Sesame germplasm resource nitrogen efficient identification method based on yield-nitrogen efficiency comprehensive index

Abstract

The invention provides a sesame germplasm nitrogen efficient identification method based on a yield-nitrogen efficiency comprehensive index, which comprises the following steps: determining a sesame variety to be evaluated, measuring each single index in a sesame mature period under the conditions of no nitrogen application and normal nitrogen application, calculating nitrogen efficiency related indexes, extracting main components, and calculating the yield-nitrogen efficiency comprehensive index. And finally, establishing a nitrogen efficiency evaluation mathematical model in the sesame mature period. According to the method, correlation and principal component analysis is carried out on indexes such as sesame yield and constituent factors thereof, a sesame nitrogen efficient evaluation system is constructed, sesame nitrogen efficiency grading is carried out, and the relationship between sesame nitrogen efficient utilization and nitrogen efficiency related indexes is determined so as to be expected to pass nitrogen efficient germplasm resource identification. A high-nitrogen-yield, high-efficiency and excellent germplasm resource and a nitrogen efficient and rapid identification method are provided for production, and theoretical and technical supports are provided for breeding and production application of sesame nitrogen efficient varieties.

Description

Technical Field

[0001] The invention relates to a sesame germplasm nitrogen-efficient identification method based on a yield-nitrogen efficiency comprehensive index, and belongs to the technical field of crop variety evaluation and identification. Background Art

[0002] Nitrogen is an essential macronutrient for crop growth and development and a crucial nutrient in determining sesame yield. While nitrogen fertilizer usage has increased annually to boost crop yields, yield growth has been slow. This waste of nitrogen fertilizer resources has significantly reduced nitrogen fertilizer efficiency, making this agricultural production model—one that relies on increasing nitrogen inputs to increase yield—unsustainable. Selecting and cultivating high-yield, nitrogen-efficient sesame varieties can ensure a synergistic increase in yield and nitrogen absorption and utilization efficiency, and is crucial for promoting the healthy development of the sesame industry.

[0003] Scientific evaluation methods are key to selecting sesame varieties with high nitrogen efficiency. Numerous evaluation indicators are involved in nitrogen efficiency. Due to the varying screening targets and objectives, different indicators are selected, resulting in a multitude of nitrogen efficiency screening and evaluation methods. Different nitrogen efficiency evaluation indicators reflect different aspects of nitrogen absorption and utilization, and the ranking of the same genotype under different nitrogen absorption and utilization efficiency evaluation indicators varies significantly. Therefore, using a single nitrogen efficiency evaluation indicator to evaluate the nitrogen efficiency characteristics of sesame varieties with different genotypes cannot fully reflect the overall nitrogen absorption and utilization capacity and may even result in the loss of some superior genotypes.

[0004] Nitrogen efficiency is the result of the combined effects of multiple physiological processes, including nitrogen absorption, assimilation, transportation, and reuse. Ideal nitrogen-efficient genotypes should exhibit both high nitrogen utilization efficiency and high absorption efficiency. Therefore, a comprehensive evaluation based on multiple nitrogen efficiency evaluation indices is necessary, which facilitates screening results that consider both nitrogen absorption and utilization. Currently, research on the evaluation of nitrogen-efficient crop varieties has been reported for crops such as wheat, corn, rice, rapeseed, and oats. However, no research has been reported on methods for identifying nitrogen-efficient sesame during maturity. Therefore, a comprehensive evaluation of nitrogen absorption and utilization efficiency of different varieties based on the entropy-weighted fuzzy membership function method can not only enrich sesame nitrogen efficiency evaluation methods but also screen for high-yield, nitrogen-efficient sesame varieties, providing a reference for the breeding and production application of high-yield, nitrogen-efficient sesame varieties. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method for identifying nitrogen efficiency of sesame germplasm resources based on the yield-nitrogen efficiency comprehensive index.

[0006] The technical solution adopted in the present invention is as follows:

[0007] A method for identifying nitrogen efficiency of sesame germplasm based on a yield-nitrogen efficiency comprehensive index comprises the following steps:

[0008] S1. Determine the sesame variety to be identified;

[0009] S2. Under normal nitrogen application and no nitrogen application conditions, measure individual indicators of mature sesame varieties;

[0010] S3. Calculation of nitrogen efficiency index;

[0011] S4, extraction of principal components;

[0012] S5. Calculation of the yield-nitrogen efficiency composite index;

[0013] S6. Establishment of a mathematical model for predicting nitrogen efficiency of sesame;

[0014] S7. Determine the nitrogen efficiency type of sesame varieties based on the GYNEI value of the mathematical model.

[0015] Step S2 comprises the following steps:

[0016] (1) Two groups were set up: a nitrogen treatment group and a non-nitrogen treatment group. The fertilizer dosage of the nitrogen treatment group was 180 kg / hm2. 2 The nitrogen fertilizer addition rate of the no nitrogen treatment group was 0; both treatment groups were given 75 kg / hm2 of phosphorus fertilizer. 2 , potash fertilizer 17.3kg / hm 2 ;

[0017] (2) For potted planting, mix the fertilizer and sand evenly and put them into a flower pot with an upper diameter of 0.38m, a lower bottom diameter of 0.25m and a height of 0.34m. Each pot should contain 20kg of sand.

