was 9.45% - 61.32%, indicating that the measured indicators had different response degrees to drought stress.

[0090] Table 3 Descriptive statistics of drought resistance coefficients of each trait under drought stress

[0091] Trait Minimum Mix Maximum Max Average Standard Deviation SD Coefficient of Variation CV(%) Total Leaf Number TL 0.67 1.11 0.91 0.09 9.45 Wilted Leaf Number WL 0.00 1.00 0.62 0.31 49.43 Plant Height PH(cm) 0.53 1.12 0.88 0.10 11.23 Stem Diameter SW(mm) 0.32 1.47 0.77 0.19 24.96 Electrical Conductivity EC 0.47 3.91 1.53 0.71 46.20 Shoot Fresh Weight TopFW(g) 0.68 1.43 0.93 0.16 16.67 Root Fresh Weight RootFW(g) 0.38 3.15 0.98 0.41 42.35 Total Fresh Weight TBFW(g) 0.65 1.45 0.93 0.16 17.54 Fresh Weight Root / Top Ratio R / TFW(%) 0.45 2.36 1.03 0.32 30.93 Shoot Dry Weight TopDW(g) 0.58 3.29 0.98 0.28 28.79 Root Dry Weight RootDW(g) 0.10 4.50 1.01 0.62 61.32 Total Dry Weight TBDW(g) 0.57 3.31 0.97 0.29 30.26 Dry Weight Root / Top Ratio R / TDW(%) 0.10 3.45 1.01 0.45 45.03

[0092] Correlation analysis showed that each index was closely related, and there were significant or highly significant correlations among all indexes. Among them, the total number of leaves was highly significantly negatively correlated with the number of wilted leaves, and highly significantly positively correlated with plant height, stem diameter and conductivity; the number of wilted leaves was highly significantly negatively correlated with stem diameter and conductivity; plant height was highly significantly negatively correlated with the fresh weight of the underground part and the fresh weight root-shoot ratio; stem diameter was highly significantly positively correlated with conductivity, the dry weight of the aboveground part and the total dry weight; there were highly significant positive correlations among the indexes of the fresh weight of the aboveground part, the fresh weight of the underground part, the total fresh weight, the fresh weight root-shoot ratio, the dry weight of the aboveground part, the dry weight of the underground part and the total dry weight, and the correlations between the fresh weight of the aboveground part and the total fresh weight, and between the dry weight of the underground part and the total dry weight were the highest, with the correlation coefficients both being 0.99; the dry weight root-shoot ratio was highly significantly positively correlated with the fresh weight of the underground part, the fresh weight root-shoot ratio, the dry weight of the underground part and the total dry weight. Based on the above analysis, the drought resistance of sesame is a comprehensive manifestation of multiple indexes. Therefore, the drought resistance can be more accurately identified by jointly evaluating multiple indexes.

[0093] 2.3 Principal component analysis of drought resistance coefficient

[0094] To comprehensively evaluate the drought resistance of sesame germplasm resources at the adult stage, principal component analysis was carried out on the drought resistance coefficient values of the 13 measured traits. According to the principle that the cumulative contribution rate is greater than 80%, 5 principal components were extracted (Table 4). The contribution rates of the first 5 principal components were 27.163%, 18.344%, 15.94%, 12.526% and 11.84% respectively, and the cumulative contribution rate was 85.813%. That is, the 13 indexes were transformed into 5 new comprehensive drought resistance indexes. By analyzing the 5 principal components, the first principal component had high loadings on the fresh weight of the underground part, the total dry weight, the dry weight of the aboveground part, the dry weight of the underground part, the fresh weight root-shoot ratio, the total fresh weight and the fresh weight of the aboveground part; the second principal component had high loadings on stem diameter, the total number of leaves and conductivity; the third principal component had high loadings on the fresh weight of the aboveground part, the total fresh weight and plant height; the fourth principal component had high loading on the dry weight root-shoot ratio; the fifth principal component had high loading on conductivity.

[0095] Table 4 Eigenvectors and contribution rates of principal components of 13 indexes of sesame germplasm resources

[0096] Trait First Principal Component Second Principal Component Third Principal Component Fourth Principal Component Fifth Principal Component Total Leaf Number TL -0.176 0.684 0.306 0.127 0.255 Wilted Leaf Number WL -0.003 -0.733 0.036 0.242 0.248 Plant Height PH(cm) -0.183 0.378 0.569 0.381 -0.48 Stem Diameter SW(mm) 0.114 0.791 -0.136 -0.351 0.102 Electrical Conductivity EC -0.037 0.633 0.152 0.176 0.523 Shoot Fresh Weight TopFW(g) 0.652 -0.248 0.66 0.017 0.13 Root Fresh Weight RootFW(g) 0.941 -0.121 0.01 -0.147 0.13 Total Fresh Weight TBFW(g) 0.73 -0.257 0.576 -0.003 0.151 Fresh Weight Root / Top Ratio R / TFW(%) 0.809 -0.058 -0.356 -0.172 0.156 Shoot Dry Weight TopDW(g) 0.873 0.24 0.025 -0.192 -0.25 Root Dry Weight RootDW(g) 0.82 0.201 -0.274 0.424 -0.092 Total Dry Weight TBDW(g) 0.898 0.233 -0.016 -0.127 -0.234 Dry Weight Root / Top Ratio R / TDW(%) 0.457 0.105 -0.385 0.749 0.054 Eigenvalue 5.025 2.481 1.579 1.22 0.851 Contribution Rate(%) 27.163 18.344 15.94 12.526 11.84 Cumulative Contribution Rate(%) 27.163 45.507 61.447 73.973 85.813

[0097] 2.4 Correlation and regression analysis between drought resistance metric value (D value) and drought resistance coefficients of each index at the seedling stage

[0098] Correlation analysis was carried out using SPSS 22 software, and the results are shown in Figures 2 to 3. The results showed that except for the number of wilted leaves and the fresh weight root-shoot ratio, which were negatively correlated with the D value, and the stem diameter, which was positively correlated with the D value, there were significant or highly significant correlations between the comprehensive drought resistance evaluation D value and the drought resistance coefficients of the 10 indicators. Among them, the total number of leaves, plant height, conductivity, aboveground fresh weight, total fresh weight, aboveground dry weight, underground dry weight, and total dry weight were highly significantly positively correlated with the D value; the underground fresh weight and dry weight root-shoot ratio were significantly positively correlated with the D value. This indicates that all these 10 indicators are closely related to the drought resistance ability of sesame and can be used as comprehensive evaluation indicators for drought resistance.

[0099] 2.5 Evaluation Indexes and Prediction Model Construction of Sesame Drought Resistance

[0100] To analyze the relationship between each index and the drought resistance of different sesame germplasms and screen out effective drought resistance identification indexes, a reliable and persuasive drought resistance evaluation model needs to be established. Through stepwise regression analysis, this invention constructed a model using the D value and the drought resistance coefficients of each index. Taking the drought resistance coefficients of each index as independent variables and the comprehensive drought resistance evaluation D value as the dependent variable, a regression equation was established: Predicted D value = -1.056 + 1.699 plant height + 0.112 underground fresh weight + 0.057 underground dry weight + 0.161 aboveground fresh weight + 0.176 total number of leaves, R 2 = 0.996, indicating that these 5 indexes, namely plant height, underground fresh weight, underground dry weight, aboveground fresh weight, and total number of leaves, determined 99.6% of the D value and had a significant linear effect on the D value.

[0101] Using the D value and predicted D value of the test materials, the estimation accuracy of the regression equation was evaluated (see Table 1). The results showed that except for the estimation accuracy of material No. 94 (Canadian Sesame No. 1) being 74.98%, the estimation accuracy of sesame germplasms was greater than 93.93%, indicating that these 5 indexes had a guiding role in screening the drought resistance of sesame germplasms. This equation can be used for the evaluation of the drought resistance of sesame germplasms, making the evaluation and identification of the drought resistance of sesame germplasms more convenient.

[0102] 2.5 Comprehensive Evaluation of Sesame Drought Resistance

[0103] Using the drought resistance coefficients of 13 indexes, namely the total number of leaves, number of wilted leaves, plant height, stem diameter, conductivity, aboveground fresh weight, underground fresh weight, total fresh weight, fresh weight root-shoot ratio, aboveground dry weight, underground dry weight, total dry weight, and dry weight root-shoot ratio, the comprehensive drought resistance D values of 158 sesame germplasm resources at the seedling stage were calculated by the membership function method.