[0018] The indicators and measurement methods measured in step S2 are:

[0019] (1) Determination of dry matter and nitrogen accumulation:

[0020] At maturity, three plants of similar growth were selected from each variety. The aboveground stems, leaves, and capsules were placed at 105°C for 30 minutes, dried at 80°C until constant weight, and then weighed. The dry matter accumulation of the stems, leaves, and capsules, as well as the total dry matter accumulation of the aboveground parts, were calculated for each variety.

[0021] The dry matter samples of each organ were pulverized, and the nitrogen content of stems, leaves, capsule peels and seeds was determined using a FOSS Kjeldahl nitrogen analyzer;

[0022] The nitrogen absorption of stems, leaves, capsules and seeds at maturity was calculated according to dry matter weight × nitrogen content. The total nitrogen absorption was the sum of the nitrogen absorption of the four tissues: stems, leaves, capsules and seeds.

[0023] (2) Determination of output and its components:

[0024] At maturity, three plants with the same growth were selected to investigate the number of capsules per plant, the number of grains per capsule, the thousand-grain weight, and the yield per plant.

[0025] The nitrogen efficiency related indicators and calculation formula in step S2 are:

[0026] Low nitrogen stress tolerance index = no nitrogen application index value / normal nitrogen application index value;

[0027] Agronomic utilization rate of nitrogen fertilizer = (grain yield in nitrogen-fertilized area - grain yield in non-nitrogen-fertilized area) / nitrogen fertilizer application rate;

[0028] Nitrogen fertilizer recovery efficiency = (total nitrogen absorbed in the nitrogen-fertilized area - total nitrogen absorbed in the nitrogen-free area) / nitrogen fertilizer application amount × 100%;

[0029] Nitrogen physiological utilization rate = (grain yield in the nitrogen-fertilized area - grain yield in the non-nitrogen-fertilized area) / (total nitrogen absorption in the nitrogen-fertilized area - total nitrogen absorption in the non-nitrogen-fertilized area);

[0030] Nitrogen dry matter production efficiency = dry matter accumulation in nitrogen application area / total nitrogen absorption by plants;

[0031] Nitrogen grain production efficiency = grain yield in nitrogen application area / total nitrogen uptake by plants.

[0032] Step S4 includes the following steps:

[0033] (1) Standardize the original measurement values determined in step S2 using SPSS 22.0 software;

[0034] (2) performing principal component analysis and path analysis on the nitrogen efficiency index calculated in step S3;

[0035] (3) Determine the number of principal components based on the eigenvalue and cumulative contribution rate, and calculate the comprehensive index value of each index in each principal component and the weight of each principal component;

[0036] (4) The principal components are extracted according to the criterion that the eigenvalue is greater than 1 or the cumulative contribution rate is greater than 80%, and the coefficients of the comprehensive indicators of each principal component are obtained.

[0037] Step S5 includes the following steps:

[0038] (1) Constructing the optimal membership matrix

[0039]

[0040] Where y ij is the preferred membership, max T ij is the maximum value of each evaluation index;

[0041] (2) Determine indicator weights using entropy weight method

[0042] First, the original data is normalized and calculated according to the larger the better indicator. The calculation formula is:

[0043]

[0044] Where max ij and min ij are the maximum and minimum values of each evaluation index respectively;

[0045] Secondly, calculate the entropy value. The entropy calculation formula for m evaluation objects and n evaluation indicators is:

[0046]

[0047] Where i = 1, 2, ···, n; j = 1, 2, ···, m; f ij The calculation formula is:

[0048]

[0049] Finally, calculate the evaluation index weight W, the calculation formula is:

[0050] and satisfy

[0051] (3) Calculate the comprehensive index of fuzzy evaluation

[0052] Multiply the weight value and the optimal membership value of each evaluation index and add them together to get the comprehensive index value C of fuzzy evaluation. The calculation formula is:

[0053]

[0054] (4) Calculation of yield-nitrogen efficiency comprehensive index

[0055] GYNEI = (yield / average yield of the group) × (nitrogen efficiency comprehensive index / average value of the nitrogen efficiency comprehensive index of the group).

[0056] The method for establishing the mathematical model in step S6 is:

[0057] Based on the yield-nitrogen efficiency comprehensive index of step S5, a nitrogen efficiency grade cluster analysis is performed on each sesame variety, and nitrogen high efficiency grades are divided to screen out high-yield and high-efficiency sesame germplasm resources; through a multivariate stepwise regression analysis method, with the yield-nitrogen efficiency comprehensive index as the dependent variable and the low nitrogen stress tolerance index of each individual indicator as the independent variable, a regression equation for predicting the mathematical model of nitrogen efficiency is established.

[0058] The regression equation of the mathematical model for predicting nitrogen efficiency is:

[0059] GYNEI=0.906-0.292X1-0.146X2-11.054X3+2.267X5+1.114X6+13.953X7-2.486X8-13.761 X9-0.276X10+0.396X11+0.897X12-0.094X13+3.002X14-1.235X15-7.308X16+14.090X18,

[0060] GYNEI is the dependent variable; X is the independent variable, representing the low nitrogen stress tolerance index of each indicator, X1: yield; X2: 1000-grain weight; X3: number of capsules per plant; X5: stem weight; X6: leaf dry weight; X7: capsule dry weight; X8: grain weight per plant; X9: total dry weight of aboveground parts; X10: stem nitrogen content; X11: leaf nitrogen content; X12: capsule nitrogen content; X13: grain nitrogen content; X14: stem nitrogen accumulation; X15: leaf nitrogen accumulation; X16: capsule nitrogen accumulation; X18: total nitrogen accumulation.

[0061] The relationship between GYNEI value and nitrogen efficiency type of sesame varieties is:

[0062] High-yield and high-efficiency type, GYNEI ≥ 2.65;

[0063] High nitrogen and high efficiency type, 1.29≤GYNEI<2.65;

[0064] Low nitrogen and high efficiency type, 0.69≤GYNEI<1.29;

[0065] Low-yield and low-efficiency type, GYNEI<0.69.

[0066] Beneficial effects of the present invention

[0067] (1) The present invention uses the entropy weight fuzzy membership function method, takes nitrogen fertilizer recovery efficiency, nitrogen fertilizer agronomic utilization rate, nitrogen physiological utilization rate, nitrogen grain production efficiency, and nitrogen dry matter production efficiency as nitrogen efficiency evaluation indicators, and establishes a comprehensive evaluation method for sesame nitrogen efficiency. Based on the yield-nitrogen efficiency comprehensive index (GYNEI), the test sesame varieties are divided into four types: high-yield and high-efficiency type, high-nitrogen and high-efficiency type, low-nitrogen and high-efficiency type, and low-yield and low-efficiency type. Seven varieties, namely N2, N11, N15, N23, N24, N25 and N30, are screened out as high-yield and high-efficiency varieties.

[0068] (2) The present invention conducts a grey correlation analysis on the individual indicators of different sesame plants and the yield-nitrogen efficiency comprehensive index GYNEI to determine the role of each indicator in the evaluation of sesame nitrogen efficiency. It is found that under different nitrogen application levels, the four indicators of sesame plant seed weight (T8), yield (T1), grain nitrogen accumulation (T17), and total dry weight of aboveground parts (T9) have a high correlation coefficient with the sesame GYNEI comprehensive index. These four indicators are used as key indicators for sesame nitrogen efficiency screening. Through multiple stepwise regression analysis, with the yield-nitrogen efficiency comprehensive index (GYNEI) as the dependent variable and the low nitrogen stress tolerance index of each individual indicator as the independent variable, a reliable mathematical model for evaluating sesame nitrogen efficiency during maturity is established.

[0069] (3) The present invention conducted correlation and principal component analysis on 23 indicators such as sesame yield and its components during the mature period, constructed a sesame nitrogen efficiency evaluation system and nitrogen efficiency grade classification, clarified the relationship between sesame nitrogen efficiency and nitrogen efficiency-related indicators, and hoped to provide high-yield nitrogen efficiency and excellent germplasm resources and nitrogen efficiency rapid identification methods for production through the identification of nitrogen efficiency germplasm resources, and provide theoretical and technical support for the selection and production application of sesame nitrogen efficiency varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 Classification of sesame varieties with different nitrogen efficiency types DETAILED DESCRIPTION

[0071] The technical solution of the present invention is further described in detail below with reference to specific embodiments.

[0072] Example 1. A method for efficiently identifying nitrogen in sesame germplasm resources

[0073] 1. Determine the sesame variety to be identified

[0074] A total of 145 sesame germplasms from various regions were selected as experimental materials, designated N1-N145. All experimental materials were provided by the Sesame Research Center of the Henan Academy of Agricultural Sciences (see Table 7). The experimental location was the Modern Agricultural Development Research Base of the Henan Academy of Agricultural Sciences (113.96°E, 35.05°N, 84 m above sea level).

[0075] 2. Determination of individual indicators of sesame during maturity under normal nitrogen application and no nitrogen application conditions

[0076] 2.1 Experimental design

[0077] (1) Two groups were set up: a nitrogen treatment group and a non-nitrogen treatment group. The fertilizer dosage of the nitrogen treatment group was 180 kg / hm2. 2 , the nitrogen fertilizer addition amount of the no nitrogen treatment group was 0, that is, no nitrogen fertilizer was used;

[0078] Both treatment groups were treated with 75 kg / hm2 of phosphorus fertilizer.2 , Potassium fertilizer 17.3kg / hm 2 The fertilizers tested were urea (N, 46%), superphosphate (P2O5, 12%), and potassium sulfate (K2SO4, 52%).

[0079] (2) For potted planting, mix the fertilizer and sand evenly and put them into a flower pot with an upper diameter of 0.38m, a lower bottom diameter of 0.25m and a height of 0.34m. Each pot should contain 20kg of sand.

[0080] Planting was carried out on May 25, 2024, with four seedlings left per pot. Harvest was conducted on August 22 of the same year for the no-nitrogen treatment and on September 10 of the same year for the nitrogen treatment. Other water and pest management procedures were the same as for general field cultivation.

[0081] 2.2 Indicators and measurement methods

[0082] (1) Determination of dry matter and nitrogen accumulation

[0083] During the sesame maturity period, three sesame plants of each variety with similar growth were selected, and the stems, leaves, and capsules of the aboveground parts were removed. They were withered at 105°C for 30 min, and then dried at 80°C to constant weight and weighed. The dry matter accumulation of the stems, leaves, capsules, and grains of each variety and the total dry matter accumulation of the aboveground parts were calculated, where the total dry matter accumulation of the aboveground parts was the sum of the dry matter accumulation of the stems, leaves, capsules, and grains.