[0104] Based on the D value, cluster analysis of the 158 sesame germplasm resources was carried out using K-means, and they could be divided into 4 categories ( Figure 4 , the numbers corresponding to the serial numbers in Table 1). According to the results of Table 1 and Figure 4 , the classification was as follows:

[0105] The division of D values is as follows:

[0106] When D ≥ 1.28, the germplasm of the sesame to be tested is of strong drought resistance type;

[0107] When 1.28 > D ≥ 0.90, the germplasm of the sesame to be tested is of drought resistance type;

[0108] When 0.90 > D ≥ 0.43, the germplasm of the sesame to be tested is of drought sensitivity type;

[0109] When 0.43 > D, the germplasm of the sesame to be tested is of extremely drought sensitivity type.

[0110] The division of the predicted D value is as follows:

[0111] When the predicted D value ≥ 1.28, the germplasm of the sesame to be tested is of strong drought resistance type;

[0112] When 1.28 > the predicted D value ≥ 0.92, the germplasm of the sesame to be tested is of drought resistance type;

[0113] When 0.92 > the predicted D value ≥ 0.43, the germplasm of the sesame to be tested is of drought sensitivity type;

[0114] When 0.43 > the predicted D value, the germplasm of the sesame to be tested is of extremely drought sensitivity type.

[0115] Perform a correlation analysis on the predicted D value and the D value ( Figure 3 , n), and there is an extremely significant correlation between the two (R 2 = 0.996); Use the D value and the predicted D value respectively to identify the drought resistance of 158 sesame germplasms in this experiment, and the drought resistance results of 152 (accounting for 96.20%) sesame germplasms are consistent. Therefore, the drought resistance of sesame germplasm resources can be identified by the predicted D value.

[0116] Although the above embodiments have described the present invention in detail, they are only a part of the embodiments of the present invention, rather than all embodiments. People can also obtain other embodiments according to this embodiment without creative efforts, and these embodiments all belong to the protection scope of the present invention.

Claims

1. A method for identifying or assisting in identifying drought resistance of sesame, characterized in that: The following steps are involved: The sesame seedlings were treated with repeated drought treatment. The plant height, underground fresh weight, underground dry weight, aboveground fresh weight and total number of leaves of the sesame seedlings after the third treatment were measured. The predicted value of the drought resistance metric of the tested sesame was calculated according to Formula I: Predicted D value = -1.056 + 1.699 plant height + 0.112 underground fresh weight + 0.057 underground dry weight + 0.161 aboveground fresh weight + 0.176 total number of leaves Formula I; Among them, the predicted D value is the predicted value of drought resistance; plant height, underground fresh weight, underground dry weight, aboveground fresh weight and total number of leaves are measured values ​​under limited units, specifically: the unit of plant height is cm, the unit of underground fresh weight is g, the unit of underground dry weight is g, the unit of aboveground fresh weight is g, and the unit of total number of leaves is piece; The higher the predicted value of the drought resistance metric, the stronger the drought resistance of the tested sesame is.

2. The method according to claim 1, characterized in that The method further includes classifying the sesame germplasm to be tested according to the predicted D value, wherein the drought resistance is ranked from strong to weak as highly drought-resistant germplasm, drought-resistant germplasm, drought-sensitive germplasm and extremely drought-sensitive germplasm: When the predicted D value is ≥1.28, the sesame germplasm to be tested is of high drought resistance type; 1.28>When the predicted D value is ≥0.92, the sesame germplasm to be tested is drought-resistant; When the predicted D value is 0.92>≥0.43, the sesame germplasm to be tested is drought sensitive; When the predicted D value is > 0.43, the sesame germplasm to be tested is extremely drought sensitive.

3. The method according to claim 1, characterized in that The sesame seedlings are sesame seedlings when the second to third pairs of true leaves are unfolded.

4. The method according to claim 1 or 3, characterized in that: The sesame seedlings are early stage sesame seedlings grown after sesame seeds are sown and germinated.

5. The method according to claim 1 or 3, characterized in that: The repeated drought method comprises: after watering the sesame seedlings, performing drought treatment, observing the wilting of leaves of the seedlings, and re-watering when 50% of the leaves of the sesame seedlings wilt permanently.

6. The method according to claim 5, characterized in that The measurement time is after the third drought treatment and before rewatering.

7. Use of the method according to any one of claims 1 to 6 in the identification or auxiliary identification of drought resistance of sesame germplasm at the seedling stage.

8. Use of the method according to any one of claims 1 to 6 in sesame drought resistance breeding.

9. The use according to claim 8, characterized in that: The sesame drought-resistant breeding comprises: screening or auxiliary screening of drought-resistant sesame germplasm.

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