[0084] Each dry matter sample was ground separately, and the nitrogen content of the stems, leaves, pericarp, and seeds was determined using a FOSS Kjeldahl nitrogen analyzer. Nitrogen uptake by the stems, leaves, pericarp, and seeds at maturity was calculated by multiplying dry matter by nitrogen content. Total nitrogen uptake was the sum of nitrogen uptake by the four tissues: stems, leaves, pericarp, and seeds.

[0085] (2) Output and composition

[0086] Three sesame plants with the same growth at maturity were selected, and the number of capsules per plant, number of grains per capsule, thousand-grain weight, and yield per plant were investigated.

[0087] 3. Calculation of nitrogen efficiency related indicators

[0088] According to the individual indicators measured in step 2, calculate the nitrogen efficiency related indicators. The calculation formula is as follows:

[0089] (1) Low nitrogen stress tolerance index (X) = no nitrogen application index value / normal nitrogen application index value;

[0090] (2) Nitrogen agronomic efficiency (NAE, kg / kg) = (grain yield in the nitrogen-fertilized area - grain yield in the non-nitrogen-fertilized area) / nitrogen fertilizer application rate;

[0091] (3) Nitrogen recovery efficiency (NRE, %) = (total nitrogen absorbed in the nitrogen-fertilized area - total nitrogen absorbed in the nitrogen-free area) / nitrogen fertilizer application rate × 100%;

[0092] (4) Nitrogen physiological efficiency (NPE, kg / kg) = (grain yield in the nitrogen-fertilized area - grain yield in the non-nitrogenized area) / (total nitrogen absorbed in the nitrogen-fertilized area - total nitrogen absorbed in the non-nitrogenized area);

[0093] (5) Nitrogen dry matter production efficiency (DME, kg / kg) = dry matter accumulation in the nitrogen application area / total nitrogen absorption by the plant.

[0094] (6) Nitrogen grain yield production efficiency (GYE, kg / kg) = grain yield in nitrogen-fertilized area / total nitrogen uptake by the plant.

[0095] 4. Principal Component Extraction

[0096] SPSS 22.0 software was used to standardize the original measured values of the following 23 indicators, including T1: yield (kg / mu); T2: 1000-grain weight (g); T3: number of capsules per plant; T4: number of grains per capsule; T5: stem dry weight (g); T6: leaf dry weight (g); T7: capsule dry weight (g); T8: grain weight per plant (g); T9: total dry weight of aboveground parts (g); T10: stem nitrogen content (g / kg); T11: leaf nitrogen content (g / kg); T12: capsule nitrogen content (g / kg); T13: grain nitrogen content (g / kg); T14: stem nitrogen accumulation (mg / plant); T15: leaf nitrogen accumulation (mg / plant);

[0097] T16: Capsule nitrogen accumulation (mg / plant); T17: Grain nitrogen accumulation (mg / plant); T18: Total nitrogen accumulation (mg / plant); T19: Physiological nitrogen utilization rate (kg / kg); T20: Nitrogen fertilizer recovery efficiency (%); T21: Nitrogen grain production efficiency (kg / kg); T22: Nitrogen dry matter production efficiency (kg / kg); T23: Agronomic nitrogen utilization rate (kg / kg).

[0098] The calculated nitrogen efficiency index was subjected to principal component analysis and path analysis, the number of principal components (n) was determined according to the characteristic value and cumulative contribution rate, and the comprehensive index value of each index in each principal component was calculated [F i, (i=1,2,…n)] and the weight of each principal component (W).

[0099] The principal components were extracted according to the criterion that the extracted eigenvalue was greater than 1 or the cumulative contribution rate was greater than 80%, and the coefficients of the comprehensive indicators of each principal component were obtained.

[0100] 5. Calculation of Yield-Nitrogen Efficiency Composite Index

[0101] 5.1 Constructing the preferential membership matrix

[0102] According to the principle of superior membership, the degree to which the fuzzy value of each individual evaluation indicator belongs to the fuzzy value of the corresponding evaluation indicator of the standard solution is called the superior membership. In this test, each evaluation indicator is calculated as the larger the better.

[0103]

[0104] Where y ij is the preferred membership, max T ij is the maximum value of each evaluation index.

[0105] 5.2 Determining indicator weights using entropy weight method

[0106] First, the original data is normalized and calculated according to the larger the better indicator. The calculation formula is:

[0107]

[0108] Where max ij 、min ij are the maximum and minimum values of each evaluation index respectively.

[0109] Secondly, calculate the entropy value. The entropy calculation formula for m evaluation items and n evaluation indicators is:

[0110]

[0111] Where i = 1, 2, ···, n; j = 1, 2, ···, m; f ij The calculation formula is:

[0112]

[0113] Finally, calculate the evaluation index weight W, the calculation formula is:

[0114]

[0115] 5.3 Calculation of comprehensive index of fuzzy evaluation

[0116] Multiply the weight value and the optimal membership value of each evaluation index and add them together to get the comprehensive index value CEV of fuzzy evaluation. The calculation formula is:

[0117]

[0118] The larger the CEV value, the better the comprehensive evaluation of the variety.

[0119] 5.4 Yield-Nitrogen Efficiency Composite Index (GYNEI)

[0120] GYNEI = (yield / average yield of the group) × (nitrogen efficiency index / average nitrogen efficiency index of the group) (7)

[0121] 6. Establishment of a mathematical model for evaluating the high efficiency of sesame nitrogen

[0122] Based on the yield-nitrogen efficiency composite index of each variety in Step 5, a cluster analysis of nitrogen efficiency levels was conducted on each sesame variety, and nitrogen efficiency grades were classified to screen for high-yield and high-efficiency sesame germplasm resources. Using a multivariate stepwise regression analysis method, the optimal regression equation for the mathematical model predicting nitrogen efficiency was established, using the low nitrogen stress tolerance index of each individual indicator as the independent variable and the yield-nitrogen efficiency composite index (GYNEI) as the dependent variable.

[0123] Example 2. Genetic diversity analysis of indicators related to nitrogen efficiency in sesame

[0124] The method of Example 1 was used to analyze the changes in yield traits and nitrogen efficiency-related indicators of each test variety under different nitrogen application levels during the sesame maturity period. The responses of various measured indicators of 145 sesame varieties at maturity to different nitrogen application levels are shown in Table 1.

[0125] Under normal nitrogen application treatment, the coefficient of variation of each indicator was 12.62% to 75.67%, among which T10 (stem nitrogen content) had the largest coefficient of variation (75.67%), and T13 (grain nitrogen content) had the smallest coefficient of variation (12.62%). The range of variation of the test variety T1 (yield) was 5.58 to 147.34 kg / mu, with an average yield of 73.48 kg / mu.

[0126] The non-nitrogen treatment group significantly inhibited sesame growth, and the dry matter accumulation of each tissue and the total dry matter accumulation were reduced. The coefficient of variation of each indicator was 8.80% to 83.29%, among which T13 (grain nitrogen content) had the smallest coefficient of variation (8.80%), and T15 (leaf nitrogen accumulation) had the largest coefficient of variation (83.29%).

[0127] It can be seen that, regardless of whether nitrogen fertilizer was applied or not, the coefficient of variation of T1 (yield) among the yield and its components was the largest; the coefficient of variation of the nitrogen content in the dry weight of each tissue was the largest in the stem.

[0128] Compared with the treatment without nitrogen application, the coefficient of variation of T2 (1000-grain weight), T10 (stem nitrogen content), T11 (leaf nitrogen content), T12 (capsule nitrogen content) and T13 (grain nitrogen content) in the nitrogen application treatment group increased, while the coefficient of variation of dry weight of each tissue (stem dry weight T5, leaf dry weight T6, capsule dry weight T7, single plant grain weight T8), total aboveground dry weight T9 and yield T1 among varieties decreased.

[0129] Except for the coefficient of variation of T22 (nitrogen dry matter production efficiency) (18.55%) which was lower than 20%, the coefficients of variation of indicators T19 (nitrogen physiological utilization rate), T20 (nitrogen fertilizer recovery efficiency), T21 (nitrogen grain production efficiency) and T23 (nitrogen fertilizer agronomic utilization rate) were all above 30%, indicating that there were large differences in nitrogen absorption and utilization efficiency among the tested sesame varieties.

[0130] Table 1 Statistics of variation of 23 indicators at maturity of 145 sesame varieties under different nitrogen application levels

[0131]

[0132] Note: The meaning of each letter in the table is: T1: yield (kg / mu); T2: 1000-grain weight (g); T3: number of capsules per plant; T4: number of grains per capsule; T5: stem dry weight (g); T6: leaf dry weight (g); T7: capsule dry weight (g); T8: grain weight per plant (g); T9: total dry weight of aboveground parts (g); T10: stem nitrogen content (g / kg); T11: leaf nitrogen content (g / kg); T12: capsule nitrogen content (g / kg); T13: grain nitrogen content (g / kg); T14: stem nitrogen T15: Leaf nitrogen accumulation (mg / plant); T16: Capsule nitrogen accumulation (mg / plant); T17: Grain nitrogen accumulation (mg / plant); T18: Total nitrogen accumulation (mg / plant); T19: Physiological nitrogen utilization rate (kg / kg); T20: Nitrogen fertilizer recovery efficiency (%); T21: Nitrogen grain production efficiency (kg / kg); T22: Nitrogen dry matter production efficiency (kg / kg); T23: Agronomic nitrogen utilization rate (kg / kg).

[0133] Example 3. Principal component analysis of different nitrogen efficiency indicators

[0134] Principal component analysis was performed on the five nitrogen efficiency-related indices in Example 2 (T19 nitrogen physiological utilization rate, T20 nitrogen fertilizer recovery efficiency, T21 nitrogen grain production efficiency, T22 nitrogen dry matter production efficiency, and T23 nitrogen fertilizer agronomic utilization rate). Based on the criterion that the cumulative contribution rate of the principal component factor is greater than 80%, two principal components were extracted, and the five individual indices were converted into two new independent comprehensive indices, represented by principal component 1 [CI(1)] and principal component 2 [CI(2)]. Their eigenvalues were 3.121 and 1.462, respectively, both greater than 1, and their contribution rates were 62.41% and 29.25%, respectively. The cumulative contribution rate was 91.66%. The principal component indices contained most of the information on the nitrogen efficiency of sesame, indicating that the two comprehensive principal factors had strong information representativeness (Table 2).

[0135] In addition, the coefficients of the principal component comprehensive indicators show that the first principal component mainly includes T19 (nitrogen physiological use efficiency) and T21 (nitrogen grain production efficiency); the second principal component mainly includes T20 (nitrogen fertilizer recovery efficiency) and T23 (nitrogen fertilizer agronomic utilization efficiency). The results show that the two independent principal components CI (1) and CI (2) comprehensive indicators can fully reflect the information contained in the original five nitrogen efficiency indicators and can accurately evaluate the nitrogen efficiency of sesame.

[0136] Table 2 Coefficients and contribution rates of comprehensive indicators of each principal component of nitrogen efficiency

[0137]

[0138] Example 4. Comprehensive evaluation of nitrogen absorption and utilization efficiency

[0139] Different nitrogen absorption and utilization efficiency evaluation indices reflect different aspects of nitrogen absorption and utilization efficiency. Nitrogen fertilizer recovery efficiency (T20) and nitrogen agronomic utilization efficiency (T23) reflect the efficiency of plant absorption and yield increase of applied nitrogen fertilizer. Nitrogen physiological utilization efficiency (T19), nitrogen grain production efficiency (T21), and nitrogen dry matter production efficiency (T22) reflect the ability of the plant to convert absorbed nitrogen into biomass and economic yield. Using these five indices, the entropy-weighted fuzzy membership function method was used to comprehensively evaluate the nitrogen absorption and utilization efficiency of the tested sesame varieties.

[0140] The results showed that the agronomic utilization efficiency of nitrogen fertilizer (T23) had the largest weight among the indicators, and the nitrogen recovery efficiency (T20) had the smallest weight (Table 3).

[0141] Table 3 Weights of evaluation indicators for nitrogen absorption and utilization efficiency

[0142]

[0143] The combined nitrogen efficiency value (CEV) of each test variety ranged from 0.104 to 0.834, with an average of 0.503 and a coefficient of variation of 28.21%. The yield-nitrogen efficiency index (GYNEI) ranged from 0.025 to 3.139, with an average of 1.106 and a coefficient of variation of 61.80% (Table 4).

[0144] Table 4 Comprehensive nitrogen efficiency values and yield-nitrogen efficiency comprehensive index of tested sesame varieties

[0145]

[0146] Example 5. Screening of high nitrogen yield and high efficiency varieties

[0147] Based on the GYNEI value, the longest distance method of the Euclidean distance in the system clustering method (within-group linkage-squared Euclidean distance) was used, and the Euclidean distance of the horizontal axis was set to 1.80. The 145 sesame varieties tested in Example 1 were divided into four types ( Figure 1 , Table 5), as shown below:

[0148] Ⅰ. High-yield and high-efficiency type, GYNEI ≥ 2.65;

[0149] Ⅱ. High nitrogen and high efficiency type, 1.29≤GYNEI<2.65;

[0150] Ⅲ. Low nitrogen and high efficiency type, 0.69≤GYNEI<1.29;

[0151] IV. Low-yield and low-efficiency type, GYNEI < 0.69.

[0152] Category I is high-yield and high-efficiency type (high GYNEI index, strong tolerance to low nitrogen, and high yield in both treatments), with 7 types, namely N2, N11, N15, N23, N24, N25 and N30, accounting for 4.83%; Category II is high-nitrogen and high-efficiency type, with 48 types, accounting for 33.10%; Category III is low-nitrogen and high-efficiency type, with 43 types, accounting for 29.66%; Category IV is low-yield and low-efficiency type (low GYNEI index, poor tolerance to low nitrogen, and low yield in both treatments), with 47 types, accounting for 32.42%.

[0153] Table 5 Classification of sesame varieties with different nitrogen efficiency types

[0154]

[0155]

[0156] Example 6. Grey correlation analysis of 18 individual indicators and GYNEI values of sesame under different nitrogen levels

[0157] Grey correlation analysis was performed on the individual indicators of different sesame varieties in Example 1 and the yield-nitrogen efficiency comprehensive index GYNEI to determine the role of each indicator in the yield-nitrogen efficiency comprehensive index (GYNEI), i.e., the nitrogen efficiency evaluation of sesame.

[0158] As shown in Table 6, among the individual indicators under normal nitrogen application levels, T8 (grain weight per plant) and T1 (yield) were most closely correlated with the yield-nitrogen efficiency index (GYNEI), with correlation coefficients of 0.829 and 0.809, respectively. Other indicators with greater correlation were T17 (grain nitrogen accumulation), T7 (capsule dry weight), T9 (total aboveground dry weight), and T5 (stem dry weight), with correlation coefficients of 0.788, 0.7037, 0.694, and 0.688, respectively. This indicates that under normal nitrogen application levels (N), these indicators are closely related to sesame nitrogen efficiency.

[0159] Among the individual indicators under the no nitrogen application level (0N), T8 (grain weight per plant) had the largest correlation coefficient with the yield-nitrogen efficiency integrated index (GYNEI), which was 0.632. The other indicators with greater correlation were T17 (grain nitrogen accumulation), T1 (yield), T9 (total dry weight of aboveground parts), T18 (total nitrogen accumulation) and T4 (number of grains per capsule), with correlation coefficients of 0.628, 0.616, 0.606, 0.605 and 0.595, respectively. This shows that under the no nitrogen application level (0N), these indicators are closely related to the high nitrogen efficiency of sesame.

[0160] It can be seen that under the two different nitrogen application levels, the four indicators of T8 (single-plant grain weight), T1 (yield), T17 (grain nitrogen accumulation), and T9 (above-ground dry weight) all had high correlation coefficients with the sesame yield-nitrogen efficiency index (GYNEI), and can be used as key indicators for screening sesame nitrogen efficiency, as shown in Table 6.

[0161] Table 6 Grey correlation between 18 individual indicators and GYNEI values of sesame varieties under different nitrogen levels

[0162]

[0163]

[0164] Example 7. Establishment and verification of the regression equation for the high-efficiency identification of sesame nitrogen

[0165] (1) Establishment of regression equation for high-efficiency identification of sesame nitrogen

[0166] In order to effectively analyze the relationship between the 18 indicators in Example 6 and the nitrogen efficiency of different sesame varieties, a multivariate stepwise regression analysis was performed with the yield-nitrogen efficiency comprehensive index (GYNEI) as the dependent variable and the low nitrogen stress tolerance index X of each individual indicator as the independent variable to establish the optimal regression equation for predicting the nitrogen efficiency mathematical model, i.e., the regression equation for nitrogen high efficiency identification at sesame maturity:

[0167] GYNEI=0.906-0.292X1-0.146X2-11.054X3+2.267X5+1.114X6+13.953X7-2.486X8-13.76 1X9-0.276X10+0.396X11+0.897X12-0.094X13+3.002X14-1.235X15-7.308X16+14.090X18

[0168] Wherein, X is the low nitrogen stress tolerance index of each indicator, and the low nitrogen stress tolerance index = no nitrogen application index value / normal nitrogen application index value. The meanings of each index are as follows: X1: yield; X2: 1000-grain weight; X3: number of capsules per plant; X5: stem weight; X6: leaf dry weight; X7: capsule dry weight; X8: grain weight per plant; X9: total dry weight of aboveground parts; X10: stem nitrogen content; X11: leaf nitrogen content; X12: capsule nitrogen content; X13: grain nitrogen content; X14: stem nitrogen accumulation; X15: leaf nitrogen accumulation; X16: capsule nitrogen accumulation; X18: total nitrogen accumulation.

[0169] When applied, the index value T of the index in the equation under normal nitrogen application and no nitrogen application conditions is measured, and the low nitrogen stress tolerance index X of each index is further calculated. The GYNEI value is substituted into formula (7) to obtain the nitrogen efficiency type according to the classification standard of Example 5.

[0170] (2) Verification of the accuracy of the results of the regression equation for determining nitrogen efficiency type during sesame maturity

[0171] Variety N1, the low nitrogen stress tolerance index of this variety is X1: 0.29, X2: 0.760, X3: 0.082, X5: 0.083, X6: 0.027, X7: 0.078, X8: 0.078, X9: 0.071, X10: 0.646, X11: 0.627, X12: 1.163, X13: 1.787, X14: 0.053, X15: 0.017, X16: 0.091, X18: 0.090.

[0172] Substituting the above-mentioned regression equation for nitrogen high efficiency identification at maturity stage into the sesame, the calculated GYNEI value was 1.626. According to the classification standard, it was determined to be a high nitrogen and high efficiency variety (1.29≤GYNEI<2.65).

[0173] To measure the accuracy of the results, the GYNEI values calculated using the regression equation were classified separately from the results calculated using the formula GYNEI = (yield / population average yield) × (nitrogen efficiency comprehensive index / population nitrogen efficiency comprehensive index average). The classification results obtained by the two methods were consistent, indicating that the identification method of the present invention is reliable.

[0174] Table 7 Information on 145 sesame germplasms from different sources and regions

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[0179]

Claims

1. A method for identifying nitrogen efficiency of sesame germplasm based on a yield-nitrogen efficiency comprehensive index, characterized in that: The following steps are involved: S1. Determine the sesame variety to be identified; S2. Under normal nitrogen application and no nitrogen application conditions, measure individual indicators of mature sesame varieties; S3. Calculation of nitrogen efficiency index; S4, extraction of principal components; S5. Calculation of the yield-nitrogen efficiency composite index; S6. Establishment of a mathematical model for predicting nitrogen efficiency of sesame; S7. Determine the nitrogen efficiency type of sesame varieties based on the GYNEI value of the mathematical model.

2. The identification method according to claim 1, wherein Step S2 includes the following steps: (1) Two groups were set up: a nitrogen treatment group and a non-nitrogen treatment group. The fertilizer dosage of the nitrogen treatment group was 180 kg / hm2. 2 The nitrogen fertilizer addition rate of the no nitrogen treatment group was 0; both treatment groups were given 75 kg / hm2 of phosphorus fertilizer. 2 , potash fertilizer 17.3kg / hm 2 ; (2) For potted planting, mix the fertilizer and sand evenly and put them into a flower pot with an upper diameter of 0.38m, a lower bottom diameter of 0.25m and a height of 0.34m. Each pot should contain 20kg of sand.

3. The identification method according to claim 1, wherein The indicators and measurement methods measured in step S2 are: (1) Determination of dry matter and nitrogen accumulation: At maturity, three plants of similar growth were selected from each variety. The aboveground stems, leaves, and capsules were placed at 105°C for 30 minutes, dried at 80°C until constant weight, and then weighed. The dry matter accumulation of the stems, leaves, and capsules, as well as the total dry matter accumulation of the aboveground parts, were calculated for each variety. The dry matter samples of each organ were pulverized, and the nitrogen content of stems, leaves, capsule peels and seeds was determined using a FOSS Kjeldahl nitrogen analyzer; The nitrogen absorption of stems, leaves, capsules and seeds at maturity was calculated according to dry matter weight × nitrogen content. The total nitrogen absorption was the sum of the nitrogen absorption of the four tissues: stems, leaves, capsules and seeds. (2) Determination of output and its components: At maturity, three plants with the same growth were selected to investigate the number of capsules per plant, the number of grains per capsule, the thousand-grain weight, and the yield per plant.

4. The identification method according to claim 1, wherein The nitrogen efficiency related indicators and calculation formula in step S2 are: Low nitrogen stress tolerance index = no nitrogen application index value / normal nitrogen application index value; Agronomic utilization rate of nitrogen fertilizer = (grain yield in nitrogen-fertilized area - grain yield in non-nitrogen-fertilized area) / nitrogen fertilizer application rate; Nitrogen fertilizer recovery efficiency = (total nitrogen absorbed in the nitrogen-fertilized area - total nitrogen absorbed in the nitrogen-free area) / nitrogen fertilizer application amount × 100%; Nitrogen physiological utilization rate = (grain yield in the nitrogen-fertilized area - grain yield in the non-nitrogenized area) / (total nitrogen absorption in the nitrogen-fertilized area - total nitrogen absorption in the non-nitrogenized area); Nitrogen dry matter production efficiency = dry matter accumulation in nitrogen application area / total nitrogen absorption by plants; Nitrogen grain production efficiency = grain yield in nitrogen application area / total nitrogen uptake by plants.

5. The identification method according to claim 1, wherein Step S4 includes the following steps: (1) Standardize the original measurement values determined in step S2 using SPSS 22.0 software; (2) performing principal component analysis and path analysis on the nitrogen efficiency index calculated in step S3; (3) Determine the number of principal components based on the eigenvalue and cumulative contribution rate, and calculate the comprehensive index value of each index in each principal component and the weight of each principal component; (4) The principal components are extracted according to the criterion that the eigenvalue is greater than 1 or the cumulative contribution rate is greater than 80%, and the coefficients of the comprehensive indicators of each principal component are obtained.

6. The identification method according to claim 1, wherein Step S5 includes the following steps: (1) Constructing the optimal membership matrix Where y ij is the preferred membership, max T ij is the maximum value of each evaluation index; (2) Determine indicator weights using entropy weight method First, the original data is normalized and calculated according to the larger the better indicator. The calculation formula is: Where max ij and min ij are the maximum and minimum values of each evaluation index respectively; Secondly, calculate the entropy value. The entropy calculation formula for m evaluation objects and n evaluation indicators is: Where i = 1, 2, ···, n; j = 1, 2, ···, m; f ij The calculation formula is: Finally, calculate the evaluation index weight W, the calculation formula is: and satisfy (3) Calculate the comprehensive index of fuzzy evaluation Multiply the weight value and the optimal membership value of each evaluation index and add them together to get the comprehensive index value C of fuzzy evaluation. The calculation formula is: (4) Calculation of yield-nitrogen efficiency comprehensive index GYNEI = (yield / average yield of the group) × (nitrogen efficiency comprehensive index / average value of the nitrogen efficiency comprehensive index of the group).

7. The identification method according to claim 1, wherein The method for establishing the mathematical model in step S6 is: Based on the yield-nitrogen efficiency comprehensive index of step S5, a nitrogen efficiency grade cluster analysis is performed on each sesame variety, and nitrogen high efficiency grades are divided to screen out high-yield and high-efficiency sesame germplasm resources; through a multivariate stepwise regression analysis method, with the yield-nitrogen efficiency comprehensive index as the dependent variable and the low nitrogen stress tolerance index of each individual indicator as the independent variable, a regression equation for predicting the mathematical model of nitrogen efficiency is established.

8. The identification method according to claim 7, wherein The regression equation of the mathematical model for predicting nitrogen efficiency is: GYNEI=0.906-0.292X1-0.146X2-11.054X3+2.267X5+1.114X6+13.953X7-2.486X8-13.761 X9-0.276X10+0.396X11+0.897X12-0.094X13+3.002X14-1.235X15-7.308X16+14.090X18, GYNEI is the dependent variable; X is the independent variable, representing the low nitrogen stress tolerance index of each indicator, X1: yield; X2: 1000-grain weight; X3: number of capsules per plant; X5: stem weight; X6: leaf dry weight; X7: capsule dry weight; X8: grain weight per plant; X9: total dry weight of aboveground parts; X10: stem nitrogen content; X11: leaf nitrogen content; X12: capsule nitrogen content; X13: grain nitrogen content; X14: stem nitrogen accumulation; X15: leaf nitrogen accumulation; X16: capsule nitrogen accumulation; X18: total nitrogen accumulation.

9. The identification method according to claim 1, wherein The relationship between GYNEI value and nitrogen efficiency type of sesame varieties is: High-yield and high-efficiency type, GYNEI ≥ 2.65; High nitrogen and high efficiency type, 1.29≤GYNEI<2.65; Low nitrogen and high efficiency type, 0.69≤GYNEI<1.29; Low-yield and low-efficiency type, GYNEI<0.69.

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